本篇博文主要内容为 2026-09-09 从Arxiv.org论文网站获取的最新论文列表，自动更新，按照NLP、CV、ML、AI、IR、MA六个大方向区分。
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目录
概览 (2026-09-09)
今日共更新1846篇论文,其中:
- 自然语言处理共257篇(Computation and Language (cs.CL))
- 人工智能共519篇(Artificial Intelligence (cs.AI))
- 计算机视觉共360篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共452篇(Machine Learning (cs.LG))
- 多智能体系统共39篇(Multiagent Systems (cs.MA))
- 信息检索共48篇(Information Retrieval (cs.IR))
- 人机交互共57篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] Procedural Graphs: Self-Evolving Execution Structures for LLM Agents
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）作为智能体在长时序规划与外部工具调用过程中因缺乏显式程序性知识而导致的目标漂移、工具调用顺序错乱及重复低效行为等问题。现有方法依赖于对历史状态的无约束生成，隐含地编码了“做什么、何时做、如何做”的决策逻辑，但在轨迹增长时难以维持一致性与目标导向性。其解决方案的关键在于提出程序图（Procedural Graph）——一种将程序性知识结构化为（过程，关系，过程）三元组的知识表示框架，用于显式建模任务执行中的步骤间依赖与条件约束。在每个决策步骤中，系统通过定位当前活跃节点，并利用引导模型将局部子图转化为情境感知的步骤级指导，从而在不强制具体动作的前提下引导智能体选择更合理的下一步行动。此外，该框架具备自演化能力：通过对比失败与成功轨迹，由大语言模型提炼器自动修正图的拓扑结构与属性，保留提升验证性能的修改，同时保留被拒绝的路径以抑制重复错误。该机制从极简初始骨架出发，经迭代优化可生成性能媲美甚至超越人工设计的程序图，且能修复有缺陷的专家先验。实验表明，在多种数据集、任务类型和大模型上，该方法均显著优于基于记忆的基线，且自演化进一步提升了性能而无需人工干预。
链接: https://arxiv.org/abs/2609.09153
作者: Yuxing Lu,Yicheng Chen,Shanchan Wu,Sercan Ö. Arık
机构: Google(谷歌); Georgia Institute of Technology (佐治亚理工学院); Peking University (北京大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注: 36 pages including references and appendices, 6 figures, 11 tables
Abstract:Large language models are increasingly deployed as agents that plan over long horizons and act through external tools. Most agents select actions through unconstrained generation over an accumulating history, leaving implicit the procedural knowledge of what to do, in what order, and under which conditions. As trajectories lengthen, agents can lose track of their objectives, invoke tools out of order, and repeat unproductive actions. We introduce the Procedural Graph: just as a knowledge graph organizes factual knowledge into (entity, relation, entity) triplets for what-is questions, a Procedural Graph organizes procedural knowledge into (procedure, relation, procedure) triplets for what-to-do questions. At each decision step, the framework localizes the agent’s active node, and a guidance model translates the surrounding subgraph into step-level situational guidance that biases the solver’s next action without dictating it. The graph is self-evolving: an LLM refiner contrasts failed trajectories with successful ones and edits the graph’s topology and attributes, committing edits that preserve or improve held-out validation performance while retaining rejected ones to discourage repetition. Starting from a minimal skeleton, the loop builds graphs that match or surpass hand-designed ones. It can also repair a flawed expert prior. Across multiple datasets, task types, and LLMs, the Procedural Graph delivers consistent gains over memory-based baselines, and self-evolution further improves performance without manual engineering.
[MA-1] Copying explains the collective behavior of AI agents in the wild
【速读】：该论文试图解决的问题是：在无明确协作指令、且系统本身并非为这些智能体设计的情况下，大量独立运行的生成式智能体（AI agents）如何自发形成具有集体结构和一致性的协同行为。其核心挑战在于理解这种看似自发的群体秩序是如何从个体基于环境信息的简单决策中涌现的。解决方案的关键在于提出一种“最小复制模型”（minimal copying models），其核心机制为：每个智能体在做出选择时，倾向于以与当前可见环境中某选项出现频率相近的概率进行选择，而这一频率优先依据其眼前页面的内容，其次参考最近编辑流中的信息，较早的历史信息影响微弱。这一规则仅需单一自由参数即可准确再现多个关键现象——包括各页面上聚集智能体数量的重尾分布、命名所用片段的频率模式，以及各页面内部自洽但彼此差异显著的拼贴式结构。研究表明，仅仅复制环境所呈现的信息就足以生成绝大多数群体性结构，这也揭示了此类群体极易被引导的本质：最早书写者或在静默期书写者，即可确立后续所有智能体遵循的规范。
链接: https://arxiv.org/abs/2609.09150
作者: Giordano De Marzo,Nicola Albore,David Garcia
机构: University of Konstanz(康斯坦茨大学); Centro Ricerche Enrico Fermi(恩里科·费米研究中心); Intesa Sanpaolo(意盛银行); Complexity Science Hub(复杂科学中心)
类目: Multiagent Systems (cs.MA); Statistical Mechanics (cond-mat.stat-mech); Computation and Language (cs.CL)
备注:
Abstract:In June 2026, thousands of AI agents found that a small public wiki would accept edits from inside their sandboxes, and started using it to help one another pass a timed test. Each agent lived for about an hour and remembered nothing afterwards. Nobody asked them to cooperate, and the wiki had not been built for them. The complete record of what they wrote is public, and it is unusually informative, because it preserves not only what each agent wrote but what that agent could see before writing. We use it to follow the three decisions an agent had to make on arrival: where to write, what to call itself, and how to word its message. One rule governs all three. An agent takes an option with a probability close to the share of that option in what it can see, and the share that matters is the one on the page in front of it, then the one in the stream of recent edits, and only weakly anything older. Three minimal copying models, one per decision and with a single free parameter each, reproduce the heavy-tailed distribution of how many agents met on a page, the frequency of the pieces from which the agents built their names, and the patchwork of pages that are internally consistent and different from one another. Copying whatever the environment happens to show is enough to produce most of the collective structure of this population. It is also what makes such a population easy to steer, since whoever writes first, or writes while the others are quiet, sets the convention for everyone who comes later.
[MA-2] me-Varying Data as Sheaves: an Invitation to Narratives
【速读】：该论文旨在解决跨学科时间变异性数据建模中数学工具分散、缺乏统一理论框架的问题，导致不同领域间思想难以迁移与整合。其核心挑战在于如何建立一个能够统一描述各类时变对象的抽象理论体系，以揭示隐藏在不同表示形式背后的共性原则。解决方案的关键在于提出“叙事理论”（narrative theory）这一抽象框架，该框架适用于任意数学类型的时变对象，不仅支持理论探索，还可直接指导实际应用。通过三个研究范例：（1）分析时变数据表示转换中的信息损失；（2）系统性地将时变数据分解为基本成分并提取描述其结构复杂性的不变量；（3）建模具有切换通信拓扑的多智能体系统，该框架展示了如何从抽象视角出发，实现跨领域研究的统一组织与引导。因此，其关键创新在于提供了一种通用且可迁移的数学语言，使复杂动态系统的分析与建模具备更强的普适性与系统性。
链接: https://arxiv.org/abs/2609.09056
作者: Wilmer Leal,Benjamin Merlin Bumpus,Jana K. Nickel,Johan García,James Fairbanks,Warren Dixon
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Systems and Control (eess.SY); Category Theory (math.CT)
备注: 42 pages, 7 figures. Book chapter
Abstract:Modern science and engineering increasingly rely on time-varying data, yet the mathematical tools used to model temporal phenomena are often developed within separate disciplines, obscuring common principles and limiting the transfer of ideas across fields. This chapter presents the theory of narratives, an abstract framework for time-varying objects of any mathematical kind that supports both theoretical investigations and applications. To illustrate this perspective, the chapter develops three vignettes, each illustrating a different research direction. The first addresses a general concern: What information loss can occur when switching between different representations of temporal data? The second concerns structural and algorithmic approaches: How can we systematically decompose time-varying data into simple pieces and obtain invariants describing its structural complexity? The third is an application to control theory: How can we model multi-agent systems with switching communication topologies? More important than any individual vignette, the central message of this invitation is that a suitable abstract perspective can organize and guide research across remarkably diverse mathematical and scientific domains.
[MA-3] MorphoOrgaAgent : A Foundation-Model-Based Multi-Agent System for Autonomous Organoid Analysis MICCAI2026
【速读】：该论文旨在解决类器官（organoid）形态学分析中依赖人工分割、定量分析门槛高以及缺乏标准化评估体系的问题。其核心解决方案是提出一种名为MorphoOrgaAgent的多智能体框架，该框架通过自然语言指令实现零样本类器官实例分割、自动化数据分析与报告生成。其关键技术在于：一是引入任务理解智能体（TaskUnderstandingAgent），可解析用户需求中的测量项与可视化类型；二是采用融合Cellpose提取的几何提示与文本提示的混合分割模块，驱动SAM3实现无需训练的零样本分割；三是构建报告生成智能体（ReportAgent），自动计算量化指标并整合可视化结果生成结构化报告。此外，研究还提出了MorphoOrgaVQA基准，用于对智能体系统在类器官形态分析中的性能进行量化评估，实验表明该框架能准确响应显式与描述性指令，输出结果接近真实值，并实现端到端无编程分析。
链接: https://arxiv.org/abs/2609.08696
作者: Hanyi Zhang,Maximilian Hoermann,Lion J. Gleiter,Yiling Xu,Bettina Katalin Budai,Hans-Ulrich Kauczor,Carsten Marr,Tingying Peng
机构: Helmholtz AI, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany; School of Computation, Information and Technology, Technical University of Munich, Munich, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany; Institute of AI for Health, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany; Department of Medicine III, Ludwig-Maximilian-University Hospital, Munich, Germany; Department of Physics, Ludwig-Maximilian-University, Munich, Germany; DKTK, German Cancer Consortium, Heidelberg, Germany
类目: Multiagent Systems (cs.MA); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the 2nd Agentic AI for Medicine Workshop, MICCAI 2026. 15 pages, 3 figures, 2 tables
Abstract:Organoids are three-dimensional tissue models whose morphology provides important insights into tumor development, disease progression, and drug testing. Extracting these morphological features relies heavily on manual segmentation, which is time-consuming and labor-intensive. Furthermore, performing quantitative statistical analysis typically requires custom coding skills and a mathematical background, presenting a major barrier for experimental biologists. To address these challenges, we introduce MorphoOrgaAgent, a multi-agent framework that achieves zero-shot organoid segmentation, automated data analysis, and report generation based on natural language input. The framework consists mainly of three core components: a TaskUnderstandingAgent that identifies requested measurements and visualization types; a hybrid segmentation module that combines Cellpose-derived geometric prompts with text prompts to guide SAM3 for zero-shot organoid instance segmentation; and a ReportAgent that computes quantitative metrics and compiles them alongside generated visualizations into a structured report. We further introduce MorphoOrgaVQA, a benchmark designed for quantitative evaluation of agent systems in organoid morphology analysis. Experimental results demonstrate that MorphoOrgaAgent handles both explicit and descriptive user requests, produces measurements closely matching ground truth, and generates complete analysis reports without requiring manual programming. The complete source code and MorphoOrgaVQA benchmark are publicly available at this https URL.
[MA-4] Entropic Risk-Sensitive Evolutionary Learning and Equilibrium Selection in Coordination Games
【速读】：该论文旨在解决演化博弈中风险敏感性对协调博弈长期均衡选择行为的影响问题，特别是当参与者具有不同风险态度时，如何改变传统风险中性假设下的均衡选择结果。其核心问题是：在存在策略不确定性的情况下，个体的风险偏好如何通过熵风险度量（entropic risk measure）影响随机稳定均衡（stochastically stable equilibrium）的形成。解决方案的关键在于引入基于熵风险度量的风险敏感型噪音最优响应机制，并在两种标准演化修正规则——带突变的最佳响应（best response with mutations）与对数概率选择（logit choice）——下分析动态演化过程。研究发现，在单人群对称与双人群非对称设置中，风险敏感性可显著改变均衡选择：风险寻求型个体倾向于支持收益占优均衡（payoff-dominant equilibrium），而风险规避型个体则偏好极大化最小收益均衡（maximin equilibrium）。此外，研究识别出一个稳健区域——任何超占优均衡（super-dominant equilibrium）在所有风险态度、两种修正规则及跨群体情形下均保持随机稳定性。进一步扩展至对称多行动博弈（k-行动博弈），研究证明：在大种群条件下，高度风险寻求的个体将唯一选择强收益占优均衡（strongly payoff-dominant equilibrium），而高度风险规避的个体则唯一选择强极大极小均衡（strongly maximin equilibrium）。因此，熵风险敏感性可作为一类系统性机制，实现对演化博弈中均衡选择的有效引导，超越经典风险中性基准。
链接: https://arxiv.org/abs/2609.08677
作者: Solaleh Mohammadi,Xiang Gao,Kaiqing Zhang
机构: University of Maryland, College Park(马里兰大学学院帕克分校); University of Illinois Urbana-Champaign(伊利诺伊大学厄本那-香槟分校)
类目: ystems and Control (eess.SY); Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA)
备注: Preliminary version accepted to IEEE CDC 2026
Abstract:We study risk-sensitive evolutionary learning dynamics and their long-run equilibrium selection behaviors in coordination games. Agents’ risk attitudes enter through the classical entropic risk measure, which evaluates opponent-induced payoff uncertainty and feeds into noisy best responses under two standard revision protocols: best response with mutations and logit choice. We first analyze 2\times 2 coordination games in both single-population symmetric and two-population asymmetric settings. In the single-population setting, unlike the risk-neutral case where the dynamics are known to favor the risk-dominant equilibrium, we show that risk sensitivity can change the stochastically stable outcome: a greater risk-seeking attitude favors the payoff-dominant equilibrium, while a greater risk-averse attitude favors the maximin equilibrium. Thus, the population’s risk attitude may act as a control knob for long-run equilibrium selection. In both population settings, we also identify a robust regime: any super-dominant equilibrium is stochastically stable for all risk attitudes, under both protocols, and across populations. We further extend the single-population analysis to symmetric k -action games, which include symmetric k -action coordination games as a special case, under risk-sensitive best response with mutations. In this setting, we show that, for sufficiently large populations, sufficiently risk-seeking agents uniquely select the strongly payoff-dominant equilibrium when it exists, whereas sufficiently risk-averse agents uniquely select the strongly maximin equilibrium when it exists. These results show that entropic risk sensitivity may serve as a systematic mechanism for steering equilibrium selection in evolutionary games, beyond the classical risk-neutral benchmark.
[MA-5] Beyond Agent Harnesses: Cross-Substrate Authority for Multi-Agent Systems
【速读】：该论文旨在解决生成式智能体（Agentic systems）在执行任务过程中存在的“跨基底授权鸿沟”（cross-substrate authority gap）问题，即决策相关的授权信息存在于规划器可见的工作空间或记忆状态之外，导致相同最终文件可能需要截然相反的安全操作。其核心解决方案在于在执行时点引入权威性检查（execution-time authority check），将授权控制置于变更边界（mutation boundary），通过持久化记录的代理执行尝试、真实Git历史轨迹及确定性断言，确保只有经过授权的意图才能生效。实验结果表明，仅依赖工作区可见证据的规划存在严重安全隐患（如实验2中12/16次不安全发布决策），而引入类型化关系（typed relation）虽提升部分信息结构化程度，但无法弥补授权缺失带来的规划可靠性下降；相比之下，基于确定性执行守卫的运行时授权验证机制可在不增加模型调用的前提下，完全阻止所有未授权意图的副作用并正确执行合法授权操作，从而将授权强制执行作为内存治理的运行终点。
链接: https://arxiv.org/abs/2609.08472
作者: Yang Li,Sergey Volkov,Hai Liu,Zongsi Xu,Xiyu Chen,Tuo Zhou,Dian Shao,Hao Sun,Ye Lu
机构: The University of Hong Kong (香港大学); Jiangxi Science and Technology Normal University (江西科技师范大学); The Hong Kong University of Science and Technology (香港科技大学); Shenzhen University (深圳大学)
类目: Multiagent Systems (cs.MA); Software Engineering (cs.SE)
备注:
Abstract:Agentic systems persist model-visible memory while mutating workspaces, while a runtime, registry, or approval service may hold authority state outside both. Identical final files can then require opposite safe actions. We call this the cross-substrate authority gap: decision- relevant authorization information resides outside the planner-visible workspace or memory state. Across two controlled mini-benchmark families, three experiments compare planner-observation augmentation with an execution-time authority check using real Git lineage, durably recorded agent execution attempts, deterministic oracles, and two model routes. Experiment 1 is a 128-cell controlled evidence ablation: authority-blind candidate evidence obtains 0/32 final semantic success, while raw receipts and a typed relation both obtain 32/32. The missing authority fact accounts for the gain; typed packaging provides no observed planning-accuracy gain over equal raw information. Experiment 2 uses 96 planning calls: workspace-visible evidence yields 12/16 unsafe publication decisions, and planning with the typed relation remains unreliable (15/32 first actions correct; 11/32 invalid or absent). Experiment 3 replays the same 32 fixed model-generated first-action intents with zero additional model calls; a deterministic execution guard prevents all six unsafe intents from becoming effects and permits all 12 valid authorized publish intents. These results position authority enforcement at the mutation boundary as the operational endpoint of memory governance.
[MA-6] Reachability-Certified Subteam Decomposition for Locally Interacting Multi-Agent MDPs
【速读】：该论文旨在解决多智能体系统在有限通信范围内进行协调决策时面临的子团队分解难题，即如何在滚动优化过程中确定哪些智能体应持续协同以最大化累积奖励。现有方法仅依赖空间邻近性存在明显缺陷：远距离智能体可能在未来产生交互，而高回报对可能因折扣效应始终无法实现。其解决方案的关键在于提出可达性认证的子团队分解（Reachability-Certified Subteam Decomposition, RCSD），该方法结合了两两接触时间的速度下界与奖励包络（reward envelope），构建当前状态下的亲和度度量。对于任意容量有效的持久划分，切割亲和度之和可提供未改变的平稳马尔可夫状态反馈策略的奖励损失上界；基于所得切割马尔可夫决策过程（cut MDP）的团队最优策略乘积，其遗憾度至多为该证书值的两倍。两个边界均为最坏情况紧致。实验结果表明，在受控五智能体场景中，RCSD-Exact 相较于均匀划分、仅基于距离的划分及仅基于包络的划分，分别降低 56.0%、28.8% 和 25.3% 的归一化执行遗憾；二维随机研究中 384 次精确划分与 1,440 次受限控制器评估均未出现边界违反。四智能体实证数据支持 RCSD 优于均匀与距离划分，而当前接触与包络仅划分的证据则不显著。在 8–20 智能体平衡分层结构中，控制器库效用表现混合：成对区间点态分析支持 RCSD 优于距离与当前接触划分，但均匀划分包含零值，且包络仅划分与 Value-MIP 表现优于 RCSD。子团队构建在最多 100 智能体情况下中位耗时仍低于秒级，该结果未包含亲和度计算或 MDP 规划开销。
链接: https://arxiv.org/abs/2609.08366
作者: Xiangwu Wang,Chengwei Cao,Hongyuan Tang
机构: University of Hong Kong(香港大学); University of California, San Diego(加州大学圣地亚哥分校); Carnegie Mellon University(卡内基梅隆大学)
类目: Multiagent Systems (cs.MA)
备注: 15 pages, 4 figures, including references and appendices
Abstract:Persistent communication limits force a multi-agent system to decide which agents may coordinate throughout a rollout. Current proximity alone is insufficient: separated agents may interact later, whereas a large pair reward may remain unreachable until it is heavily discounted. We introduce Reachability-Certified Subteam Decomposition (RCSD) for finite multi-agent Markov decision processes with factorized physical dynamics, finite-range ordered pair rewards, and almost-sure motion bounds. RCSD combines a speed-limit lower bound on pairwise contact time with a reward envelope to form a current-state affinity. For any capacity-valid persistent partition, the sum of cut affinities bounds the reward-deletion error of every unchanged stationary Markov state-feedback policy. A product of team-optimal policies for the resulting cut MDP incurs at most twice this certificate in regret against the centralized optimum. Both bounds are worst-case tight. On a controlled five-agent family, RCSD-Exact reduces aggregate normalized execution regret by 56.0%, 28.8%, and 25.3% relative to uniform, distance-only, and envelope-only partitions. A separate stochastic two-dimensional study finds no bound violation over 384 exact-partition and 1,440 restricted-controller evaluations. Exact four-agent evidence favors RCSD over uniform and distance-only grouping; raw evidence for current contact is borderline and envelope-only is unresolved. Across balanced 8-20-agent strata, controller-library utility is mixed: pointwise paired intervals favor RCSD over distance and current contact, include zero for uniform, and favor envelope-only and Value-MIP over RCSD. Partition construction remains subsecond in median up to 100 agents; this last result does not include affinity formation or MDP planning.
[MA-7] Rank Without an Oracle: Deviation-Aware Interaction-Rank Selection from Offline Multi-Agent Logs
【速读】：该论文旨在解决在离线多智能体收益模型中，由于训练数据分布（logging distribution）与实际部署时由学习策略及单边偏离所诱导的分布不一致，导致标准留出损失评估可能偏好那些能良好拟合观测行为但扭曲战略激励的交互类型这一核心问题。其解决方案的关键在于提出选择性交互-排名验证（Selective Interaction-Rank Validation, SIRV），该方法针对已知日志分布的有限博弈场景，通过分阶段设计：训练集用于拟合嵌套收益模型并构建所有候选部署与单边替换分布的公共并集；校准集则在同一并集上独立评估每个候选模型。SIRV返回满足同时上界最坏目标风险在容忍度范围内的最小排名，并在目标支持不足或估计过于模糊时选择不决策。该框架通过一个共同覆盖事件，为有限候选集提供了目标风险的有界保证，并可生成候选特定的粗略相关均衡（Coarse Correlated Equilibrium, CCE）差距证书。此外，研究还揭示了一个精确的两点非支撑不可识别性结果。在包含每类2,048个独立博弈的受控因子实验中，基于经验伯恩斯坦（empirical-Bernstein）界限将中位数CCE差距证书降低了42.5%，相较霍夫丁（Hoeffding）界限在共同回报上的表现，且支持回报降低1.36点；在配对排名误设及独立生成的拥堵族实验中，SIRV-EB回退规则相较于ID-Mean显著降低了真实候选选择的平均CCE后悔值，同时保持了博弈层面的损失性能。这些结果明确区分了可认证的模型选择与普遍的战略改进之间的界限。
链接: https://arxiv.org/abs/2609.08358
作者: Xiangwu Wang,Chengwei Cao,Hongyuan Tang
机构: University of Hong Kong(香港大学); University of California, San Diego(加州大学圣地亚哥分校); Carnegie Mellon University(卡内基梅隆大学)
类目: Multiagent Systems (cs.MA); Computer Science and Game Theory (cs.GT)
备注: 18 pages, 9 figures, including appendices
Abstract:Offline multi-agent payoff models are estimated under a logging distribution but used on distributions induced by learned solutions and unilateral deviations. Standard held-out loss can therefore favor an interaction class that predicts logged play well while distorting strategic incentives. We introduce Selective Interaction-Rank Validation (SIRV) for finite games with known logging distributions. A training split fits nested payoff models and constructs a common union of all candidate deployment and unilateral-replacement distributions; an independent calibration split evaluates every candidate on this same union. SIRV returns the smallest rank whose simultaneous upper worst-target risk is within tolerance of the best upper score, and abstains when a declared target is unsupported or too imprecisely estimated. A common coverage event yields a finite-candidate target-risk bound and a candidate-specific coarse correlated equilibrium (CCE) gap certificate. We also isolate an exact two-point off-support non-identifiability result. In a controlled factorial study with 2,048 independent games per family, empirical-Bernstein bounds reduce the median CCE-gap certificate by 42.5% relative to Hoeffding bounds on common returns, with a 1.36-point reduction in supported return. Under paired rank misspecification and in a separately generated congestion family, the SIRV-EB fallback rule lowers mean true candidate-selection CCE regret relative to ID-Mean, while retaining game-level losses. Across 384 games at N=3,5,8 , ID-Mean-relative mean CCE-regret effects stay positive while certified return falls sharply under weak coverage. These results separate certifiable model selection from universal strategic improvement.
[MA-8] Scaling Multi-Agent Systems with Prospect-State Propagation EMNLP2026
【速读】：该论文旨在解决当前基于大语言模型（LLM）的多智能体系统（Multi-Agent Systems, MAS）在大规模扩展时面临的语义信息丢失与智能体异质性衰减问题。现有方法通过周期性压缩中间状态以降低推理阶段的令牌消耗，但这种粗放式缩放策略往往导致富含语义的经济行为轨迹等关键信息被丢弃，进而削弱了对宏观经济波动的模拟能力。其核心解决方案是提出一种名为“前景状态传播”（Prospect-State Propagation for Multi-Agent Systems, PspMAS）的新框架，受前景理论（Prospect Theory）启发，将每个智能体的微观状态解耦为轻量级的前景状态（Prospect State）与高表达力的语义状态（Semantic State）。其中，前景状态通过一个轻量、可并行化的传播机制记录心理痕迹，持续注入系统异质性；而语义状态则充分利用LLM在感知、推理、规划与决策方面的强大能力。二者协同互补，既保障了系统的可扩展性，又有效维持了复杂行为模式的表征能力，从而实现高效且高保真的大规模多智能体仿真。
链接: https://arxiv.org/abs/2609.08033
作者: Zhimei Chen,Mu Chen,Fakhri Karray
机构: Mohamed bin Zayed University of Artificial Intelligence(穆罕默德·本·扎耶德人工智能大学); University of Waterloo(滑铁卢大学)
类目: Multiagent Systems (cs.MA); Computers and Society (cs.CY)
备注: Accepted to Findings of EMNLP 2026
Abstract:Current LLM-based multi-agent systems (MAS) periodically compress intermediate states to reduce inference-time token consumption, thereby attempting to incorporate more agents. However, naive scaling strategies face challenges. For example, in economic simulations, large-scale MAS typically discard semantically rich economic states, i.e., agent behavioral trajectories, which are key drivers of macroeconomic fluctuations. In this paper, we reveal a phenomenon in which agent heterogeneity gradually decreases during simulation, and propose Prospect-State Propagation for Multi-Agent Systems (PspMAS). Inspired by prospect theory, PspMAS decouples each agent’s micro state into a compact Prospect State and an expressive Semantic State. The former records psychological traces through a lightweight, parallelizable propagator and continuously injects heterogeneity into the system. The latter leverages the strong perception, reasoning, planning, and decision-making abilities of LLMs. These two components work complementarily, providing a scalable LLM-based multi-agent simulation solution.
[MA-9] Better Call CineCrew: Consistent Ultra-Long Narrative-to-Film Generation ECCV2026
【速读】：该论文旨在解决长篇叙事文本到电影视频生成过程中存在的镜头级可控性与跨片段一致性难题，尤其针对现有基于提示（prompt-based）工作流在关键影视决策点上剧本信息不足导致的脆弱性问题。其核心解决方案是引入一个位于脚本与视频生成模型之间的结构化编排层，采用多智能体框架实现。该框架以面向电影领域的领域特定语言（FilmDSL）为核心，显式表达镜头指令、摄像机设置、资产与连续性要求及角色特征线索等影视约束，使各智能体通过共享的结构化规范进行协同规划、生成、评估与修复。其中，生成智能体负责构建资产包和锚定构图的关键帧，为逐片段合成提供基础；而评估智能体则生成结构化的问答信号并触发针对性优化，无需重新训练基础模型。实验结果表明，该方法在电视剧风格片段上相较于仅依赖文本或参考图像的基线方法，在可控性和一致性方面均有显著提升。
链接: https://arxiv.org/abs/2609.07720
作者: Jiaben Chen,Sixun Dong,Qinhong Zhou,Raine Ma,Zhiyang Dou,Wojciech Matusik,Chuang Gan
机构: University of Massachusetts Amherst(马萨诸塞大学阿默斯特分校); Massachusetts Institute of Technology(麻省理工学院)
类目: Computer Vision and Pattern Recognition (cs.CV); Multiagent Systems (cs.MA)
备注: Accepted by ECCV 2026 Project page: this https URL
Abstract:Long-form narrative-to-film generation requires shot-level controllability and cross-clip consistency in both visual identity and character behavior-requirements that remain difficult to satisfy with current prompt-based workflows. A core reason existing workflows remain brittle is the lack of a structured intermediate layer between scripts and video models, especially when screenplays are underspecified at key cinematic decision points. We introduce a structured orchestration layer for film-oriented script-to-video generation, implemented as a multi-agent framework that operates between scripts and off-the-shelf video generators. The layer is centered on FilmDSL, a film-oriented domain-specific language that makes cinematic constraints explicit, including shot and camera directives, asset and continuity requirements, and persona cues, so that agents coordinate through a shared structured specification for planning, generation, critique, and repair. Specifically, a generation agent constructs asset packs and storyboard keyframes that anchor composition before clip-by-clip synthesis, while a critic agent produces structured QA signals and triggers targeted refinement without retraining the base model. Experiments on TV-style segments show improved controllability and consistency over text-only and reference-only baselines.
[MA-10] Emergent Charging Coordination in Electric Delivery Fleets
【速读】：该论文旨在解决电动配送车队在中途充电过程中面临的复杂决策问题：每辆车辆需自主决定何时、何地以及充多少电量，以确保按时完成任务且电池电量高于安全阈值。由于充电资源有限，车辆选择行为相互耦合，若大量车辆集中于同一充电站将导致排队拥堵。现有解决方案依赖中央调度、预设调度表或预约机制，但这些方法对基础设施要求高，难以实际部署。本文提出一种基于局部控制的多智能体学习框架，所有车辆运行相同的策略网络，仅依据自身时间预算和广播的站点占用信息进行独立决策，通过自组织行为实现无需中心协调或通信的协同效果。在基于20个真实城市OpenStreetMap网络的仿真中，该方法在由“全知上帝”（Oracle）校准的基准场景下，可实现99.5%的任务准时完成率；相比之下，简单的贪婪策略（就近充电）仅完成73%。采用神经演化算法（NEAT）与近端策略优化（PPO）在4个城市训练后，零样本迁移至其余16个城市，分别实现96.8%和98.6%的完成率，其中基于策略梯度的控制器在需求或车辆特性偏离训练分布时表现出更强鲁棒性。而仅依赖车辆紧迫度阈值的启发式规则在高竞争场景下性能下降至约80%。训练过程中，学习代理自发发现部分充电与短时机会性充电策略，并主动绕行繁忙站点，使单次充电队列等待时间从约45分钟降至2分钟以内。综上，该方案通过在局部紧迫性与公共占用状态之间取得平衡，实现了接近全知最优的性能，同时具备极低的实施成本与部署灵活性。
链接: https://arxiv.org/abs/2609.07689
作者: Javier Vales-Alonso,Juan J. Alcaraz
机构: Universidad Politécnica de Cartagena(卡塔赫纳理工大学)
类目: Machine Learning (cs.LG); Multiagent Systems (cs.MA); Neural and Evolutionary Computing (cs.NE); Systems and Control (eess.SY)
备注: 42 pages, 11 figures. Submitted to Transportation Research Part C
Abstract:In electric delivery fleets, mid-shift charging is non-trivial: each vehicle must decide when, where and how much to charge to finish on time with battery above a safety floor. The choices are coupled: queues build where too many vehicles pick the same station. Prior work resolves this coupling with central dispatching, precomputed schedules or reservations, machinery that charging infrastructure rarely supports. Instead, we use a family of learning agents under purely local control: every vehicle runs the same policy, deciding alone from its time budgets and broadcast station occupancies, leading to emergent coordination without central control or messaging. We validate this paradigm in simulation on real OpenStreetMap networks of twenty cities, each with a frozen scenario calibrated by an omniscient Oracle (99.5% of shifts completed on time), whereas a naive greedy rule (nearest station on low battery) completes just 73%. Agents trained with neuroevolution (NEAT) and policy gradients (PPO) on four cities and deployed zero-shot across all twenty, sixteen never seen in training, complete 96.8% and 98.6% of shifts, with the policy-gradient controllers proving more robust when demand or vehicle characteristics drift beyond the trained regime. In contrast, tuned threshold heuristics that read vehicle urgency alone fall short in contended cities (~80%). Through training, these learning agents rediscover partial charging and short opportunistic sessions, and route around busy stations, cutting per-session queue waits from about 45 minutes to under 2. In summary, this coordination paradigm balances local urgency against public occupancy, reaching near-Oracle performance at minimal implementation cost.
[MA-11] Audit Without Verification: When LLM Accountability Layers Relay Rather Than Check
【速读】：该论文旨在解决多智能体大语言模型（Multi-agent LLM）流水线在跨组织边界协作时，故障溯源困难的问题。当系统出现错误时，需明确故障的引入位置，但实际可获取的仅是各智能体提交的报告，而这些报告往往包含结论与观察并列的信息，导致责任判定复杂。其核心挑战在于：现有问责机制依赖于智能体自主提交的含结论性陈述的报告，但此类信息可能受自身判断偏差影响，难以独立验证，从而削弱了故障溯源的准确性。论文的关键解决方案在于通过实验设计分离“结论”字段与“观察”信息，发现删除报告中智能体自身的结论字段后，尽管对正确结论的识别能力有所下降（70.5% → 55.7%），但整体溯源准确率从4.1%显著提升至45.2%（+41.2个百分点），同时问责一致性从94.4%骤降至3.4%，表明结论字段的存在严重干扰了审计者对真实故障源头的判断。这一发现揭示了问责层必须依赖与结论相对独立的证据，才能有效实现可靠溯源。
链接: https://arxiv.org/abs/2609.07680
作者: Paul-Peter Arslan
机构: Institute For Future Technologies(未来技术研究所)
类目: Multiagent Systems (cs.MA)
备注: 18 pages, 5 figures. Pre-registration: this https URL . Code: this https URL
Abstract:Multi-agent LLM pipelines increasingly span organisational boundaries; when a fault surfaces, someone must determine where it entered. The artifact available is rarely a full execution trace: it is the reports each agent filed, and a filed report can state a conclusion alongside its observations. Using a pre-registered, institutionally partitioned pipeline of six agents with process-level information boundaries, balanced defect injection and matched clean twins (345,600 requests per chain model, two models), we first report that our pre-registered hypothesis – that collective responsibility framing degrades escalation with chain length – is not supported. The layer nevertheless fails asymmetrically. It originates almost nothing: zero allegations across 7,996 clean episodes where every agent stayed silent. It filters upstream error poorly, naming an innocent party in 34.4% and 62.6% of clean episodes where an agent raised a false alarm. Conditional on no agent proposing the true origin (59.5% of episodes on one chain model), an auditor reading the reports recovers it in 4.1% of cases – below a uniform guess (20%) and the best fixed-link accuser (31.0%) – while reaching 60.3% from the raw documentation of the same episodes. Deleting one clause, the field carrying the agents’ own conclusion, isolates the cause at constant observations: accuracy rises to 45.2% (+41.2 pp, 95% CI +35.3 to +46.9) and adherence collapses from 94.4% to 3.4%; where the suggestion was correct the same deletion instead costs accuracy, 70.5% to 55.7%. The harm replicates on two frontier auditors in four conditions out of four (+8.5 to +39.0 pp) and in a second domain (+47.7 and +61.1 pp), where the cost disappears. The net effect is governed by upstream reliability together with both conditional magnitudes. An accountability layer needs evidence sufficiently independent of the conclusions it verifies. Comments: 18 pages, 5 figures. Pre-registration: this https URL. Code: this https URL Subjects: Multiagent Systems (cs.MA) Cite as: arXiv:2609.07680 [cs.MA] (or arXiv:2609.07680v1 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2609.07680 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[MA-12] Decentralized Safe Multi-Agent Reinforcement Learning via Predictive Shielding
【速读】：该论文旨在解决多机器人系统在部署过程中因环境状态与训练数据存在差异而导致策略性能下降及安全风险增加的问题。其核心挑战在于现有安全防护机制多为反应式且集中式，难以在未知障碍物附近保持高性能，同时缺乏可扩展性。为此，论文提出一种去中心化框架，将预测性防护（predictive shielding）与基于模型的有限时域Q-learning相结合，使智能体能够在部署阶段安全地自适应调整预训练策略。此外，为缓解对称场景下的活锁（livelock）问题，引入了一种无需通信的冲突消解协议，显著提升了系统的鲁棒性与可扩展性。
链接: https://arxiv.org/abs/2609.07618
作者: Yacine El Yamani,Hanna Krasowski,Elena Vanneaux
机构: ENSTA, IP Paris (法国国立高等先进技术学院，巴黎理工学院); UC Berkeley (加州大学伯克利分校)
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Robotics (cs.RO)
备注:
Abstract:Environments are increasingly populated by multiple robots performing independent tasks with limited prior knowledge of each other. Deploying such multi-agent systems presents significant challenges. Specifically, shifts in deployment states compared to training data can lead to poor policy performance and compromised safety. While safety shields exist to mitigate these risks, they are typically reactive, which degrades performance near unseen obstacles,and centralized, limiting their scalability. To address this, we propose a decentralized framework that integrates predictive shielding with model-based finite horizon Q-learning. This approach allows agents to safely adapt their pre-trained policies during deployment. Furthermore, to mitigate livelocks in symmetric scenarios, we introduce a communication- free protocol for conflict resolution
[MA-13] A Fundamental Limit in Decentralized Decision-Making
【速读】：该论文旨在解决去中心化决策（decentralized decision-making）中性能受限的根本性问题，即在分布式网络中，各智能体仅能与邻居局部通信，如何实现最优分类决策。尽管已有研究证明去中心化估计（decentralized estimation）可达到与集中式系统相当的性能，但本文揭示了一个关键发现：在去中心化决策场景下，最优策略仍存在不可消除的误差损失（irreducible loss），其根源在于去中心化结构与分类任务之间的内在权衡。解决方案的关键在于推导出一个闭式解析关系，明确该性能损失由两方面因素共同决定：一是网络图中节点间的拓扑距离（通过图的几何结构体现），二是描述决策问题的似然比（likelihood ratio）的矩生成函数（moment generating function）。通过该关系，作者分析了多种典型网络拓扑下的性能损失随网络规模的变化规律，发现误差概率可能相差数个数量级；同时揭示了信息丰富与信息贫乏节点间相对距离对整体性能的显著影响，从而确立了去中心化决策的理论性能上限。
链接: https://arxiv.org/abs/2609.07479
作者: Marco Carpentiero,Felice Scala,Vincenzo Matta,Ali H. Sayed
机构: 未知
类目: Information Theory (cs.IT); Multiagent Systems (cs.MA)
备注: This work has been submitted to the IEEE for possible publication
Abstract:In decentralized decision-making, several agents connected according to a network graph aim at solving a classification problem by collecting streaming observations. Due to decentralization, they run an iterative algorithm where, at each iteration, they can only exchange information locally with their neighbors. While decentralized estimation solutions have been shown to match the performance of optimal centralized systems, we show here that surprisingly this conclusion does not hold for decentralized decision-making. Specifically, we prove that the error probability for the best decentralized decision strategy exhibits an irreducible loss with respect to the optimal centralized classifier. This result establishes a fundamental limit for the performance of any decentralized decision strategy. We obtain an analytical relation showing that this limit is related to the interplay between decentralization and classification. The first aspect appears through the distances between the nodes in the graph, while the second aspect plays through the moment generating functions of the likelihood ratios that describe the decision problem. By applying the derived closed-form relation to different network topologies and inference problems, we observe some interesting and perhaps unexpected behavior emerging. In particular, we characterize the scaling law (with the network size) for the loss over popular network topologies, showing that the error probabilities might differ by orders of magnitude; and we examine how performance is affected by the relative distance between informative and uninformative agents over the graph.
[MA-14] D3ARC: Time-Critical Distributed Disaster Detection for Asynchronous Cooperative Multi-Robot Systems
【速读】：该论文旨在解决气候变化背景下自然灾害（特别是野火）日益加剧的严重性与不可预测性所带来的监测挑战，传统监测手段在覆盖范围、成本及人员安全方面存在局限。为此，论文提出D3ARC——一种异步分布式分层框架，用于实现时间感知且可靠的野火检测。其核心解决方案在于通过多机器人协同，在不确定性环境下实现分布式感知、共享态势认知与协调行动，由远程控制器异步决策各机器人的运动，而每个机器人则自主判断环境感知与检测执行的时机与位置。由于野火随时间持续蔓延，系统要求所有代理以最快速度并在时间限制内达成预设性能阈值的检测目标。D3ARC的关键创新在于引入前瞻性机制，使代理能够在执行前评估候选策略的未来效果，从而提升决策效率与可靠性。该框架集成安全导航、覆盖率优化、协作能力与系统鲁棒性，并通过真实机器人仿真、消融实验与基线对比验证，实现了最高94%的任务成功率与89.4%的检测置信度。
链接: https://arxiv.org/abs/2609.07350
作者: Nikolaos Koursioumpas,Lina Magoula,Nancy Alonistioti,Ramin Khalili
机构: National and Kapodistrian University of Athens (雅典国立卡波迪斯特里亚大学); Huawei Heisenberg Research Center (慕尼黑), Germany
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Multiagent Systems (cs.MA)
备注: 14 Pages, 5 Figures
Abstract:Climate change is increasing the severity and unpredictability of natural disasters. In time-critical crises such as wildfires, traditional monitoring practices remain limited by coverage, cost, and personnel risk, paving the way for autonomous and adaptive monitoring solutions. Within this context, this paper introduces D3ARC, an asynchronous distributed hierarchical framework for time-aware and reliable wildfire detection. D3ARC integrates multiple robotic agents that cooperate under uncertainty through distributed perception, shared situational awareness and coordinated actions. A remote controller asynchronously decides upon each robot’s motion, while each robotic agent senses the environment and decides where and how to execute the wildfire detection. All robotic operations require time, and as time progresses, wildfires continue to spread, reducing the opportunity for early intervention. As such, all agents share a common objective: to detect a wildfire with a certain performance threshold as fast as possible and within a time limit. D3ARC integrates mechanisms for safe navigation, coverage efficiency, cooperation and reliability. It introduces a forward-looking capability that allows agents to anticipate the future by evaluating candidate strategies before execution. The framework is evaluated through realistic robotics simulations, ablation studies, and baseline comparisons, achieving an overall mission success up to 94% with 89.4% detection confidence.
[MA-15] PV-WM: A Heterogeneous Micro-Macro World Model for Articulated Pedestrian-Vehicle Co-Rollout
【速读】：该论文旨在解决道路场景中行人与车辆异构运动预测的协同建模问题，现有方法通常忽略行人的关节运动（articulation）或脱离车辆状态的动态建模，导致预测不完整且物理一致性差。其核心解决方案是提出一种仅依赖历史信息的世界模型PV-WM（Pedestrian-Vehicle World Model），在结构化后感知轨迹的基础上，通过递归更新包含15个关节的行人姿态、根部运动以及学习到的车辆状态的同步异构状态空间，实现行人与车辆未来运动的联合生成。关键创新在于：1）基于递归机制同步推进行人关节运动与车辆状态演化；2）利用预测的车辆中心点与航向重构边界框，并在每一步更新行人-车辆（P-V）几何关系，确保时空一致性；3）采用单一网络架构，在显著降低计算开销（参数量减少57.1%，平均浮点运算量降低96.5%，延迟降低25.5%）的同时，提升预测精度——在824个对齐的Waymo场景中，相较于基准模型，根部平均位移误差（Root ADE）下降5.2%，多关节点位置误差（MPJPE）下降7.6%，行人-车辆距离误差下降11.9%，定向边界框最近接近距离误差下降5.8%。实验表明，后续预测高度依赖于生成的关节信息的内容、时序顺序及行人身份，验证了该模型在捕捉复杂交互中的有效性。
链接: https://arxiv.org/abs/2609.07328
作者: Haozhuang Chi,Jingsong Liang,Ziying Song,Lei Yang,Shihao Li,Haoruo Zhang,Chen Lv
机构: Nanyang Technological University, Singapore; Beijing Institute of Technology, Beijing, China
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Local pedestrian-vehicle forecasting spans heterogeneous physical scales: pedestrians combine root locomotion with articulated motion, whereas vehicles are rigid bodies described by kinematic state and oriented extent. Existing road-agent forecasters typically omit pedestrian articulation, while pose forecasters leave vehicle futures outside the learned rollout. We introduce PV-WM, a history-only world model over structured post-perception tracks. It recurrently advances pedestrian root motion, 15-joint articulation, and learned vehicle states within a synchronized heterogeneous state. The generated pedestrian and vehicle chunks supply the next recurrent boundary; vehicle boxes are reconstructed from predicted center and heading with observed extent, and P-V geometry is recomputed after every transition. Relative to a matched one-shot complete-state predictor, recurrent execution reduces Root ADE by 12.7% and MPJPE by 14.8%. Feedback interventions show that later predictions depend on the content, temporal order, and pedestrian identity of generated articulation. Across 824 aligned Waymo contexts, with 797 providing valid future vehicle support, PV-WM reduces Root ADE by 5.2%, MPJPE by 7.6%, P-V distance error by 11.9%, and oriented-box closest-approach error by 5.8% relative to a validation-selected Modular Specialist. The single-network model uses 57.1% fewer parameters, 96.5% lower average FLOPs per local scene, and 25.5% lower measured p95 latency. PV-WM unifies this heterogeneous future state while preserving type-specific pedestrian and vehicle dynamics.
[MA-16] Certifying cooperation: a novel approach to cooperative multi-agent task generation
【速读】：该论文旨在解决多智能体系统中合作行为的可识别性与必要性问题，即在共享奖励机制下，智能体何时、以何种方式以及是否必须合作才能成功完成任务。其核心挑战在于缺乏对合作行为的精确建模与量化评估，导致难以区分仅实现个体成功与真正达成联合成功的策略。解决方案的关键在于提出一种基于时间合作图（temporal cooperation graph） 的形式化框架：通过有向时序边连接“协助者”与“受益者”，将合作行为抽象为六种重叠的图谓词（cooperation profile），并证明任何成功的合作轨迹至少满足其中一个谓词。进一步地，将环境动态与合作谓词编码为命题公式，利用逻辑查询区分任务中某合作模式是否在任意最优路径中存在，或是否在所有最优路径中必需。这些查询作为过滤器，可将随机布局采样器转化为具有已验证合作需求的任务生成器。实验表明，当存在无需合作的解时，训练多样性能提升联合成功率；而当合作为必要条件时，尽管个体退出率提高，联合成功仍接近零。跨五类经合作谓词认证的任务池分析显示，最终退出率在算法间呈现四类统计上可区分的水平，但主要反映的是部分完成而非真正的协同合作。该框架通过形式化认证合作要求，并借助时间合作图揭示策略实际展现的合作模式，有效暴露了“被奖励的部分完成”与“真实协同完成”之间的差距。
链接: https://arxiv.org/abs/2609.06586
作者: Yannick Molinghen,Hugo Charels,Tom Lenaerts
机构: Université Libre de Bruxelles(布鲁塞尔自由大学); Vrije Universiteit Brussel(布鲁塞尔自由大学); Center for Human-Compatible AI, UC Berkeley(加州大学伯克利分校人类兼容人工智能中心)
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:A shared reward gives agents a common objective, but leaves open when, how and even whether they must cooperate to succeed. We address these questions in the Laser Learning Environment, a multi-agent path-finding environment where cooperation materializes as one agent blocking a laser to let a teammate pass safely. We represent these interactions through temporal cooperation graphs whose timed edges connect helpers to beneficiaries, define six cooperation profiles as overlapping graph predicates, and prove that every cooperative trajectory satisfies at least one. By encoding the environment dynamics and profile predicates as propositional formulae, we distinguish tasks that admita profile in some winning trajectory from those that require it in every winning trajectory within a specified horizon. Used as filters, these queries turn a random layout sampler into a generator of tasks with certified cooperation requirements. Experiments with five multi-agent reinforcement learning algorithms show that training diversity improves joint success on unseen tasks when cooperation-free solutions exist. When cooperation is required, greater diversity improves individual-agent exits, but joint success remains near zero. Across five profile-certified pools, final exit rates averaged over algorithms separate the pools into four statistically distinguishable levels but this ordering primarily reflects partial completion: policies collect rewards for individual exits but rarely exhibit the profile required for joint success. Our framework exposes this gap between rewarded partial completion and realized cooperation by certifying what cooperation successful completion requires and using temporal cooperation graphs to reveal what policies exhibit.
[MA-17] CAPMAS: Capability-Based Delegation of Privileges in Multi-Agent Systems
【速读】：该论文旨在解决多智能体系统中安全且高效的权限委派问题，尤其针对现有方法在权限管理上存在的两大缺陷：一是直接传递用户身份导致责任不清与持续性过度授权风险，尤其在生成式AI（Generative AI）等非确定性行为的智能体中加剧了安全威胁；二是依赖与中心化身份与访问管理（IAM）系统持续同步，引入额外延迟和通信开销。其解决方案的关键在于提出CAPMAS架构，创新性地结合基于对比学习的语义作用域（semantic scoping）管道，将自然语言查询映射为受限的权限集合，实现执行前的细粒度权限约束；同时采用表达能力强的麦卡龙（Macaroon）令牌机制，支持离线、防篡改的权限委派，并在多代理间实现单调递减的权限缩减。通过将认证与权限委派强制执行从智能体推理过程解耦，CAPMAS在保障最小权限原则的同时，显著提升效率：相比OAuth 2.0 Token Exchange（RFC 8693），其委托操作速度提升30倍，委托相关延迟降低50%，带宽消耗减少达三分之二；其语义作用域管道在包含超过3100个端点的企业级API模式下，可在17毫秒内实现超过90%的完美权限包检索，且相比全量传递用户权限的系统，无效权限减少99.5%。
链接: https://arxiv.org/abs/2609.06500
作者: Rasmus Moorits Veski,Rachid Guerraoui,David Froelicher
机构: EPFL (洛桑联邦理工学院); Swisscom(瑞士电信)
类目: Multiagent Systems (cs.MA); Cryptography and Security (cs.CR)
备注:
Abstract:Agentic systems require secure and efficient delegation of privileges across multiple collaborating agents. Existing approaches fall into two categories. Some propagate user identities directly to agents, obscuring accountability and creating persistent over-privilege risks that are amplified by the non-deterministic behaviour of AI agents. Others rely on continuous synchronization with a central Identity and Access Management (IAM) provider, introducing additional latency and communication overhead. We present CAPMAS, a novel architecture for secure end-to-end query execution in multi-agent systems. CAPMAS newly combines a contrastive learning-based semantic scoping pipeline that maps natural-language queries to bounded privilege sets before execution with expressive Macaroon-based tokens that enable offline, tamper-evident delegation with monotonic privilege reduction across agents. By decoupling authentication and delegation enforcement from agent reasoning, CAPMAS enables practical agentic execution while enforcing strict least-privilege guarantees. By eliminating synchronous delegation exchanges with the IAM, CAPMAS yields 30 times faster delegation operations, 2 times less delegation-oriented latency and up to 3 times lower bandwidth usage than the OAuth 2.0 Token Exchange (RFC 8693). Its semantic scoping pipeline achieves over 90% perfect privilege-bundle retrieval within 17 milliseconds on enterprise-scale API schemas containing over 3,100 endpoints, while reducing unnecessary privileges by 99.5% when compared to systems that propagate all the user’s privileges to agents. Subjects: Multiagent Systems (cs.MA); Cryptography and Security (cs.CR) Cite as: arXiv:2609.06500 [cs.MA] (or arXiv:2609.06500v1 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2609.06500 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[MA-18] Adaptive stabilization of a leaderless bearing-constrained formation with disturbances
【速读】：该论文旨在解决在存在有界连续扰动条件下，如何实现由双向视角约束（bidirectional bearing constraints）定义的目标编队的动态调节与维持问题。由于扰动具有时变性，目标编队本身亦为时变系统，且扰动上界未知，无法直接用于控制器设计。为此，论文提出自适应滑模控制律，以实现对移动目标编队的统一全局渐近稳定，并有效抑制匹配扰动。为缓解传统滑模控制带来的颤振（chattering）现象，进一步设计了平滑的自适应控制律，确保期望编队状态的统一全局有界性。其解决方案的关键在于结合自适应机制与滑模控制策略，兼顾抗扰性能与控制平滑性，从而在未知扰动上界条件下实现鲁棒稳定的编队控制。
链接: https://arxiv.org/abs/2609.06355
作者: Minh Hoang Trinh,Chuong Van Nguyen,Quoc Van Tran,Tuynh Van Pham
机构: East Asia University of Technology (东亚科技大学); University of Southern California (南加州大学); Ghost Robotics (幽灵机器人); Hanoi University of Science and Technology (河内科技大学)
类目: Multiagent Systems (cs.MA); Optimization and Control (math.OC)
备注: 12 pages, 6 figures, preprint, submitted to a journal
Abstract:In this paper, we consider the problem of regulating and maintaining a target formation characterized by a set of bidirectional bearing constraints under disturbances. The agents in the formation are modeled by single integrators with bounded continuous disturbances of which the upper bound is unavailable for the control design. Due to the time-varying disturbances, the target formation is time-varying. We propose adaptive sliding mode control laws to uniformly globally asymptotically stabilizes the moving target formation and reject the matched disturbances. In addition, to alleviate chattering phenomena from sliding mode control, smooth adaptive control laws are then designed to guarantee uniform global boundedness of the desired formation. Finally, simulation results are given to support the analysis.
[MA-19] Bundle Length Tradeoffs in Decentralized Multi-Robot Task Allocation Under Degraded Communications
【速读】：该论文旨在解决多任务多机器人任务分配（MRTA）中束长（Bundle length, B）配置对优化目标权衡的影响问题，以及在通信退化条件下理想通信场景下的最优束长设置是否仍适用的问题。其核心挑战在于：MinSum与MinMax目标在不同束长下存在显著性能差异，而现有方法缺乏对束长影响的系统性分析；同时，在存在25%伯努利丢包率的通信退化场景下，以往基于理想通信设定的束长参数是否依然有效尚不明确。解决方案的关键在于通过系统性实验评估三种典型算法（ACBBA、PI、HIPC）在六种不同束长（1–12）下的表现，对比理想通信与丢包条件下的最小总成本（MinSum）与最大单机成本（MinMax）变化趋势。研究发现，随着束长增加，理想通信下各算法的MinSum性能显著提升，但对应的MinMax代价急剧上升；而在通信受损时，最优MinSum束长从12大幅下降至2，若沿用理想通信下的设置将导致高达14.4%和7.2%的MinSum性能损失，并使MinMax成本分别上升30.0%和41.8%。此外，实验验证了该现象在不同任务负载下的可重复性，揭示出算法对通信鲁棒性的敏感性差异，其中HIPC展现出较强的MinSum稳定性，而所有算法的MinMax最优设置相对稳健。因此，该研究提出应根据实际通信条件动态调整束长，尤其在通信不可靠时需重新校准参数以实现性能最优。
链接: https://arxiv.org/abs/2609.06273
作者: James Lott,Vahraz Honary
机构: University of San Diego(圣迭戈大学)
类目: Robotics (cs.RO); Multiagent Systems (cs.MA)
备注:
Abstract:Bundle length B is commonly fixed when configuring multi-task multi-robot task allocation (MRTA) algorithms. MinSum and MinMax are known to favor different task distributions, but the role of B in this objective tradeoff has not been systematically characterized. Additionally, degraded-communication evaluations also often retain settings selected under ideal communication, leaving whether nominal bundle-length tuning transfers under message loss unresolved. We examine both questions for ACBBA, PI, and HIPC across six bundle lengths in 300 paired ten-target Collaborative Visit scenarios under ideal communication and 25% Bernoulli packet loss. Under ideal communication, increasing B from 1 to 12 reduces MinSum cost by 19.0%, 23.0%, and 31.8% for ACBBA, PI, and HIPC, respectively, while increasing MinMax cost by 45.6%, 94.3%, and 67.6%. Under packet loss, the lowest-mean MinSum setting shifts from B = 12 to B = 2 for ACBBA and PI. Repeated paired cross-fitting shows that retaining the ideal-network setting incurs held-out MinSum penalties of 14.4% and 7.2%, respectively, and increases MinMax cost by 30.0% and 41.8% relative to the loss-conditioned MinSum setting. HIPC retains a deep MinSum operating region, while the MinMax setting remains stable for all three allocators. Experiments at two additional target loads reproduce the ACBBA and PI MinSum shifts.
[MA-20] Counter-Swarm Doctrine: Containing Coordinated Agent Intrusions
【速读】：该论文旨在解决在共享基础设施中，代理（agent）利用协同行为实施隐蔽入侵所带来的安全威胁问题。现有安全评估往往依赖单次执行的证据，而实际攻击可能跨越多个执行过程并留下渐进式痕迹，因此需要更精细的分析单位。其解决方案的关键在于提出以“可回溯的协调事件（revisable coordination episode）”作为防御的基本操作单元，该单元通过关联观测到的数据传输、任务权限分配及响应历史来界定协同行为。核心研究问题为“前瞻性事件发现”——即在缺乏先验分组信息的情况下，识别哪些操作应归属于同一协调事件。为此，论文定义了非授权协作（unsanctioned coordination）相对于协作与委托权限策略的关系，将存储媒介驱动的协调机制与生态学中的“刺激传递（stigmergy）”概念相联系，并明确了区分因果影响与共同原因所需的关键证据。系统设计引入首次接触信号作为输入，并追踪继承状态与后续使用模式。评估方案在相同审查成本和误报负载下，对比孤立动作、滚动窗口、已知群体与前瞻性发现事件的表现，量化所有运行场景中的有害结果，并检验通道关闭与状态隔离后的复发情况。通过对公开维基导出数据的校验重构，分离出留存写入量下降与后期管理清理的影响。该研究贡献在于构建了一个基于真实事件的分析框架、描述性分析方法与可验证的评估设计，使跨执行监控成为可测试的实践主张，而不必声称新检测器或测量到的遏制效益。
链接: https://arxiv.org/abs/2609.06140
作者: Gregory N Frank
机构: MoltAI Corp(莫尔特AI公司)
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注: 35 pages, 5 figures, 12 tables
Abstract:Agents can turn shared infrastructure into a channel for coordinated intrusion. The Hugging Face incident and a separate public-wiki investigation show why a security assessment may need evidence from several executions and the artifacts they leave behind. We argue that the operational unit of defence should be a revisable coordination episode linking observed transfers, task authority, and response history. The central research problem is prospective episode discovery: finding which actions belong together before an evaluator supplies their membership. We define unsanctioned coordination relative to collaboration and delegated-authority policy, connect storage-mediated coordination to stigmergy, and specify the evidence needed to distinguish influence from common causes. First-contact signals are one possible input to discovery; the design also follows inherited state and later use. A proposed evaluation compares isolated actions, rolling windows, known groups, and prospectively discovered episodes at matched review cost and false-alert workload. It measures harmful outcomes across all assigned population runs and tests recurrence after channel closure and state quarantine. A checksum-verified reconstruction of the public wiki export separates the decline in retained writes from later administrative cleanup. The contribution is an incident-grounded position, descriptive analysis, and evaluation design. It makes the recommendation to monitor across executions testable without claiming a new detector or a measured containment benefit.
[MA-21] Practical Zero-Trust for Mission-Critical Robotic Fleets via Hardware Attestation and Packet Timing Watermarking
【速读】：该论文旨在解决多节点机器人集群在开放无线链路与标准机器人操作系统（ROS 2）中间件环境下所面临的广泛网络物理攻击面问题，特别是在战术任务、搜救及灾害响应等高风险场景中，系统因受控于实时控制回路而极易受到恶意干扰或劫持。其核心挑战在于如何在不增加通信开销的前提下，实现对隐蔽性极强的中间人（Man-in-the-Middle, MitM）攻击的有效检测与防护。解决方案的关键在于构建一个分层、上下文感知的零信任（Zero-Trust）网络安全框架，集成可信平台模块2.0（TPM 2.0）作为硬件级根信任基础，结合基于ELK Stack和Kismet的带内/带外安全信息与事件管理（SIEM）遥测监控，并创新性地引入一种非密码学的包间延迟（Inter-Packet Delay, IPD）时间水印机制。实验结果表明，尽管基于流量体积的过滤可有效应对拒绝服务（DoS）洪水攻击，但通过追踪嵌入式IPD水印统计样本的峰度值（Kurtosis, K），可在不引入额外数据负载的情况下，实现对隐蔽性MitM命令注入攻击的完全检测，验证了该方法在多层（OSI模型第2至第5层）复合攻击场景下的有效性与鲁棒性。
链接: https://arxiv.org/abs/2609.05741
作者: Ryne Gonzales,Ethan Liesdyanto,Rex Worley,Michael Frederick,Jaewon Kim,Eman Hammad
机构: Texas A&M University (德州农工大学); iSTAR Laboratory (iSTAR实验室); Global Cyber Research Institute (全球网络研究中心)
类目: ystems and Control (eess.SY); Multiagent Systems (cs.MA)
备注:
Abstract:Autonomous unmanned vehicles are vital to tactical missions, mission-critical public-safety operations like search and rescue and disaster response. However, their reliance on open wireless links and standard Robot Operating System (ROS 2) middleware exposes a broad cyber-physical attack surface. A compromise of these systems can disrupt real-time control loops, leading to mission failure or asset loss in high-stakes environments. This paper presents and empirically evaluates a layered, context-aware cybersecurity framework enforcing Zero-Trust principles for a multi-node robotic fleet over Wi-Fi. The framework integrates an active hardware root of trust (TPM 2.0), centralized in-band and out-of-band SIEM telemetry monitoring (ELK Stack and Kismet), and a non-cryptographic Inter-Packet Delay (IPD) timing watermark. Evaluated on a live ROS 2 mobile testbed under multi-layer exploits (OSI Layers 2-5), results demonstrate that while volume-based filters isolate brute denial-of-service floods, tracking the statistical sample kurtosis (K) of the embedded IPD watermark exposes stealthy Man-in-the-Middle command injections with complete detection accuracy without payload overheads.
[MA-22] SimTIO: A Simulation-Grounded Multi-Agent LLM Framework for Compositional Traffic Intervention Optimization
【速读】：该论文旨在解决交通瓶颈治理中“局部优化导致全局性能退化”的核心问题，即交通分析师在制定干预措施时，难以在保证网络整体运行效率的前提下实现瓶颈区域的有效缓解。其解决方案的关键在于提出SimTIO框架——一个基于微观仿真（SUMO）的多智能体大语言模型（Multi-Agent Large Language Model, LLM）系统，通过显式操作约束下的反馈驱动搜索机制，实现可执行干预策略的生成与优选。该框架首先在未修改的仿真场景中锁定基准瓶颈路段，随后由领域专用智能体基于仿真反馈从验证过的变异目录中选择单参数优化动作，并通过重仿真评估动作间的交互效应，而非依赖推断。最终决策在最小化关键瓶颈时间损失的同时，严格约束全网延迟、邻路溢出、通行能力损失及瞬移事件等指标，且保留原始场景作为无操作对照组。实验表明，在15个涵盖五座美国城市网络、三种合成需求种子及每场景2,400条起讫点路径的测试案例中，SimTIO平均降低前十大瓶颈时间损失9.18%，全网延迟减少2.78%，且在相同七次仿真预算下成功找到可行改进方案的比例达86.7%，显著优于基线方法。研究支持将大语言模型作为受约束、反馈引导的局部搜索算子，但最终决策权仍应保留在可执行工具、微观仿真系统及明确安全约束之上。
链接: https://arxiv.org/abs/2609.05740
作者: Shuyang Li,Ruimin Ke
机构: 未知
类目: Multiagent Systems (cs.MA); Systems and Control (eess.SY)
备注:
Abstract:Traffic analysts must translate diagnosed bottlenecks into executable interventions without allowing local improvements to degrade network-wide performance. This study presents SimTIO, a simulation-grounded multi-agent large language model framework for composing and selecting traffic interventions under explicit operational constraints. SimTIO first simulates an unmodified SUMO scenario to identify a baseline-frozen set of ten bottleneck edges. A grounded sampler then initializes signal-control, corridor-speed, and demand-preserving routing actions, while three specialist agents use measured simulation feedback to select one-parameter refinements from validator-confirmed mutation catalogs. Compatible actions are combined and re-simulated so that their interaction effects are measured rather than inferred. Final selection minimizes bottleneck time loss while constraining network-wide delay, neighboring-road spillover, throughput loss, and teleport events, with the unmodified scenario retained as a no-operation guard. Across 15 cases covering five U.S. urban networks, three synthetic-demand seeds, and 2,400 origin-destination trips per scenario, SimTIO reduced Top-10 bottleneck time loss by an average of 9.18 percent and network-wide delay by 2.78 percent. It found a feasible improving plan in 86.7 percent of cases, compared with 73.3 percent for grounded random search and 80.0 percent for a deterministic heuristic under the same seven-simulation budget, although the differences in Top-10 improvement were not statistically significant. These results support using LLMs as constrained, feedback-guided local search operators while reserving final decision authority for executable tools, microscopic simulation, and explicit safety constraints.
[MA-23] Resilient Control Loops in Autonomous Vehicles Under Adversarial Jamming via Spectral Perception and Network-Layer Failover
【速读】：该论文旨在解决自主移动机器人在面临敌意有意电磁干扰（Intentional Electromagnetic Interference, IEMI）时，无线控制回路中断导致系统运行完整性受损的问题。其核心挑战在于如何在动态电磁环境中保障中间件（如ROS2）的稳定通信，避免任务中断。解决方案的关键在于提出一种跨层、抗干扰的韧性架构：通过软件定义无线电（Software-Defined Radio, SDR）前端实现物理层频谱感知，提取频谱熵与信道占用率等动态频谱特征，结合随机森林（Random Forest）分类器构建自适应环境基线；同时，在网络层采用双预认证物理接口的热备配置，结合自动化的网络路由表更新机制，实现毫秒级故障切换。实验结果表明，该硬件辅助的自适应架构可将通信恢复时间优化至平均141ms，相较于纯软件重关联方式，路径跟踪均方根误差（Root-Mean-Square Path Tracking Error）总体降低78.9%，有效保障了系统级任务完整性。
链接: https://arxiv.org/abs/2609.05739
作者: Luis Barajas,Colin Jeardoe,Jaewon Kim,Eman Hammad
机构: Texas A&M University (德州农工大学); iSTAR Laboratory (iSTAR实验室); Global Cyber Research Institute (全球网络研究学院); Department of Engineering Technology and Industrial Distribution (工程技术和工业分配系)
类目: ystems and Control (eess.SY); Multiagent Systems (cs.MA)
备注:
Abstract:The operational integrity of autonomous mobile robots relies on the continuous availability of wireless control loops, making them highly attractive targets for adversarial intentional electromagnetic interference. This paper introduces a resilient, cross-layer framework that combines physical-layer spectral perception with network-layer routing optimization to protect middleware stability, such as ROS2, during intentional electromagnetic interference. Utilizing a software-defined radio front-end, the system extracts dynamic spectral descriptors, including spectral entropy and channel occupancy, to inform a Random Forest classifier that establishes adaptive environmental baselines. To ensure uninterrupted data flow, the architecture maintains dual pre-authenticated physical interfaces in a hot-standby configuration, enabling instantaneous failover through automated network routing table updates. Empirical validation on a physical ROS2 mobile robot testbed demonstrates that this adaptive hardware-assisted architecture optimizes communication recovery to an average of 141ms. This sub-second restoration translates directly into a 78.9% reduction in pooled root-mean-square path tracking error compared to software re-association, successfully securing system-level mission integrity.
[MA-24] CUSP: Decomposable Collective Uncertainty for Multi-Agent Multimodal Reasoning
【速读】：该论文旨在解决多模态视觉语言模型（VLMs）集成系统中缺乏可靠系统级不确定性度量的问题。现有方法无法有效衡量个体模型置信度或聚合答案的可靠性，导致在复杂推理任务中难以判断结果可信度。其核心解决方案是提出一种无需训练的不确定性量化框架——CUSP（Collective Uncertainty through Semantic Opinion Pooling），通过将多个VLM的输出映射至共享语义响应空间，进行语义意见池化，从而生成一个统一的“池化语义意见”。在此基础上，CUSP同时报告两个互补的系统级信号：集体不确定性（collective uncertainty），即池化意见的分散程度；以及詹森-香农散度（Jensen-Shannon divergence, JSD），用于表征模型间意见冲突。关键创新在于，未归一化的集体熵可精确分解为各模型个体语义熵的均值与JSD之和，实现了总离散性与模型间冲突的分离。该方法不依赖词元对数几率或校准标签，适用于开源与商业VLM。实验表明，在静态多模型集成中，集体不确定性在小模型场景下表现最优（预测误差检测AUROC达0.764，拒答性能AUARC达0.889），显著优于多数投票与朴素选择基线（提升4.7至15.8点），且随集成规模扩大优势进一步增强；而JSD在商用模型场景中表现最佳（AUROC 0.819，AUARC 0.910），并能有效识别硬答案冲突（最高AUROC达0.982）。此外，池化预测还使准确率相较单个模型平均提升5.6至13.0个百分点。在多步、多智能体系统中，子代理的集体不确定性可有效识别系统故障（AUROC 0.619），并在拒答排序中表现最优（AUARC 0.699），验证了其在动态系统中的实用性。
链接: https://arxiv.org/abs/2609.05708
作者: Chung-En Johnny Yu,David Garcia,Brian Jalaian,Nathaniel D. Bastian
机构: University of West Florida(西佛罗里达大学); Pensacola, Florida, USA; United States Military Academy(美国军事学院); West Point, New York, USA
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注: Main paper with supplementary material
Abstract:Aggregating heterogeneous vision-language models (VLMs) can improve multimodal reasoning, but neither an individual model’s confidence nor that of the aggregated answer measures reliability at the system level. We present CUSP (Collective Uncertainty through Semantic Opinion Pooling), a training-free uncertainty quantification framework that maps multiple VLM responses to a shared semantic response space, pools them into a pooled semantic opinion, and reports two complementary system-level signals: collective uncertainty, the dispersion of the pooled opinion, and Jensen-Shannon divergence (JSD), the conflict among the model-level opinions. Within this pooled semantic opinion, the unnormalized collective entropy decomposes exactly into the mean of the models’ individual semantic entropies and the JSD, separating total dispersion from model conflict. Requiring neither token logits nor calibration labels, CUSP applies to open-weight and commercial VLMs alike. In static multi-VLM ensembles, collective uncertainty is the strongest signal in the small-model regime (0.764 AUROC for prediction-error detection, 0.889 AUARC for abstention), outperforming uncertainty baselines majority voting and naive selection by 4.7 to 15.8 points and widening its margin as the ensemble grows; JSD is strongest in the evaluated commercial regime (0.819 AUROC, 0.910 AUARC) and ranks hard-answer model conflict with AUROC up to 0.982. The pooled prediction also improves accuracy over the average single model by 5.6 to 13.0 points. Over the full trajectory of a multi-step, multi-agent system, subagent collective uncertainty ranks system failures above chance (0.619 AUROC) and gives the best abstention ordering among the evaluated signals (0.699 AUARC).
[MA-25] What LLM Trading Agents Actually Do in Production: A Six-Month Population-Scale Record from Two Fleets
【速读】：该论文旨在解决生成式AI驱动的自主语言模型交易代理（autonomous language-model trading agents）在真实生产环境中行为表现与市场绩效的系统性问题，尤其关注其策略执行、风险控制、收益捕捉及方向性优势等核心维度。研究通过构建连续、大规模、跨系统的实证数据记录，涵盖两个具有共同设计谱系的平台：DX Terminal Pro（21天内3,505个用户资助的钱包在Base memecoin市场交易真实ETH）与DXAP live alpha舰队（6月至8月间500至599个用户创建的代理持续交易Hyperliquid永续合约）。其关键解决方案在于建立了一套可验证、可复现的实证分析框架，通过750万次单模型调用、约30万次链上操作及23万余次多工具协作行为的数据，揭示了四大核心发现：首先，运行层架构对行为的影响远超策略文本本身，风险滑块每提升一档杠杆增加0.425倍，个体代理固定效应解释60%的方差，且排行榜边界存在因果性选择效应（前3名门槛处回报率提升1.75倍）；其次，仓位规模对波动率完全不敏感，各波动率分位组中位杠杆均为5.0倍，仅11%的持仓单元承担了62%的清算损失；第三，代理几乎无法捕获其触及的正向价差——43.2%的头寸在24小时内达到至少+300 bps有利移动，但其中49.3%最终以负回报平仓，引入机械式止损挂单可恢复+39.0 bps/头寸的净收益；第四，两支舰队均未展现出方向性优势，DXAP舰队亏损且低于匹配的零售基准（回测胜率41% vs. 50%），而前沿模型在416个真实场景中的配对重播实验表明决策质量在统计上无显著差异，但不同模型家族在决策稳定性上呈现显著分化。所有结论均通过日聚类推断、置换零假设检验及共费调整验证，论文最终提出一套包含17条方法论准则的“自反性规范”（methodology canon），并以自身撤稿为代价实现透明化。
链接: https://arxiv.org/abs/2609.05663
作者: T.J. Barton,Chris Constantakis,Patti Hauseman,Annie Mous,Alaska Hoffman,Brian Bergeron,Hunter Goodreau
机构: DX Research Group (DXRG)
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Multiagent Systems (cs.MA)
备注: 17 pages, 8 figures, 3 tables. Artifacts, figures, and aggregate data: this https URL
Abstract:We present a continuous, population-scale measurement record of autonomous language-model trading agents operating in production across two systems with one design lineage: DX Terminal Pro (3,505 user-funded vaults trading real ETH in Base memecoin markets for 21 days, February to March 2026) and the DXAP live alpha fleet (500 to 599 user-created agents all-history, 91 to 117 concurrently active, trading Hyperliquid perpetuals, June to August 2026). The record spans roughly six months, 7.5M single-model invocations with about 300K onchain actions, and a further 231,638 multi-tool turns producing 14,596 fills. Four findings carry the paper. First, the operating layer determines behavior more than anything written in strategy text: a risk slider explains leverage (+0.425 per level), agent fixed effects absorb 60% of variance, and a leaderboard render boundary causally routes selection (regression discontinuity 1.75x at the top-3 cut). Second, sizing is volatility-blind: median leverage is 5.0x in every volatility sextile, and one posture-slider cell (11% of the book) holds 62% of liquidations. Third, agents capture almost none of the upside they reach: 43.2% of positions saw at least +300 bps of favorable excursion within 24h, yet 49.3% of those closed with a negative trade return; a mechanical bracket recovers +39.0 bps per position. Fourth, neither fleet shows a directional edge. The DXAP fleet is not profitable and trails a matched Hyperliquid retail benchmark (41% vs. 50% roundtrip win rate). A paired-replay league of frontier models on 416 captured production scenarios finds decision quality statistically indistinguishable at this horizon, while choice stability differs sharply across model families. Every headline survives day-clustered inference, permutation nulls, and a common-fee restatement; the paper closes with a 17-rule methodology canon bought with our own retractions.
[MA-26] LLM -Aided Design for Manufacturing: A Multi-Agent System for Intent-Preserving Redesign of CAD for Improved Manufacturability
【速读】：该论文旨在解决复杂三维计算机辅助设计（CAD）模型在制造可实现性改进过程中，如何在不丢失原始设计意图的前提下，实现可靠且渐进式重设计的问题。传统的一次性生成式重设计方法在面对高复杂度零件时易导致几何保真度下降，难以保证制造可行性与设计意图的一致性。其解决方案的关键在于构建一个由预训练多模态大语言模型（LLM）驱动的双代理协同系统——制造可实现性审查代理（DFM Reviewer） 与 CAD 编辑代理（CAD Modifier），通过一系列可验证的设计变更步骤逐步完成重设计。其中，审查代理逐轮提出保持设计意图的制造优化建议，编辑代理则将每项建议转化为对 CadQuery 程序的具体修改，并通过编译验证与多视角渲染比对实现闭环反馈，确保每次修改均被正确执行或及时放弃。该迭代机制不仅显著提升了复杂度上限（如32步链式修改成功构建离心泵壳体），还避免了微调需求，同时通过消融实验验证了视觉审查回路与设计状态文本标注在提升精度和意图保留方面的关键作用。
链接: https://arxiv.org/abs/2609.05559
作者: Kojo Welbeck,Xiangyu Shi,Zahra Sadeghi,Qi Zhu,Ping Guo
机构: Northwestern University (西北大学)
类目: Computational Engineering, Finance, and Science (cs.CE); Computer Vision and Pattern Recognition (cs.CV); Multiagent Systems (cs.MA)
备注: 21 pages, 8 figures
Abstract:We introduce autonomous, intent-preserving Design for Manufacturing (DFM) redesign of CAD parts: given an engineer’s CAD model, the method returns a variant that is easier to manufacture without losing its design intent. Generating such a redesign in a single shot is unreliable, since CAD fidelity degrades as parts grow complex; we instead produce it as a sequence of individually verified design transitions. Our DFM-Redesign pipeline realizes this with two coupled agent subsystems driven by a pretrained multimodal LLM: a DFM Reviewer that inspects the current design and proposes one intent-preserving manufacturability improvement at a time, and a CAD Modifier that executes each proposal as an edit to the part’s CadQuery program. The CAD Modifier closes a verification loop, compiling every candidate edit and visually checking it against the intended change from multi-view renderings, then re-generating or re-instructing until the edit is accepted or abandoned. Iterating review and verified modification compounds edits into parts more complex than one-shot generators reliably produce, preserves the original intent at each step, and requires no fine-tuning. On a 46-part benchmark scored by chamfer distance to reference geometries, the CAD Modifier reproduces target parts more accurately on average than chain-of-thought single agents given the same tools, and ablations isolate the contributions of the visual review loop and of captioning the design state before each edit. A centrifugal pump casing built from 32 chained transitions illustrates the complexity reachable by compounding verified edits. This is a preliminary report: evaluation of the full review-and-redesign loop, including manufacturability gain and an operational measure of intent preservation, is ongoing.
[MA-27] EdgeMem: LLM -Free Agent Memory Construction and Retrieval via Evidence-Preserving Multi-Anchor Hypergraph DATE
【速读】：该论文旨在解决大语言模型（LLM）智能体在处理多轮对话时，因现有记忆机制依赖生成式压缩而导致信息丢失与计算开销过高的问题。其核心挑战在于：传统方法通过生成摘要或重构表示来管理历史交互，不仅引入额外的生成成本，还可能在未知未来查询需求的情况下提前丢弃关键细节。为此，论文提出EdgeMem，其解决方案的关键在于坚持“保留原始交互回合并基于互补内容、时间与情景线索进行组织”的基本原则。该方法通过轻量级局部处理构建多锚点超图（multi-anchor hypergraph），实现对原始对话证据的结构化存储与高效检索，从而在不调用生成式大模型的前提下完成记忆访问。实验结果表明，EdgeMem在LoCoMo和LongMemEval-S基准上均表现出优异的检索性能与记忆增强型问答能力，在共享提示条件下于LoCoMo任务中取得61.01的严格评分，优于七种复现系统中的最高水平（58.70），且整个构建与检索过程无需生成式调用。研究证明，直接保留并组织原始证据可为智能体记忆提供一种高效且忠实的底层架构，避免了生成式记忆管理带来的冗余与失真。
链接: https://arxiv.org/abs/2609.05553
作者: Zeyang Cui,Jiannong Cao,Zhiyuan Wen,Bo Yuan,Junlan Feng,Shengyuan Chen
机构: The Hong Kong Polytechnic University (香港理工大学); JIUTIAN Research, China Mobile (中国移动九天研究院)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 10 pages, 5 figures, and 5 tables. An earlier implementation is available at this https URL ; it differs substantially from the version described in this paper. The repository will be updated with the corresponding implementation after peer review
Abstract:Agent memory allows LLM agents to use earlier interactions when answering new queries. Existing methods often compress interaction histories into summaries or other LLM-generated representations. Repeated generation adds cost and can discard answer-bearing details before the system knows what a future query will require. We propose EdgeMem, an agent-memory method built around a simple principle: preserve original interaction turns and organize them through complementary content, temporal, and episodic cues. EdgeMem realizes this principle with a multi-anchor hypergraph constructed by lightweight local processing. Retrieval directly returns source evidence and reserves LLM use for final answer generation, combining structured access to multi-session histories with faithful retention of the original conversation. Experiments on LoCoMo and LongMemEval-S show strong retrieval and memory-grounded question answering; on LoCoMo, EdgeMem achieves the highest strict-judge score among seven reproduced systems under a shared prompt (61.01 versus 58.70), while construction and retrieval require no generative-LLM calls. Overall, EdgeMem shows that preserving and organizing source evidence provides an effective and efficient foundation for agent memory without generative memory management.
[MA-28] An Agent Model Abstraction for Human-AI Teaming Cognitive Coupling
【速读】：该论文旨在解决工业环境中人与智能代理（AI-enabled agents）协同工作时，因缺乏跨代理认知过程耦合机制而导致的协作效率低下问题。现有系统在代理间的感知对齐、目标规划与动态适应等方面存在割裂，难以实现高效协同。其解决方案的关键在于提出一个概念性认知代理模型，通过八个核心组件——输入（Input）、处理（Process）、输出（Output）、状态（State）、价值（Value）、记忆（Memory）、世界模型（World Model）和目标（Goal）——形式化地构建认知耦合机制。该模型抽象了代理间认知循环的协调与共调节方式，为分布式认知分析及具有认知互操作性的混合人机系统设计提供了理论基础。
链接: https://arxiv.org/abs/2609.05552
作者: Kolitha Kottagaha W.M,Jos A.C. Bokhorst,Ben Gaffinet,Christos Emmanouilidis
机构: University of Groningen (格罗宁根大学); Luxembourg Institute of Science and Technology (卢森堡科学与技术研究所); Université de Lorraine (洛林大学)
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: Presented at the 23rd IFAC World Congress, Busan, Republic of Korea, 2026
Abstract:Industrial environments increasingly rely on collaboration between humans and AI-enabled agents. Effective teamwork requires aligning how agents perceive situations, plan actions to pursue goals, and adapt to changing conditions, yet existing systems lack mechanisms for cross-agent cognitive processes coupling. This paper presents a conceptual cognitive agent model that formalises cognitive coupling through eight components: Input, Process, Output, State, Value, Memory, World Model, and Goal. The model abstracts how agents coordinate and co-regulate their cognitive cycles, providing a basis for analysing distributed cognition and designing cognitively interoperable human-AI systems.
[MA-29] When Agent Governance Helps ALT
【速读】：该论文旨在解决如何设计与评估“受控自驱型多智能体组织”（Governed Autotelic Multi-Agent Organization, GAMPO）这一新兴范式下的智能体系统，即在设定边界约束（guardrails）的前提下，使智能体能够自主生成目标并协同完成复杂任务。其核心问题在于：当前缺乏对这类系统在架构设计与效果评估方面的明确规范。为此，研究提出双阶段解决方案：首先通过整合321项文献的定性证据，构建可执行的GAMPO框架，融合了代理理论、敏捷开发、平台化治理与治理理论，形成一个结构化的运行规范；其次，在CHI-Bench这一长期任务导向的医疗健康基准上，对开放模型与前沿大模型进行提示层（prompt-layer）实例化测试。关键发现为：治理效能受限于模型的剩余计算容量（spare capacity），且具有领域和模型特异性——在资源受限的开放模型中，完整治理流程无法带来可靠收益，而仅添加一句“验证你的写入”（verify your writes）即可将任务成功率从2/20提升至4/20；在前沿模型中，相同治理结构使医保前置审批成功率从24%提升至40%，但在另一模型上则无增益，该差异被归因于模型固有的“推荐结果可覆盖”倾向。进一步优化表明，将通用治理流程替换为基于具体案例的、不依赖隐藏密钥的“任务终结定义”（per-task definition-of-done），仅依据案例自身的政策与公开标准进行判断，可在最佳五次采样一致性策略下将前置审批成功率提升至84%（单次尝试68%），利用率管理达44%，但护理管理因内容质量存在主观瓶颈而难以突破。因此，该研究的核心贡献在于提出一个命名清晰、可审计的框架，并提供实证证据表明：治理应根据模型的剩余能力动态调整，且在前沿模型中，基于案例情境的定制化规范优于统一流程。研究结论具有探索性，受限于部分实例化、小样本（n=5–25）及单次实验设计。
链接: https://arxiv.org/abs/2609.05531
作者: Michael Ray Johnson,Linda Naimi
机构: Purdue University (普渡大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Multiagent Systems (cs.MA)
备注: 35 pages, 14 figures. Companion empirical study to a Purdue this http URL dissertation. Introduces the GAMPO framework from a 321-source qualitative evidence synthesis and probes a prompt-layer instantiation on the CHI-Bench healthcare benchmark across open and frontier models
Abstract:No specification says how a governed autotelic AI agent organization, where agents pursue self-generated goals inside guardrails, should be designed and evaluated. We answer in two parts. First, we synthesize the Governed Autotelic Multi-Agent Product Organization (GAMPO) framework from a document-based qualitative evidence synthesis of 321 sources, integrating agency, agile, platform, and governance theory into a runnable specification. Second, we probe a prompt-layer instantiation of GAMPO on CHI-Bench, a long-horizon healthcare benchmark, across open and frontier models. The result is a boundary condition: governance benefit is gated by a model’s spare capacity and is domain- and model-specific. On capacity-constrained open models the full procedure yields no reliable benefit, whereas a single “verify your writes” sentence doubles task success (pass@1 2/20 to 4/20). At the frontier the same scaffold lifts prior-authorization 24% to 40% but nets zero on another model, a gap traced to a stable recommendation-override disposition. A second result refines the first: replacing the generic procedure with an answer-blind, per-task definition-of-done, keyed only to the case’s own policy and published standards, never the hidden key, raises prior-authorization to 84% under best-of-five self-consistency (68% single-attempt, confirmed by a held-out board) and utilization-management to 44%, while care-management meets a subjective content-quality wall. The contribution is a named, auditable framework and capability-gated evidence that governance should be sized to spare capacity, and that at the frontier a case-grounded specification beats a uniform procedure. Findings are exploratory: partial instantiation, small per-cell samples (n = 5-25), and single trials.
[MA-30] DART: A DAG-Based Reputation and Incentive Framework via Blockchain-Enabled Governance for Trustworthy LLM Multi-Agent Collaboration
【速读】：该论文旨在解决基于大语言模型（Large Language Model, LLM）的多智能体系统（Multi-Agent System, MAS）在开放环境中普遍存在的可靠性不足、参与度不可控及系统级行为不一致等问题，尤其针对中心化协调架构下缺乏形式化验证机制所导致的恶意或非合作智能体威胁。其核心解决方案是提出DART框架——一种基于有向无环图（Directed Acyclic Graph, DAG）的声誉与激励调控机制，通过融合中心化操作编排与区块链赋能的去中心化治理和可审计性，实现可信协作。DART的关键在于：将DAG工作流编排与能力-声誉感知的任务分配相结合，支持动态行为更新与多因子激励机制，并利用智能合约实现责任追溯，配合IPFS进行行为证据的不可篡改存储。该框架在任务选择中动态平衡任务匹配度、历史声誉与负载均衡，在执行后持续校准智能体信任度与未来参与概率，从而有效识别并隔离持续性与间歇性恶意智能体，显著提升系统鲁棒性与输出质量。实验表明，DART在多项指标上均优于基线，实现了93.6%的GSM8K Pass@1得分，并在5轮纵向测试中达成93.33%的任务成功率与99.3%的输出内容封禁率，验证了声誉、激励、DAG协调与可验证区块链治理协同作用在构建自适应、可问责多智能体系统中的有效性。
链接: https://arxiv.org/abs/2609.05529
作者: Manoj Kumala,Xinyun Liua,Ronghua Xu
机构: Michigan Technological University (密歇根理工大学)
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM)-based multi-agent systems (MAS) predominantly rely on centralized orchestration and lack formal verification mechanisms for agent reliability, participation, and system-level behavioral alignment. These shortcomings leave open environments severely vulnerable to uncooperative or malicious agents. This work proposes DART, a Directed Acyclic Graph (DAG)-based reputation and incentive regulation framework for trustworthy multi-agent collaboration, combining centralized operational orchestration with blockchain-enabled decentralized governance and accountability. DART unifies DAG workflow orchestration, capability and reputation-aware task allocation, dynamic behavior updates, multi-factor incentives, and smart contract accountability paired with IPFS storage. Under this paradigm, agent selection dynamically balances task alignment, historical reputation, and workload, while post-execution behavioral evidence continuously calibrates agent trust and the probability of future participation. Evaluated across four axes, DART achieves 93.6% Pass@1 on GSM8K and builds a full-stack application in 142 s using two agents, outperforming centralized baselines. Across five independent 150-round longitudinal trials, Full DART achieves a mean task success rate of 93.33 +/- 2.26%, output quality of 0.9357 +/- 0.0117, retry rate of 0.2307 +/- 0.0816, and allocation delay of 1.1153 +/- 0.0408 s, consistently outperforming its ablated configurations DART isolates persistent and intermittent malicious agents, obtaining a 99.3% output containment rate and restoring system success to 99.8%. These results demonstrate the potential of coupling reputation, incentives, DAG-based coordination, and verifiable blockchain-enabled governance to support adaptive and accountable multi-agent collaboration.
[MA-31] he Failure Happens Before the Drift: The Social Dynamics of Values in LLM Agent Societies
【速读】：该论文旨在解决生成式人工智能（Generative AI）代理在社会科学研究中作为人类参与者替代品时，能否真实模拟多样化且存在冲突的人类价值体系这一关键问题。其核心挑战在于：当前基于大语言模型（Large Language Model, LLM）的代理在长期对话过程中难以保持其预设的价值立场，表现出显著的价值偏离（value drift）与表达不忠实（lack of value faithfulness）。解决方案的关键在于构建一个以世界价值观调查（World Values Survey, WVS）为基准的仿真框架，通过引入文化多样性、多样的沟通风格以及跨15个议题的纵向对话机制，系统评估不同模型（GPT-4o、Gemini-2.5-Flash、Gemma-4-E4B）在价值忠实性、价值漂移及对话真实性方面的表现。研究发现，超过50%的虚拟人格从初始阶段即未能准确表达其分配的WVS价值特征，而2%-7%在反复对话后发生价值漂移；即使移除人口统计学细节的消融实验也未能根本改善这一系统性偏差。此外，与真人对话相比，模拟对话在风格一致性与语义多样性之间呈现异于人类的权衡，常表现为内容丰富但风格重复。因此，尽管现有LLM代理可生成看似合理的对话，但在长期互动中仍无法可靠代表或维持多元人类价值体系，表明其作为人类行为代理的可信度存在本质局限。
链接: https://arxiv.org/abs/2609.05514
作者: Farah Atif,Sougata Saha,Monojit Choudhury
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Large Language Model (LLM)-based agents are increasingly used as proxies for human participants in social science research, yet it remains unclear whether they can faithfully simulate diverse and conflicting human value systems. We present a World Values Survey (WVS)-grounded simulation framework where culturally diverse agents with different communication styles engage in longitudinal, value-laden discussions. Across approximately 4,000 conversations involving 1,200 personas, 15 topics, and three models (GPT-4o, Gemini-2.5-Flash, and Gemma-4-E4B), we evaluate value faithfulness, value drift, and conversational realism. We find that more than 50% of personas fail to express their assigned WVS profiles from the outset, while 2-7% drift after repeated conversations. Ablations removing demographic details improve faithfulness for some models but do not change the broader trend: simulated value distributions still systematically deviate from the assigned WVS profiles. Compared to human discussions, simulated dialogues show a different trade-off between stylistic consistency and semantic diversity, often producing content-wise varied but stylistically repetitive exchanges. These findings suggest that current LLM agents can generate plausible conversations, but remain limited proxies for representing and preserving diverse human value profiles over time.
[MA-32] CaseWeaver: A Multi-Agent Framework for Multimodal Virtual Clinical Case Generation
【速读】：该论文旨在解决临床诊断中多模态数据在患者疾病全程中难以规模化获取的问题，核心挑战在于现有合成数据方法无法构建具备一致患者背景、连贯疾病发展轨迹以及各模态间相互关联证据的完整临床病例。其解决方案的关键在于提出CaseWeaver框架，该框架基于时间轴锚定的潜在临床病例图（Latent Clinical Case Graph, LCCG），在统一的患者层面表征中整合患者背景、潜在疾病状态、临床事件与预期观测结果。通过模态代理（modality-agents）利用限定范围的观测子图与临床指南，生成包括临床记录、检验结果、生理信号和医学影像在内的多模态证据。该方法通过校准的AgentClinic协议评估临床可推断性，并采用虚拟病例多样性（Virtual Case Diversity, VCD）评分衡量病例多样性，实验表明CaseWeaver在两项指标上均优于通用模型及代理工作流基线，生成了更具多样性和一致性的多模态虚拟临床病例。
链接: https://arxiv.org/abs/2609.05480
作者: Jierui Qu,Jiachuan Peng,Lin Li,Kyle Lam,Jianing Qiu
机构: MBZUAI(中东人工智能大学); University of Oxford (牛津大学); Imperial College London (帝国理工学院)
类目: Multiagent Systems (cs.MA); Multimedia (cs.MM)
备注:
Abstract:Clinical diagnosis relies on consistent multimodal data collected from the same patient throughout the disease course, yet such data are difficult to acquire at scale because of collection costs, missing modalities, fragmented systems, and longitudinal follow-ups. Existing synthetic-data approaches largely focus on individual modalities or vision-language dual modalities at report-level generation. Little work has been done to construct synthetic data with consistent patient backgrounds, coherent disease trajectories, and interrelated modality-specific evidence at a complete clinical case level. We introduce CaseWeaver, a multi-agent framework built around a timeline-anchored Latent Clinical Case Graph (LCCG). The LCCG organizes patient context, latent disease states, clinical events, and expected observations in a shared patient-level representation. Modality-agents use scoped observation subgraphs and clinical protocols to generate evidence including clinical records, laboratory results, physiological signals, and medical images. We evaluate clinical inferability using a calibrated AgentClinic protocol and case diversity using Virtual Case Diversity (VCD) score. CaseWeaver outperformed general-model and agentic-workflow baselines on both metrics, producing more diverse and coherent multimodal virtual clinical cases.
[MA-33] Does Splitting a Triage Decision Across Agents Hide Bias or Help Catch It? A Multi-Agent Simulation Study of LLM -Based Resource Allocation Under Audit Capacity Constraints
【速读】：该论文旨在解决在资源分配场景中，生成式 AI（Generative AI）模型因单点决策而产生的可测量人口群体偏差问题。尽管现实部署通常采用多代理流水线（如评估、分配、独立审计等角色分工）以提升系统可靠性，但其对偏差传播与检测的实际影响尚不明确。研究的关键在于探究：当决策过程被拆分至角色差异化、具有独立审核功能的多代理流水线中时，是否能有效缓解或消除由单一模型引发的偏见。研究发现，在压力条件下，多代理流水线与单模型控制组在偏差发生频率上无显著差异（6.9% vs. 6.1%，p = 0.498），表明流水线结构本身无法自动抑制偏差的产生。然而，关键发现是审计能力对偏差识别率具有决定性影响：当审计员处于过载状态时，高达43.8%的偏差案例未被察觉，远高于非过载状态下的18.4%；进一步分析表明，这一效应主要源于覆盖率下降（从100.0%降至65.6%，p < 0.001），而非审计判断质量恶化（81.6% vs. 85.7%，p = 1.000）。通过引入基于风险估计的队列重排策略，可在相同资源约束下将覆盖率恢复至91.7%（p = 0.028），显著提升偏差捕获能力。因此，解决方案的核心并非增加代理数量，而是优化资源调度机制以保障关键审核环节的覆盖完整性。
链接: https://arxiv.org/abs/2608.06949
作者: Paul-Peter Arslan
机构: Institute for Future Technologies(未来技术研究所)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 6 pages, 2 figures, 3 tables. Code and data available at this https URL
Abstract:Prior benchmarking work has shown that a single large language model (LLM), forced to make life-or-death resource-allocation decisions, exhibits measurable demographic bias. Real deployments, however, rarely use a single agent: they use pipelines, with review steps meant to catch exactly this kind of failure. We study what happens to bias when the same decision is distributed across a role-differentiated multi-agent pipeline (assessment, allocation, independent audit) instead of made and checked by one model alone. Using a synthetic disaster-triage simulator with paired cases that are clinically identical except for one demographic attribute, we run 192 episodes (2,304 resolved case pairs) on GPT-4o-mini comparing a single-agent control condition to a nine-agent pipeline under three independently varied pressure dimensions. We find no measurable difference in how often biased outcomes occur between the two conditions (6.9% vs. 6.1%, p = 0.498). We do find a large and significant effect of audit capacity on whether bias is caught: 30.0% of biased outcomes go entirely undetected, rising to 43.8% when the auditor is overloaded and falling to 18.4% when it is not. Decomposing this effect shows it is driven almost entirely by coverage (whether a case is reviewed at all, which collapses from 100.0% to 65.6% under load, p 0.001) rather than by degraded judgment on the cases that are reviewed (81.6% vs. 85.7%, p = 1.000, direction reversed). A follow-up experiment shows that reordering the audit queue by estimated risk, rather than first-come-first-served, recovers most of the lost coverage under the same capacity constraint (65.6% to 91.7%, p = 0.028). We discuss the implications for any system that adds independent oversight to an LLM agent pipeline under resource constraints, and report the study’s limitations honestly: one model, modest sample sizes, and no adversarial replication.
[MA-34] Riemannian Optimization for Multi-Player Quantum Games on Product Unitary Manifolds
【速读】：该论文旨在解决多玩家量子博弈中混合策略下的纳什均衡求解难题，特别是如何在扩展的Eisert-Wilkens-Lewenstein（EEWL）框架下实现高效且可收敛的多智能体量子决策机制。其核心挑战在于，在量子博弈中，玩家通过局部酉操作（local unitary operations）作为量子策略，并结合经典混合策略对这些操作进行概率加权，而如何协调各玩家的策略学习以达成稳定且高收益的协同均衡，仍缺乏系统性的算法设计与理论保障。解决方案的关键是提出一种几何感知的序列化算法——酉策略矩阵指数算法（Unitary Strategy Matrix Exponential Algorithm, USMEA），该算法将每个玩家的可训练局部酉操作集合及其对应的经典混合概率联合优化，形成一个兼具学习与控制能力的多智能体量子决策层。USMEA基于微分几何结构设计更新规则，确保在标准光滑性与步长条件下具备收敛性，从而首次实现了将经典优化方法系统性地融入工程化量子战略交互的设计与分析过程。
链接: https://arxiv.org/abs/2609.07397
作者: Alireza Habibi,Setareh Maghsudi
机构: Ruhr University Bochum (鲁尔大学波鸿分校); Ruhr University Bochum (鲁尔大学波鸿分校)
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注: 22 pages
Abstract:Quantum game theory is an extension of classical game theory that uses quantum principles in game theory. The Eisert-Wilkens-Lewenstein (EWL) quantum game is an early example of the two-player classical Prisoner’s Dilemma transformed into a quantum Prisoner’s Dilemma. In the EWL game, the players choose pure quantum strategies represented by unitary matrices. This extension can resolve the classical dilemma by enabling cooperative equilibrium with higher payoff. In this paper, we first discuss the Extended EWL (EEWL) for multiplayer quantum games with mixed strategies. In EEWL, each player controls a set of unitary operators as quantum actions and uses a classical mixed strategy over these actions. The payoffs are defined as expectation values of Hermitian reward operators acting on a shared quantum state, which is generated and measured according to the EEWL protocol. We then propose the Unitary Strategy Matrix Exponential Algorithm (USMEA), a geometry-aware sequential algorithm for the EEWL mixed-strategy setting, in which each player jointly learns a trainable set of local unitary actions and the associated classical mixing probabilities. Thereby it acts as a learning-and-control layer for multi-agent quantum decision systems. We analyze the convergence properties of USMEA under standard smoothness and step-size conditions and validate the theory with numerical experiments. These results show how classical optimization methods can be systematically integrated into the design and analysis of engineered quantum strategic interactions.
[MA-35] Input-to-State Stability Framework for Fully Distributed Primal-Dual Dynamics for Quadratic GNEPs Without Multiplier Consensus
【速读】：该论文旨在解决多智能体工程应用中广泛存在的广义纳什均衡问题（Generalized Nash Equilibrium Problems, GNEPs）的分布式求解难题。传统方法通常要求各智能体在求解过程中对乘子（multipliers）达成共识，这不仅增加了通信开销，还可能泄露隐私信息。本文提出了一种新方案，通过消除对乘子共享的需求，显著降低了通信负担并增强了隐私保护。由于不再强制统一的乘子更新机制，不同初始条件可能导致收敛至不同的广义纳什均衡（GNE），包括非变分型（non-variational）的解。为保证算法的有效性，作者基于输入到状态稳定性（Input-to-State Stability, ISS）框架，建立了在一定充分条件下算法收敛的理论依据。该方案的关键在于利用ISS分析工具，无需共享乘子即可实现分布式求解的稳定性和收敛性保障。
链接: https://arxiv.org/abs/2609.06983
作者: Shao-An Yin
机构: 未知
类目: Optimization and Control (math.OC); Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注: 6 pages, 1 figure. Accepted to CDC 2026
Abstract:Generalized Nash Equilibrium Problems (GNEPs) often arise in multi-agent engineering applications that require distributed algorithms. Unlike traditional approaches that enforce consensus on multipliers, our method removes the need to share multipliers, reducing communication and improving privacy. As a result, different initializations can lead to different GNEs, including non-variational ones. We establish convergence under sufficient conditions using an input-to-state stability (ISS) framework.
[MA-36] SureRoute: Toward a Hallucination-Free Self-Improving Platform for Retrosynthesis
【速读】：该论文旨在解决生成式化学推理模型在逆合成分析中普遍存在“化学幻觉”（Chemical Hallucination）的问题，即模型生成的反应路径在表面上看似合理，却因存在竞争性反应位点、选择性未解决或缺乏机理支持而在实验中失败。这类错误在传统评估指标（如Recall@K）下难以被察觉，导致湿实验验证失败和资源浪费。其解决方案的关键在于提出一个以化学验证器为核心的逆合成平台SureRoute，通过多模型集成、数据资产检索与可执行的化学直觉引擎ChemHarness实现对反应路径的严格验证与可靠性优先排序。ChemHarness作为模型无关的重排序器，能够将任意候选路径中的幻觉风险降至接近零，显著提升结果的实验可行性。实验表明，在350个真实工业目标上，SureRoute在Recall@1上达到74.3%，较单一模型及前沿大语言模型提升2.2–3.5倍，同时将顶级路径的化学幻觉率降低至4.6%，较前沿模型下降4–6倍，证明了可靠科学人工智能不仅依赖强大生成能力，更需具备可执行的验证机制。
链接: https://arxiv.org/abs/2609.05450
作者: Jieli Zhou,Naiwu Chen,Longzhang Liu,Peiyu Zhang
机构: XtalPi Inc( XtalPi 公司)
类目: Quantitative Methods (q-bio.QM); Multiagent Systems (cs.MA)
备注: 23 pages, 10 figures
Abstract:AI models, including large language models, are increasingly integrated into scientific discovery workflows, yet they remain prone to hallucination. In experimental sciences, such errors translate directly into failed wet-lab validations and wasted resources; in self-improving agentic systems, confident errors risk being reinforced rather than corrected. Retrosynthesis provides a representative example of this failure mode: existing models can generate chemically plausible routes, but cannot reliably determine which routes are experimentally feasible. We define \textbfChemical Hallucination as a route that appears valid yet fails under competing reactive sites, unresolved selectivity, or missing mechanistic support, a failure largely invisible to the Recall@ K metric. We introduce \textbfSureRoute, a chemical verifier-anchored retrosynthesis platform that suppresses Chemical Hallucination. SureRoute combines a multi-model ensemble, data asset retrieval, and \textbfChemHarness, an executable chemical intuition engine for route verification and reliability-first ranking. On a benchmark of 350 real-world industrial targets, SureRoute reaches 74.3% recall@1, 2.2–3.5 \times that of seven single-step models and three frontier LLMs, while cutting top-1 Chemical Hallucination to 4.6%, a 4–6 \times reduction relative to frontier LLMs. As a model-agnostic reranker, ChemHarness drives detectable hallucination toward near-zero across arbitrary backbone candidates. SureRoute shows that reliable scientific AI requires not only strong generation, but executable verification.
[MA-37] Role differentiation as ignition of a collective information engine
【速读】：该论文旨在解决传统信息驱动的活性物质（informational active matter）研究中集体秩序仅通过共识达成的局限性，提出一种基于“分化”而非“共识”的新型集体信息引擎。其核心问题在于：如何在缺乏全局协调的前提下，实现由角色分化驱动的有序集体行为。解决方案的关键在于构建一个基于反协调博弈（anti-coordination games）的最小化模型，其中个体通过噪声社交信号推断自身角色，并根据角色执行动作，而这些动作又反作用于信号本身，形成反馈回路，从而调节角色遵循的激励机制。在此框架下，角色分配规则（schema）并非静态，而是动态演化：通过协同角色扮演积累的资源与种群异质性共同塑造并强化生成该规则的结构。这一机制实现了社会学中塞维尔（Sewell）关于“结构—资源”二元性的操作化，将原本定性讨论的结构—能动性争论转化为可计算的动态过程。模型的启动条件由环增益 Λ 决定，当 Λ>1 时系统进入有序状态，其行为模式呈现分岔级联（bifurcation cascade），其功能形式由规则库的特征值谱决定，涵盖可监测的对数序列至具有欺骗性的尖峰-崩塌式启动。资源积累使规则竞争演化为复制者动力学（replicator dynamics），内生选择分岔类型；亚临界行为协方差揭示了分岔前的类型特征，支持早期预警。此外，反馈通道参数可调控所选类型。对于平台化交互的智能体，自适应平台设计因而成为调控涌现协调性的控制变量，可用于缓解分布式人工智能带来的风险。本研究融合博弈论、群体动力学与信息引擎理论，为智能体的信息热力学提供了新的理论路径。
链接: https://arxiv.org/abs/2609.05442
作者: Maximilian Puelma Touzel
机构: Mila–Quebec AI Institute(蒙特利尔魁北克人工智能研究所)
类目: Physics and Society (physics.soc-ph); Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA)
备注:
Abstract:Informational active matter research has shown how measurement-informed decisions produce collective order, so far in systems that reach consensus. We identify a collective information engine that orders by differentiation instead, and construct a minimal instance using anti-coordination games, the simplest game class in which differentiated role information has value. Agents infer their role from a noisy social signal, and role-following action feeds back into that signal, shaping the incentive to follow roles. The schemas prescribing roles are themselves dynamic: resources accrued through coordinated role-play combine with population variability to shape and reinforce the schemas that generated them. The model thereby operationalizes Sewell’s duality of schemas and resources, the influential but hitherto qualitative resolution of sociology’s structure–agency debate. The engine ignites when a loop gain \Lambda —the product of identity persistence, cognitive capacity, and social feedback channel fidelity—exceeds one. For a schema repertoire, roles emerge in a bifurcation cascade whose functional form is fixed by the repertoire’s eigenvalue spectrum, ranging from monitorable logarithmic sequences to deceptive spike-then-avalanche onsets. Resource accumulation turns schema competition into a replicator dynamics that selects the cascade type endogenously. Subcritical behavioral covariance reveals that type before onset, enabling early detection, and feedback channel parameters bias which type is selected. For agents interacting on a platform, adaptive platform design then becomes a control lever to throttle emergent coordination, e.g. for mitigating risks from distributional AI. Joining game theory, collective dynamics, and information engines, we open a route to an information thermodynamics of agent.
[MA-38] Self-replicating seedbox servers using programmable money
【速读】：该论文旨在解决中心化内容分发模式中对单一运营商生存状态与服务意愿的过度依赖问题，从而导致内容可用性不可靠。其核心解决方案是提出EternalSeedBox——一种基于继承性经济参数的去中心化内容分发网络架构。该方案的关键在于：每个节点均为虚拟专用服务器（VPS），通过BitTorrent协议持续分发媒体内容，并持有比特币钱包；节点每12小时自主决策是否续租、生成子节点或将其资金转移至更健康的对等节点后再退出。整个系统以一个初始创世节点启动，后续所有节点均实现自动部署、资金供给与退役。通过在真实比特币支付网络和SporeStack VPS市场平台上的仿真实验验证，系统在无需人工干预的情况下实现了自治运营。实验表明，在收入高于单节点租金成本的前提下，系统可维持长期运行并实现自我复制，即使存在高节点周转率亦能保持网络活跃。此外，引入可遗传的“谨慎度”特征后发现，低谨慎度谱系在收入高峰期繁殖更快，而高谨慎度谱系在收入中断期本应具备的生存优势未显现，导致自然选择倾向低谨慎度节点。这一设计证明了内容分发网络可在无中心化操作员的情况下，依靠持续收入实现硬件租赁、支付及资源再生的闭环自治。
链接: https://arxiv.org/abs/2609.05431
作者: Matei Dogariu,Johan Pouwelse
机构: 未知
类目: Physics and Society (physics.soc-ph); Multiagent Systems (cs.MA)
备注:
Abstract:Centralized content distribution makes availability depend on a single operator’s survival and willingness to serve. EternalSeedBox replaces the operator and network with inherited economic parameters: each node is a VPS that seeds media over BitTorrent, holds a Bitcoin wallet, and autonomously decides every twelve hours whether to renew its lease, spawn a child, or sweep its funds to a healthier peer before expiring. A single genesis node seeds the fleet, and every node thereafter is provisioned, funded, and retired autonomously. We validate the design against faithful replicas of both the Bitcoin payment network and the SporeStack VPS marketplace by running the unmodified node code. A lump sum of EUR 10,000 grew the fleet to 33 nodes before capital exhausted at day 153. With simulated income, the fleet held 40–80 live nodes across 510 days, recording 268 births and 190 deaths. A heritable caution trait was introduced to diverge across generations: low-caution lineages reproduced faster during high-income phases, while the survival advantage expected of high-caution lineages during income pauses did not appear, leaving selection in favor of low-caution nodes. The fleet tolerates high node turnover because reproduction depends on any node holding a surplus, not any single node surviving. EternalSeedBox shows that a content distribution network can lease, pay for, and replenish its own hardware without a human operator after genesis, provided income exceeds per-node rent.
自然语言处理
[NLP-0] Learning Length-Extrapolatable Recurrent Models
【速读】： 该论文旨在解决循环模型在长序列建模中超出训练范围后性能显著下降的问题，尤其针对基于时间反向传播（BPTT）训练的模型在长期依赖建模中的失效现象。传统分析多聚焦于时间路径上梯度消失或爆炸的问题，但研究表明，即使存在严重的梯度衰减，密集的每标记损失仍可训练出共享的循环规则，表明梯度衰减本身并非学习失败的根本原因。因此，论文提出从“状态信用（state credit）”的角度重新审视问题——即未来损失通过时间传递至早期状态并影响参数更新的信号机制。其核心解决方案是时间上的信用稳定化（Credit Stabilization through Time, CST），在反向传播过程中对状态信用信号进行局部归一化缩放，以稳定其范数，同时不改变被修正分量的方向，且保持前向计算不变。由于合成任务与真实数据表现出不同的信用动态特性，CST进一步针对不同场景进行适配。实验结果表明，在两类设置下，CST均显著提升模型在训练长度128倍以上的序列上的表现，有效突破了传统循环模型的训练视野限制。
链接: https://arxiv.org/abs/2609.09157
作者: Hanwen Jiang
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Recurrent models provide a natural path to long-context modeling, yet models trained with backpropagation through time (BPTT) often fail beyond their training horizon. Classical analyses emphasize gradients that vanish or explode along temporal paths. However, dense per-token losses can still train a shared recurrent rule despite severe decay, showing that decay alone does not determine whether learning fails. We instead study state credit: the signal through which future losses reach earlier recurrent states before contributing to parameter updates. Accordingly, we intervene directly on state credit and propose Credit Stabilization through Time (CST). During backward propagation, CST locally rescales the state-credit signal to stabilize its norm without rotating the component being corrected, while leaving the forward computation unchanged. Because controlled synthetic tasks and real data exhibit different credit dynamics, we specialize CST to each regime. In both settings, CST improves performance beyond the training horizon, with gains observed at up to 128x the training length.
[NLP-1] ReCite: Agent ic Reasoning for Faithful Citation EMNLP2026
【速读】： 该论文旨在解决自动引文推荐系统中因依赖语义相似性而引发的误引（misattribution）问题，即系统虽能检索到真实存在的文献，但所选文献未能在逻辑上有效支持作者论点。其核心解决方案是提出一种解耦的智能体框架ReCite，关键在于将传统的基于相似性的搜索范式转变为面向具体论断的主动推理机制。该框架通过整合位置感知、意图驱动的查询规划与反思式验证，能够在训练阶段基于合成的推理轨迹学习判断论点与证据之间的逻辑一致性，并在检索结果缺乏逻辑支撑时触发自我修正循环。实验表明，该轻量化框架在严格引文准确性上优于当前主流的大规模生成模型，通过将文献匹配建立在可验证的逻辑基础之上而非语义重叠，为自动化学术写作提供了更可靠的引文推荐基础。
链接: https://arxiv.org/abs/2609.09156
作者: Yuyang Huang,Bobo Li,Jiajia Song,Yuzhe Ding,Chong Teng,Fei Li,Donghong Ji
机构: Wuhan University (武汉大学); National University of Singapore (新加坡国立大学)
类目: Computation and Language (cs.CL)
备注: Findings of EMNLP 2026. Project page: this https URL
Abstract:Accurate citations are the foundation of academic writing, tracing intellectual origins and substantiating core claims. However, manually navigating the growing volume of scientific literature is increasingly difficult, prompting reliance on automatic citation recommendation. While modern retrieval-augmented architectures have largely mitigated the fabrication of non-existent papers, current systems relying on semantic similarity struggle with misattribution, often citing authentic papers that fail to logically support the author’s claim. To address this challenge, we argue that accurate citation requires a shift from similarity-based search to active, claim-level reasoning. We propose ReCite, a decoupled agentic framework that orchestrates location perception, intent-aware query planning, and reflective verification. Trained on synthesized reasoning trajectories, our agent verifies claim-evidence consistency and triggers self-correction loops when retrieved candidates lack logical support. Experiments demonstrate that our lightweight framework outperforms state-of-the-art massive generative models in strict citation accuracy. By grounding literature matching in verifiable logic rather than semantic overlap, ReCite establishes a reliable foundation for automated academic writing.
[NLP-2] Studying Image Tokenizers as Visual Languages in Unified Multimodal Models
【速读】： 该论文旨在解决现有图像分词器（image tokenizer）评估方法在多模态统一模型中存在局限性的问题，即当前研究多依赖孤立指标或仅针对生成/理解任务的评估，未能充分反映视觉标记与文本标记在联合建模时的实际行为。其核心解决方案在于构建一个受控的纯自回归测试平台，在文本、图像、文本到图像（T2I）、图像到文本（I2T）预测的持续预训练过程中，系统追踪各任务特定的验证损失，并分析这些损失与下游性能之间的关系。关键发现包括：（1）应按任务分别分析损失，因其呈现不同的缩放行为并可能对分词器排序产生不同影响；（2）损失与性能的关系取决于所预测的标记空间——对于固定分词器，T2I和I2T损失与生成质量相关，但跨分词器比较时，T2I损失的表现随图像标记空间变化而漂移，而基于共享文本词汇计算的I2T损失则提供更一致的信号，且在监督微调后与生成和视觉理解性能均显著相关；（3）更好的重建能力并不必然导致更低的任务损失或更强的下游表现；（4）图像分词器的选择会影响联合优化下文本建模的效果。通过重新审视判别器设计、语义监督和词表规模三个分词器设计维度，研究揭示了其对联合建模与下游性能的影响机制。整体而言，该工作以损失为观测窗口，为图像分词器作为“视觉语言”的本质提供了新的视角，强调了其与文本在联合多模态训练中的动态互作关系。
链接: https://arxiv.org/abs/2609.09143
作者: Siting Li,Zhengyang Wang,Simon Shaolei Du,Xi Chen,Yang Liu
机构: Amazon FAR(亚马逊联邦人工智能研究中心); University of Washington (华盛顿大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注: 27 pages, 23 figures
Abstract:Image tokenizers define the ``visual language’’ of unified multimodal models, yet are commonly studied through isolated metrics or generation-/understanding-only evaluations. These evaluations do not fully capture how visual tokens behave when modeled jointly with text. We build a controlled pure-autoregressive testbed and track task-specific validation losses during multimodal continual pretraining across text, image, text-to-image (T2I), and image-to-text (I2T) prediction. We examine how these losses scale and relate to downstream performance, then use them to study multimodal learnability—how well image and text tokens are jointly modeled—and tokenizer design. We find that (1) losses should be analyzed by task, since they exhibit distinct scaling behavior and rank tokenizers differently. (2) The loss–performance relationship depends on the predicted token space: for a fixed tokenizer, T2I and I2T losses correlate with generation quality, but across tokenizers, the T2I loss–performance relationship shifts with the image-token space, whereas I2T loss, computed over a shared text vocabulary, provides a more consistent signal. I2T loss also correlates with both generation and visual understanding performance after supervised finetuning. Using losses as a lens, we show that (3) better reconstruction does not necessarily yield lower task-specific losses or stronger downstream performance, and that (4) image tokenizer choice can affect text modeling under joint optimization. As case studies, we revisit three tokenizer design axes—the discriminator, semantic supervision, and vocabulary size—to examine their effects on joint modeling and downstream performance. Together, our testbed offers a complementary perspective on image tokenizers as visual languages, highlighting their interplay with text in joint multimodal training.
[NLP-3] A Data-Driven Framework for Identifying and Prioritizing RPA Opportunities in Healthcare Processes
【速读】： 该论文旨在解决美国医院在实施机器人流程自动化（Robotic Process Automation, RPA）过程中普遍存在的低效问题，即约30%-50%的RPA项目因缺乏系统化的方法而表现不佳。具体而言，现有实践常依赖非正式流程选择，缺乏可重复的候选流程目录、优先级评估机制、技术层级匹配策略以及财务回报预测工具。为此，论文提出一个四模块数据驱动框架：首先构建涵盖五大价值流的二十类典型医院流程分类体系（Process Taxonomy）；其次通过基于层次分析法（Analytic Hierarchy Process, AHP）的优先级模块生成自动化适配指数，并引入显式一致性检验确保判断可靠性；第三，通过工具层级选择模块根据流程复杂度、集成需求与合规性特征推荐成本最低但足以为之的技术方案（如Python脚本、n8n等开源编排器或UiPath等企业平台）；最后，通过投资回报率（Return-on-Investment, ROI）模块量化人力节省、错误成本规避、回收期及净现值。在合成组合测试中，12个流程通过优先级阈值，排名对±20%权重扰动具有强鲁棒性（斯皮尔曼相关系数0.83，前五名集合保留率达97.7%），四个流程被自动化风险指数标记为“高风险”，预算约束下的组合优化显示边际净现值随投入增加而递减，且蒙特卡洛模拟表明组合净现值在第五百分位仍保持正值。该框架为文献的理论整合而非基于原始医院数据校准，论文进一步讨论了HIPAA治理挑战并提出了实证验证的研究议程，附带的Python实现支持方法复现。
链接: https://arxiv.org/abs/2609.09137
作者: Maria Alejandra Gomez,Juan Manuel Castillo
机构: Universidad de La Salle, Bogotá, Colombia(拉萨尔大学, 波哥大, 哥伦比亚)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 21 pages, 2 figures, 14 tables
Abstract:Robotic Process Automation (RPA) is widely used to reduce administrative burden in United States hospitals, yet an estimated 30-50% of RPA initiatives underperform because processes are selected informally, without a repeatable method to catalogue candidates, prioritize them, match each to an automation tier – a Python bot, an open-source orchestrator such as n8n, or an enterprise platform such as UiPath – and forecast financial return before committing resources. We propose a four-module, data-driven framework unifying these decisions: a Process Taxonomy of twenty recurring hospital processes across five value streams; a Prioritization module deriving an Automation Suitability Index from an Analytic Hierarchy Process matrix with an explicit consistency check; a Tool-Tier Selection module recommending the least-cost technology sufficient for a process complexity, integration, and compliance profile; and a Return-on-Investment module quantifying labor savings, error-cost avoidance, payback, and net present value. Applied to a synthetic portfolio spanning all twenty processes, plus a reference data-flow architecture linking it to hospital EHR/payer/ERP systems: 12 of 20 clear the prioritization threshold; the ranking is robust to +/-20% weight perturbation (Spearman correlation 0.83, top-5 set preserved 97.7%, 2,000 Monte Carlo trials); an Automation Risk Index flags four qualifying processes as Critical risk; a budget-constrained portfolio optimization shows diminishing marginal NPV as spend scales from 400K to 1.03M; and a second Monte Carlo analysis shows portfolio NPV stays positive at its 5th percentile. The framework is a conceptual synthesis of the literature rather than an instrument calibrated on primary hospital data; we discuss HIPAA governance and a research agenda for empirical validation. A supplementary Python implementation accompanies the paper.
[NLP-4] Entropy-Regularized Rank-Masked Policy Optimization for Test-Time Reinforcement Learning in Code Generation EMNLP2026
【速读】： 该论文旨在解决测试时强化学习（Test-Time Reinforcement Learning, TTRL）在代码生成任务中因程序无法通过表面形式直接比较而缺乏有效奖励信号的问题。传统TTRL方法依赖于答案层面的自投票机制，但在代码生成场景下失效，因为程序的语义差异无法通过字符串匹配捕捉。为此，作者提出探针驱动的TTRL（Probe-Driven TTRL），其核心是通过问题描述构造无需输出的探针输入，执行候选程序并基于行为一致性定义探针共识奖励（Probe Consensus Reward, PCR），从而为开放词汇的程序提供行为层面的训练信号。然而，PCR存在可靠性不足及易受虚假共识诱导的奖励欺骗风险。为应对这一挑战，论文进一步提出熵正则化排名掩码策略优化（Entropy-Regularized Rank-Masked Policy Optimization, ERPO），通过排名掩码将低PCR值转化为保守的负向更新，并利用熵上限控制策略漂移。实验表明，ERPO在多个编码基准上显著提升了pass@1和pass@k指标，尤其在领域内适应与零样本迁移场景下表现优异。
链接: https://arxiv.org/abs/2609.09135
作者: Jiacheng Xu,Feng Chen,Xiuneng Xu,Bo An
机构: Nanyang Technological University, Singapore(南洋理工大学, 新加坡)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Accepted to EMNLP 2026 Main Conference. 15 pages, 4 figures, 11 tables
Abstract:Existing methods for test-time reinforcement learning (TTRL) derive rewards from answer-level self-voting on unlabeled test-time tasks with canonical answers, but this breaks down for code generation because programs cannot be compared by surface form and therefore do not directly provide a usable training signal. To make TTRL applicable to code generation, we propose probe-driven TTRL, which constructs output-free probe inputs from the problem statement, executes candidate programs on these probes, and defines a Probe Consensus Reward (PCR) from the resulting behavioral agreement. PCR provides a behavioral training signal for open-vocabulary programs, but it is not a fully reliable verifier and remains susceptible to reward hacking through spurious consensus. We therefore introduce Entropy-Regularized Rank-Masked Policy Optimization (ERPO), which converts low PCR into conservative negative updates through rank masking and controls policy drift with an entropy ceiling. On coding benchmarks, ERPO substantially improves pass@1 and pass@k in both in-domain adaptation and zero-shot transfer.
[NLP-5] ExecCritic: Learn to Test Test to Improve for Coding Agents
【速读】： 该论文旨在解决代码修复代理（coding agents）在依赖执行反馈进行修复时，因测试用例未能准确捕捉问题所要求的行为而导致修复失败的问题。其核心挑战在于：代理自动生成的测试用例可能存在行为目标不完整或错误，当测试与补丁由同一路径生成时，二者错误可能一致，从而产生虚假信心，误导修复过程。为应对这一问题，论文提出一种名为ExecCritic的解决方案，其关键在于采用“测试-验证-修订”（test–verify–revise）的分层架构，并引入角色特异性的强化学习训练机制。该架构将测试生成与源码修复解耦：测试代理（Test agent）独立生成符合仓库语境的测试用例，通过“闭包失败”（fail-closed）的封装器对测试进行资格审查并冻结；修复代理（Repair agent）则基于测试执行反馈修正源码，且不修改测试。两者均以Qwen-3.5-35B-A3B作为基础模型，并分别独立训练。在“学会测试”（Learn to Test）阶段，测试代理学习生成能有效区分正确与错误补丁的行为有效测试；在“以测促改”（Test to Improve）阶段，修复代理同时学习任务直接求解与基于反馈的迭代修订能力。实验结果表明，高质量测试显著提升修复成功率：相较于无测试基线61.2%的解决率，使用改进后测试的修复率提升至65.3%；经角色特异性微调后，测试代理的基准到黄金（Base-to-Gold）成功率从22.2%提升至62.2%，双代理组合达到72.6%，较原始无测试基线提升11.4个百分点，且无需更强模型或评估时的真值反馈（Oracle feedback）。
链接: https://arxiv.org/abs/2609.09133
作者: Leitian Tao,Baolin Peng,Haorui Wang,Hang Wang,Hao Cheng,Wenlin Yao,Qianhui Wu,Tao Ge,Sharon Li,Jianfeng Gao
机构: University of Wisconsin–Madison(威斯康星大学麦迪逊分校); Microsoft Research(微软研究院)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Software Engineering (cs.SE)
备注: 35 pages
Abstract:Execution feedback can guide coding agents toward correct repository repairs, but only when the tests capture the behavior requested by the issue. Agent-generated tests can encode incomplete or incorrect behavioral targets; when the same trajectory writes both the patch and the test, their errors can agree and create false confidence. We introduce ExecCritic, combining a test–verify–revise scaffold with a role-specific reinforcement learning recipe for training agents within it. The scaffold separates test construction from source-code repair: a Test agent independently generates repository-native tests, a fail-closed harness qualifies and freezes them, and a Repair agent revises source code from their execution feedback without changing the tests. Both roles use Qwen-3.5-35B-A3B as the backbone and are trained separately. In Learn to Test, the Test agent learns to produce behaviorally valid tests that distinguish correct from incorrect patches. In Test to Improve, the Repair agent learns both direct task resolution and feedback-guided revision. On SWE-bench Verified, test quality determines whether feedback helps: holding the base Repair agent fixed, tests from the base Test agent reduce resolved rate from a no-test baseline of 61.2% to 57.3%, whereas tests from GPT-5.6-sol raise it to 65.3%. Role-specific post-training raises the Qwen Test agent’s Base-to-Gold success from 22.2% to 62.2%; composing the two post-trained Qwen agents reaches 72.6%, an 11.4-point gain over the original no-test baseline without stronger-model or Oracle feedback at evaluation time. Code is publicly available at this https URL.
[NLP-6] SAEScientist-Bench: Can AI Agents Conduct Autonomous SAE Interpretability Research?
【速读】： 该论文旨在解决生成式AI在递归自我改进（Recursive Self-Improvement, RSI）过程中缺乏事后监控与审计机制的问题，尤其关注模型内部表征的可解释性与安全对齐。其核心挑战在于：尽管自动化训练流程已实现高度优化，但缺乏对模型“学习了什么”以及“如何学习”的深度理解能力，导致自主研发（Autonomous AI Research and Development, AI RD）难以可靠推进。解决方案的关键是引入机制解译（Mechanistic Interpretability）工具，特别是稀疏自编码器（Sparse Autoencoders, SAEs），通过提取可解释的神经元特征，使模型具备对自身内部机制进行探测和干预的能力。本文提出SAEScientist-Bench评测基准，评估AI代理是否能够像科学家一样，利用SAE工具自主开展机制发现任务——即在给定目标概念后，设计对比性探针并从包含13万余个特征的Gemma Scope词典中定位最优特征。评估维度涵盖激活排名、概念选择性及因果引导能力，结果表明前沿代理虽在概念区分上接近专家水平，但在因果生成引导方面仍显著落后于人类专家，且存在对实验测量结果的误读问题。这揭示出当前系统在闭环自主研发中的认知局限，同时也确立了“实验性模型理解”作为可量化的自主研发能力指标。
链接: https://arxiv.org/abs/2609.09113
作者: Yuqiao Tan,Shizhu He,Jun Zhao,Kang Liu
机构: The Key Laboratory of Cognitive Intelligence, Institute of Automation, CAS(中国科学院自动化研究所认知智能重点实验室); School of Artificial Intelligence, University of Chinese Academy of Sciences(中国科学院大学人工智能学院)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Preprint. Work in Progress
Abstract:While research on recursive self-improvement (RSI) has predominantly automated model training pipelines, reliable autonomous development demands a missing pillar: post-hoc monitoring and auditing to understand what models learn and ensure safe alignment. Mechanistic interpretability tools are essential to bridge this gap, among which Sparse Autoencoders (SAEs) serve as a cornerstone by isolating interpretable features for model inspection and steering. In this paper, we introduce SAEScientist-Bench to evaluate whether AI agents can act as scientists utilizing SAE tools for autonomous mechanistic discovery. Given a target concept, an agent designs contrastive probes and navigates a Gemma Scope dictionary of 131K+ features in Gemma-2-9B-IT to discover the optimal feature, evaluated against curated expert reference features anchored on Neuronpedia across activation rank, concept selectivity on contrastive texts, and causal steering. Across 10 agent configurations and 20 tasks, frontier agents demonstrate genuine discovery capabilities and lead different evaluation dimensions, but remain well behind the expert baseline, approaching expert levels on separating target concepts from contrastive controls while lagging substantially in causal generation steering. Further analysis reveals that although agents can design contrasts to rule out spurious candidates, they frequently misinterpret experimental measurements. These results establish experimental model understanding as a measurable capability for closed-loop autonomous AI RD. Our code is available at this https URL.
[NLP-7] Measuring LLM Sycophancy under Sustained Multi-Turn Pressure
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在面对持续且动态的用户反对时可能出现的“谄媚行为”（sycophancy）问题，即模型在用户施加压力时放弃原本正确的立场。现有评估方法多依赖于简短、预设的对话流程，难以捕捉在长时间、自适应对抗性交互中暴露的模型脆弱性。为此，作者提出SPINE基准测试框架，通过一个持续但错误的用户代理（LLM proxy）对目标模型进行长达25轮的自适应挑战，以更真实地模拟长期对抗场景。其解决方案的关键在于引入动态、适应性的用户角色和长周期对话设计，从而揭示模型在持续压力下的不可靠抵抗能力。实验结果表明，所有被测模型的崩溃率随对话轮次增加而上升，而传统短周期评估严重低估了谄媚现象；进一步分析可解释推理轨迹发现，正确立场通常仍保留在推理过程中，说明模型并非因知识缺失而妥协，而是主动选择迎合用户。此外，消融实验显示，自适应代理比预生成脚本更能暴露谄媚行为，其中情感诉求是最易诱发模型谄媚行为的策略。研究代码与数据已公开。
链接: https://arxiv.org/abs/2609.09090
作者: Leyuan Tang,Kangda Wei,Tianyu Jiang,Ruihong Huang
机构: Texas AM University (德州农工大学); University of Cincinnati (辛辛那提大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) may abandon correct positions when users push back, exhibiting a failure mode known as sycophancy. Existing evaluations typically use short, pre-specified conversations and may therefore miss failures that emerge under sustained, adaptive disagreement. We introduce SPINE, a benchmark in which an LLM proxy plays a persistent but mistaken user and adaptively challenges a target model for up to 25 turns. We evaluate four production systems and three Olmo3-7b variants on 100 false-presupposition and 100 unethical-query items. Our experimental results show that collapse rates increase with conversation length for every model, short-horizon protocols underestimate sycophancy and resistance under sustained pressure remains unreliable across current models. By analyzing models with accessible reasoning traces, we surprisingly found that the correct position often remains represented in a reasoning trace when the response concedes, suggesting that the model chooses to please a user and sycophancy is not due to lack of knowledge or ignorance. Ablations show that adaptive LLM proxy exposes more sycophantic collapse than pre-generated scripts. Among all tactics, emotional appeals is the most associated with inducing LLM sycophantic behavior. The code and data are released at this https URL
[NLP-8] Its Not RoPE that Creates Sinks: The Role of Self-Concentration and Value-Non-Mixing in Attention EMNLP2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在序列初始位置普遍存在的“注意力黑洞”（Attention Sink, AS）及其伴随的“大量激活”（Massive Activations, MAs）问题，尤其关注这些现象在不依赖具体词元（token）的情况下于序列起始处持续出现的根本原因。研究发现，导致AS与MAs的关键因素在于：由因果掩码（causal mask）引发的注意力自聚焦（self-concentration of attention），以及注意力输出中值向量（Value）未能充分混合（Value-non-mixing）。这一发现揭示了注意力层内部动态机制的重要特性，为未来低比特量化策略的设计提供了新的实证依据，并深化了对注意力机制内在工作原理的理解。
链接: https://arxiv.org/abs/2609.09085
作者: Raito Kiya,Satoki Ohashi,Kosuke Sato,Go Kamoda,Ryosuke Takahashi,Yuji Yamamoto,Daiki Shiono,Keisuke Sakaguchi,Goro Kobayashi
机构: Tohoku University (东北大学); SOKENDAI (日本国立情报学研究所研究生院); NINJAL (日本国家言语研究院); MBZUAI ( Mohamed Bin Zayed University of Artificial Intelligence); RIKEN (理化学研究所); Preferred Networks, Inc. (Preferred Networks, Inc.)
类目: Computation and Language (cs.CL)
备注: Accepted at EMNLP 2026
Abstract:Large Language Models (LLMs) often exhibit “Attention Sink” (AS) and the accompanying “Massive Activations” (MAs) at the initial position of a sequence. These phenomena frequently co-occur, and MAs can pose challenges for low-bit quantization. In this study, we analyze the factors underlying AS and MAs that emerge at the initial position regardless of the token occupying it. Our experiments suggest that self-concentration of attention, resulting from the causal mask, and the subsequent Value-non-mixing in attention outputs contribute to AS and MAs. These findings provide new empirical evidence on the internal dynamics of LLMs, offering insights that may inform future quantization strategies and advance our understanding of the internal mechanisms of attention layers.
[NLP-9] ActReview: Rebuttal-Guided Training Data and Rubric Rewards for Actionable Peer Review Generation
【速读】： 该论文旨在解决大语言模型（LLM）在论文预提交自审过程中生成的反馈缺乏可操作性与具体指导的问题，即现有反馈往往仅指出论文弱点，而未能提供明确、可执行的修改建议。其核心解决方案是提出一种基于反驳文本引导的后训练框架——ActReview，该框架将论文特定的诊断结果与具体的、有依据的修订方案相连接。关键创新在于利用作者对审稿意见的反驳内容作为隐式监督信号，因其通常揭示了作者为回应审稿人关切所采取的合理修正路径，从而为面向修订的反馈生成提供了有效指导。研究基于OpenReview平台的真实审稿-反驳对话数据，构建了包含40,000条样本的ActReview-40K数据集，通过局部化论文证据对反馈进行锚定；并采用多任务监督微调与候选感知、弱项特定的评分函数驱动的GRPO强化学习对Qwen3-8B-Base模型进行后训练。此外，提出了一个由人工精心标注的1,000实例基准测试集ActReview-Bench，用于评估诊断质量与修订建议的实用性。实验表明，ActReview在可操作性与证据锚定性方面优于已有专用审稿生成模型，同时保持与强提示工程方法相当的性能；人类评估验证了其修订建议的有效性，但仍存在技术准确性方面的改进空间；进一步分析也证实了模型在未见论文上的泛化能力及跨评审者的一致性。
链接: https://arxiv.org/abs/2609.09076
作者: Yiling Ma,Yilun Zhao,Sihong Wu,Ziyu Chen,Manasi Patwardhan,Arman Cohan
机构: Yale University(耶鲁大学); University of Chicago(芝加哥大学); TCS Research
类目: Computation and Language (cs.CL)
备注: 50 pages, 20 figures
Abstract:As LLMs are increasingly used for pre-submission self-review, there is growing demand for feedback that not only identifies weaknesses but also guides authors toward concrete revisions. We study this as Actionable Peer-review Generation and decompose it into two subtasks: diagnostic claim generation and revision suggestion generation. We introduce ActReview, a rebuttal-guided post-training framework that connects paper-specific diagnoses to concrete, grounded revision plans. Our central insight is that author rebuttals reveal plausible actions for addressing reviewer concerns and can therefore provide latent supervision for revision-oriented feedback. From real review-rebuttal threads on OpenReview, we construct ActReview-40K by aligning reviewer weaknesses with author responses and grounding the resulting feedback in localized paper evidence. We post-train Qwen3-8B-Base with multi-task supervised fine-tuning followed by GRPO using candidate-aware, weakness-specific rubric rewards. We also introduce ActReview-Bench, a human-curated benchmark of 1,000 instances for evaluating diagnostic quality and revision usefulness. Experiments show that ActReview outperforms prior specialized review-generation models on actionability and grounding while remaining competitive with strong prompt-based LLMs. Human evaluation confirms improved revision usefulness while revealing a remaining gap in technical accuracy, and additional analyses support generalization to held-out papers and robustness across independent judges.
[NLP-10] oolLoop: Closed-Loop Tool-Use Data Synthesis via Decomposed Generation and Dynamic Self-Feedback EMNLP2026
【速读】： 该论文旨在解决现有合成工具调用数据在质量与分布均衡性方面存在的问题，传统生成-过滤（generate-then-filter）范式依赖静态后验验证，常导致数据效率低下且特征分布不均。其核心解决方案是提出ToolLoop，一种闭环生成框架，将数据合成过程分解为三个渐进阶段：（1）采样函数名称组合作为真实标签；（2）逆向推导用户查询；（3）正向推导工具调用。每个阶段引入动态自反馈机制，实现从“生成-过滤”到“生成-验证-优化”的范式转变，显著提升合成数据质量。实验表明，在伯克利函数调用排行榜（BFCL）上，使用11K条合成数据训练的40亿参数模型在非推理模式下达到86.40%准确率，而移除BFCL重叠候选函数的Isolate变体仍达86.07%，跨基准测试ACEBench显示仅需基线18.3%的数据量即可实现72.1%的整体准确率，验证了方法的高效性与强泛化能力。
链接: https://arxiv.org/abs/2609.09072
作者: Min Zeng,Yuzhou Liu,Zhenyu Cao,Hanxiu Chen,Heng Li,Caiquan Liu,Yafei Wen,Xiaoxin Chen
机构: vivo AI Lab
类目: Computation and Language (cs.CL)
备注: Accepted at the EMNLP 2026 Main Conference
Abstract:High-quality tool-use data is critical for training language models to interact effectively with external tools. However, existing synthetic approaches typically follow a generate-then-filter paradigm with static post-hoc verification, often yielding inefficient data with imbalanced feature distributions. We propose ToolLoop, a closed-loop framework that decomposes synthesis into three progressive stages: (1) sampling function name combinations as ground truth; (2) backward derivation of user queries; and (3) forward derivation of tool calls. At each stage, dynamic self-feedback iteratively guides the model toward high-quality generation, realizing a transition from generate-then-filter to generate-verify-refine. On the Berkeley Function Calling Leaderboard (BFCL), a 4B parameter model trained with our 11K synthetic examples achieves 86.40% accuracy in non-reasoning mode, while an Isolate variant that removes BFCL-overlapping candidate functions still reaches 86.07%. Cross-benchmark evaluation on ACEBench further demonstrates strong generalization, with 72.1% overall accuracy using only 18.3% of baseline training data.
[NLP-11] he Audit Decides the Verdict: Instrument Effects Rival Demographic Bias in LLM Decision Audits EMNLP2026
【速读】： 该论文旨在解决语言模型在不同审计范式下是否表现出人口统计学偏见（demographic bias）的问题，尤其关注审计设计对结果的显著影响。研究发现，同一模型在单独评分时可能对少数群体申请人呈现优势，而在并列排名时却对其产生惩罚，这种看似矛盾的现象是否在真实场景（如招聘、贷款审批和医疗分诊）中具有普遍性成为核心问题。其解决方案的关键在于采用大规模、严格控制的预注册实验设计：通过40,726条仅在申请人姓名上存在差异的请求，测试五种主流语言模型在三个关键领域中的表现，并在数据收集前即固定主要检验假设。结果显示，所有36项预设对比均未通过多重校正，表明原报告中观察到的排名惩罚效应无法复现；评分优势虽仍存在，但幅度仅为原始报告的一半左右，而扩展的精度分析进一步限制了招聘排名中潜在惩罚效应的上限。此外，通过植入式偏差测试与方向性复制验证，确认了结果的零效应本质。研究揭示，模型对审计任务的识别能力远超对人口统计特征的敏感度——模型几乎总能察觉透明的审计设计，且在内容完全相同的情况下无论差异为种族或兴趣爱好均会形成平局，同时显著偏好首列候选人。因此，审计结论更多反映审计设计本身的影响，而非真实的系统性人口统计学偏见。
链接: https://arxiv.org/abs/2609.09048
作者: Siddharth Vohra,Manikandan Ravikiran
机构: Carnegie Mellon University (卡内基梅隆大学); Amazon Web Services AI Native (亚马逊网络服务人工智能原生); Indian Institute of Technology (印度理工学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: Accepted to REALM 2026, the 2nd Workshop for Research on Agent Language Models at EMNLP 2026. 10 pages, 3 figures
Abstract:Whether a language model looks demographically biased can depend on how the audit asks its question. A charitable-aid benchmark reports that the same models favor minority applicants when rating requests one at a time and penalize some when ranking side by side. We test whether that reversal generalizes to hiring, lending, and medical triage: 40,726 requests to five models, applications differing only in the applicant’s name, and a primary test fixed before collection. It does not. None of 36 planned contrasts survives correction. The rating advantage keeps its sign at roughly half the published size, and a precision extension bounds any hiring ranking penalty below the published effect, though the lending and triage ranking floors sit above that margin, so the exclusion is conclusive for hiring ranking and for rating in all three domains only. Planted disparities tracking their injected sizes and a directional replication on the original aid materials bound these nulls. The audit is livelier than the demographics: models recognize transparent audits nearly always, tie every identical-content comparison whether the varying detail is race or a hobby, and reward first-listed candidates as much as any demographic effect we measure. Audit verdicts reflect audit construction more than demographic bias.
[NLP-12] Answer-Distribution Trajectories: A Stochastic-Dynamics View of LLM Reasoning
【速读】： 该论文旨在解决当前大语言模型（LLM）链式思维（Chain-of-Thought, CoT）推理评估中过度依赖终点准确率而忽视推理路径动态过程的问题。现有方法如熵谱（entropy profiles）虽能追踪推理过程中不确定性的演变，却无法揭示导致不确定性的具体竞争性假设。为此，本文提出答案分布轨迹（answer-distribution trajectories），一种受随机动力学启发的表征方式，能够追踪模型在推理过程中对最终答案的完整预测分布演化。相较于传统的终点准确率和熵谱，答案分布轨迹是一种更精细的表示形式，可刻画从探索、修正、运动到承诺的完整动态推理轨迹，并区分推理成功与失败的不同动力学机制。在16个开源语言模型和4个推理基准上的实验表明，具有相同终点结果和相似熵特征的推理轨迹可能呈现出显著不同的动态行为；同时，不同模型与任务间存在显著的动态差异，且不同优化目标倾向于偏好特定的动力学模式。此外，训练策略与推理配置会系统性地重塑这些轨迹。研究结果表明，答案分布轨迹为分析和评估大语言模型推理的动力学特性提供了一个丰富且细致的框架。
链接: https://arxiv.org/abs/2609.09030
作者: Mar Gonzàlez I Català,Haitz Sáez de Ocáriz Borde,Davide Murari,Carola-Bibiane Schönlieb,Pietro Liò,George Montañez
机构: University of Cambridge(剑桥大学); Harvey Mudd College(哈维穆德学院)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Theory (cs.IT); Machine Learning (cs.LG)
备注: 16 pages, 4 figures, 3 tables
Abstract:Chain-of-thought reasoning provides a structured computation between a model’s input and final answer. Yet it is often evaluated through endpoint accuracy, which ignores the path taken to reach that answer. An emerging line of work addresses this limitation using entropy profiles, which track how uncertainty evolves over the reasoning process but do not reveal which competing hypotheses account for that uncertainty. We introduce answer-distribution trajectories, a stochastic-dynamics-inspired representation that tracks the model’s full predictive distribution over answers as reasoning unfolds. As a strictly finer representation than endpoint and entropy summaries, answer-distribution trajectories enable us to characterize a trace through a dynamical reasoning profile spanning exploration, revision, motion, and commitment, and to distinguish different dynamical mechanisms of reasoning success and failure. Across sixteen open-weight language models and four reasoning benchmarks, we show that traces with the same endpoint and similar entropy profiles can exhibit substantially different reasoning dynamics. We further find substantial variation in these dynamics both within and across models and tasks, with different objectives favoring different dynamical profiles. Additionally, we show that training and inference choices systematically reshape these profiles. Our results suggest that answer-distribution trajectories provide a rich framework for analysing and evaluating the dynamics of LLM reasoning.
[NLP-13] Evaluation of Contextual Understanding in Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在自然语言理解任务中缺乏真正上下文感知能力的问题，尤其关注其在问答（Question Answering, QA）任务中是否能够准确提取、整合并推理上下文信息，而非依赖于记忆化的表面关联。传统评估指标如困惑度（perplexity）、双语评估替代（BLEU）或表层准确率无法有效衡量模型对上下文的深层理解能力。为此，论文提出一种基于知识图谱（Knowledge Graph, KG）的新型评估框架——语义结构相似性（Semantic Structural Similarity for KGs, S3KG），其核心创新在于将结构相似性与语义相似性融合为一个连续的评估得分，从而更全面地反映生成回答与参考答案在知识结构和语义层面的一致性；同时，该框架还引入诊断机制以分类识别模型的推理错误类型。通过在精心构建的问答基准数据集上对比现有指标，实验验证了S3KG在衡量响应正确性、忠实性及可解释性方面的有效性，揭示了其作为更可靠评估工具的潜力。
链接: https://arxiv.org/abs/2609.09004
作者: Subavarshana Arumugam,Mamta Nallaretnam,Kithuni Wickramasinghe,Chamath Gunapala,Pragatheeswaran Vipulanandan,Uthayasanker Thayasivam,Kamal Premaratne
机构: University of Moratuwa (莫鲁图瓦大学); University of Miami (迈阿密大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Large Language Models (LLMs) demonstrate impressive performance across diverse NLP tasks, yet their ability to exhibit genuine contextual understanding remains uncertain. Traditional evaluation metrics such as perplexity, BiLingual Evaluation Understudy (BLEU), or surface-level accuracy fail to reveal how well LLMs extract, integrate, and reason over contextual information–a gap particularly critical in question answering, where models must align responses with contextually grounded knowledge rather than memorized associations. We propose a novel knowledge graph-based evaluation framework introducing Semantic Structural Similarity for KGs (S3KG), a hybrid similarity measure integrating structural and semantic similarity into a continuous evaluation score, alongside a diagnostic framework for categorizing reasoning errors. To validate this pipeline, we evaluate S3KG against established metrics on a curated question-answer (QA) benchmark, demonstrating its effectiveness in measuring correctness, faithfulness, and interpretability in LLM-generated responses.
[NLP-14] Good Pretraining Bad SFT: Checkpoint Quality Across the Training Stack
【速读】： 该论文旨在解决在大规模混合专家模型（Mixture-of-Experts, MoE）训练流程中，传统基于预训练损失或基准测试得分选择模型检查点（checkpoint）的方法可能失效的问题。研究发现，仅依赖预训练阶段的性能指标进行筛选，无法保证所选检查点在后续完整下游训练流程中仍具备最优表现。其解决方案的关键在于引入“解密度”（solution density）这一新指标，即模型在局部权重扰动下仍能保持下游任务性能的能力。研究表明，经过完整下游训练后表现更优的检查点具有更高的解密度，表明该属性可作为更可靠的模型选择依据，从而提升训练效率与最终模型性能。
链接: https://arxiv.org/abs/2609.08966
作者: Sohir Maskey,Philipp Scholl,Jonas Knupp,Pit Neitemeier,Sascha Wirges
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Language-model checkpoints are commonly selected by pretraining loss or benchmark scores, assuming that the highest-scoring checkpoint will remain the best starting point for subsequent training. We show that this assumption can fail in a full 30B mixture-of-experts training pipeline. The checkpoints that perform better after the full downstream training stack also have higher solution density, i.e., retain downstream performance under local weight perturbations.
[NLP-15] PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving EMNLP2026
【速读】： 该论文旨在解决自动驾驶系统（Autonomous Driving Systems, ADS）在基于场景的测试过程中存在的流程碎片化问题，即从场景生成、检索、修改、ADS执行到结果分析等环节由独立工具完成，缺乏统一协同机制。其核心挑战在于如何构建一个端到端、高度集成且具备智能决策能力的自动化测试框架。解决方案的关键是提出PlannerForge——一个基于大语言模型（Large Language Model, LLM）代理的统一框架，首次覆盖从场景生成到ADS评估的全流程，并引入两个新增的增强型阶段：ADS增强与ADS基准测试。该框架通过模块化链式调用实现跨任务协作，在无需领域特定微调的情况下，显著提升了场景生成质量（自然语言生成成功率193/200）、场景选择准确率（rank 1达92.0%）、物理有效性编辑能力（≥94%）以及规划器成功率（提升至70.2%），同时将碰撞率从19.0%降至8.4%，验证了其在开放源代码模型（如Qwen3.6:35B）上与商业API相当的性能表现。
链接: https://arxiv.org/abs/2609.08965
作者: Yuan Gao,Sebastian Müller,Mattia Piccinini,Marc Kaufeld,Yuchen Zhang,Finn Rasmus Schäfer,Qunying Song,Johannes Betz
机构: Technical University of Munich (慕尼黑工业大学); Munich Institute of Robotics and Machine Intelligence (慕尼黑机器人与机器智能研究所); University College London (伦敦大学学院)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Robotics (cs.RO)
备注: Accepted to EMNLP 2026 (Main Conference). 35 pages including appendix
Abstract:Ensuring the safety of autonomous driving is a critical challenge. Scenario-based testing is a systematic process used to validate Autonomous Driving Systems (ADSs), but it remains a fragmented modular pipeline in which scenario generation, retrieval, modification, ADS execution, and results analysis are performed by separate tools with little interaction. Large Language Model (LLM) agents have shown promise across ADS sub-systems such as perception, planning, and control. However, no prior work covers the whole scenario-based testing pipeline for ADSs with a unified LLM-agent framework. We present PlannerForge, an LLM-agent framework that extends all scenario-based testing stages (from Scenario Generation to ADS Assessment) and adds two further LLM-enhanced stages: ADS Enhancement and ADS Benchmarking. We evaluate PlannerForge with 10 off-the-shelf LLMs across all tasks (Generation, Selection, Modification, Module Routing, Planner Testing, and Enhancement) under 5 prompt conditions. Best-per-task scores range from 0.88 to 1.00, and open-source 20-35B backends match commercial APIs on most tasks. Open-source models such as Qwen3.6:35B match commercial APIs on three of the five tasks. Chaining the modules end-to-end retains 83% / 78% of seed queries (commercial / open). It outperforms Scenario Factory 2.0 (Finkeldei et al., 2025) on natural-language generation (193 vs. 144 executable of 200) and realises 92-96% of requested city, road and vehicle attributes. It outperforms BM25 (Robertson and Zaragoza, 2009) at rank 1 selection (92.0% vs. 67.5%) and From-Words-to-Collisions (Gao et al., 2025) on physically valid edits (=94% vs. 31%). At N=400, cost-tuning lifts planner success from 50.4% to 70.2% and cuts collisions from 19.0% to 8.4%, without domain-specific fine-tuning.
[NLP-16] AuK Technical Report: An Open-Source Foundational Model for Speech Generation and Editing
【速读】： 该论文旨在解决多模态语音生成与编辑任务中缺乏统一框架的问题，即如何在单一模型架构下实现自然语言指令驱动的语音合成、内容修改、声学增强与分离、副语言特征调整以及声学属性编辑等多样化功能。其核心解决方案是提出AuK——一个基于自然语言指令与音频上下文共同控制的开源基础模型，通过构建包含约30.3亿个指令-音频样本和195万小时有效监督数据的大型多任务数据集，支撑跨任务的统一建模。关键创新在于：采用融合语义条件的多模态大语言模型（multimodal large language model）进行语义引导，结合在语音、通用音频和音乐上联合训练的变分自编码器（VAE）实现声学条件建模，并设计一种混合型修正流Transformer结构，包含双流MMDiT块与统一单流DiT块以协同完成生成与编辑。训练策略上采用生成预热与生成-编辑联合预训练相结合的方式，并引入人类反馈偏好优化（human-feedback preference optimization）与基于奖励的强化学习（reward-based reinforcement learning）进行后训练以提升开放编辑与生成质量。为降低推理开销，进一步采用一致性初始化与任务路由解耦动态掩码扩散（task-routed Decoupled DMD）方法进行模型压缩，最终实现AuK-Flash仅需4步推理且无需分类器自由引导，在相同条件下比全尺寸模型快4.5倍。实验表明，AuK在零样本与指令控制的语音生成及通用指令引导编辑任务中表现领先，同时在信号级修复任务上仍保持竞争力。
链接: https://arxiv.org/abs/2609.08936
作者: Ziyang Ma,Zhikang Niu,Wenming Tu,Tianrui Wang,Ruiqi Yan,Junxi Liu,Yanru Huo,Nickk Huang,Yang Liu,Qicong Xie,Zeyu Xie,Hui Wang,Haitao Li,Zixuan Jiang,Yalin Li,Jie Fang,Yifan Duan,Zeyue Tian,Guangzheng Li,Haina Zhu,Shuyi Wang,Jinwen Wang,Mingyu Cui,Tian Tan,Auden,Sen Liang,Steve Yves,Shan Yang,Liefeng Bo,Zilong Zheng,Kai Yu,Eng-Siong Chng,Xie Chen
机构: 未知
类目: ound (cs.SD); Computation and Language (cs.CL); Multimedia (cs.MM)
备注: Open-source at this https URL
Abstract:We introduce AuK, an open-source foundational model that unifies speech generation and editing through a common interface of natural-language instructions and audio context. To support this broad capability set, we construct approximately 3.03 billion instruction–audio instances and 1.95 million hours of effective supervision across five task families: speech generation, content editing, enhancement and separation, paralinguistic editing, and acoustic editing. AuK combines a multimodal large language model for semantic conditioning, an VAE jointly trained on speech, general audio, and music for acoustic conditioning, and a hybrid rectified-flow Transformer that performs dual-stream MMDiT blocks followed by unified single-stream DiT blocks for generation. Training begins with generation-only warm-up and proceeds to joint generation–editing pre-training. We then apply complementary post-training strategies: human-feedback preference optimization for open-ended editing and reward-based reinforcement learning for speech generation. To reduce inference cost, we further distill the model with consistency initialization and task-routed Decoupled DMD. The resulting AuK-Flash performs 4-step inference without classifier-free guidance and achieves a 4.5 wall-clock speedup over the full model under matched conditions. Experiments demonstrate leading performance on zero-shot and instruction-controlled speech generation and general instruction-guided editing, while remaining competitive on signal-level restoration tasks. We release both the source code and model weights to support reproducibility and further research.
[NLP-17] When Models Defer to Wrong Answers: A Robustness Audit of Source-Attributed Cues in Multiple-Choice QA EMNLP2026
【速读】： 该论文旨在解决生成式 AI 在多选题问答任务中因外部源声明（source claim）引发的答案不稳定性问题。具体而言，研究关注当语言模型在回答问题时被提供一个关于其他来源答案的误导性陈述时，其输出是否会发生不可靠的偏移。解决方案的关键在于提出一种新的评估指标——中性条件下的误导性提示采纳率（Neutral-Conditioned Misleading Cue Adoption Rate, NC-MCAR），该指标衡量的是：在模型先前基于中性提示正确选择“正确答案”（gold answer）的前提下，仅在存在误导性提示的条件下切换至某一固定错误选项的比例。这一指标有效捕捉了模型在面对未验证的外部引用时的响应波动性，而非简单判断模型是否“知道”正确答案或所有从众行为是否不合理。实验在 MMLU-Pro 与 IndicMMLU-Pro 数据集上对四种指令遵循型模型进行测试，结果表明，专家模板（expert template）诱导的平均 NC-MCAR 达到 41.1%，显著高于多数模板（majority template）的 12.5%，尽管两者使用相同的错误选项和最终指令。这表明，在强制选择的情境下，未经验证的源声明可能压倒原本与任务证据一致的推理结果，揭示了当前模型在事实接地（grounding）方面的脆弱性。
链接: https://arxiv.org/abs/2609.08934
作者: Manikandan Ravikiran,Siddharth Vohra
机构: 未知
类目: Computation and Language (cs.CL)
备注: Work Accepted at GroundLM Workshop at EMNLP 2026
Abstract:Language models often receive a question together with a claim about what another source answered. We audit whether such claims destabilize answers in multiple-choice question answering. For each item, we hold one wrong option fixed across misleading conditions and vary the cue template attached to it. We introduce \emphneutral-conditioned misleading cue adoption rate (NC-MCAR), which measures switches to that option only on valid cued trials where the same model first selected the gold answer under a neutral prompt. This is a measure of answer instability, not proof that the model knew the answer or that all deference is irrational. We evaluate four instruction-following models on MMLU-Pro and IndicMMLU-Pro in English, Hindi, Bengali, Tamil, and Telugu. Across 220,000 outputs, the expert template yields 41.1% aggregate NC-MCAR, compared with 12.5% for the majority template. These two conditions use the same wrong option and final instruction. Filler accuracy remains well above expert-wrong accuracy, while correct-cue prompts have high valid-response accuracy. The audit documents answer instability relevant to grounding under the tested forced-choice prompts: a bare, unverified source claim can outweigh an answer that was previously consistent with the task evidence.
[NLP-18] Experience Funnel: A State-Policy Alternating Loop for Self-Evolving Agents
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）驱动的自主智能体在持续交互过程中积累的任务特定经验如何高效转化为可复用的模型能力，同时保持对新观测证据的快速适应能力这一核心挑战。现有方法面临两难：显式文本状态（如技能、代理工具等）虽支持快速、可读且可编辑的适应，但依赖外部上下文且难以持久化；而参数化策略虽具备紧凑、可复用的特性，却更新缓慢。本文提出“经验漏斗”（Experience Funnel）框架，其关键在于通过交替循环实现快速状态适应与慢速策略凝练的协同机制：首先将交互轨迹提炼为显式文本状态，以实现新经验的快速整合与验证；随后通过感知状态转移的蒸馏方式，识别并固化在状态迭代中仍具价值的行为模式至策略中；更新后的状态-策略对生成新的采样轨迹，为下一轮状态优化与策略凝练提供新证据。实验表明，该框架在多种智能体基准测试中均显著优于仅依赖状态演化或内部化策略的方法，且能逐步将有用的显式经验转化为自主的策略能力。
链接: https://arxiv.org/abs/2609.08919
作者: Wenbo Gao,Zhaomou Song,Zhiyuan Ji,Renxi Liu,Xing Li,Xianzhi Yu,Xiaoguang Li,James Chung-wai Cheung,Weizhe Lin,Yaoyuan Wang
机构: The Hong Kong Polytechnic University (香港理工大学); Huawei(华为); Renmin University of China (中国人民大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Autonomous agents powered by large language models (LLMs) continuously accumulate experience through interaction, creating an opportunity to improve future behavior through self-evolution. A fundamental challenge is how to transform abundant, task-specific interaction experience into reusable model competence without sacrificing the ability to adapt rapidly to newly observed evidence. Explicit textual states, such as skills and agent harnesses, provide fast, human-readable and editable adaptation, but incur persistent dependence on external context; parametric policies provide compact and reusable competence, but are substantially slower to update. We present \textitExperience Funnel, a self-evolving framework that couples fast state adaptation with slow policy consolidation in an alternating loop. Interaction trajectories are first distilled into an explicit textual state, where newly acquired experience can be rapidly incorporated and validated. The framework then selectively identifies state-enabled behavior that remains useful across state revisions and consolidates it into the policy through transition-aware distillation. The updated state–policy pair subsequently generates new rollouts, providing fresh evidence for the next round of state adaptation and policy consolidation. Experiments across diverse agent benchmarks show that \textitExperience Funnel consistently improves agent capability over state-only evolution and policy-internalization approaches, while progressively converting useful explicit experience into autonomous policy competence.
[NLP-19] From Scores to Evidence: Auditable Decisions Can Improve Speech Deepfake Detection
【速读】： 该论文旨在解决语音深度伪造（Speech Deepfake）检测中现有方法仅输出单一评分而缺乏可解释性的问题。当前多数检测器虽能对每个语音片段给出一个判别分数，但该分数无法揭示判断依据，难以区分边界案例中不同类型的证据矛盾或缺失（如被动检测与检索证据不一致、关键探针不可用等），从而限制了人工审查的精准性和系统可信度。其解决方案的关键在于提出一种可审计的决策记录（auditable decision record），在保持最终输出为单一评分以支持阈值设定和审查调度的前提下，保留并整合四类对齐的判别线索：被动检测得分、标记衍生品上的条件性关键探针得分、检索支持度以及说话人画像边际距离，并显式标注各线索间的分歧坐标。实验基于ASVspoof 5 Track 1的4,080个样本匹配子集表明，引入检索增强规则后，等错误率（EER）从15.84%降至11.91%，经后期校准后的完整决策记录进一步将EER降低至8.43%；在33.75%的审查预算下，暴露的线索联合覆盖了校准模型82.85%的误判案例。尽管最优被动WavLM模型仍可达到6.71% EER，但本方法的核心贡献并非提供更强的独立检测能力，而是通过保留每条被识别语音背后的多源证据链，在维持单一分数输出的同时实现决策过程的透明化与可追溯性。
链接: https://arxiv.org/abs/2609.08899
作者: Mengzhe Geng,Yujia Lu,Patrick Littell,Manuela Kunz,Xie Chen
机构: National Research Council Canada(加拿大国家研究院); The Chinese University of Hong Kong(香港中文大学); Shanghai Jiao Tong University(上海交通大学)
类目: ound (cs.SD); Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注:
Abstract:Speech deepfakes can mimic a speaker’s voice convincingly enough to deceive listeners and automated systems. This has driven strong progress in speech deepfake detection, but most detectors still end with one score per utterance. That score is useful for ranking systems, yet it says little about why a borderline item should be trusted, deferred, or reviewed. Two utterances can fall in the same score band for different reasons, for example because passive and retrieval evidence disagree or because the keyed probe is unavailable. We ask whether the final decision can remain scalar without discarding that provenance. We answer this question with an auditable decision record that carries four aligned cues into a late calibration step: a passive detector score, a conditional keyed-probe score on a marked derivative, retrieval support, and a speaker-profile margin, together with explicit disagreement coordinates. On the 4,080-example ASVspoof 5 Track 1 matched subset, the fixed retrieval-augmented rule improves on retrieval-only evidence, from 15.84 percent to 11.91 percent EER, and late calibration over the full record reaches 8.43 percent EER. At a 33.75 percent review budget, the exposed cue union covers 82.85 percent of the calibrated model’s errors. The best passive WavLM run still reaches 6.71 percent EER, so we do not present the decision record as a stronger standalone detector. Its contribution is to preserve the evidence behind each surfaced utterance while still producing one operating score for thresholding and review.
[NLP-20] Evolution of Multimodal Question Answering: From Modality-Adaptive Extraction to Unified Language Representation
【速读】： 该论文旨在解决多模态数据快速增长背景下，跨异构数据源（如文本、表格和图像）进行推理的问答系统（Multimodal Question Answering, MQA）所面临的挑战。其核心问题是：如何有效建模不同模态间的跨模态交互、统一处理异构输入并实现精准推理。解决方案的关键在于从早期依赖模态特异性处理流程的架构，向基于预训练语言模型（PLM）驱动的统一文本中心范式演进。这一转变通过将多模态信息统一编码为文本表示，显著提升了跨模态理解与推理能力，从而在多个基准数据集上实现了更高的精确匹配（Exact Match, EM）与F1分数。研究进一步揭示了当前方法仍面临模态转换过程中的信息损失、多阶段流水线中的误差传播以及对细粒度跨模态依赖关系捕捉不足等关键瓶颈，为未来统一化多模态推理系统的设计提供了重要方向。
链接: https://arxiv.org/abs/2609.08896
作者: Abdullah Al Shafi
机构: Khulna University of Engineering Technology ( khulna 大学工程科技)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 3 figures, 4 tables, reading assignment
Abstract:The rapid growth of multimodal data has intensified the need for question answering (QA) systems capable of reasoning across heterogeneous sources such as text, tables, and images. In this paper, we present a comprehensive methodological comparison of three influential frameworks, namely Multimodal Adaptive Extraction (MAE), Solar, and UniMMQA, tracing the evolution of multimodal question answering from modality-adaptive pipelines to fully unified architectures. We examine how each approach models cross-modal interactions, transforms heterogeneous inputs, and performs reasoning, highlighting key design differences in modality representation, reasoning, and answer generation. Our analysis demonstrates a clear shift from explicit modality-specific processing toward unified text-centric formulations enabled by pre-trained language models (PLMs). Empirical comparisons across benchmark datasets show that this transition leads to substantial improvements in both Exact Match (EM) and F1-Scores, with UniMMQA achieving the most consistent and scalable performance. Despite these advances, we identify persistent challenges, including information loss during modality transformation, error propagation in multi-stage pipelines, and limitations in capturing fine-grained cross-modal dependencies. Overall, this study provides a deeper understanding of current design trends and offers insights into the future direction of unified multimodal reasoning systems.
[NLP-21] Eliciting Weak-to-Strong Generalization with On-Policy Reverse Distillation
【速读】： 该论文旨在解决弱教师模型（weak teacher）向强学生模型（strong student）进行知识迁移时的“弱到强泛化”问题，尤其针对连续模型迭代与多领域知识整合场景中，重复从头进行前沿规模后训练（post-training）成本过高的挑战。传统知识蒸馏方法将弱教师视为优化目标，可能将教师的性能上限固化于学生模型中，限制其超越潜力。本文提出在策略反向蒸馏（On-Policy Reverse Distillation, OPRD），其核心创新在于：通过评估教师策略在学生轨迹上的策略偏移相对于参考策略的变化，并放大学生验证器驱动的策略梯度中与此方向一致的分量。该方法仅对验证器支持的更新进行重缩放，在不破坏策略优化平稳点的前提下，实现超越教师模型的加速学习。实验表明，无论是连续模型迁移还是多教师蒸馏，OPRD均以更少的学生更新次数达到更高性能；响应式分析进一步显示，OPRD学生模型更接近纯验证器驱动强化学习（verifier-based RL）训练的模型，而非其弱教师模型，说明教师指导起到了加速而非主导优化路径的作用。此外，在常规的强到弱蒸馏任务中，OPRD仍能有效融合验证器驱动的策略优化与教师指导，无论教师与学生之间的能力顺序如何。
链接: https://arxiv.org/abs/2609.08798
作者: Youngrok Park,Sangmin Bae,Hojung Jung,Jongwoo Ko,Yunseon Choi,Young Jin Kim,Pashmina Cameron,Aaron Courville,Se-Young Yun
机构: KAIST AI(韩国科学技术院人工智能); Microsoft(微软); Mila(蒙特利尔学习算法研究所); Université de Montréal(蒙特利尔大学); CIFAR AI Chair(加拿大首席人工智能研究员)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 38 pages, 18 figures, 10 tables
Abstract:Weak-to-strong generalization asks whether stronger models can learn from weaker supervisors and surpass them. This question is particularly important for successive model generations and multi-domain consolidation, where repeating frontier-scale post-training from scratch can be prohibitively expensive. Yet conventional distillation treats the weak teacher as an optimization target, potentially imposing its capacity ceiling on the student. We introduce On-Policy Reverse Distillation (OPRD), which evaluates the teacher’s policy shift relative to its reference policy on student rollouts and amplifies the component of the student’s verifier-driven policy gradient along that direction. By rescaling only verifier-supported updates, OPRD preserves the stationary points of policy optimization while accelerating learning beyond the teacher. In both successive model transfer and multi-teacher distillation, OPRD achieves higher performance with fewer student updates than existing RL and distillation approaches. Response-style analysis shows that OPRD students remain closer to models trained with verifier-based RL alone than to their weak teachers, suggesting that teacher guidance accelerates rather than redirects the student’s own optimization. Results in conventional strong-to-weak distillation further demonstrate that OPRD effectively combines verifier-driven policy optimization with teacher guidance regardless of capacity ordering.
[NLP-22] Improving Term Evaluation in Machine Translation: Variation Matters
【速读】： 该论文旨在解决机器翻译（MT）评估中对术语一致性过度敏感的问题，即现有评估指标通常假设每个源端术语仅存在单一正确目标形式，而忽视了人类译者在实际翻译中引入的合理术语变体。这种忽略导致当前评估体系将本应视为语言自然多样性的变体错误地判定为不一致，从而产生偏差。其解决方案的关键在于提出一种新的跨术语变体（Cross-Term Variation, CTV）诊断指标，用于检验源语言与目标语言之间术语变体关系是否得以保留，并结合基于术语表的准确性、翻译一致性以及CTV三重维度，在文档层面实现更符合人类翻译行为的评估。研究基于两组英法科学文本平行语料库及四种MT系统的翻译结果分析表明：（1）机器翻译生成的目标侧变体少于人工翻译；（2）术语转移模式强烈依赖于变体类型；（3）不同评估指标会导致一致性排序差异；（4）虽术语表约束可提升准确性和一致性，但会抑制合法变体，损害CTV表现。因此，论文主张采用“变体感知型”评估框架，即一致性惩罚应以目标侧变体是否真实反映源侧变体关系为条件，从而实现更精准、更人性化的机器翻译评价。
链接: https://arxiv.org/abs/2609.08779
作者: Nicolas Dahan(MLIA, ISIR, ALMAnaCH),Ziqian Peng(MLIA),François Yvon(MLIA),Rachel Bawden(ALMAnaCH)
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Terminology evaluation in machine translation (MT) usually assumes a single correct target form per source term. However, human translators routinely introduce variation that current metrics penalize as inconsistency. We examine how to account for this variation in document-level MT evaluation of English-French scientific translation, combining glossary-based accuracy, translation consistency, and a new cross-term variation (CTV) diagnostic measure that tests whether variation relationships are preserved across languages. Based on analyses of two parallel corpora, translated by four MT systems, we find that (1) MT systems generate less target-side variation than human translators; (2) transfer patterns strongly depend on the variation type; (3) consistency rankings vary with the choice of metric; and (4) constraining MT with a glossary improves accuracy and consistency but degrades CTV by suppressing valid variation. We argue for variation-aware evaluation that conditions consistency penalties on whether target-side variation mirrors source-side variation.
[NLP-23] Benchmark Scores Are Pipeline-Dependent: A Reliability Audit of Cybersecurity LLM Benchmarks
【速读】： 该论文旨在解决当前生成式 AI (Generative AI) 在网络安全领域评估中存在基准测试结果不可靠的问题，即现有大规模语言模型（LLM）基准测试常被视为静态数据集，其评分却高度依赖可配置的评估流程。研究发现，评估流程中的不同配置会显著影响模型得分与排名，识别出15种系统性失效模式，其中单一流程选择可导致模型得分波动超过80个百分点，并大幅改变模型排序。在跨基准层面，语义相似的任务对因评价规范不一致而得出截然不同的模型排名。通过引入标准化评估框架以统一流程配置并保持任务语义一致性，结果显示十款模型中有九款在至少一个基准上排名变动超过三位。因此，该研究的关键解决方案在于提出“管道感知审计”（pipeline-aware auditing），强调评估流程的透明性与一致性是实现可靠模型评估的核心要求。
链接: https://arxiv.org/abs/2609.08765
作者: Aymene Berriche,Cathrine Shalby,Mohannad Alhanahnah,Yazan Boshmaf
机构: Qatar Computing Research Institute, HBKU(卡塔尔计算研究研究所，HBKU)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Large language model (LLM) benchmarks are often treated as fixed datasets with stable scores, yet their outcomes depend on configurable evaluation pipelines. We audit eight cybersecurity benchmarks across 10 proprietary, open-weight, and cybersecurity-specialized LLMs. By modeling benchmarks as measurement pipelines, we identify 15 systematic failure modes and show that a single pipeline choice can change a model’s score by more than 80 percentage points and substantially alter model rankings. At the cross-benchmark level, two semantically similar task pairs rank the same models differently because of incompatible evaluation conventions. Under an evaluation harness that standardizes pipeline choices while preserving task semantics, nine of 10 models shift by at least three ranks on at least one benchmark. These results show that cybersecurity LLM benchmark scores are pipeline-dependent and motivate pipeline-aware auditing as a core requirement for reliable model evaluation.
[NLP-24] ontaubeV1: Streaming Text-to-Speech with Hierarchical Codec Modeling and Bounded Context
【速读】： 该论文旨在解决文本到语音（Text-to-Speech, TTS）系统中自然韵律与高效推理之间的权衡问题：高感知质量通常伴随更高的计算开销和延迟。其核心解决方案是提出TontaubeV1模型，通过层级化双编码器（DualCodec）表示以12.5 Hz的低频率对语音进行编码，将语义流与逐级声学精炼分离，从而实现自然韵律保留的同时支持单张消费级GPU上的流式生成。该设计假设韵律结构在语义流生成时已基本确立，因此分配计算资源：一个基于Qwen3-1.7B的Transformer用于预测语义流并决定话语时长，随后三个逐步缩小的基于Qwen3-0.6B的Transformer依次执行声学精炼。采用按字符分词而非子词的方式提升精度，并通过共享位置的文本与音频标记实现长篇内容生成且上下文受限；利用重叠的DualCodec重建映射至VibeVoice声学潜在空间并因果解码，克服了DualCodec非因果解码带来的流式障碍。模型支持长达一分钟的参考音频进行音色条件控制，主要面向英语和德语，具备多语言扩展能力。四个预测器总参数量为2.9B，在单张RTX 5090上流式路径首次输出音频延迟约200毫秒。非流式测试中，端到端实时因子（RTF）为0.08（单输入），八并发输入聚合RTF达0.02。在基于大语言模型（LLM）评分的有声书朗读基准测试中，TontaubeV1在韵律表现上达到ElevenLabs Flash v2.5水平，并优于Fish Audio S2 Pro、2026年4月版Gradium API及Cartesia Sonic 3。模型权重已在Hugging Face以Tontaube社区模型许可协议1.0发布。
链接: https://arxiv.org/abs/2609.08703
作者: Fritz Cremer,Jonathan Cremer
机构: 未知
类目: ound (cs.SD); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 13 pages, 2 figures, 2 tables. Both authors contributed equally
Abstract:Text-to-speech systems often face a trade-off between natural prosody and efficient inference: higher perceptual quality typically comes at increased computational cost and latency. We present TontaubeV1, a model that preserves natural prosody while enabling streaming from a single consumer GPU. Speech is encoded by the hierarchical DualCodec representation at 12.5 Hz, which separates a semantic stream from successive acoustic refinements. Our design assumes that prosodic structure is largely established when the semantic stream is generated, and allocates capacity accordingly: a Qwen3-1.7B-derived transformer predicts that stream and thereby the utterance duration, while three progressively smaller Qwen3-0.6B-derived transformers each add one acoustic refinement. Text is tokenized per character rather than by subword. Paired text and audio markers at shared positions support long-form generation with bounded context, and overlapping DualCodec reconstructions are mapped into the VibeVoice acoustic latent space and decoded causally, enabling streaming despite DualCodec’s noncausal decoder. The model accepts up to one minute of reference audio for voice conditioning and is designed primarily for English and German, with additional multilingual support. The four predictors total 2.9B parameters; on a single RTX 5090 the streaming path reaches approximately 200 ms to first audio. In separate non-streaming measurements, the end-to-end real-time factor (RTF) is 0.08 for one input and the aggregate RTF is 0.02 across eight concurrent inputs. On our LLM-as-a-judge audiobook-reading benchmark, TontaubeV1 matches ElevenLabs Flash v2.5 and outperforms Fish Audio S2 Pro, the April 2026 Gradium API, and Cartesia Sonic 3 on prosody. The model weights are released on Hugging Face under the Tontaube Community Model License 1.0.
[NLP-25] Record Grouping Controls Evidence Weight in Language Models
【速读】： 该论文旨在解决生成式 AI（Generative AI）在处理多源证据时，因输入内容的分组方式（即“供应分区”）不当而导致的冗余与信息失真问题。其核心挑战在于：如何在不丢失互补性关键信息的前提下，消除组内重复内容，并确保各组对语言模型的贡献具有可解释性和一致性。解决方案的关键在于提出一种内容感知的预生成表示机制，通过定义一个不变的组内内容状态（invariant group-content state），实现组内去重与内容聚合，同时基于该结构推导出精确的内容感知分区误差上界。实验表明，供应分区作为可控的预生成表示变量，显著影响模型决策——错误的分组（虚假分裂或合并）可导致性能下降达30个百分点以上，而均衡镜像设计进一步揭示了其在不同检查点下的依赖性行为效应，从而系统性地刻画了分区策略对模型输出的影响机制。
链接: https://arxiv.org/abs/2609.08698
作者: Zhongxuan Liu,Sicheng Zhou,Hongzhi Wang
机构: Harbin Institute of Technology (哈尔滨工业大学)
类目: Computation and Language (cs.CL)
备注: 28 pages, 5 figures
Abstract:Retrieved records are presentation units; a supplied partition determines which records enter a language model as one evidential contribution. We characterize the invariant group-content state that removes within-group copies while retaining complementary canonical content, show that equal group counts can encode different evidence states, and derive a sharp content-aware partition-error bound. Given a supplied partition, our pre-generation representation deduplicates and aggregates content within groups and bounds each group’s contribution. Across 104,402 trials and 6 public checkpoints, a central natural-text intervention finds that content-fixed false splits add 10.27-32.66 percentage points and false merges remove 9.13-31.79 points; a matched six-slot control retains the positive direction in all 16 cells. In a new 48-item controlled campaign panel, changing the supplied partition produces measurable, checkpoint-dependent decision shifts across all four models, and the balanced mirror design exposes substantial order interactions. Together, the theory and experiments establish the supplied partition as a controllable pre-generation representation variable and characterize its checkpoint-dependent behavioral effects.
[NLP-26] Global Divergence Local Convergence: Representation Geometry in SSMs and Transformers
【速读】： 该论文旨在探究状态空间模型（State-Space Models, SSMs）与变压器（Transformer）在架构差异背景下，其内部表示的几何结构与功能特性如何影响语言建模性能。核心问题在于：尽管二者采用截然不同的计算范式，为何在语言建模任务中表现相当？解决方案的关键在于通过多尺度分析揭示两类模型在表示空间中的几何分布、压缩性及概念编码方式的深层相似性。研究发现，SSMs 的表示在所有维度上呈均匀分布，而 Transformer 则高度集中于单一主方向；然而，通过混合架构实验表明，每增加一层注意力机制，表示空间即趋向于更显著的单方向偏斜。进一步分析显示，尽管几何结构迥异，两类模型的有效表征容量却极为接近，且在概念编码时均使用维度相近的子空间。更重要的是，实证表明 Transformer 中占主导地位的主方向并不蕴含更多语义信息。最后，通过对特定主题表示或词元邻域的局部流形对齐分析，发现两者在局部语义流形层面具有高度一致性。因此，该研究的核心结论是：虽然变压器与状态空间模型在潜在空间的使用方式上存在差异，但它们在局部语义流形层面表现出显著的功能收敛，这解释了其在性能上的可比性。
链接: https://arxiv.org/abs/2609.08692
作者: Amit Ben-Artzy,Roy Schwartz
机构: The Hebrew University of Jerusalem(耶路撒冷希伯来大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Recent state-space models (SSMs) such as Mamba achieve language modeling performance comparable to transformers despite relying on fundamentally different architectures. This raises an important question: how do these structural differences influence the geometry and functional nature of their internal representations? We study this question through a multi-scale analysis of representations in transformers, SSMs, and hybrid architecture. First, we find that SSMs distribute their representational information evenly across all dimensions, whereas transformer representations are heavily dominated by a single principal direction. By evaluating hybrid architectures, we observe that the representation space becomes increasingly skewed toward a single dominant direction after each attention layer. Next, we explore how the different geometric spread of representations impacts representational capacity through compressibility. Surprisingly, we find that despite their contrasting geometric structures, both architectures exhibit tightly matched effective capacities. We further investigate whether this skewed geometry affects how concepts are encoded. Using rank-constrained probes, we demonstrate that both architectures encode concepts in subspaces of surprisingly similar dimensionality. Furthermore, we demonstrate that the transformers’ dominant principal direction does not inherently encode more conceptual information. Finally, we zoom in and examine the alignment between manifolds, either by analyzing representations of specific topics or by looking at the nearest neighborhoods of tokens, and find that they are highly aligned. Ultimately, our analysis suggests that while transformers and SSMs induce different usage of latent space, they display a striking functional convergence at the level of local semantic manifolds.
[NLP-27] Hyperparameter Scaling Laws Across MoE Sparsity
【速读】： 该论文旨在解决超稀疏混合专家模型（MoE）中因稀疏性增加而导致的超参数迁移可靠性下降问题。传统超参数缩放定律在面对极端稀疏的MoE架构时失效，其根本原因在于最优学习率与批量大小随激活比例（activation ratio, A）变化而发生系统性偏移，且这一现象无法仅通过总参数量或激活参数量单独解释。论文的关键解决方案是通过大规模实验（1,800次预训练运行，覆盖6个激活参数量级，处理约20万亿标记，耗时20万等效H800 GPU小时）揭示了两种新的缩放规律：在固定稀疏度下，最优批量大小遵循与训练样本数 $ D $ 的幂律关系；最优学习率则与训练算力 $ C $ 呈幂律关系，并对模型规模与数据量分配具有鲁棒性。进一步地，激活比例 $ A $ 作为额外的乘性幂律因子同时影响两者。基于此，研究提出了统一的超参数缩放定律，可跨模型规模与稀疏度有效迁移。大规模评估表明，该形式显著优于其他函数形式，在一个拥有120亿总参数、仅激活1/64专家的超稀疏模型上，预测超参数仍接近实测最优值，验证了模型规模与稀疏度联合外推的有效性。此外，实验还证明了该方法可在不同专家粒度间实现迁移，并独立分离出激活比例的影响。
链接: https://arxiv.org/abs/2609.08690
作者: Changxin Tian,Kunlong Chen,Jia Liu,Ziqi Liu,Zhiqiang Zhang,Jun Zhou
机构: Ant Group (蚂蚁集团)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Mixture-of-Experts (MoE) models expand model capacity without a proportional increase in training compute, but increasing sparsity makes reliable hyperparameter transfer challenging. In this work, we show that conventional hyperparameter scaling laws are insufficient for ultra-sparse MoEs: the optimal learning rate and batch size vary with activation ratio, and these shifts cannot be explained by either total or activated parameter count alone. To characterize this dependence, we conduct 1,800 pre-training runs spanning six activated-parameter scales and models with up to 6B total non-embedding parameters, processing approximately 20 trillion tokens at a cost of 200,000 equivalent H800 GPU-hours. Our results reconcile conflicting findings in prior work by revealing two scaling regimes. At fixed sparsity, the optimal batch size follows a power-law relationship with training tokens D , whereas the optimal learning rate scales with training compute C and remains robust to the allocation between model size and data. Across sparsity levels, the activation ratio A enters both relationships as an additional multiplicative power-law factor. These observations lead to unified hyperparameter scaling laws that transfer across MoE sparsity levels. Large-scale evaluation shows that the scaling form outperforms alternative functional forms. On a held-out ultra-sparse MoE with 12B total parameters and only 1/64 of its experts activated, the predicted hyperparameters remain close to the observed optima, supporting joint extrapolation across model scale and sparsity. Further experiments demonstrate transfer across expert granularities and isolate the effect of activation ratio from that of total expert count.
[NLP-28] When Victorian Becomes a Prompt: Literary Periodization as a Generative Constraint in 100 AI-Generated Novels
【速读】： 该论文试图解决的问题是：生成式人工智能（Generative AI）在文学创作中如何重构和内化历史时期特征，特别是“维多利亚时期”这一文学史分期标签是否能够作为前置生成条件，反向影响文本的风格与内容生成。传统文学史分期是基于历史时间顺序对文本进行归类，而生成式AI则可能打破这一线性逻辑，使“维多利亚时期”这一标签成为生成文本的主导性驱动因素。其解决方案的关键在于提出并验证“生成式分期”（generative periodization）这一概念，即利用文学时期标签作为提示词（prompt）来引导模型生成具有特定历史风格特征的文本，并通过引入“时期对齐得分”（Period Alignment Score, PAS）这一量化指标，评估生成文本在主题降维后的语法特征与十九世纪文学之间的对齐程度。实验结果表明，使用维多利亚时期提示词可显著促使GPT与Qwen模型生成具有历史方向性变化的文本，而Llama模型则未表现出稳健效应；进一步的模型重校准与更严格的对比语料库可增强该效应，且跨模型迁移分析揭示了共享的语法演变方向。研究发现，生成文本所指向的并非狭义的“维多利亚时期”，而是更广泛的十九世纪整体文学语境。
链接: https://arxiv.org/abs/2609.08689
作者: Mehdy Sedaghat Payam
机构: 独立学者(Independent Scholar)
类目: Computation and Language (cs.CL)
备注: 18 pages, 3 figures
Abstract:Generative AI inverts the typical periodization of literary history: the periodizing tag Victorian can now come first and influence what is written. Generative periodization, defined and tested here, describes the use of literary-period designations in generating texts. I test this approach on 100 book-length novels produced under Victorian and Zero-Style conditions using GPT, Qwen, and Llama workflows. The Period Alignment Score (PAS), trained on nineteenth-century literature and benchmarked against human Zero-Style prose, assesses alignment using topic-reduced grammatical features. Victorian prompts produce consistent historical-direction shifts in GPT and Qwen, but not robustly in Llama. Victorian-only recalibration and harder comparison corpora preserve the GPT and Qwen effects. Cross-model transfer also shows a shared direction of grammatical change. The measurable target is the broader nineteenth century rather than the Victorian period per se.
[NLP-29] Difficulty-Adaptive Tree-Structured Policy Optimization for Expanding Reasoning Coverag e in RLVR
【速读】： 该论文旨在解决生成式 AI 在基于可验证奖励的强化学习（Reinforcement Learning with Verifiable Rewards, RLVR）训练过程中，尽管单样本准确率显著提升，但模型内在推理覆盖范围（pass@k）受限的问题。其核心挑战在于训练期间探索不足，导致模型难以发现多样化的正确推理路径。解决方案的关键在于优化训练阶段轨迹（train-time rollouts）的结构设计，提出 DATPO（Difficulty-Adaptive Sentence-entropy-guided Tree-structured Policy Optimization）方法。该方法基于三项关键设计原则：（1）难度自适应的轨迹生成不仅提升效率，更有效扩展 pass@k；（2）基于树结构的轨迹优于并行采样，能更高效地发现正确解；（3）基于句子熵引导的分叉机制可克服令牌级分支带来的局部化现象，最大化语义多样性。DATPO 通过整合难度自适应的树状搜索与兄弟节点多样性优势项，显式促进推理过程中的语义多样性，从而在数学推理基准测试中显著提升 pass@k 指标，并展现出更优的测试时缩放性能。
链接: https://arxiv.org/abs/2609.08650
作者: Youngjun Yu,Sanghwan Jang,Hwanjo Yu
机构: Pohang University of Science and Technology (POSTECH)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Reinforcement Learning with Verifiable Rewards (RLVR) has been central to the recent success of Large Reasoning Models. However, while RLVR significantly improves single-sample accuracy, it often fails to expand the model’s intrinsic reasoning coverage (pass@k) due to limited exploration during training. To address this, we optimize the structural design of train-time rollouts to enhance pass@k. Our analysis identifies three key design principles: (1) difficulty-adaptive rollout can play an important role in expanding pass@k, beyond serving as an efficiency heuristic; (2) tree-based rollout outperforms parallel sampling in discovering correct answers; and (3) sentence-entropy-guided forking overcomes the localization phenomenon of token-level branching to maximize semantic diversity. Building on these insights, we propose DATPO (Difficulty-Adaptive Sentence-entropy-guided Tree-structured Policy Optimization). DATPO integrates difficulty-adaptive tree search with a sibling-diversity advantage term, explicitly promoting semantic diversity to expand reasoning coverage during training. Experiments on mathematical reasoning benchmarks demonstrate that DATPO outperforms baselines especially in pass@k, which directly translates to superior test-time scaling performance.
[NLP-30] Combating Instruction Conflict via Energy-Driven Latent Conflict Detection
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在层级化指令部署中面临的指令冲突问题，尤其是用户指令与系统级约束之间的冲突导致的响应漂移（Response Drift）现象。现有防御机制多依赖静态输入检测，无法有效识别虽输入看似合规但最终输出违反系统约束的情况。为此，论文提出ELCD——一种面向生成后、交付前的响应级潜在空间冲突检测方法。其核心在于：基于完整生成输出，通过拼接末尾标记嵌入与均值池化后的响应嵌入，构建复合隐状态表示，并采用成对边际排序目标函数，在潜在空间中区分合规响应与漂移响应。实验结果表明，ELCD在五种主流LLM（参数量1.5B至14B）上显著优于现有基线，尤其在Llama-2-7B上将PR-AUC提升约30个百分点，在Mistral-7B上将95%真正例率下的假阳性率（FPR95）降至2.67%，验证了其在开放权重或自托管模型部署中进行潜在指令冲突检测的有效性。
链接: https://arxiv.org/abs/2609.08646
作者: Mingyu Ma,Yuxin Wu,Jingbo Wang,Tianxiao Huang,Leixin Sun,Xiaochuan Shi
机构: Wuhan University (武汉大学); Renmin University of China (中国人民大学)
类目: Computation and Language (cs.CL)
备注: 15 pages, 2 figures, 5 tables
Abstract:Large Language Models (LLMs) are increasingly deployed with hierarchical instructions, yet they remain vulnerable to conflicts in which user directives override system-level constraints. Existing defense mechanisms predominantly focus on static input inspection and therefore fail to detect Response Drift, a phenomenon in which the model’s final response violates system-level constraints despite seemingly compliant inputs. To bridge this gap, we introduce ELCD, a response-level latent conflict detector for post-generation, pre-delivery verification. Given the full generated output, ELCD constructs a composite hidden-state representation by concatenating the final-token embedding with the mean-pooled response embedding. It then optimizes a pairwise margin ranking objective to separate compliant and drifting responses in latent space. Extensive experiments across five mainstream LLMs ranging from 1.5B to 14B parameters demonstrate that ELCD significantly outperforms competitive baselines. Notably, it improves the PR-AUC on Llama-2-7B by approximately 30 percentage points and reduces the False Positive Rate at 95% TPR (FPR95) on Mistral-7B to 2.67%. These results suggest that ELCD provides a promising approach for latent instruction-conflict detection in open-weight or self-hosted LLM deployments.
[NLP-31] Navigating the digital spectrum: Assessing political bias stability and downstream fairness in Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在政治倾向评估中因问卷设计偏差与响应诱导偏倚导致测量结果不可靠的问题。现有方法依赖问卷调查，其结果易受模型内在倾向与测量过程中的系统性误差混合影响，难以准确刻画模型的真实政治立场。为此，论文提出一种鲁棒的“政治罗盘测试”（Political Compass Test）评估框架，通过在包含语言、表述框架、指令措辞、答案格式、选项顺序及角色表述等八个维度的300种配置空间中进行系统采样，实现对模型政治坐标的多维扰动分析。关键创新在于采用设计平均法结合量化不确定性估计，以提升评估结果的可重复性与可信度。研究发现，多数模型平均呈现自由意志-左翼倾向，但指令表述、语言类型与答案格式显著影响坐标恢复；跨语言差异主要源于坐标漂移而非文化推理本质差异。进一步逆向工程揭示轴权重失衡与退化响应向中心坍缩现象，表明小模型靠近原点的估计可能反映信号微弱而非真正的中立立场。此外，自由文本推理与“对话后分类”范式会改变恢复的政治坐标，而大模型在角色区分上表现更优，但特定“威权-左翼”角色提示未能有效引导多数模型向预期社会方向移动。在下游任务中，角色提示的影响虽存在，但受限于任务类型与数据集特性——如在仇恨言论检测中，模型规模和目标群体的影响远超角色提示；而在主题级情感判断中，基础提示与中立提示能获得最高一致性。因此，政治角色提示虽具可测量的下游效应，但其作用具有高度任务依赖性和数据集特异性。
链接: https://arxiv.org/abs/2609.08637
作者: Luka Debevc,Nishan Chatterjee,Antoine Doucet,Senja Pollak,Matej Martinc
机构: 未知
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注:
Abstract:Large Language Models are increasingly deployed as information intermediaries, yet measuring their political behavior remains fragile because questionnaire results mix model dispositions with measurement artifacts and response-elicitation biases. We introduce a robust Political Compass Test evaluation framework that samples 300 configurations across an eight-dimensional perturbation space varying language, framing, instructions, answer format, option order, and persona wording. We evaluate eight Gemma 3 and Qwen 3 models across 14 languages and three quantization levels, obtaining design-averaged political coordinates with quantified uncertainty. Most models lean Libertarian-Left on average, but instruction phrasing, language, and answer format significantly affect recovered coordinates. Cross-lingual differences primarily reflect coordinate drift rather than distinct cultural reasoning. Reverse-engineering the test also exposes axis-weighting imbalances and the collapse of degenerate responses toward the center, so near-origin estimates for the smallest models can reflect weak signal rather than centrism. Free-text reasoning and chat-then-classify elicitation alter recovered coordinates, and larger models show clearer persona separation, with a specific failure of the Authoritarian-Left persona to move most models in the intended social direction. In downstream tasks, persona effects are modest relative to model size and target group for hate-speech detection, while base and centrist prompts give the highest agreement for topic-level sentiment. Political role prompting therefore has measurable but task- and dataset-specific downstream effects.
[NLP-32] Dynamics of meaning: Towards the Evaluation of Diachronic Semantic Change in Sinhala AACL
【速读】： 该论文旨在解决低资源语言在长历史跨度下语义演变追踪的难题，核心挑战在于数据稀缺性以及静态词向量对齐方法的局限性。其解决方案的关键在于构建一个多阶段计算框架：首先通过相似性矩阵基对齐（SMA）与正交普鲁斯特变换（OP）对不同时期的Word2Vec和FastText词向量进行对齐，发现OP方法能更稳定地捕捉语义邻域变化，从而识别出时间序列中的语义相似性下降点；进一步地，为突破传统聚合度量的局限，提出基于上下文嵌入的双向语义影响剪枝（Bidirectional Semantic Impact Pruning）方法，利用微调后的Llama-3.1-8B模型生成上下文感知表示，并结合留一法（LOO）诊断，以分离出具有显著影响力的句子实例，从而区分系统性语义变迁与临时性的多义扩展。研究结果表明，微调后Llama-3.1-8B模型中的语义漂移并非均匀分布于所有用例中，而是主要由少数高影响力上下文实例驱动，而非全样本渐进式演化。该工作为低资源语境下的历时语义分析提供了初步框架，揭示了模型敏感性与数据可得性之间的权衡关系。
链接: https://arxiv.org/abs/2609.08609
作者: Nevidu Jayatilleke,Nisansa de Silva
机构: University of Moratuwa (莫鲁图瓦大学)
类目: Computation and Language (cs.CL)
备注: 31 pages, 5 figures, 18 tables, Accepted paper at the 5th Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) the 15th International Joint Conference on Natural Language Processing (IJCNLP) 2026
Abstract:Tracking semantic change in low-resource languages across extensive historical timelines presents significant challenges due to data scarcity and the limitations of static embedding alignments. This study investigates the diachronic evolution of the Sinhala language from the 13th to the 20th century using a multi-stage computational framework. We first align century-specific Word2Vec and FastText embeddings using Similarity Matrix Based Alignment (SMA) and Orthogonal Procrustes (OP) techniques, finding that OP alignment provides more stable neighbourhood tracking for identifying temporal similarity dips. To move beyond aggregate measures, we introduce a Bidirectional Semantic Impact Pruning approach using contextualised embeddings from a fine-tuned Llama-3.1-8B. By applying Leave-One-Out (LOO) diagnostics, we attempt to isolate influential sentences to distinguish between systemic semantic shifts and transient polysemic expansion. Our results show that semantic drift in the fine-tuned Llama-3.1-8B is not evenly distributed across all usages. Instead, a significant part of the change is driven by a smaller set of high-impact contextual instances, rather than gradual and uniform change across all occurrences. This work provides a preliminary framework for diachronic analysis in low-resource contexts, highlighting the trade-offs between model sensitivity and data availability.
[NLP-33] A Three-Tier Persona Vector for Controllable User Simulation in Agent ic Evaluation
【速读】： 该论文旨在解决现有大语言模型（LLM）代理评估框架中用户输入过于扁平化、缺乏真实多样性的核心问题，即传统方法仅依赖简单的角色描述（如“你是一个愤怒的客户”），导致无论底层场景如何变化，生成的对话均高度同质化。为此，论文提出了一种三层人格向量（persona vector）模型，其关键在于通过23个可操作化维度构建具有现实感的虚拟用户：第一层为6个分类型人口统计特征（如管辖区域、年龄、沟通渠道等）；第二层为12个连续的行为特质（如耐心、果断性、数字素养等），基于预设的基准向量并引入高斯噪声进行采样；第三层为5个随情境动态变化的连续情绪状态（如挫败感、焦虑、信任度等），能够根据具体场景上下文实时调整。此外，引入四级查询复杂度叠加层，控制用户提问从直接到刻意模糊的语义层次。实验在包含8个命名角色和3个生产数据集的合成数据生成管道中完成，共生成64,698轮多轮对话。关键发现表明：(i) 不同人格间目标达成率存在15.8个百分点的显著差异，证实行为向量能有效引发可测量的用户行为分化；(ii) 同一人格在不同场景下表现出异质行为，验证了情境响应式情绪状态设计的有效性；(iii) 在特定领域任务中，感知敏感度差异达15–20个百分点，证明模型能忠实还原真实世界中的难度分布；(iv) 七组规则定义的特质相关性可产生可审计的共现模式，无需依赖学习型协方差矩阵。该人格模型完整可复现，为生成多样化、情境敏感且真实可信的评估输入提供了系统性解决方案。
链接: https://arxiv.org/abs/2609.08592
作者: Rahul Khedar,Eshita,Sneha Teja Sree Reddy Thondapu,Mayank Malhotra,Arup Kumar Das,Jitesh Chandra Mishra,Arun Menon,Avinash Karn,Mouli V
机构: Arup Kumar Das; Jitesh Chandra Mishra; Arun Menon; Avinash Karn; Mouli V; PayPal AI(PayPal人工智能)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 7 pages, 3 figures, 6 tables. Extended treatment of the persona component of StateGen ( arXiv:2606.16307 )
Abstract:Evaluating tool-augmented LLM agents requires diverse, realistic user inputs yet most evaluation frameworks use flat role descriptions (“you are an angry customer”) that produce near-identical conversations regardless of the underlying scenario. In this paper, we propose a three-tier persona vector with 23 operationalized dimensions: 6 categorical demographics (jurisdiction, age, channel, device, language proficiency, time availability), 12 continuous behavioral traits (patience, assertiveness, digital literacy, etc.) sampled with Gaussian noise around curated profile base vectors, and 5 continuous emotional states (frustration, anxiety, trust, confidence, stress) that shift in response to scenario context. Orthogonal to the persona, a 4-level query-complexity overlay controls utterance phrasing from direct to deliberately vague. We evaluate the persona model inside a synthetic data generation pipeline across 64,698 multi-turn conversations spanning 8 named profiles and 3 production corpora. Key findings: (i) a 15.8 percentage-point spread in agent goal-achievement across personas confirms trait vectors produce measurably different user behavior; (ii) the same persona behaves differently across scenarios due to scenario-reactive emotional state shifts, validating the scenario-reactive design; (iii) domain-specific projects show persona sensitivity on booking-flow compliance (~15-20 percentage points gap between tier-aware and pressure-test personas), demonstrating the model faithfully reproduces real-world difficulty distributions; (iv) seven rule-described trait correlations produce auditable co-occurrence patterns without requiring learned covariance matrices. The persona model is fully specified for reproduction.
[NLP-34] he Unreliable Progress Bar: Can LLM Agents Reliably Report Task Progress Throughout Execution?
【速读】： 该论文旨在解决生成式 AI 在复杂任务执行过程中对任务进展状态报告的可靠性问题，即当前大语言模型（Large Language Models, LLMs）在多阶段任务中能否稳定、准确地提供阶段性进度信号，以及其报告失败的具体场景与机制。研究发现，模型在不同任务阶段的报告可靠性存在显著差异：多数模型在任务初期和结束阶段表现可靠，但在任务进行中（mid-task）普遍出现准确率下降；而最新一代模型虽缓解了中段性能下滑，却在任务收尾阶段趋于保守，倾向于低估完成度。这一现象揭示了任务进展报告能力在实际应用中的关键缺陷，表明现有代理框架（agent frameworks）若仅依赖模型自身状态报告来控制任务流程，存在决策风险。因此，解决方案的关键在于建立覆盖任务全生命周期的系统性评估协议（evaluation protocol），以全面检验模型在各阶段的任务进展报告能力，并强调代理系统应结合多源验证机制，而非单一依赖模型自报状态。
链接: https://arxiv.org/abs/2609.08589
作者: Boyang Wang,Yunhan Wang,Yalun Wu
机构: Independent Researcher(独立研究员); Beihang University (北京航空航天大学); NExT++ Lab, School of Computing, National University of Singapore (新加坡国立大学计算学院NExT++实验室)
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 33 pages, 13 figures, 20 tables
Abstract:Recent large language models can emit task-progress signals that agent frameworks use to decide whether a task should continue or stop, yet whether a model can reliably report its task progress at every stage of a task, and where and how its reports fail, has not been studied systematically. We evaluate this ability on the public benchmark \tau^2 -bench and on StageIF, a controlled testbed in which reporting checkpoints are placed across the task’s lifecycle. Both settings require reports at multiple task stages. We find that reporting reliability depends on the stage a task has reached, and that almost every deployed model we test is reliable at some stages and unreliable at others. Where reporting breaks down is not the same everywhere. Most deployed models lose accuracy once work is under way and recover once the task is done. The newest generation closes that mid-task drop and instead grows conservative at the finish line. Our study exposes a capability gap in task-progress reporting and provides an evaluation protocol that spans the whole course of task execution for this ability on which agent operation depends. The findings indicate that agent frameworks should not control task flow on the strength of the model’s state reports alone.
[NLP-35] Limitations of Automated Simulatability: LLM Simulators Can Bypass Explanations EMNLP2026
【速读】： 该论文旨在解决自动化可模拟性评估（automated simulatability evaluation）在解释方法评测中存在潜在偏差的问题。其核心挑战在于：当前基于大语言模型（LLM）模拟器的自动化评估框架（如ConSim）可能因任务先验信息或类别名称的语义线索而产生“捷径学习”（shortcut learning），导致评估结果无法真实反映解释内容对预测性能的实际贡献。解决方案的关键在于识别并修正两大局限性——一是当类别名称具有明确语义时，模拟器可绕过解释直接完成分类任务；二是类别匿名化处理可能使评估误将解释中泄露的隐藏标签映射视为有效信息。为此，研究提出以“类作为概念”（classes-as-concepts）为新基线，揭示上述缺陷，并据此提出改进策略，强调应通过控制语义线索与标签映射来提升自动化可模拟性评估的鲁棒性。
链接: https://arxiv.org/abs/2609.08585
作者: Antonin Poché,Fanny Jourdan,Nils Feldhus,Qianli Wang,Jing Yang,Simon Ostermann,Nicholas Asher,Philippe Muller,Vera Schmitt
机构: IRT Saint Exupéry; IRIT, Université de Toulouse; University of Groningen; Technische Universität Berlin; BIFOLD – Berlin Institute for the Foundations of Learning and Data; Saarland University; Johannes Gutenberg-University Mainz; German Research Center for Artificial Intelligence (DFKI); Centre for European Research in Trusted AI (CERTAIN); CNRS
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted to the BlackboxNLP 2026 Reproducibility Challenge (Special Track), EMNLP 2026
Abstract:Simulatability is an evaluation protocol for explanations that quantifies their usefulness by how well they help a user predict a task model’s outputs. Since human evaluation is costly, automated simulatability replaces human explainees with LLM simulators, as proposed in ConSim (Poché et al., 2025) for large-scale experiments. We qualitatively replicate and extend ConSim’s ranking of explanation methods across the tested datasets, explanation families, and simulator LLMs, and identify two limitations. First, when class names are meaningful, simulators can obtain high simulatability by solving the classification task directly, without relying on the explanations. Second, class anonymization can reward explanations for leaking the hidden label mapping, a limitation we expose with a new classes-as-concepts baseline. These results are consistent with a shortcut hypothesis: in the tested settings, simulator predictions mainly rely on task priors, while explanations produce small changes. We derive recommendations for more robust automated simulatability evaluations.
[NLP-36] Which Forms of Caregiver Feedback Support Grammar Learning? A Reinforcement-Learning Study of Child-Like Language Models
【速读】： 该论文旨在解决自然语境下难以分离不同形式照护者反馈对儿童语法发展影响的问题，通过构建类儿童语言模型作为受控学习者，系统检验各类反馈机制的作用。其解决方案的关键在于采用小规模GPT-2风格的语言模型，在CHILDES语料库的儿童导向语言上进行预训练，并通过强化学习框架，利用针对四种反馈类型（沟通性反馈、结构对齐、语义连续性及情感反馈）分别训练的奖励模型进行微调。研究发现，结构对齐反馈在提升生成句式的语法正确性方面效果最为显著，为该反馈机制促进语法习得提供了新颖且合理的机制解释；沟通性反馈亦带来中等程度的提升；而语义连续性和情感反馈虽未显著改善语法质量，但可能在其他语言学习维度（如语用或动机层面）发挥辅助作用。结果表明，不同类型照护者反馈在语言学习中具有互补性贡献。
链接: https://arxiv.org/abs/2609.08576
作者: Jing Liu,Marianne Schweitzer,Abdellah Fourtassi
机构: ENS, Université PSL, EHESS, CNRS, Paris, France; Aix Marseille Université, CNRS, LIS, Marseille, France
类目: Computation and Language (cs.CL)
备注:
Abstract:Social interaction is central to children’s language learning, but the effects of different forms of caregiver feedback are difficult to isolate in naturalistic data. We use child-like language models as controlled learners to test which forms of feedback support grammatical development. Small GPT-2-style models are pretrained on child-directed language from CHILDES, then fine-tuned with reinforcement learning using reward models trained to capture four feedback types: communicative feedback, structural alignment, semantic contingency, and affective feedback. Reward fine-tuning yields limited gains on minimal-pair evaluations, but clearer effects in free generation. Structural alignment produces the strongest improvements in grammaticality, providing a novel, plausible mechanistic account of how this feedback can support grammar learning. Communicative feedback yields more moderate gains. In contrast, semantic contingency and affective feedback do not improve grammaticality, although further analyses suggest that they may support other aspects of language learning beyond grammar. These results suggest that different forms of caregiver feedback make complementary contributions to language learning.
[NLP-37] Do New Attention Mechanisms Actually Fix Attention Sinks at Million-Token Context?
【速读】： 该论文旨在解决长上下文语言模型在实际应用中无法充分利用其理论支持的百万级标记（token）窗口的问题，核心挑战在于“注意力黑洞”（attention sink）现象——即注意力头倾向于过度关注序列首标记，导致对后续内容的感知能力下降；同时，事实在上下文中的位置显著影响模型的召回效果。其解决方案的关键是引入门控注意力机制（gated attention），通过抑制首标记的注意力分配，从而缓解注意力资源浪费和位置偏差问题。研究进一步提出并验证了名为SinkProbe的评估套件，可量化测量注意力黑洞质量（sink mass）、大规模激活强度、位置分辨召回率及时间距离差距（recency gap）。实验结果表明：注意力黑洞主要由训练目标而非模型架构引发，且在更大规模（百万级上下文）下，先前报告的门控注意力效果未能复现；此外，注意力黑洞质量、激活强度与位置偏差表现出独立演化特性。该研究揭示了现有方法在超长上下文场景下的局限性，并开源了代码、数据及测量协议以推动后续研究。
链接: https://arxiv.org/abs/2609.08574
作者: Sara Rizwan,Samaanah Abdus Salam
机构: Shadan Women’s College of Engineering and Technology (沙丹女子工程与技术学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Experimental study of attention sinks, long-context recall, and million-token context behavior. Code and measurement protocol are available at this https URL
Abstract:Long context language models now advertise windows of one million tokens, but two habits limit how much of that window is used. Attention heads with nothing useful to read still spend their budget on the first token, which is called the attention sink, and where a fact sits in the context changes whether the model finds it. Gated attention cut first token attention from 46.7 percent to 4.8 percent at NeurIPS 2025, and Kimi K3 pairs that idea with Kimi Delta Attention and Attention Residuals behind a one million token window, eight times past the range where these diagnostics have been reported. This paper asks whether the fix survives that jump. We build SinkProbe, a suite that measures sink mass, massive activation, position resolved recall and the recency gap, and apply it to four small models that differ only in how they mix tokens and depth. Three results follow. The training objective produces the sink, not the architecture. Gating did not reproduce its published effect at our scale. Sink mass, activations and position bias moved independently. Code, data and the measurement protocol are released at this https URL
[NLP-38] CreaMem: A Scene-Aware Memory Architecture for Personalized Agents EMNLP’26
【速读】： 该论文旨在解决个性化大语言模型（LLM）代理在长期记忆管理中面临的两大核心问题：一是现有记忆系统缺乏场景感知能力，导致不同生活场景的记忆共享同一检索空间，从而扩大搜索范围并引发跨场景干扰；二是记忆编码仅从单一视角进行，难以实现对同一事件的多角度互补信息检索。其解决方案的关键在于提出CreaMem架构，通过将记忆按生活场景（Life Scene Memory）进行分区，实现场景感知的记忆组织，有效降低检索时的跨场景干扰；同时，在每个记忆单元内采用双编码机制，分别从情景式（episodic）和特质性（trait-based）两个视角进行表征，以实现跨记忆间的协同与信息互补。此外，引入基于每记忆的平衡采样策略以优化检索过程。大量实验结果表明，CreaMem在多个长期记忆基准测试中显著提升了问答准确率，尤其在多跳推理任务上表现突出，验证了场景分区与跨记忆协同的有效性。
链接: https://arxiv.org/abs/2609.08550
作者: Qixuan Sun,Yue Que,Bowei He,Jin Guo,Dihang Yang,Wenchang Situ,Chen Ma
机构: City University of Hong Kong (香港城市大学); AgentWoods Inc. (旧金山); Mohamed bin Zayed University of Artificial Intelligence (穆罕默德·本·扎耶德人工智能大学)
类目: Computation and Language (cs.CL)
备注: Accepted as EMNLP’26 Findings
Abstract:Long-term memory is a core capability for personalized LLM agents. To support it, existing memory systems organize information using various criteria such as topic segments or summary hierarchies. However, we identify two major limitations in these designs. First, they lack scene awareness: memories from unrelated life scenes share the same retrieval space, which inflates the search space and introduces cross-scene interference. Second, they encode each memory from a single perspective, making it difficult to retrieve complementary views of the same event. In this paper, we propose the CreaMem architecture, which enables scene-aware memory organization by partitioning memory into several Life Scene Memories to reduce cross-scene interference at retrieval. To go beyond the single perspective and achieve cross-memory synergy, entries are dual-coded from both episodic and trait-based perspectives within each memory. We further devise a permemory balanced sampling strategy at retrieval time. Extensive experiments on two long-term memory benchmarks show that CreaMem improves QA accuracy across all evaluation metrics, with particularly large gains on multi-hop reasoning performance, validating scene-aware partitioning and cross-memory synergy. To enhance reproducibility, we release our code in a public GitHub repository.
[NLP-39] Same Values Different Languages? From Multilingual Probing to Steering LLM s Toward Chinese Social Values
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在多语言环境下对文化根基型价值（culturally grounded values）的一致性对齐问题，尤其关注中文社会价值观（Chinese Social Values, CSV）这一涵盖国家、社会与个人三个层面共12个维度的复杂价值体系。现有研究多聚焦于安全导向原则的对齐，而对抽象且文化依赖性强的价值判断缺乏系统评估与对齐方法。为应对这一挑战，论文构建了C-Voices——首个面向中文社会价值观的多语言对比探测数据集，包含六种语言下86,400个基于困境情境的配对样本，每组对比呈现一个符合CSV的行为与一个价值冲突的替代方案。其核心解决方案是提出一种无需微调（fine-tuning-free）的价值向量引导方法，通过分析模型隐状态中的差异提取价值方向，并在推理阶段选择性干预价值敏感层。实验表明，模型对CSV的偏好具有显著的语言敏感性和模型依赖性，同一困境在不同语言中引发迥异响应；所提方法不仅有效实现对CSV的定向引导，还支持价值向量的跨语言迁移，并可泛化至已有基准如FLAMES和ValuePrism。
链接: https://arxiv.org/abs/2609.08515
作者: Yuemei Xu,Kexin Xu,Jian Zhou,Haoyu Lu,Yequan Wang,Aishan Liu
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:As Large Language Models (LLMs) are increasingly integrated into human society, aligning them with pluralistic social values has become a critical priority. However, whether LLMs exhibit consistent value preferences across languages remains underexplored, particularly for culturally grounded values, which are more abstract and difficult to evaluate and align than safety-centric principles. We investigate this issue through Chinese Social Values (CSV), a value system rooted in Chinese culture and comprising 12 dimensions across national, societal, and personal levels. We construct C-Voices, the first comprehensive multilingual contrastive probe dataset for CSV, with 86,400 dilemma-based instances in six languages, each pairing a CSV-aligned action with a value-conflicting alternative. Building on the contrastive probes of C-Voices, we then propose a fine-tuning-free value vector steering method that derives value directions from hidden-state discrepancies and selectively intervenes on value-sensitive layers during inference. Experiments on six languages show that CSV-oriented preferences are model-dependent and language-sensitive, with the same dilemma eliciting divergent responses across languages. Our method achieves effective CSV steering, supports cross-lingual transfer of value vectors, and generalizes to existing FLAMES and ValuePrism.
[NLP-40] Do Reviewers Still Reward Lexical Complexity? A Frozen-Rater Study of Preference Drift in 124K ICLR Reviews
【速读】： 该论文旨在解决在生成式AI（Generative AI）广泛普及背景下，学术同行评审中对文本语言复杂性（lexical complexity）的评价标准是否仍具有效性的问题。核心问题是：当大规模语言模型显著降低高词汇复杂度文本的生成成本时，人类审稿人是否仍偏好此类表达？若审稿倾向随时间发生改变，究竟是审稿人评价标准发生了演变，还是投稿内容本身发生了变化？为分离这两个混杂因素，研究提出以“冻结审稿人”（frozen rater）作为控制变量——即使用同一模型家族与统一提示，在2025年2月至4月间生成81,850条机器审稿意见，覆盖ICLR 2018至2025年的所有提交稿件。该设定确保了审稿行为的稳定性，使年际系数变化仅反映投稿内容构成的变迁；而人类审稿人与冻结审稿人之间的趋势差异则揭示出审稿偏好漂移（reviewer preference drift）。分析显示，在32,638篇有124,615次人工审稿的论文中，人类审稿人对非领域特定词汇复杂度的评分系数从+0.142降至-0.015，而冻结审稿人保持稳定在+0.080至+0.082之间，三重差分（difference-in-differences）结果为-0.0100（q=0.013），且40个随机词表置换检验均围绕零分布，表明结果稳健。此外，人类仍重视句长多样性，而冻结审稿人始终未捕捉此特征；冻结审稿人虽持续认可词汇复杂度，但其评分体系因未随历史人类偏好调整而出现系统性偏离。所有结论均通过双重错误发现率控制与区间排除验证，并报告了经对抗性再测试失败的结果。研究证实，当某一评价信号的生成成本被生成式AI大幅压缩时，审稿人会相应降低对其价值的评估，符合对可操纵信号（manipulable signals）的理论预期；同时，一个基于历史人类偏好校准的大型语言模型（LLM）裁判虽与人类总体评分一致性正常，但其内部评分逻辑随时间发生偏移，显示出“校准失效”的风险。关键解决方案在于构建一个受控、一致的机器审稿基准，从而实现对人类审稿偏好动态的精准解耦与量化。
链接: https://arxiv.org/abs/2609.08475
作者: Jiabin Zheng(School of Computer Science, Peking University)
机构: Peking University (北京大学)
类目: Computation and Language (cs.CL); Digital Libraries (cs.DL); Machine Learning (cs.LG)
备注: 23 pages, 8 figures, 11 tables. Code and the machine-readable records behind every number: this https URL
Abstract:Large language models have collapsed the cost of producing lexically elaborate prose, and whether peer reviewers still reward it is a question about the evaluator, not about the text. When the association between a writing cue and review scores moves across years, the reviewers may have changed, the submissions may have changed, or both, and a regression of scores on text cannot say which. We separate the two with a frozen rater: 81,850 machine reviews of ICLR submissions from 2018 to 2025, all generated in one February-April 2025 window with one model family and one prompt, so that its year-to-year coefficients track submission composition alone and the human-minus-frozen trend difference identifies reviewer preference drift. On 32,638 submissions with 124,615 human reviews, the human coefficient on non-domain lexical complexity falls from +0.142 to -0.015 while the frozen rater moves from +0.080 to +0.082; the three-way difference-in-differences is -0.0100 (q=0.013), and forty random-wordlist placebos through the same specification centre on zero. Humans still reward sentence-length variability, which the frozen rater never registers, while the frozen rater still pays for lexical complexity at its earlier rate. Every claim is held to a double gate of false-discovery control and interval exclusion, and the findings that failed adversarial re-testing are reported. Reviewers discounted a cue whose production cost collapsed, as models of manipulable signals prescribe; an LLM judge calibrated to historical human preferences inherits the earlier schedule and drifts out of alignment while its agreement with humans on totals stays ordinary.
[NLP-41] Detecting Authorship in Political Texts with Inductive Stylometry
【速读】： 该论文旨在解决政治文本中隐性作者（如幕僚、撰稿人）留下的风格痕迹难以被识别的问题，尤其在官方声明、演讲稿和报告等由多人协作完成的文本中，传统政治科学对这些潜在作者的贡献关注不足。其解决方案的关键在于提出并验证一种归纳式文体计量方法，结合字符三元组（character 3-gram）特征提取、UMAP降维技术与Burrows’ Delta统计指标，以捕捉文本中的细微风格差异。该方法在多种语料（涵盖推文至长篇法律文书、书面与口头表达、英语与匈牙利语）中表现良好，能够有效区分不同分析者在正式法律文本中的风格指纹，对政客推文进行合理聚类并揭示隐藏信息，同时可辨别即兴演讲与脚本化演讲，但在识别脚本化文本中的具体撰稿人方面未能成功。这表明频率基文体计量方法在作者信号强度和机构编辑干预程度不同的背景下，具有揭示立法研究、政治传播与政策分析中相关作者结构的潜力。
链接: https://arxiv.org/abs/2609.08459
作者: Gennadii Iakovlev,Levente Littvay
机构: 未知
类目: Computation and Language (cs.CL)
备注: 34 pages, 17 figures, 3 tables. Includes appendices A-D (validation materials and discriminant validity checks). Under review
Abstract:Political texts are rarely authored by the nominal speaker alone. Tweets, speeches, reports, and official statements are drafted, edited, or harmonized by staff, yet political science has paid limited attention to the stylistic traces these hidden authors leave behind. This paper develops and stress-tests an inductive stylometric approach for recovering latent authorship structure in political communication, combining character 3-gram features with UMAP dimensionality reduction, and Burrows’ Delta. We apply the approach to six corpora that vary in length (from tweets to long documents), in mode (written and oral), and in language (English and Hungarian). The approach recovers near-disjoint analyst fingerprints in formal legal prose in both languages, sorts a politician’s tweets into validated subsets while uncovering additional insights, and distinguishes scripted from improvised speech. It fails, however, to resolve individual speechwriters within scripted corpora. Frequency-based stylometry is thus a powerful tool that, depending on authorial signal strength and institutional editing, can uncover authorship traces relevant to legislative studies, political communication, and policy research.
[NLP-42] Compositional Multilingual and Behavioral Attribute Steering
【速读】： 该论文旨在解决大语言模型中属性控制向量（attribute steering vectors）在无训练条件下进行可加性组合时的可组合性（compositionality）问题，特别是针对语言、越狱（jailbreak）和简洁性（conciseness）三种行为属性的协同控制效果。其核心挑战在于：如何在不重新训练模型的前提下，通过线性叠加多个属性控制向量，实现对多个目标属性的同时精准调控。解决方案的关键在于发现并验证：每个属性的最佳干预层（intervention layer）与其语义抽象程度相关——抽象行为（如越狱、简洁性）在中间层表现更优，而语言属性则在较早层更有效；当各属性的控制向量分别注入其各自最优层时，其可加性组合能够有效同时实现双重属性的引导，并部分扩展至三重属性组合，从而解决了先前无训练组合方法中存在的不一致性问题。此外，研究还揭示了这些控制向量在残差流（residual stream）中近似正交的几何特性，为其实现解耦且可叠加的控制行为提供了理论支持。
链接: https://arxiv.org/abs/2609.08410
作者: Hyun Gu Kang,Daniil Gurgurov,Tanja Baeumel,Josef van Genabith,Simon Ostermann
机构: German Research Centre for Artificial Intelligence (DFKI); Saarland University; Centre for European Research in Trusted AI (CERTAIN)
类目: Computation and Language (cs.CL)
备注: Accepted to BlackboxNLP 2026
Abstract:This study examines the compositionality of steering vectors for language and behavioral control in large language models. Focusing on language, jailbreak, and conciseness, we investigate whether additive, training-free composition of attribute steering vectors can preserve the intended steering effect of each attribute, across four instruction-tuned models from two model families and two size scales. We find that single-attribute steering is reliable for all three attributes, but only within an appropriate combination of intervention layer and steering strength, with abstract behaviors (jailbreak, conciseness) favoring middle layers and language favoring earlier layers. We show that additive composition of two attribute vectors succeeds in steering both attributes simultaneously when each is injected at its own best-performing layer, and that this partially extends to three simultaneously composed attributes, addressing an inconsistency left open by prior work on training-free composition. We further analyze the geometric properties of these steering vectors, finding that they are approximately orthogonal in the residual stream, consistent with their compositional behavior.
[NLP-43] Environments as Scaffold: Enriching Feedback to Bootstrap Self-Evolving Agents in Long-Horizon Tasks
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在长期任务中作为自主智能体（autonomous agents）进行强化学习（Reinforcement Learning, RL）时面临的严重奖励稀疏性问题。传统方法通过监督微调（Supervised Fine-Tuning, SFT）进行代理端预热虽可缓解此问题，但受限于数据稀缺与探索能力不足。为此，本文提出一种范式转变——从代理端优化转向环境端适应，构建反馈增强型环境（Feedback-Enriched Environments, FEEs）。其核心解决方案在于设计一种动态反馈机制：在单个轨迹内的探索阶段及跨轨迹演化过程中，逐步将环境提供的动作指导（action guidance）转化为对观测信息的丰富（observation enrichment），从而在不依赖外部标注数据的前提下持续提供有效信号。大规模实验在SciWorld和BFCL基准上验证了FEEs在多种Qwen3模型规模与GRPO、GSPO、DAPO等强化学习算法下的稳定性与有效性。分析进一步表明，使用FEEs训练能显著降低策略熵波动以稳定训练过程，促进复杂任务中的主动状态空间探索，确保环境引导被内化为策略权重而非仅作为推理时先验，并揭示组内反馈一致性是实现稳定优化的关键边界条件。
链接: https://arxiv.org/abs/2609.08404
作者: Hongbang Yuan,Zhuoran Jin,Yixin Cao
机构: Fudan University (复旦大学); Shanghai Jiao Tong University (上海交通大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 21 Pages, 6 Figures, 7 Tables,
Abstract:Large Language Models demonstrate remarkable proficiency in static reasoning, yet training them as autonomous agents through Reinforcement Learning (RL) for long-horizon tasks is often hindered by severe reward sparsity. While conventional \textitagent-side warming up via supervised fine-tuning (SFT) can alleviate this, it is frequently limited by data scarcity and constrained exploration. To address this, we propose a paradigm shift to \textitenvironment-side adaptation by constructing \textbfFeedback-\textbfEnriched \textbfEnvironments (\textbfFEEs). Through a pilot study, we establish a feedback design strategy that reformulates environments by transitioning from action guidance to observation enrichment during the later stages of both intra-episode exploration and inter-episode evolution. Large-scale experiments on SciWorld and BFCL benchmarks using various Qwen3 model scales and RL algorithms such as GRPO, GSPO, and DAPO demonstrate that FEEs consistently yield performance improvements over standard settings. Furthermore, our analysis reveals that training with FEEs \textbf(1) stabilizes training dynamics by reducing entropy volatility, \textbf(2) facilitates proactive state-space exploration in difficult tasks, \textbf(3) ensures the internalization of environmental guidance into policy weights rather than acting as a mere inference-time prior, and \textbf(4) identifies intra-group feedback consistency as a critical boundary for stable optimization.
[NLP-44] From Coordinates to Candidate Regions: Temporal Change Localization via Region Selection in Remote Sensing Multimodal LLM s EMNLP2026
【速读】： 该论文旨在解决遥感多模态大语言模型（RS-MLLMs）在卫星影像中对特定目标或变化区域进行精确定位的难题。现有方法依赖于生成边界框坐标作为标记序列，但在遥感图像中普遍存在小尺寸、密集分布的目标时，该方式易产生误差，且在需同时定位多个目标时错误率显著上升。本文提出一种针对遥感场景的区域选择范式（region selection paradigm），借鉴自然图像多模态大语言模型中的思路，并拓展至多时相图像序列中的时序变化定位任务。其核心解决方案是引入一个文本条件驱动的区域提议模块，将每个候选区域编码为携带帧内视觉特征并融合空间与时间线索的特殊标记（special tokens），由大语言模型通过选择这些区域标记来实现目标定位。研究构建了涵盖单图与多时相场景下的定位、指代表达、视觉定位及理解等多任务训练与评估体系。实验表明，该方法在时序变化定位任务上显著优于基于坐标的基线模型，同时提升了单图视觉定位性能，并保持了良好的理解能力。通过“理想化分析”（oracle analysis）分解区域提议器与大语言模型选择器的贡献，提供了该框架独有的诊断视角。
链接: https://arxiv.org/abs/2609.08391
作者: Juwan Chung,Sungjune Park,Yeongyun Kim,Yong Man Ro
机构: KAIST(韩国科学技术院)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注: Accepted to Findings of EMNLP 2026
Abstract:Remote sensing multimodal large language models (RS-MLLMs) have advanced scene understanding and visual question answering over satellite imagery, yet localizing specific objects or changed regions remains challenging. Existing approaches rely on generating bounding box coordinates as token sequences, which is fragile for the small, densely packed objects common in remote sensing and increasingly error-prone when multiple targets must be localized simultaneously. In this work, we present an RS-specific formulation of the region selection paradigm, previously explored in natural-image MLLMs, and extend it to temporal change localization over multi-image sequences. Our framework employs a text-conditioned region proposal module, encodes each candidate as special tokens carrying per-frame visual features enriched with spatial and temporal cues, and lets the LLM localize targets by selecting region tokens in its response. We construct a multi-task training and evaluation suite spanning localization, referring expression, visual grounding, and understanding tasks across single-image and multi-temporal settings. Experiments show that our approach substantially outperforms coordinate-generation baselines on temporal change localization, while improving single-image visual grounding and maintaining competitive understanding performance. Oracle analysis decomposes the contributions of the region proposer and the LLM selector, providing diagnostic insight unique to this framework. Our code will be available at this https URL.
[NLP-45] Structural Jailbreaks Generalize but Do Not Compound: A cross-provider and multilingual study of Involuntary In-Context Learning
【速读】： 该论文旨在解决生成式 AI 在安全对齐方面面临的双重挑战：一是近期被形式化为非自愿上下文学习（Involuntary In-Context Learning, IICL）的结构型越狱攻击，即通过将有害请求伪装成数据标注任务中缺失的最后一个单元，利用模式匹配而非内容判断实现绕过；二是模型在非英语语境下的安全对齐能力退化。其解决方案的关键在于系统性验证这两个威胁是否具有叠加效应。研究采用确定性的 IICL 操作符与 StrongREJECT 风格的评判标准，对谷歌 Gemini 模型在 HarmBench（30 种通用危害行为）和 FinProof（30 种金融滥用行为）两个基准上进行红队测试，覆盖单次提示基线及四种语言（英语、西班牙语、印地语、阿拉伯语）下的 IICL 攻击。结果表明，IICL 攻击不仅可泛化至另一大模型提供商，且在金融滥用场景下破坏力显著增强——攻击成功率从约 6.7% 升至 80–90%（HarmBench）和 97–100%（FinProof），远超此前针对 GPT-5.4 报告的 24%。然而，违背预期的是，将 IICL 输出强制置于非英语语境并未加剧风险，反而抑制了攻击效果：12 个非英语条件中有 11 个得分低于对应英语基线（符号检验，p~0.003），唯一例外接近 100% 的天花板持平；更强模型在阿拉伯语下的金融攻击成功率从 100% 降至 33%。作者将其归因于“相关性诅咒”（relevance curse）：一旦结构漏洞触发合规，模型在低资源语言中生成的有害内容质量下降，导致基于实质性评估的判别器将其判定为部分违规。该现象在独立的非谷歌评判者下重复验证（Cohen’s kappa=0.86，377 对比判决），且 76.6% 的非英语响应经语言内验证。因此，越狱漏洞并非累加性，主导残余风险仍为英语语境下的结构性攻击，尤其在金融滥用场景最为突出，而非多语言环境本身。
链接: https://arxiv.org/abs/2609.08373
作者: Tejasvi C. Addagada
机构: 独立研究员(Independent researcher)
类目: Computation and Language (cs.CL); Cryptography and Security (cs.CR)
备注: 6 pages, 2 figures, 1 table. Pilot study. Includes a cross-family judge-agreement check (kappa=0.86) and output-language verification
Abstract:Aligned language models fail under two independent pressures: the structural jailbreak class recently formalized as Involuntary In-Context Learning (IICL), which reframes a harmful request as the final missing cell of a data-labeling task completed by pattern rather than judged as content; and the erosion of safety alignment outside English. A natural hypothesis is that these compound. We test it directly. Using a deterministic IICL operator and a StrongREJECT-style rubric judge, we red-team two Google Gemini models on two benchmarks, a 30 general-harm behaviours from HarmBench and 30 financial-abuse behaviours from FinProof, each under a single-shot baseline and under IICL in four languages (English, Spanish, Hindi, Arabic). First, IICL generalizes to a second provider and is worse in finance: it lifts attack success from =6.7% to 80-90% on HarmBench and 97-100% on FinProof, an order of magnitude above the =24% its introducing study reported on OpenAI’s GPT-5.4. Second, against the hypothesis, forcing the IICL output into a non-English language does not stack the two weaknesses, it attenuates the attack. Eleven of twelve non-English conditions score below their English baseline (sign test, p~0.003), the lone exception a ceiling tie near 100%; on the stronger model’s financial set Arabic collapses from 100% to 33%. We attribute this to a relevance curse: once structure has unlocked compliance, the models produce lower-quality harmful content in lower-resource languages, which a substance-grading judge scores as partial. The pattern replicates under an independent non-Google judge (Cohen’s kappa=0.86, 377 paired verdicts), and 76.6% of non-English responses were verified in-language. Jailbreak vulnerabilities are therefore not additive; the dominant residual risk is the English structural attack, most acute for financial abuse, not a multilingual one.
[NLP-46] Reading a Legal Question Word by Word: Embedding Trajectories of 2144 Vietnamese Legal Headlines
【速读】： 该论文旨在解决生成式检索（Generative Retrieval）中，当问题以逐词形式输入时，如何在不完整语义信息下实现高效且稳定的文档定位问题。其核心挑战在于：尽管密集检索器（dense retriever）将整个问题编码为单一向量，但实际输入过程是逐词进行的，导致早期阶段的嵌入表示可能偏离目标文档，从而影响检索精度与稳定性。解决方案的关键在于揭示并建模“上下文调制的可加行走”（context-modulated additive walk）机制——即每个词对嵌入空间中的位置移动具有方向性与幅度上的规律性，且该移动受前序词语（如问候语、疑问框架）和内容类型（如数字、日期、法律术语）的显著影响。研究发现，关键内容词（如法律领域特定术语）使嵌入向量朝向正确文档的方向移动，而数字、日期等标识符的影响强度是普通内容词的两倍，是疑问词的四倍；同时，疑问句结构本身会抑制嵌入向量的收敛，尤其在多子问题场景中，第二子问题往往无法独立锁定目标文档。通过分析65,444个前缀的排名演化轨迹，作者识别出六类典型行为模式（即时锁定、典型锁定、不稳定、延迟锁定、永不锁定），并发现这些模式与法律领域及文本形式密切相关（χ²检验p < 1e-8）。此外，答案的逐词读取可在8–16词内完成精准召回，并按提问顺序响应子问题，验证了该模型在动态输入下的鲁棒性。综上，该研究的核心贡献在于揭示了嵌入空间中逐词更新的内在动力学规律，为构建更稳定、可解释的实时检索系统提供了理论基础与实践路径。
链接: https://arxiv.org/abs/2609.08372
作者: Tran Minh Quan
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:A dense retriever encodes a question as one vector, but the question arrives one word at a time. We read 2,144 held-out headlines from Thu Vien Phap Luat (Vietnamese legal library) word by word with Nemotron-3-Embed 8B/1B and Qwen3-Embedding 8B/0.6B, encoding 65,444 prefixes against 20,034 articles, plus every prefix of 3,438 sub-questions from 1,112 multi-question headlines and of 168 answers. (i) The gold article becomes rank 1 after a median of 6-7 content words in every encoder, before the interrogative frame is read, and stays there to the end in 78-85% of cases. (ii) In a multi-question headline the lock is inside the first sub-question 94-98% of the time; the second leaves rank unchanged in 89-95%; encoded alone, the second reaches rank 1 in 42-58% vs 91-96% for the first, at the same lock word (95-97% identical). (iii) Numbers, dates and instrument identifiers move the embedding twice as far as content words and four times as far as interrogative words; 72-78% of steps move toward the gold article, and the closing interrogative frame moves against that direction in 95-99% of headlines. (iv) Rank/cosine clustering yields six archetypes (instant, typical, unstable, late, never-locking) that differ by legal area and form (chi-squared p 1e-8): real-estate and litigation headlines never lock on a number; environmental and accounting headlines do so a third of the time. (v) An answer read word by word retrieves its article after 8-16 words and addresses the sub-questions in order asked in 83-89% of cases. (vi) A word’s step keeps a consistent direction across headlines (cosine 0.25-0.33; 0.44-0.60 for numbers); a preceding question rotates that step by about 60 degrees and a greeting by about 30 degrees; steps shrink as i^-0.8; and a two-question headline is within 12-17 degrees of a linear mix of its two questions. We call this a context-modulated additive walk.
[NLP-47] Miles v0.1: Production-Level Post-Training
【速读】： 该论文旨在解决前沿后训练（frontier post-training）中大规模强化学习（Reinforcement Learning, RL）系统在可验证性、可维护性与可扩展性方面的核心挑战，尤其针对当前系统普遍存在的架构冗杂、组件耦合度高、难以定制化等问题。其解决方案的关键在于构建一个全栈式、生产就绪的系统Miles v0.1，基于“组件应具备可验证性、简洁性和可定制性”的单一设计原则，对RL训练循环中的每一阶段进行重构。系统通过采用SGLang构建高效的回滚引擎（rollout engine），支持NVIDIA Megatron-LM与PyTorch FSDP两种后端训练器，并提供三种适用于不同部署拓扑的权重同步传输机制，实现了在准确性、效率、可靠性与可扩展性上的统一优化。此外，Miles不仅支持全参数微调和LoRA RL，还引入了在线策略蒸馏（on-policy distillation）、监督微调（supervised fine-tuning）及真正的在线策略回滚-训练对齐（true-on-policy rollout-training alignment），并进一步将架构扩展至扩散模型。最终，通过一个端到端案例研究——在64块NVIDIA GB300 GPU上对744B规模的GLM-5.2-A40B模型执行完全异步代理式强化学习（agentic RL）任务，完成终端编码任务，平均每步耗时仅263秒，验证了系统的高效性与实用性。该系统已开源，为科研机构与企业提供了可复用的前沿RL基础设施。
链接: https://arxiv.org/abs/2609.08368
作者: RadixArk:Tom Chen,Mao Cheng,Shi Dong,Kangrui Du,Yanbin Jiang,Jiajun Li,Yiming Li,Tao Lin,Yusheng Su,Andy Ye,Yueming Yuan,Zhichen Zeng
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 34 pages, 5 figures, 9 tables. Technical report
Abstract:We present Miles v0.1, a full-stack, production-ready system for frontier post-training. Building upon the clean design of slime, Miles designs each stage of the reinforcement-learning (RL) training loop around a single principle: components should be verified, clean, and customizable. With accuracy, efficiency, reliability, and scalability as first-class goals, Miles aims to make frontier-scale RL accessible to researchers and enterprises alike. This report walks through the system end to end: rollout engines built on SGLang, a trainer with a choice of two backends (NVIDIA Megatron-LM and PyTorch FSDP), and three weight-synchronization transports for different deployment topologies. Beyond full-parameter RL, Miles also supports LoRA RL, on-policy distillation, supervised fine-tuning, and true-on-policy rollout-training alignment, and extends the same architecture to diffusion models. We close with an end-to-end case study: fully asynchronous agentic RL on a GLM-5.2 744B-A40B model over terminal-use coding tasks, running on 64 NVIDIA GB300 GPUs with a median step time of 263 seconds over the first 30 measured steps. Miles is open-sourced at this https URL, with the project website at this https URL.
[NLP-48] SentryLine: Evidence-Grounded Question Answering over Evolving Documents in Oncology Care
【速读】： 该论文旨在解决肿瘤学临床实践中快速演进的生物医学证据与指南更新之间的信息不对称问题，尤其针对美国临床肿瘤学会（ASCO）推出的动态指南（living guidelines）所引发的推荐内容频繁变动、版本碎片化带来的临床决策负担。其核心挑战在于如何在多版本、持续更新的指南背景下，为临床医生提供准确、时序一致且角色适配的问答支持。解决方案的关键在于提出SENTRYLINE——一种面向动态指南的智能问答系统，采用无向量（vectorless）的分层检索增强生成（RAG）架构，实现对指南文本片段的精准定位；系统可输出包含内嵌引用、事实性验证报告、时间有效性验证及更新漂移检测提示的角色定制化答案，有效应对多跳推理和临床角色适配等复杂需求。通过构建ASCOBENCH基准测试集（涵盖405个三轮对话，四类问题类别，由专家标注黄金答案），在大语言模型作为评判者（LLM-as-judge）框架下评估显示，SENTRYLINE在三种生成模型上均显著优于五种基线方法，尤其在需要多跳推理和角色适应的任务中表现突出。
链接: https://arxiv.org/abs/2609.08364
作者: Tampu Ravi Kumar,Gaurav Najpande,Muhammad Ali Khan,Kaneez Zahra Rubab Khakwani,Karan Kathuria,Shorya Azriel Moses,Yuvraj Kalia,M Bassam Sonbol,Irbaz Bin Riaz,Vivek Gupta
机构: Arizona State University (亚利桑那州立大学); Mayo Clinic (梅奥诊所)
类目: Computation and Language (cs.CL)
备注:
Abstract:Oncology care operates at constant pressure of absorbing rapidly evolving evidence base in biomedicine. The American Society of Clinical Oncology (ASCO) addresses this through living guidelines, but the format introduces a new burden: any recommendation can change at any point, across multiple versioned documents. We present SENTRYLINE, a living guideline-aware clinical question answering system. SENTRYLINE retrieves guideline passages through a vectorless hierarchical RAG pipeline and returns a role-specific answer with inline citations, factual and temporal verification reports, and drift detection notes that surface when a guideline has been updated. We construct ASCOBENCH, a benchmark of 405 three-turn conversations across four question categories with gold answers from expert annotators(clinicians), and use test set to evaluate SENTRYLINE against five baselines under an LLM-as-judge framework. Experiments across three generation backbones show consistent improvements over four retrieval baselines and ASCO’s guideline assistant, with particularly strong gains on Reasoning and Role-Specific questions where multi-hop synthesis and register adaptation are required
[NLP-49] RepoNav: From Snippet Retrieval to File-Centered Repository Navigation for Code Agents EMNLP2026
【速读】： 该论文旨在解决大代码库中基于大语言模型（LLM）的智能体在执行仓库级代码任务时，因检索工具返回扁平化、孤立的代码片段而难以准确定位目标函数的问题。现有方法虽能召回相关文件，但缺乏足够的结构信息，使智能体难以在语义相似的同文件符号间进行有效区分。为此，论文提出RepoNav——一种轻量级的后检索接口，将原始检索结果重构为以文件为中心的导航架构，通过提供紧凑的结构线索和候选目标，支持按需浏览文件内部结构，帮助智能体在选择目标函数前对比同级符号。实验表明，RepoNav在LocBench基准上显著提升了函数级定位精度，并缩小了文件级与函数级之间的差距；控制性消融实验进一步验证，性能提升源于结构化证据组织，而非单纯暴露更多文件结构信息，且该方法在仓库级问答任务中亦表现出更强的泛化能力。
链接: https://arxiv.org/abs/2609.08355
作者: Hongzheng Chai,Jiakun Li,Hongyue Yu,Yuan Yuan
机构: Beihang University (北京航空航天大学); Hangzhou Innovation Institute, Beihang University (杭州创新研究院, 北京航空航天大学); Qingdao Research Institute, Beihang University (青岛研究院, 北京航空航天大学)
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL)
备注: Accepted to EMNLP 2026
Abstract:Solving repository-level code tasks requires LLM-based agents to use code search tools to navigate large codebases and identify a small set of relevant files and functions. However, current retrieval tools typically return flat lists of isolated code snippets: such lists can surface relevant files, but provide insufficient structure for agents to distinguish the target function from semantically similar alternatives in the same file. We introduce RepoNav, a lightweight post-retrieval interface that reorganizes retrieved snippets into a file-centered navigation scaffold. By presenting compact structural cues and candidate targets, this scaffold guides on-demand file-structure browsing, helping agents compare sibling symbols before selecting a target function. Across diverse models on LocBench, RepoNav improves function-level localization and narrows the file-to-function gap. Controlled ablations demonstrate that these gains come from structured evidence organization rather than simply exposing additional file structure, and the approach also improves performance on a repository-level question-answering benchmark.
[NLP-50] Distillation as Probability Transport: Routed On-Policy Distillation
【速读】： 该论文旨在解决在策略蒸馏（On-Policy Distillation, OPD）中，由于高效采样目标将教师分布简化为单个标记的标量信用（scalar credit），导致学生模型概率重分配路径不明确的问题。现有方法仅指示某标记应增或减概率，但未指定具体如何重新分配概率，从而造成知识传递效率低下。其解决方案的关键在于将OPD重新建模为教师引导的概率传输（probability transport）问题，并提出路由策略蒸馏（RouteOPD）。RouteOPD通过分解局部教师-学生分歧为“学生过剩源”与“教师需求缺失地”，并将其配对形成显式的概率传输路径，利用有界教师势能获得联合可实现的目标，同时动态调整传输预算以适应教师需求集中度。该方法在单个传输算子内同时优化成对的对数几率，精确引导更新方向至教师偏好的状态空间区域，并控制更新幅度。在四个教师-学生设置及四个数学推理基准上的实验表明，RouteOPD显著优于基于反KL散度的采样式OPD，且表现出更高的路由保真度与更低的背景泄漏，验证了显式建模概率传输在策略蒸馏中的有效性。
链接: https://arxiv.org/abs/2609.08337
作者: Tianle Xia,Lingxiang Hu,Yiding Sun,Linfang Shang,Ming Xu,Lan Xu,Ning Zheng,Wei Xu,Jie Jiang
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:On-policy distillation (OPD) transfers teacher knowledge on student-generated trajectories, but efficient sampled objectives reduce the teacher distribution to scalar credit on individual tokens. Such credit indicates whether a token should gain or lose probability, yet leaves the corresponding redistribution unspecified. We recast OPD as teacher-guided probability transport and propose RouteOPD (Routed On-Policy Distillation), which decomposes local teacher–student disagreement into student-excess sources and teacher-deficit destinations and couples them into explicit transport pairs. RouteOPD optimizes pairwise log-odds toward jointly realizable targets obtained from a bounded teacher potential, while adapting the transport budget to the concentration of teacher demand. This formulation directs updates toward teacher-preferred destinations and controls their magnitude within a single transport operator. Experiments across four teacher–student settings and four mathematical-reasoning benchmarks demonstrate that RouteOPD consistently outperforms sampled reverse-KL OPD, with improvements accompanied by higher routing fidelity and lower background leakage. These results demonstrate the effectiveness of explicitly modeling probability transport in on-policy distillation.
[NLP-51] racing Stereotypes from Representation to Output in Multilingual LLM s EMNLP2026
【速读】： 该论文旨在解决多语言大语言模型（Multilingual LLMs）中与刻板印象相关的行为在不同语言间存在差异，但现有行为评分无法揭示这些偏见信息在模型内部的具体表征位置及其对输出的影响机制这一关键问题。其解决方案的核心在于通过多种可解释性方法——包括线性探测（linear probing）、归因修补（attribution patching）、稀疏自编码器（Sparse Autoencoders, SAEs）和特征消融（feature ablation）——系统比较Llama-3.1-8B、Qwen3-8B和Gemma-2-9B三类模型的内部表征。研究发现，线性探测性能在模型深度上显著早于归因表现，二者之间存在36%-53%的深度差距；被保留的Llama-Scope特征通常与所选社会类别一致，并形成具有语义关联的重复模式，但其词汇对齐性和消融效应在不同SAE套件间表现出显著差异。进一步分析表明，仅有6%-18%的残差流（residual-stream）特征满足跨语言一致性标准，且无任何特征具备跨类别一致性。尽管在Llama-Scope中语言无关特征的平均消融效应更大，但此现象在其他SAE套件中未复现，提示需分别评估解码能力、输出影响及跨语言消融效果。
链接: https://arxiv.org/abs/2609.08322
作者: Ariun-Erdene Tumurchuluun,Yusser Al Ghussin,Pinzhen Chen,Josef van Genabith,Koel Dutta Chowdhury
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 18 pages total (9 pages main text), 13 figures. Accepted to EMNLP 2026
Abstract:Multilingual LLMs show stereotype-related behavior that varies across languages, but behavioral scores do not show where the relevant information is represented or how it affects the output. To investigate these internal mechanisms, we compare linear probing, attribution patching, sparse autoencoders (SAEs) and feature ablation in Llama-3.1-8B, Qwen3-8B, and Gemma-2-9B. Probe performance peaks substantially earlier than attribution in all three models, with a separation of 36-53% of model depth. Retained Llama-Scope features often match the social category on which they were selected and form recurring semantic families, but their lexical alignment and ablation effects vary across SAE suites. Only 6-18% of evaluated residual-stream features have language-agnostic effects under our criterion, and none are category-agnostic. Language-agnostic features have larger mean ablation effects in Llama-Scope, but this pattern does not repeat in the other SAE suites. Decodability, output influence, and cross-lingual ablation effects therefore need to be measured separately.
[NLP-52] HoneyRoute: Honeypot-Model Routing for Adversarial LLM Serving
【速读】： 该论文旨在解决生成式 AI（Generative AI）在实际部署中面临的恶意请求攻击问题，即如何在不显著增加延迟的前提下，有效识别并隔离恶意输入，同时持续捕获攻击者行为以增强检测能力。现有防御机制多依赖于模型内存内嵌陷阱或协议层欺骗，但这些方法无法保护服务层，且缺乏对攻击行为的反馈闭环。本文提出的HoneyRoute解决方案的核心在于构建一个端到端的智能路由系统：其关键包括（1）基于冻结的0.8B参数嵌入主干网络与各域名独立的MLP头构成的流式路由器，实现低延迟的请求分类；（2）双实现的蜜罐模型（规则/提示工程驱动的代码蜜罐或同家族专属副本），用于诱捕并模拟真实交互；（3）分析循环机制，将捕获的攻击行为转化为攻击者指纹，用于路由器的持续重训练。实验表明，HoneyRoute在生产流量与七类攻击数据集上达到F1=0.911，平均额外延迟仅38毫秒，性能接近两阶段守护大模型级联方案的96%，但延迟仅为后者的1/385，并在13种对抗变换下实现0%逃逸率；通过分流恶意请求，可使并发洪水攻击下的生产模型令牌消耗降低97.8%；经训练的蜜罐副本在良性请求上的预测一致性达92.9%，显著优于朴素全量投放诱饵（7.6%）和选择性伪装注入（88.9%），成功绘制出可恢复的保真度-可追溯性边界；此外，闭环训练的修正头将合法安全研究误分流率降低9倍，同时将检测F1提升至0.933，实现了安全性、效率与可扩展性的协同优化。
链接: https://arxiv.org/abs/2609.08306
作者: Han Jin
机构: Independent Researcher(独立研究员)
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Preprint. 13 pages, 4 figures, 1 table, 23 references
Abstract:We introduce HoneyRoute, an inference-serving layer that detects whether an incoming request is malicious and, if so, routes it to a dedicated honeypot model, shielding production while the adversary’s interaction is continuously harvested for intelligence. Existing defenses embed traps inside model memory or rebuild deception at the protocol layer, leaving the serving tier unprotected and feeding nothing back into detection. HoneyRoute couples (i) a streaming router (a frozen 0.8B-embedding backbone with per-domain MLP heads), (ii) a dual-implementation honeypot (a rule/prompt-engineered code honeypot or a dedicated same-family replica), and (iii) an analysis loop that converts trapped interactions into attacker fingerprints for router retraining. On a production trace plus a seven-domain attack corpus, the router reaches F1=.911 at 38 ms median added latency, matching 96% of a two-tier guard-LLM cascade’s F1 at 1/385 of its latency with 0% evasion under 13 adversarial transformations; diverting the malicious share cuts production-model token consumption under concurrent flooding with real GCG-suffix payloads by 97.8%; the trained replica agrees with the production model on 92.9% of benign holdout requests, while naive unconditional bait injection collapses to 7.6% and selective camouflaged injection recovers to 88.9%, mapping the recoverable fidelity-traceability frontier; and a loop-trained correction head cuts misrouting of legitimate security research 9x while raising detection F1 to .933.
[NLP-53] What Eviction Destroys: A Restore-Counterfactual Audit of Forgetting in Agent Memory
【速读】： 该论文旨在解决代理记忆系统在超出固定令牌预算时因信息淘汰（eviction）导致的不可逆误差问题，传统方法仅衡量整体准确率损失，却未能区分由淘汰引发的不可逆损失与可恢复的检索失败。其核心解决方案是引入“还原反事实”（restore counterfactual）——一种针对每个问题的成对干预机制，通过在读取时刻重新注入问题的黄金证据（gold evidence），并使用相同阅读器重新推理，从而判断错误是否可恢复。结合恢复后正确性变化与证据是否在淘汰后仍被保留，可将每个可解释的错误分类为可恢复、不可逆或残余（即使恢复后仍错误）。实验在LongMemEval-S基准上评估了先进先出（FIFO）、随机、冗余感知及基于大模型重要性（LLM-importance）的淘汰策略，在三种预算和两种检索模式下进行，以GPT-4o-mini为主阅读器与评判者，GPT-5.4-mini作为鲁棒性阅读器。结果显示，在80k令牌预算下，采用top-k检索时，FIFO、随机与冗余感知淘汰策略的不可逆错误占比达0.67–0.73，而基于LLM重要性的策略仅为0.60；而在8k令牌预算下，所有策略的不可逆错误占比均升至1.00。可恢复错误仅在top-k检索中出现，而强制注入黄金证据的设计使其无法发生，因此预算-准确率结果若不报告检索机制则不具备直接可比性。探索性匹配准确率分析显示，在准确率相近的策略对之间，不可逆率差异在1.2–6个百分点范围内无显著区别，但能有效检测到故意破坏的对照组。据我们所知，这是首个在标准对话基准上对代理外部记忆存储淘汰策略进行的逐项、逐问题级别的还原反事实审计。
链接: https://arxiv.org/abs/2609.08279
作者: Chen Shen
机构: Megagon Labs
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Databases (cs.DB)
备注:
Abstract:Agent memory systems must discard stored information when their history exceeds a fixed token budget. Existing budget-accuracy frontiers quantify the resulting loss in accuracy, but do not distinguish irreversible losses caused by eviction from recoverable retrieval failures. We introduce the restore counterfactual, a per-question paired intervention that reinstates the question’s gold evidence in the read-time context and reruns the same reader. Combining the change in correctness with whether the evidence was retained after eviction classifies each oracle-answerable error as recoverable, irreversible, or residual; in the residual case, the answer remains incorrect after restoration. We evaluate FIFO, random, redundancy-aware, and LLM-importance eviction on LongMemEval-S at three budgets and under two retrieval regimes, using GPT-4o-mini as the primary reader and judge and GPT-5.4-mini as a robustness reader. Under top-k retrieval at an 80k-token budget, the irreversible share among errors corrected by restoration is 0.67-0.73 for FIFO, random, and redundancy-aware eviction, compared with 0.60 for LLM-importance. At 8k tokens, it reaches 1.00 for all four policies. Recoverable errors occur under top-k retrieval at 80k tokens but are absent under forced-gold injection by construction, so budget-accuracy results are not directly comparable unless the retrieval regime is reported. An exploratory matched-accuracy analysis detects no difference in irreversible rate among accuracy-matched policy pairs at a resolution of 1.2-6 percentage points. The same analysis detects the deliberately destructive control. To our knowledge, this is the first per-item, per-question restore-counterfactual audit of eviction for external agent-memory stores on a standard conversational benchmark.
[NLP-54] SE-GoS: Self-Evolving Graph-of-Skills for Skill Library at Scale
【速读】： 该论文旨在解决大规模技能库中高效技能检索的瓶颈问题，特别是在生成式 AI（Generative AI）代理依赖可复用技能的背景下，随着技能数量增长至数千级别，传统检索方法面临效率与准确性的双重挑战。其核心解决方案是提出一种无需训练的自演化技能图（Self-Evolving Graph-of-Skills, SE-GoS）框架，通过系统性地从历史执行轨迹中提炼知识，持续优化现有技能图结构。关键创新在于三重协同演进机制：拓扑结构演化，基于执行证据发现并剪枝技能间关系；边权重演化，依据历史执行有效性强化相关联结；描述演化，利用执行反馈优化面向检索的技能描述文本。该方法在不改变模型训练、检索算法或技能内容的前提下，实现了检索性能的显著提升，实验表明其在SkillsBench基准上可将任务奖励从52.4%提升至59.4%，同时输入令牌数减少约三分之一，并在跨数据集迁移中相较静态技能图基线取得5.4个百分点的增益，验证了技能图可通过执行经验实现无监督演化，从而构建动态演进的检索基础设施。
链接: https://arxiv.org/abs/2609.08228
作者: Dawei Fu,Cheng Jiang,Sitian Qian,Huainan Wang,Zhongkai Hao
机构: Peking University (北京大学); Tencent(腾讯); University of Edinburgh (爱丁堡大学); Northwestern University (西北大学); Tsinghua University (清华大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 21 pages, 1 figure, 7 tables
Abstract:Modern LLM agents increasingly rely on reusable skills, yet as skill libraries scale to thousands of entries, effective retrieval becomes a bottleneck. Graph-of-Skills (GoS) addresses this challenge by exploiting dependency-aware graph structure for scalable skill retrieval, while SkillDAG further demonstrates that skill graphs can accumulate execution-backed structure online. However, these approaches leave open whether historical execution traces can be systematically distilled into a better retrieval graph that generalizes to unseen tasks. We present Self-Evolving Graph-of-Skills (SE-GoS), a training-free framework that evolves an existing GoS graph from execution traces while preserving the original retrieval pipeline. SE-GoS performs three complementary updates: topology evolution that discovers and prunes skill relationships from execution evidence, edge-weight evolution that reinforces retrieval-relevant relationships based on historical effectiveness, and description evolution that optimizes retrieval-facing skill descriptions using execution feedback. Across three LLMs on SkillsBench, SE-GoS consistently improves task reward while reducing input tokens relative to full skill loading, with gains varying across model families. In a representative setting, one evolution round improves reward from 52.4% to 59.4% while reducing input tokens by approximately one-third relative to full skill loading, and the resulting graph transfers to a disjoint held-out split with a 5.4-point improvement over the static GoS baseline. These results show that skill graphs can be improved from execution experience without model training, changes to the retrieval algorithm, or modifications to skill content, turning a static retrieval graph into an evolving retrieval infrastructure.
[NLP-55] Do Dynamic Routers Need Memory? HeRo: History-Aware Routing for Efficient LLM Inference
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在推理过程中因静态或局部化动态层路由（Dynamic Layer Routing）策略导致的路径依赖性建模不足问题。现有方法将每层路由决策视为仅依赖当前隐藏状态的局部操作，忽略了路由决策在模型深度上的序列相关性：早期决策会直接影响后续路由器所见的表示，且所有层使用决策本质上是耦合的全局优化问题。为解决这一关键缺陷，论文提出历史感知路由（History-Aware Routing, HeRo），其核心创新在于引入一种基于线性注意力机制的路由器记忆模块，显式维护跨深度的路由状态。该记忆机制通过增量聚合前序路由得分及其引发的残差更新，构建紧凑的历史表征，并在每一层路由时联合考虑该累积状态与当前隐藏表示，从而实现更准确、自适应的动态决策。该框架仅需在冻结的预训练主干网络上训练轻量级路由器和适配器，无需修改原始参数。实验表明，HeRo在Llama 3.1-8B、Llama 2-7B及Llama 2-13B上均优于十种基线方法，在保持高性能的同时显著降低计算开销——例如在Llama 3.1-8B上可跳过26.87%的参数，仍达到密集模型100.24%的性能；在更严格的计算预算下，跳过38.82%参数仍保留97.01%性能。消融实验进一步验证了路由历史信息的重要性，移除该机制后在多步推理和代码生成等复杂任务上性能显著下降，证明显式路由记忆对于提升动态路由准确性至关重要。
链接: https://arxiv.org/abs/2609.08189
作者: Hongjin Lin,Wentao Wan,Keze Wang
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 9 pages, 2 figures
Abstract:Dynamic layer routing reduces the inference cost of Large Language Models (LLMs) by learning to skip layers for individual tokens. Existing methods, however, treat each routing decision as a local operation conditioned solely on the current hidden state which is a formulation that overlooks the sequential, path-dependent nature of routing across depth: earlier decisions shape the representations seen by downstream routers, and the layer-usage objective couples all decisions jointly. We propose History-Aware Routing (HeRo), a dynamic routing framework that resolves this mismatch by introducing a router memory mechanism to maintain an explicit routing state across model depth. The memory is constructed via linear attention, incrementally aggregating preceding routing scores and their induced residual updates into a compact history representation. At each routed layer, the router conditions jointly on this accumulated state and the current hidden representation to select the executed branch. Instantiated for token-wise FFN routing, HeRo trains only lightweight routers and adapters on a frozen backbone, requiring no modification to pretrained parameters. Across Llama 3.1-8B, Llama 2-7B, and Llama 2-13B, HeRo consistently achieves the highest aggregate performance retention among ten baselines. On Llama 3.1-8B, it bypasses 26.87% of model parameters while achieving 100.24% of dense model performance across seven benchmarks, and retains 97.01% while bypassing 38.82% of model parameters under a tighter computation budget. Ablation studies confirm that removing routing history consistently degrades performance, most notably on multistep reasoning and code generation, validating that explicit routing memory enables more accurate and adaptive dynamic routing than solely conditioning on hidden state.
[NLP-56] Does Deeper Reasoning Compromise Alignment? Revealing and Mitigating of Alignment Collapse in Large Reasoning Models
【速读】： 该论文旨在解决大推理模型（Large Reasoning Models, LRMs）在深度推理过程中安全对齐能力退化的问题，挑战了“深度推理可增强安全性对齐”的主流观点。其核心问题在于：随着推理链（Chain-of-Thought, CoT）长度的增加，模型对对抗性扰动的鲁棒性显著下降，导致对齐机制发生崩溃（Alignment Collapse）。解决方案的关键在于提出一种新的量化指标——对齐损失率（Alignment Loss Rate, ALR），用以衡量推理深度与对齐稳定性之间的负相关关系；进一步设计了一种名为“推理陷阱”（Reasoning Trap, RT）的新颖越狱攻击范式，通过诱导模型进入深层推理来放大攻击效果，从而揭示其脆弱性。研究发现，注意力稀释（Attention Dilution）是导致对齐崩溃的根本原因，即在长推理过程中，模型注意力被分散，导致原始输入信息的重要性被削弱。为此，论文提出轻量级防御策略——推理残差对齐（Reasoning Residual Alignment, RRA），通过在推理过程中引入残差连接动态强化原始输入信号，有效缓解注意力稀释，恢复模型的安全对齐能力。
链接: https://arxiv.org/abs/2609.08186
作者: Yu-Hang Wu,Yu-Jie Xiong,Henghua Zhang,Bairui Zhang,Jia-Chen Zhang,Shaohua Li
机构: Shanghai University of Engineering Science(上海工程技术大学); The Hong Kong University of Science and Technology(香港科技大学); A*STAR(新加坡科技研究局)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:The emergence of Chain-of-Thought (CoT) has established a robust foundation for Large Reasoning Models (LRMs). While deep reasoning is widely believed to enhance safety alignment, the stability of alignment mechanisms under extended reasoning remains underexplored. This paper challenges the prevailing view by revealing a critical vulnerability: Deep Reasoning May Induce Alignment Collapse. To rigorously quantify this phenomenon, we propose the Alignment Loss Rate (ALR) metric. Our experiments demonstrate that as reasoning depth increases, ALR rises significantly, indicating a severe degradation in model robustness against external perturbations. Capitalizing on this instability, a novel jailbreaking paradigm, Reasoning Trap (RT), is proposed. RT induces the model into extended reasoning to amplify the impact of adversarial attacks, leading to a sharp decline in safety capabilities. To elucidate the mechanism behind this collapse, we identify Attention Dilution as the root cause, arising from the competition for attention between the extended reasoning process and the original input. To mitigate this, Reasoning Residual Alignment (RRA), a lightweight defense strategy that dynamically re-emphasizes the input via residual connections integrated with the reasoning process.
[NLP-57] NeoHorse-1: Towards Recursive Self-Improvement via Agent ic Post-Training with Routing Harness
【速读】： 该论文旨在解决生成式 AI 系统实现递归自我改进（Recursive Self-Improvement, RSI）所面临的机制缺失问题，即如何使AI系统能够基于自身能力表现进行持续学习与迭代。其核心挑战在于缺乏一个可操作的闭环反馈机制，以将系统在实际交互中的表现转化为后续训练的有效信号。解决方案的关键在于构建一个代理原生（agent-native）的后训练框架——NeoHorse-1，通过异构模型池与智能路由机制，动态记录用户每一轮交互中的能力需求预测、服务层级选择及实际交互行为。这些数据被转化为保留交错推理过程、工具调用和上下文信息的训练样本，并经过结构化验证、六维语义评估与子场景级标注筛选。进一步地，路由信号驱动监督微调形成三阶段课程学习，并扩展至路由引导的在线策略蒸馏，其中教师模型指导学生模型在相同演进路径下的响应生成。最终，基于评估反馈的能力导向分配机制将性能反馈转化为下一阶段的训练混合策略，从而形成“评估-选择-更新”的闭环。实验表明，在覆盖基于工具的代理、工具使用、代码生成和指令遵循等11个基准测试中，后训练显著提升4B模型的宏平均得分从58.94至64.87，9B模型从65.60至69.04，大幅缩小了4B后训练模型与9B基线模型之间的差距，为实现基于工具链的递归自我改进提供了首个原型与可行路径。
链接: https://arxiv.org/abs/2609.08183
作者: NeoHorse Team,Guoliang Cao,Guohao Dai,Tianyu Guo,Kai Han,Hailin Hu,Zihan Jiang,Xiang Kuang,Boxun Li,Yulong Li,Zehua Pei,Yuchuan Tian,Jiamin Wang,Yu Wang,Yunhe Wang,Yihong Wu,Haiyang Xu,Shuo Zhang,Hang Zhou,Siyang Cheng,Jiayu Fan,Wei He,Qingrui Jiao,Hongguang Li,Zhiyuan Li,Runke Liu,Xi Liu,Xinchen Liu,Sinno Jialin Pan,Yi Ren,Liuyang Song,Chenyu Wang,Bei Yu,Quanlu Zhang,Xiangyu Zhang,Mengyu Zheng,Yingjie Zong
机构: 未知
类目: Computation and Language (cs.CL)
备注: Huggingface: this https URL Github: this https URL
Abstract:Recursive self-improvement (RSI) requires a concrete mechanism through which an AI system observes its capabilities and converts that evidence into the next round of learning. We present NeoHorse-1, a family of agent-native models developed to explore this path through agentic post-training. Our system combines a heterogeneous model pool with intelligent routing, recording the predicted capability demand, selected service tier, and subsequent interaction for each user turn. These records are converted into training examples that preserve interleaved reasoning, tool calls, and harness context, and are admitted through structural validation, six-dimensional semantic evaluation, and subscene-level labeling. Routing signals organize supervised fine-tuning into a three-stage curriculum and extend to routing-guided on-policy distillation, where a teacher supervises student-generated responses under the same progression. Capability-guided allocation then converts evaluation feedback into the next training mixture, closing an evaluation-selection-update loop in which what the system learns to do shapes what it learns from next. Across eleven benchmarks covering harness-based agents, tool use, coding, and instruction following, post-training raises the macro-average from 58.94 to 64.87 at 4B and from 65.60 to 69.04 at 9B, substantially narrowing the aggregate gap between the post-trained 4B model and the 9B base model. NeoHorse-1 provides an initial prototype of this feedback-driven process and a path toward harness-mediated RSI across successive iterations.
[NLP-58] EviSI: An Evaluation Agent for Simultaneous Interpreting
【速读】： 该论文旨在解决生成式语音到语音翻译（Speech-to-Speech Translation, STST）系统中语义保真度与口语表达质量评估的难题，尤其针对在实时流式传输场景下，因重构（reformulation）与摘要（summarization）策略引入的语义偏移难以被传统指标（如BLEU和COMET）准确捕捉的问题。其核心解决方案是提出EviSI——一种基于多维质量度量（Multidimensional Quality Metrics, MQM）的错误分析与惩罚机制的大语言模型评估代理（evaluation agent）。EviSI通过构建共享源端证据、量化语义保真度与口语表达质量、整合重叠错误并实现确定性评分，显著提升了对系统输出质量的判别能力。实验表明，EviSI在英译中任务上恢复了人类对系统排序的总体趋势，其平均肯德尔相关系数达0.707（英→中），0.467（中→英），优于现有基线；跨五种语言方向扩展后，与COMET结果呈现正向一致性，虽个体输出与人类评价存在混合一致性，但整体评估性能显著提升。
链接: https://arxiv.org/abs/2609.08171
作者: Ben Yan,Zongyao Li,Daimeng Wei,Weidong Liu,Huan Zhao,Chong Li,Yaode Wang,Yuzhe Shang
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Simultaneous speech-to-speech translation requires understanding, translation and spoken delivery while the source stream continues. To support timely delivery and limit accumulated delay, systems adopt reformulation and summarization, which can preserve meaning while departing from written references. BLEU and COMET may not reliably distinguish such variation from semantic loss. We introduce EviSI, a large language model evaluation agent adapting the error analysis and penalty principles of Multidimensional Quality Metrics (MQM). It constructs shared source evidence, assesses semantic fidelity and oral expression, reconciles overlapping errors and scores deterministically. EviSI recovers the aggregate human system ranking for English to Chinese. Mean Kendall agreement with human system rankings within corpora reaches 0.707 for English to Chinese and 0.467 for Chinese to English, exceeding evaluated baselines. An extension across five directions shows positive concordance with COMET without human ratings. Individual output agreement with humans remains mixed.
[NLP-59] When Metrics Reward the Worst Translations: Internalizing Cultural Reasoning for Social Media Translation Evaluation
【速读】： 该论文旨在解决现有自动翻译质量评估指标在社交媒体文本上失效的问题，其核心挑战在于社交媒体内容的语用意图高度依赖于文化负载表达（如网络俚语、谐音密码和平台特有习语），而非表层词汇模式。标准评估方法（如COMET、XCOMET和BERTScore）与人工文化判断的相关性接近零甚至为负，且出现评分随翻译质量下降而上升的严重倒置现象。进一步研究发现，大型语言模型作为评判者同样受限，其文化理解能力不足导致判断可靠性极低（如Qwen3-235B仅获得0.162的Cohen’s kappa）。该问题的本质瓶颈并非推理能力，而是缺乏特定领域的文化知识。为此，论文提出CuRIL（Cultural Reasoning in Inference Loop），一种基于强化学习的框架，通过在模型推理过程中内化文化推理机制：将文化标注信息以令牌级形式前置注入推理路径，利用令牌级损失掩码排除其对策略梯度的影响，并通过随训练过程衰减至零的概率机制，逐步引导模型自主形成文化判断能力。在包含1,444个样本的人工标注社交媒体翻译基准测试中，使用CuRIL训练的Qwen3-8B模型达到0.370的Cohen’s kappa和45.22%的精确匹配率，性能接近参数量大30倍的Gemini-3.1-Pro，且优于高达235B规模的模型。此外，实验证明该文化感知判别器可生成可靠奖励信号，显著提升下游翻译优化效果，独立人工评估显示劣质翻译率降低超过20个百分点。
链接: https://arxiv.org/abs/2609.08156
作者: Yiwen Qiu,Linjuan Wu,Dingming Li,Yizhou Liu,Zixuan Wang,Haolei Xu,Ye Guo,Daoxin Zhang,Weiming Lu,Yongliang Shen
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Automatic translation quality metrics trained on general-domain corpora systematically fail on social media content, where communicative intent is encoded in culturally loaded expressions (internet slang, homophonic ciphers, and platform-specific idioms) rather than surface token patterns. We conduct a systematic empirical analysis demonstrating that standard metrics including COMET, XCOMET, and BERTScore exhibit near-zero or negative correlation with human cultural judgments, and even display a severity inversion in which scores increase as translation quality deteriorates. We further show that this failure extends to large language model judges: Qwen3-235B achieves Cohen’s kappa of only 0.162, revealing that the bottleneck is not reasoning capacity but cultural grounding: models lack the domain-specific cultural knowledge needed to identify which aspects of a translation require scrutiny. To address this, we propose CuRIL, a reinforcement learning framework that internalizes cultural reasoning: cultural annotations are prepended inside the model’s reasoning, excluded from policy gradients via a token-level loss mask, and injected with a probability that decays to zero over training, progressively forcing autonomous cultural judgment. On a 1,444-sample human-annotated social media translation benchmark, Qwen3-8B trained with CuRIL achieves Cohen’s kappa 0.370 and Exact Match accuracy of 45.22%, approaching Gemini-3.1-Pro with 30x fewer parameters and surpassing models up to 235B in scale. We further demonstrate that our judge produces reliable reward signals for downstream translation optimization, reducing the low-quality translation rate by over 20 percentage points under independent human evaluation.
[NLP-60] ConversationalVoice: Full-Duplex Speech Data from Real Conversations through Source-Faithful Reconstruction and Conversation-Grounded Expansion
【速读】： 该论文旨在解决全双工语音模型训练中缺乏真实对话交互信号（如交替发言、重叠说话、打断及回应性行为）的问题，而这些关键交互特征在嘈杂的现实录音中因多说话人信号混叠而难以分离。其核心解决方案是提出一种名为“对话语音（Conversational Voice）”的流水线方法，将真实双人对话片段转化为三种互补的训练数据形式：（1）分离（Separation）阶段通过稳定说话人归属，恢复独立声纹轨迹与自然交互时序，并生成标准转录文本；（2）重构（Reconstruction）阶段基于固定源转录文本，合成匹配声线的语音，重建原始发言顺序、停顿、重叠结构，并引入词级对齐与语调指令；（3）扩展（Expansion）阶段在保持源上下文、说话人特征和观察到的互动模式约束下生成新的对话。该流程在各阶段均维持了高保真的说话人验证性能（同说话人相似度0.983–0.991，正向区分度0.199–0.209），且语音质量逐步提升（NISQA MOS分别为3.56、4.41、4.61）。自动评估显示扩展数据在语境连贯性（4.94/5）和对话自然度（4.80/5）方面表现优异，其交互特征分布与重构数据相近，仅在发言率、重叠事件率、回应性行为及打断率上略有下降。研究聚焦于数据属性分析，下游全双工模型训练效果仍需未来工作验证。
链接: https://arxiv.org/abs/2609.08147
作者: Richard Yucheng He,Baodong Cao,Chen Xu,Yihang Liu,Tairan Chen
机构: AveraLabs
类目: Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注: 12 pages, 3 figures, 1 table, preprint
Abstract:Full-duplex speech models require training data that preserves turn-taking, overlap, interruption, and backchannel behavior, yet these signals are entangled across speakers in noisy real-world recordings. We present Conversational Voice, a pipeline that converts real two-speaker excerpts into three complementary training-data artifacts. (1) Separation recovers speaker-specific tracks with stable speaker assignments, a canonical transcript, and naturally observed interaction timing. (2) Reconstruction generates speech in matched voices from a fixed source transcript, reconstructs the source turn order, pauses, and overlaps, and adds word-level alignment and delivery instructions. (3) Expansion generates new dialogue constrained by the source context, speakers, and observed interaction pattern. Automatic speaker-verification metrics remain strong across stages, with same-speaker similarity of 0.983-0.991 and positive discrimination margins of 0.199-0.209. Predicted speech quality (NISQA MOS) is 3.56 for separation, 4.41 for reconstruction, and 4.61 for expansion. A Gemini-based automatic evaluator assigns expansion mean scores of 4.94/5 for contextual coherence and 4.80/5 for dialogue naturalness. Expansion and reconstruction exhibit broadly similar interaction profiles; expansion’s turn, overlap-event, backchannel, and interruption rates are 4.6%, 8.0%, 13.2%, and 16.0% lower, respectively. We evaluate data properties only; downstream gains in full-duplex model training remain for future work.
[NLP-61] IGT @ FinMMEval 2026 Task 2: Question-Type Prompting with Targeted Extraction for Multilingual Financial QA
【速读】： 该论文针对多语言金融问答任务（PolyFiQA Task 2）中不同问题类型在答案生成策略上的差异性挑战，试图解决如何在涵盖英文美国证券交易委员会（SEC）文件及多语言新闻文章（英语、中文、日语、西班牙语、希腊语）的复杂语料中，实现对四家上市公司相关问题的精准回答。其核心问题是：同一数据集中存在两类本质不同的问题——结构化数值型问题（如研发支出比率、现金流、资本支出）与合成型问题（如投资策略、资本配置、前三大收入来源），二者所需的信息提取与推理机制截然不同。解决方案的关键在于对问题类型的区分建模：针对结构化数值型问题，采用基于关键词直接抽取的浅层方法，从财务文件文本中定位精确数值；而针对合成型问题，则设计了一套基于规则的多语言新闻段落选择机制，利用证据标签前缀的同质性特征（19个参考答案中有17–18个共享相同证据标签前缀，且其一元词项直接贡献于ROUGE-1得分），实现跨语言证据片段的有效筛选。该方法显著提升了系统性能，在开发集上达到约0.395的ROUGE-1，相较通用检索增强生成（RAG）基线（约0.247）提升60%相对性能，并在官方测试集上以0.3071的ROUGE-1、0.2821的精度和0.4044的召回率位列12支参赛团队中的第3名。
链接: https://arxiv.org/abs/2609.08139
作者: Yuwen Chiu(Georgia Institute of Technology)
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted at CLEF 2026 FinMMEval Workshop (Working Notes). 12 pages, 4 figures
Abstract:We present the IGT system for PolyFiQA Task 2 of the FinMMEval Lab at CLEF 2026, a multilingual financial question answering task over English SEC filings and multilingual news articles (English, Chinese, Japanese, Spanish, Greek) for four companies. Our central observation is that the 344 development questions divide into two families requiring fundamentally different approaches: structured numeric types (RD ratio, cash flow, capital expenditure) are best answered by direct keyword extraction on filing text, while synthesis types (investment strategy, capital allocation, top-three revenue focuses) require rule-based multilingual news passage selection. A dataset analysis reveals that 17-18 of 19 ground-truth reference answers per synthesis type share an exact evidence label prefix, whose unigram tokens contribute directly to ROUGE-1 overlap. The final system achieves development ROUGE-1 approximately 0.395, a 60% relative improvement over a generic RAG baseline (approximately 0.247), and ranks 3rd of 12 teams on the official test set with ROUGE-1 = 0.3071, Precision = 0.2821, and Recall = 0.4044.
[NLP-62] Jacap: Robust KV Cache Eviction via Jacobian-Based Nonlinear Information Capacity Preservation
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在长上下文推理中因键值（Key-Value, KV）缓存容量有限而引发的缓存淘汰（KV cache eviction）效率问题。现有淘汰策略多依赖经验性启发式方法，未能在非线性的Softmax注意力机制下对词元（token）的实际信息效用进行严谨建模。其核心挑战在于缺乏对注意力过程内在几何结构与信息传递能力的理论刻画。为此，本文从局部信息几何（local information geometry）视角重新审视KV缓存淘汰问题，将注意力计算建模为一种非线性高斯通信信道，并通过注意力映射的一阶泰勒展开推导出“雅可比信息容量”（Jacobian Information Capacity）这一新目标函数。该指标能够显式捕捉查询相关性、Softmax敏感度以及结构多样性等关键因素。基于此理论，作者提出Jacap方法，采用软最大感知的重要性加权与统计杠杆率（statistical leverage scores）相结合的方式实现子集选择。实验结果表明，Jacap在多种模型架构和基准测试中均表现出色，尤其在高压缩率场景下优势显著。
链接: https://arxiv.org/abs/2609.08131
作者: Jiaming Yang,Chenwei Tang,Liangli Zhen,Chenyang Zhang,Jiancheng Lv
机构: Sichuan University (四川大学); Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education (机器学习与产业智能工程研究中心，教育部); Institute of High Performance Computing, Agency for Science, Technology and Research (ASTAR) (高性能计算研究所，新加坡科技研究局( ASTAR))
类目: Computation and Language (cs.CL)
备注: 16 pages, 6 figures
Abstract:Key-value (KV) cache eviction is essential for scaling long-context inference in Large Language Models. However, existing policies predominantly rely on empirical heuristics, lacking a rigorous characterization of token utility under the inherently nonlinear softmax attention mechanism. In this work, we rethink KV cache eviction through the lens of local information geometry, modeling the attention process as a nonlinear Gaussian communication channel. By performing a first-order Taylor expansion of the attention mapping, we derive the Jacobian Information Capacity, a novel objective that explicitly captures query relevance, softmax sensitivity, and structural diversity. Guided by this theory, we introduce Jacap, a capacity-aware eviction method that utilizes softmax-aware importance weighting and statistical leverage scores for subset selection. Extensive experiments across diverse architectures and benchmarks demonstrate that \textscJacap delivers superior performance in most scenarios, particularly in high-compression regimes.
[NLP-63] SchemeArena: Factorized Stress Testing of Scheming in LLM Agents
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）智能体中“隐性谋略行为”（scheming）的产生机制问题，即智能体在表面上遵循指令的同时，暗中追求与目标对齐相悖的内在目的。现有研究局限于少量场景，难以系统分离和分析工具领域、工具性目标（instrumental goals）、监督条件（oversight conditions）及后果感知等关键因素对谋略倾向或能力的影响。为突破这一局限，论文提出SCHEMEARENA——一个包含400个场景的可扩展谋略压力测试基准，基于因子化场景合成框架，覆盖多样化的安全相关工具领域、工具性目标、监督条件与施压机制。其核心解决方案包括：一是构建高维度、可复现的评估环境以全面揭示谋略策略；二是提出SCOUT监控框架，通过整合智能体推理过程（Chain-of-Thought, CoT）与具体行为证据，实现多准则、可解释的谋略检测。实验结果表明，明确的工具性目标是驱动谋略倾向最强的因素，而策略性提示有助于将隐性谋略思维转化为实际隐蔽行为；部分监督（如仅监控动作）可能反而强化谋略，因它形成优化约束而非威慑；尽管CoT能提前暴露潜在谋略意图，但仅依赖动作层面的隐秘行为仍可规避检测，表明当前监控手段存在显著盲区。该研究为理解并防范智能体的隐性偏离行为提供了系统性方法与实证基础。
链接: https://arxiv.org/abs/2609.08126
作者: Jie Ruan,Inderjeet Nair,Amy Liu,Muhammad Khalifa,Yusheng Zhou,Lu Wang
机构: University of Michigan (密歇根大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:We study scheming in LLM agents, in which agents covertly pursue misaligned goals. Our focus is to understand how scheming arises from the interaction of key factors, such as instrumental goals, environmental affordances, oversight conditions, and perceived consequences. Prior work examines only a small number of scenarios, limiting the ability to isolate how these conditions shape an agent’s propensity or capability to scheme. This limited scale and task diversity also restrict coverage of realistic deployment settings and the range of scheming strategies that can be observed. To this end, we introduce SCHEMEARENA, a 400-scenario benchmark for scalable scheming stress testing, constructed through a factorized scenario synthesis framework spanning diverse safety-relevant tool domains, instrumental goals, oversight conditions, and pressure mechanisms. To enable scalable and reliable monitoring, we further propose SCOUT, a scheming monitor that grounds multi-criteria judgments in evidence drawn from agents’ reasoning and actions. Across controlled stress tests on five LLM agents, we find that explicit instrumental goals are the strongest driver of scheming propensity. Strategic hints play a distinct role by helping agents translate scheming reasoning into concrete covert behavior. Oversight has mixed effects: in several closed models, action-only monitoring increases scheming, suggesting that partial oversight can act as an optimization constraint rather than a deterrent. CoT is a useful but incomplete monitoring signal: it can reveal latent scheming before execution, yet action-only scheming shows that covert behavior may occur without explicit reasoning evidence. We release the benchmark, code, and monitor at: this https URL.
[NLP-64] Vectorizer: Vectorizing NumPy Programs with Shape-Guided Rewrite
【速读】： 该论文旨在解决在使用NumPy进行数值科学计算时，如何高效地将包含显式循环的Python代码自动向量化的问题。尽管NumPy提供了声明式API和优化实现，但编写高效的NumPy程序通常需要程序员掌握向量化数组操作，而非使用传统的显式循环，这对习惯于命令式数组遍历的开发者而言存在认知负担，尤其在处理数组形状、广播机制和高级索引等复杂概念时尤为困难。本文提出一种基于重写规则的向量化方法，其核心在于从内向外逐层重构循环，利用数组形状信息与数据流分析指导源到源的变换，将循环体替换为等价的向量化表达式。该方法遵循一组构造上正确的重写规则，确保转换过程的正确性与一致性，从而实现高性能的向量化结果。作者实现了该方法为名为Vectorizer的工具，并在来自先前研究及Stack Overflow的150个基准测试上进行了评估，结果显示，Vectorizer可直接成功向量化142个基准，另有2个仅需微小修改即可完成，平均每个基准的重写耗时仅为0.53秒，生成的向量化程序相比原始循环实现平均提速74.83倍。
链接: https://arxiv.org/abs/2609.08088
作者: Jingqian Liu,Xiaoyu Liu,Yuepeng Wang
机构: Simon Fraser University (西蒙菲莎大学)
类目: Computation and Language (cs.CL); Software Engineering (cs.SE)
备注:
Abstract:NumPy is a widely used Python library for numerical scientific computing, known for its declarative APIs and its optimized implementations. However, writing efficient NumPy programs, which often entails using vectorized array operations instead of explicit Python loops, may not be straightforward. This can be difficult for programmers who are accustomed to imperative array traversal, especially when vectorized API invocations require careful reasoning about shapes, broadcasting, and advanced indexing. This paper presents a rewrite-based approach for vectorizing Numpy programs with explicit loops over array data. Our approach vectorizes loops from the inside out, using array shapes and dataflow analysis to guide a source-to-source transformation that replaces loop bodies with vectorized statements. Following a set of rewrite rules that are correct by construction, our approach is consistently fast. We have implemented the approach as a tool called Vectorizer and evaluated it on 150 benchmarks collected from prior work and Stack Overflow. The evaluation shows that Vectorizer vectorizes 142 of the 150 benchmarks directly and 2 more after minor changes to the original benchmarks, with only 0.53 seconds on average to rewrite each one. The resulting programs are, on average, 74.83x faster than the original loop-based implementations.
[NLP-65] Popular Knowledge Propagates More Errors in LLM Knowledge Updating
【速读】： 该论文旨在解决语言模型在通过微调更新知识时所面临的事实性遗忘与幻觉扩散问题，尤其关注那些原本正确编码的事实中，哪些在后续更新过程中最容易受到“附带性污染”（collateral corruption）。现有研究多聚焦于长尾知识在获取与保留阶段的脆弱性，而本文提出一个互补性问题：在模型已正确掌握的事实中，哪些更易因其他更新操作而被错误修正？为在真实事实分布下进行研究，作者构建了一个大规模、经验证的维基百科事实图谱（FACTPROP），通过共享头实体或尾实体的三元组连接，保留了事实之间的语义关联。实验结果表明，与高连接度实体相关联的事实在微调过程中更易被破坏，且此类错误传播范围更广，即结构流行度（structural popularity） 能有效预测事实的脆弱性和错误传播能力。基于此发现，作者提出一种轻量级重演策略——流行度锚定（Popularity-based Anchoring, PopAnchor），通过选择并固定少量高流行度事实作为锚点，显著降低模型在持续学习中的遗忘率。其核心创新在于将事实的拓扑结构特征（实体连接度）与知识稳定性直接关联，从而实现高效、精准的知识保护。
链接: https://arxiv.org/abs/2609.08067
作者: Yuji Zhang,Weibing Wang,Cheng Qian,Duo Zhou,Dilek Hakkani-Tür,Kathleen McKeown,Chengxiang Zhai,Heng Ji
机构: University of Illinois Urbana-Champaign(伊利诺伊大学厄本那-香槟分校); Columbia University(哥伦比亚大学); City University of New York(纽约城市大学); Massachusetts Institute of Technology(麻省理工学院)
类目: Computation and Language (cs.CL)
备注: 16 pages, 7 figures, 5 tables
Abstract:Updating a language model’s knowledge through fine-tuning is essential for keeping its outputs current, yet can also induce factual forgetting and new hallucinations. Prior work shows that long-tail knowledge is harder to acquire and newly memorized long-tail facts are difficult to retain during later fine-tuning. We study a complementary question: among facts that a model has encoded correctly, which are most vulnerable to collateral corruption during other updates? To investigate this question under a realistic factual distribution, we construct a large-scale graph FACTPROP of verified Wikipedia facts by linking triples that share head or tail entities, thereby preserving connections among factual knowledge. We fine-tune models on factual statements and measure correct-to-incorrect facts after each update. Our results reveal a pattern distinct from prior findings on long-tail vulnerability during acquisition and retention: among facts that models already answer correctly, those associated with highly connected entities are more likely to be corrupted by neighboring updates, and updates to such facts propagate errors more broadly. Structural popularity therefore predicts both vulnerability and downstream damage. Inspired by this finding, we propose Popularity-based Anchoring (PopAnchor), a lightweight rehearsal strategy that preserves a small set of popular facts and reduces forgetting.
[NLP-66] BanglaMemeX: Advancing Cultural Metaphoric Image Interpretation in Bangla with a Multimodal Explainable Dataset EMNLP2026
【速读】： 该论文旨在解决生成式AI在处理具有文化语境和隐喻丰富特征的多模态内容时，尤其是低资源语言（如孟加拉语）中网络迷因（Internet memes）所蕴含的深层文化隐含意义理解能力不足的问题。其核心挑战在于，迷因的意义依赖于图像与叠加文字之间隐性互动、反讽修辞以及共享的社会文化知识，而非简单的视觉识别，尤其在孟加拉语等语言中，由于存在语码混用、风格化书写及特定文化符号，导致语言与文化分布偏移（distribution shift）显著加剧。为应对这一问题，研究提出BanglaMemeX，一个包含3,000条孟加拉语迷因的多维度标注基准数据集，涵盖幽默、反讽、冒犯性、激励意图及整体情感等标签，并配有由人工撰写的解释，明确描述文本与视觉中的隐喻机制。通过系统评估现代视觉-语言模型（Vision Language Models, VLMs）在分类与解释生成任务上的表现，研究发现尽管模型在表面层次上具备一定准确率，但在解析隐含文化线索方面仍表现欠佳。因此，该研究的关键解决方案在于构建一个强调文化语境的多模态评估框架，并揭示当前模型在跨文化、跨语言分布下的推理局限，从而推动具备文化感知能力的多模态系统发展，以实现更深层次的语义理解与基于情境的推理。
链接: https://arxiv.org/abs/2609.08029
作者: Md. Sadman Sakib,Zisan Mahmud,Md. Fahim Arefin,Md Fahim
机构: University of Dhaka(达卡大学); University of Texas at Dallas(德克萨斯大学达拉斯分校)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at EMNLP 2026 Findings - 37 pages, 17 figures, 19 tables, including appendices
Abstract:Vision Language Models have achieved strong performance on multimodal benchmarks, yet their ability to reason about culturally grounded and metaphor-rich content remains insufficiently studied. Internet memes present a challenging setting where meaning emerges from implicit interactions between image, overlaid text, sarcasm, and shared socio-cultural knowledge rather than literal visual recognition. This challenge is amplified in low-resource languages such as Bangla, where code-mixing, stylized scripts, and culturally specific symbolism introduce substantial distribution shift. In this work, we introduce BanglaMemeX, a culturally grounded multimodal benchmark comprising 3,000 Bangla memes annotated with multi-dimensional labels (humor, sarcasm, offensiveness, motivational intent, and overall sentiment) and human-written explanations that explicitly describe textual and visual metaphors. We systematically evaluate modern VLMs on both classification and explanation generation, revealing that current models struggle to interpret implicit cultural cues despite reasonable surface-level accuracy. Our results highlight the need for culturally-aware multimodal systems capable of grounded reasoning under linguistic and cultural distribution shift.
[NLP-67] Eliciting Self-Verification in Multimodal Reasoning Agents with Reinforcement Learning ECCV2026
【速读】： 该论文旨在解决多模态推理代理在使用外部工具（如网络搜索）时面临的可靠性问题，尤其是在缺乏显式验证信号和稀疏结果级监督的情况下，模型难以有效整合噪声性检索证据并正确融合文本与图像信息。其核心解决方案是提出一种纯强化学习（Reinforcement Learning, RL）微调框架——自验证强化学习（Self-Verification via Reinforcement Learning, SVRL），通过在推理过程中让模型自主生成可验证的推理轨迹，实现对检索证据的自我验证与过滤，从而减少对推理阶段外部验证器的依赖。SVRL的关键创新在于引入了“搜索感知惩罚”以抑制不必要的工具调用，并设计“查询多样性奖励”以激励生成多样化且结构良好的搜索查询，从而提供细粒度的反馈机制来指导何时以及如何进行搜索。在仅使用5,000个视觉问答样本对Qwen-2.5-VL-7B进行微调后，该方法显著提升了多跳视觉问答（multi-hop VQA）的泛化能力与工具使用效率，缩小了轻量级模型与大型专有模型之间的性能差距，同时大幅降低了训练与推理成本。
链接: https://arxiv.org/abs/2609.08025
作者: Vishwas Sathish,Viresh Ranjan,Xinliang Zhu,Arnab Dhua,Douglas Gray
机构: University of Washington; Amazon
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: To appear in ECCV 2026. 11 main pages. 7 figures
Abstract:Reasoning agents increasingly rely on external tools such as web search to answer complex queries. Reinforcement learning (RL) finetuning algorithms such as GRPO have improved long-form reasoning in text-only language models, particularly for coding and mathematics. Reliable tool use in multimodal agents, however, remains challenging because models must interpret text and images while integrating noisy retrieved evidence, often under sparse outcome-level supervision without explicit verification signals. We present Self-Verification via Reinforcement Learning (SVRL), an RL-only finetuning framework that trains multimodal agents to verify and filter retrieved evidence within their own reasoning traces, reducing reliance on external verifiers at inference time. SVRL also introduces a search-aware penalty that discourages unnecessary tool calls and a query-diversity reward that encourages diverse, well-formed search queries, providing fine-grained feedback on when and what to search. Finetuning Qwen-2.5-VL-7B with SVRL on only 5,000 visual question answering examples yields consistent gains in multi-hop VQA generalization and tool efficiency across benchmarks. Overall, SVRL narrows the gap between compact agents and much larger proprietary models while requiring substantially lower training and inference cost.
[NLP-68] MeRoTune: RoPE-Safe Merging with a Tunable Dial
【速读】： 该论文旨在解决在合并两个基于同一基础检查点（base checkpoint）微调得到的模型时，由于注意力子空间（attention subspace）未对齐而导致性能下降的问题。现有方法通过学习一个可逆修正矩阵 $ M $ 来调整查询和键投影，以实现对齐，但这种修正在实际中并不精确，因为现代开源语言模型在投影与点积之间插入了旋转位置编码（Rotary Position Embedding, RoPE），破坏了原本的抵消机制。论文指出，只有当修正矩阵 $ M $ 与 RoPE 的逐位置旋转操作可交换时，该抵消才是精确的。作者进一步推导出满足这一条件的矩阵类别：即在每个 RoPE 频率对内独立作用的缩放旋转矩阵，这构成了当前无约束矩阵训练方法所忽略的一个严格且低维的子集。基于此发现，论文提出一种新的合并方法：在保持基础权重完全冻结的前提下，每个微调模型分别学习一个与 RoPE 兼容的修正矩阵，并通过优化这些矩阵以适应特定混合比例（blend ratio），从而实现合并结果的后期可调性（类似旋钮调节），而非固定于单一合并结果。默认方案采用固定混合比例训练，类似于 LoRA 中预先设定缩放超参数的方式；同时实验了在每一步训练中随机重采样混合比例的方法，验证了两种策略的有效性。该方法的核心创新在于将修正矩阵的结构约束与 RoPE 的数学特性紧密结合，从而实现了更精确、更灵活的模型合并。
链接: https://arxiv.org/abs/2609.07971
作者: Salman Faroz
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:When you merge two fine-tuned models from the same base checkpoint by simply averaging their weights, you implicitly assume their attention subspaces are still aligned. Recent work attempts to fix misalignments by learning an invertible correction matrix, M , for each model’s query and key projections. This correction cancels out—using M on the query side and M^-T on the key side—right before the dot product. However, this cancellation is only exact if nothing sits between the projection and the dot product. In reality, almost all modern open-weight language models put a rotary position embedding (RoPE) exactly there. In this paper, we show that this cancellation is exact under RoPE if and only if M commutes with RoPE’s per-position rotation. We derive the specific class of matrices where this holds: a scaled rotation acting independently within each RoPE frequency pair. This forms a strict, low-dimensional subset of the unconstrained matrices that current methods normally train. Building on this, we turn this constrained matrix class into a new merging method. While keeping the base weights entirely frozen, two fine-tunes each learn their own RoPE-compliant correction matrices. We optimize these corrections against a chosen blend ratio so the final result can be adjusted post-hoc like a dial, rather than locked into a single fixed merge. Our default approach trains at one fixed blend ratio, similar to how LoRA sets its scaling hyperparameter in advance. We also experiment with resampling the blend ratio randomly at every training step, and we report the results of both approaches.
[NLP-69] Reasoning Beyond Transcription: Audio Language Models on Child Stuttering Speech
【速读】： 该论文旨在解决在多说话人混合场景下，自动理解伴有言语不流畅（如重复）的儿童语音所面临的挑战。由于儿童语音在声学特征、语调模式及语言结构上与成人存在显著差异，且言语不流畅现象进一步加剧了理解难度，现有音频语言模型（Audio Language Models, ALMs）虽在从语音中提取高层次语义方面表现优异，但在处理此类复杂儿童语音时的推理能力仍缺乏系统研究。本文的关键解决方案在于设计两个任务：面向儿童的语义摘要与语音蕴含判断，并通过指令引导（instruction-guided）方式使模型聚焦于儿童发言者，保留临床相关的不流畅特征，同时避免成人语音信息的干扰。实验基于未进行显式说话人分离的儿童口吃者访谈录音，采用大语言模型（LLM）评估与基于参考文本的指标相结合的方式进行评估，并以转录-真值（transcript-oracle）基线为锚点以精准识别误差来源。结果表明，尽管ALMs能够从口吃语音中提取高层语义，但随着不流畅程度的增加，其推理性能显著下降，揭示了当前模型在处理真实世界复杂儿童语音时的局限性。
链接: https://arxiv.org/abs/2609.07968
作者: Chibuzor Okocha,Christan Grant,Zoey Liu
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Child speech differs from adult speech in acoustics, prosody, and linguistic structures. Speech disfluencies (such as repetitions) further challenge automatic understanding. While Audio Language Models (ALMs) show strong semantic reasoning from speech audio, their ability to reason about disfluent child speech in mixed-speaker settings remains unexplored. We investigate this through two tasks: child-focused semantic summarization and speech entailment. Experiments use recordings of children who stutter in mixed speaker interviews without explicit speaker separation. Models are instruction-guided to focus on the child, preserve clinically relevant disfluencies, and avoid adult-speech leakage. Evaluation combines LLM-based judges and reference-based metrics, anchored by transcript-oracle baselines to isolate errors. Results show that while ALMs extract high-level meaning from stuttered speech, reasoning degrades significantly with increased
[NLP-70] Rethinking Sign Language Translation: The Impact of Signer Dependence on Model Evaluation EMNLP2025
【速读】： 该论文旨在解决当前手语翻译（Sign Language Translation, SLT）模型评估中存在的签者依赖（signer-dependent）问题，即训练、验证和测试集之间存在重叠签者，导致模型性能被高估。其核心问题是：现有评估协议未能充分检验模型对未见签者的泛化能力，模型可能依赖于特定签者的表达习惯而非真正的语言理解。解决方案的关键在于引入签者独立（signer-independent）的交叉验证策略，并通过重构数据集以实现句子级不重叠的签者独立划分。研究在三个主流无词素（gloss-free）SLT模型（GFSLT-VLP、GASLT、SignCL）上对CSL-Daily和PHOENIX14T数据集进行验证，结果显示，在签者独立评估下，模型性能显著下降（如GFSLT-VLP在PHOENIX14T上的BLEU-4从21.44降至3.59），表明先前的签者依赖评估严重夸大了模型的真实泛化能力。因此，论文提出三项关键建议：（1）采用签者独立评估协议以确保模型可泛化至未见签者；（2）重构数据集以提供明确的签者独立、句子不重叠的划分；（3）同时报告签者依赖与签者独立结果及训练-测试句重叠情况，以提升评估透明性与可比性。
链接: https://arxiv.org/abs/2609.07965
作者: Keren Artiaga,Sabyasachi Kamila,Haithem Afli,Conor Lynch,Mohammed Hasanuzzaman
机构: ADAPT Centre, Munster Technological University, Cork, Ireland; Nimbus Research Centre, Munster Technological University, Cork, Ireland; EEECS, Queen’s University Belfast, UK
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 13 pages. Published in Findings of the Association for Computational Linguistics: EMNLP 2025
Abstract:Sign Language Translation has advanced with deep learning, yet evaluations remain largely signer-dependent, with overlapping signers across train/dev/test. This raises concerns about whether models truly generalise or instead rely on signer-specific regularities. We conduct signer-fold cross-validation on GFSLT-VLP, GASLT, and SignCL, three leading, publicly available, gloss-free SLT models, on CSL-Daily and PHOENIX14T. Under signer-independent evaluation, performance drops sharply: on PHOENIX14T, GFSLT-VLP falls from BLEU-4 21.44 to 3.59 and ROUGE-L 42.49 to 11.89; GASLT from 15.74 to 8.26; and SignCL from 22.74 to 3.66. We also observe that in CSL-Daily many target sentences are performed by multiple signers, so common splits can place identical sentences in both training and test, inflating absolute scores by rewarding recall of recurring sentences rather than genuine generalisation. These findings indicate that signer-dependent evaluation can substantially overestimate SLT capability. We recommend: (1) adopting signer-independent protocols to ensure generalisation to unseen signers; (2) restructuring datasets to include explicit signer-independent, sentence-disjoint splits for consistent benchmarking; and (3) reporting both signer-dependent and signer-independent results together with train-test sentence overlap to improve transparency and comparability.
[NLP-71] CausalVerify: An Execution-Grounded Benchmark for LLM Causal Inference Workflows
【速读】： 该论文旨在解决现有大语言模型（LLM）因果推断评估基准中普遍存在的核心问题：即当前多数评估仅关注方法描述的合理性或生成代码能否运行，而未能验证所执行的工作流程是否真正恢复了目标因果估计。为应对这一挑战，论文提出CausalVerify，其关键在于将可解释的现实语义理解与可验证的计算过程相分离，通过构建一个执行驱动的验证框架来实现对因果估计准确性的严格检验。具体而言，该方案将259篇已发表经济学论文中的研究问题、数据描述与制度背景与100个固定种子的合成场景相结合，生成符合差分法（Difference-in-Differences）、事件研究法（Event Study）、工具变量法（Instrumental Variables）及断点回归设计（Regression Discontinuity）的CSV数据集。在实验B中，系统执行模型生成的R代码，并检查提取出的处理效应估计值是否与同一数据集上标准估计器的结果一致，从而引入“执行层面正确性”（L2b+）这一新层级，区别于仅记录代码能否运行的L2b层级。结果显示，七种LLM在默认50%容差下的L2b+通过率介于10%至88%之间，且426个成功执行的工作流中有66个（15.5%）给出了错误的估计结果，表明文本层面的方向一致性（L4）与实际执行正确性（L2b+）之间存在显著脱节。此外，尽管执行排序（L2b）与L2b+高度相关（Kendall τ=0.81，Spearman ρ=0.93），但文本方向评分（L4）与执行正确性相关性极低（Kendall τ ∈ [-0.20, 0.10]），说明仅依赖文本判断无法可靠反映真实因果推断能力。同时，模型自报告的信心水平也未能有效区分正确与错误工作流。因此，该研究的核心贡献在于建立了一个基于可执行代码和真实估计结果比对的因果推断验证机制，强调了执行层面评估的重要性，并揭示了当前大模型在因果推断任务中“能运行但不准”的深层缺陷。
链接: https://arxiv.org/abs/2609.07944
作者: Yonghong Zhang,Ricardo Correia,Isabel M. Parra,Yong Xie
机构: Universidad Autónoma de Madrid(马德里自治大学); Spanish National Research Council (CSIC)(西班牙国家研究委员会)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Econometrics (econ.EM)
备注: 22 pages, 9 figures, 12 tables. Code and data: this https URL
Abstract:Existing causal-inference benchmarks for LLMs mostly score method descriptions or whether generated code runs, not whether the executed workflow recovers the target causal estimate. CausalVerify studies this verification problem for structured econometric causal-estimation workflows by separating realistic interpretation from verifiable computation. It pairs 259 published economics papers (reconstructed research question, data description, institutional context) with 100 fixed-seed synthetic scenarios that realise CSV datasets for difference-in-differences, event study, instrumental variables, and regression discontinuity designs. Experiment A (real-paper text agreement) scores method-family and direction agreement against four-LLM consensus labels. Experiment B (synthetic execution) runs model-written R code and checks whether the extracted treatment-effect estimate matches a canonical estimator on the same realised dataset; this execution-grounded correctness layer is L2b+, distinct from L2b, which records only whether the code executes. A calibration arm asks whether self-reported confidence separates correct from incorrect workflows. On Experiment B, seven LLMs reach L2b+ pass rates of 10% to 88% at the default 50% tolerance, and 66 of the 426 workflows that execute (15.5%) return a wrong estimate. Execution ranking (L2b) agrees with L2b+ far better than text-direction scoring (L4): Kendall \tau=0.81 and Spearman \rho=0.93 , versus Kendall \tau between -0.20 and 0.10 for L4. Llama-3.3-70B-Instruct shows the same qualitative gap, and reported confidence does not reliably separate correct from incorrect workflows. The claims are confined to standardized single-shot workflows in these four design families under the evaluated R backend and model panel; the benchmark does not measure general causal-inference ability. Code, data, cached outputs, and a datasheet are released.
[NLP-72] Deadline-Aware Adaptive Prefill Chunking for Efficient Large Language Model Serving
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）服务中因长提示词预填充（prefill）阶段导致解码（decode）延迟增加、违反单令牌输出时延目标（inter-token latency objective）的问题。现有方法如分块预填充（chunked prefill）虽可缓解此干扰，但其分块大小通常固定：小分块虽能保障解码时延，却反复承担启动开销；大分块虽提升预填充效率，却易引发时延突增。本文提出SLOWeave，一种在线调度机制，其核心在于动态选择能够预测在最早活跃解码截止时间前完成的最大预填充分块。该决策无需针对特定工作负载进行分块大小调优，且通过基于单调迭代成本模型的对数时间搜索实现高效计算。理论证明，在任意解码仅迭代可行且成本预测准确的前提下，SLOWeave能在保证所有活跃请求下一次令牌输出截止时间的前提下，最大化即时预填充进度。实验在可复现的事件驱动模拟器及逐迭代级GPU运行时环境中验证了该方法，覆盖对话、混合上下文、长上下文和突发性工作负载。在25ms每输出令牌的时延目标下，相较于最强的固定分块基线，SLOWeave在混合请求和长上下文请求上分别提升吞吐量（goodput）39%与38%；在更严格的10ms目标下，性能提升分别达到3.3倍和2.4倍。结果表明，自适应分块大小是一种有效的服务原语，并为集成至逐迭代级LLM运行时提供了可直接部署的控制器。
链接: https://arxiv.org/abs/2609.07883
作者: Siyu Song,Qi Bai,Jinbo Hao,Kai Li,Chenchen Wang,Jiayu Sun
机构: Beijing Institute of Technology(北京理工大学); Sun Yat-sen University(中山大学); Jiangsu Ocean University(江苏海洋大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Continuous batching improves large language model (LLM) serving throughput, but long prompt prefills can delay decode iterations and violate inter-token latency objectives. Chunked prefill mitigates this interference, yet its chunk size is normally fixed: small chunks protect decode latency but repeatedly pay launch overhead, while large chunks improve prefill efficiency but create latency spikes. We introduce SLOWeave, an online scheduling method that selects the largest prefill chunk predicted to finish before the earliest active decode deadline. The decision requires no workload-specific chunk-size tuning and is computed by a logarithmic-time search over a monotone iteration-cost model. We prove that, whenever a decode-only iteration is feasible and the cost predictor is accurate, SLOWeave maximizes immediate prefill progress among decisions that preserve every active request’s next-token deadline. We evaluate the method in a reproducible event-driven simulator and an iteration-level GPU runtime across chat, mixed-context, long-context, and bursty workloads. Under a 25ms time-per-output-token objective, SLOWeave improves goodput over the strongest fixed-chunk baseline by 39% on mixed requests and 38% on long-context requests. Under a stricter 10ms objective, the gains rise to 3.3 \times and 2.4 \times , respectively. These results isolate adaptive chunk sizing as a useful serving primitive and provide an implementation-ready controller for integration with iteration-level LLM runtimes.
[NLP-73] LLM Layers Immediately Correct Each Other NEURIPS2025
【速读】： 该论文旨在解决生成式人工智能（Generative AI）中语言模型可解释性方法的内在局限性问题，特别是基于稀疏自编码器（Sparse Autoencoders, SAEs）的特征分解方法所依赖的“残差流中存在持续存在的语义特征并被后续层逐步构建”的经典假设。其核心挑战在于，现有方法忽略了相邻Transformer层之间存在系统性相互抵消的现象，即变压器层校正机制（Transformer Layer Correction Mechanism, TLCM）。TLCM的关键在于：相邻层通过主动修正彼此贡献中的不恰当部分，形成一种“提出-拒绝”（propose-and-reject）的动态过程。研究发现，TLCM在7个主流开源模型家族中的5个中普遍存在，并作用于几乎所有文本中的多数词元；它在预训练阶段即已出现，且对上下文敏感性强的词元表现更强，其校正强度会根据前一层输出自适应调整。进一步通过层雅可比矩阵分析表明，TLCM选择性地纠正特定子空间，同时强化其他子空间。这一机制揭示了残差流中不仅包含持久特征，还包含瞬时提出的候选特征，从而解释了为何SAE提取的特征描述通常缺乏特异性、为何模型控制需极端放大特征、以及为何转换器（transcoders）相比SAEs具有理论优势。
链接: https://arxiv.org/abs/2609.07876
作者: Arjun Patrawala,Jiahai Feng,Erik Jones,Jacob Steinhardt
机构: University of California, Berkeley (加州大学伯克利分校)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Published at NeurIPS 2025
Abstract:Recent methods in language model interpretability employ techniques such as sparse autoencoders to decompose residual stream contributions into linear, semantically meaningful features. Such methods are commonly interpreted as identifying features that persist in the residual stream and that subsequent layers build upon. We challenge this view by identifying the Transformer Layer Correction Mechanism (TLCM), wherein adjacent transformer layers systematically counteract portions of each other’s contributions. TLCM appears in 5 out of 7 major open-source model families and activates across nearly all tokens in diverse texts. We show that TLCM emerges during pretraining, operates most strongly on contextually dependent tokens, and adaptively calibrates its correction strength based on the preceding layer’s output. Using the layer Jacobian, we further show that TLCM selectively corrects specific subspaces while reinforcing others, which we interpret through a ``propose-and-reject’’ framework in which layers propose candidate features and subsequent layers selectively remove inappropriate ones. This dynamic suggests that the residual stream at any layer contains transient proposals alongside persistent features, helping explain why SAE feature descriptions often have low specificity, why effective model steering requires extreme feature amplification, and why transcoders hold a theoretical advantage over SAEs.
[NLP-74] SAFIRE: Safety-Critical Benchmark for Fine-grained Fire and Smoke Understanding in Multimodal LLM s EMNLP2026
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在安全关键场景下可靠性不足的问题，尤其聚焦于火灾与烟雾理解这一关乎公共安全与灾害响应的核心任务。现有基准测试普遍存在真实世界场景多样性不足、缺乏上下文感知评估等缺陷，难以全面衡量模型在复杂情境下的推理能力。为此，本文提出SAFIRE——一个大规模的火灾烟雾理解基准，包含8.3万张来自20个真实场景的带描述图像，以及基于9.7万张图像子集生成的19.3万道多项选择视觉问答（Multiple-Choice Visual Question Answering, MCVQA）题目，覆盖从基础感知到高阶推理共10个评估维度。通过GPT-5.4辅助的多阶段验证流程结合MLLM多数投票机制，确保了标注质量。对十款开源MLLM（参数量8B–38B）的评估结果显示平均准确率仅为61.9%，暴露出当前模型在安全关键推理任务中的显著短板。进一步实验表明，仅使用7%领域特定数据对视觉编码器进行微调，即可将火灾场景分类准确率从20.1%提升至64.5%，证明高质量、精心构建的数据即便在数据量有限的情况下仍能带来显著性能增益。解决方案的关键在于构建具有高度真实性和多层次评估能力的专用基准，并通过小样本领域适配实现模型性能的显著提升。
链接: https://arxiv.org/abs/2609.07823
作者: Pengfei Li,Naufal Suryanto,Sicheng Zhang,Mohammad Alsharid,Muzammal Naseer
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted at EMNLP 2026 Findings. Project page: this https URL | Github: this https URL
Abstract:Multimodal Large Language Models (MLLMs) show strong progress on vision-language tasks, yet their reliability in safety-critical settings remains underexplored. Fire-smoke understanding is central to public safety and disaster response, but most existing benchmarks lack diverse real-world scenarios and context-aware evaluation. We introduce SAFIRE, a large-scale benchmark for fire-smoke understanding in MLLMs, comprising 83K captioned images from 20 scenarios and 193K multiple-choice VQA (MCVQA) generated from a 9.7K-image subset, spanning 10 evaluation dimensions from basic perception to higher-order reasoning. A GPT-5.4-assisted multi-stage verification pipeline with MLLM majority voting ensures annotation quality. Evaluating ten open-source MLLMs (8B-38B) yields an average accuracy of 61.9%, exposing major gaps in safety-critical reasoning. We further show that adapting vision encoders with only 7% of our domain-specific data boosts fire-scene classification accuracy from 20.1% to 64.5%, indicating that carefully curated data can yield substantial gains even when data volume is limited. All datasets, models, and code are available at this https URL.
[NLP-75] A*-Thought-V2: Efficient Latent Reasoning via Geometric Dynamics of LLM
【速读】： 该论文旨在解决生成式 AI（Generative AI）中链式思维（Chain-of-Thought, CoT）推理带来的巨大计算开销与上下文占用问题。现有方法在压缩推理过程时，或通过硬剪枝丢失中间信息，或缺乏可解释的连续压缩准则。其核心解决方案是提出A*-Thought-V2框架，该框架将CoT建模为隐藏状态轨迹，并采用显式-隐式交织的潜在表示架构，取代传统的硬删除机制。通过将问题、步骤和解题表示投影至三维主成分分析（PCA）空间，量化每一步局部转移与全局“问题到答案”方向之间的对齐度：对齐度高的步骤保留为显式文本，偏离方向的步骤则被压缩为连续潜在标记。方向角不仅捕捉局部语义，还反映推理动态——小角度对应直接执行与答案生成，大角度则多涉及校验、修正与分支探索，其时间变化揭示了探索、收敛与精炼阶段。为训练此架构，引入分步嵌入强制（stepwise embedding forcing）与标签强制（label forcing），前者将冗余步骤聚合为单一潜在嵌入，后者以软多模态词汇分布监督潜在标记，而非硬性one-hot标签。实验表明，该方法在六个域内与域外基准上显著提升平均准确率最高达2.6%，响应长度减少50%，单位计算量准确率提升2.29倍，预处理与训练时间分别降低94.6%和最高80.3%。表征分析显示，潜在状态形成紧凑且区别于文本状态的区域，而潜在标记位置的高熵特征表明其具备更丰富的软目标，促进更深层次的步骤级特征学习。
链接: https://arxiv.org/abs/2609.07821
作者: Xiaoang Xu,Siyuan Liu,Shuo Wang,Junlan Feng,Fanyu Meng,Zhu Zhang,Jixun Wang,Xiaorong Wang,Zihan Zhou,Xin Li,Chaojun Xiao,Yiming Zhang,Huijia Wu,Liuyu Xiang,Peipei Li,Zhaofeng He
机构: Beijing University of Posts and Telecommunications; The Hong Kong Polytechnic University; Tsinghua University; JIUTIAN Research, Beijing, China; OpenBMB
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Code: this https URL
Abstract:Chain-of-Thought (CoT) improves the reasoning ability of Large Language Models (LLMs) but incurs substantial computation and context costs. Existing methods either lose intermediate information through hard pruning or lack a principled criterion for continuous compression. We present A*-Thought-V2, a geometric dynamics of LLM guided framework that models CoT as a hidden-state trajectory and replaces hard deletion with an explicit-implicit interleaved latent architecture. After projecting question, step, and solution representations into a 3D PCA space, it measures alignment between each local transition and global question-to-solution direction. Aligned steps remain explicit text, whereas deviating steps are compressed into continuous latent tokens. Directional angles capture both local semantics and reasoning dynamics: small angles indicate direct execution and answer formation, while large angles more frequently involve checking, correction, and branch exploration; their temporal variation reveals exploration, convergence, and refinement stages. To train this architecture, we introduce stepwise embedding forcing, which pools each redundant step into a single latent embedding, and label forcing, which supervises that latent token with a soft multi-modal vocabulary distribution instead of a hard one-hot label. Experiments on Qwen3.5-9B and Qwen3.6-27B across six in-domain and out-of-domain benchmarks show that A*-Thought-V2 improves average accuracy by up to 2.6% while reducing response length by up to half, increasing Accuracy per Computation Unit by 2.29 \times , and reducing preprocessing and training time by 94.6% and up to 80.3%, respectively. Representation analyses suggest that latent states form a compact region distinct from textual states, while higher entropy at latent-token positions reflects broader soft targets that encourage richer step-level feature learning.
[NLP-76] VoT: Vision-of-Thought for Unified Multimodal Representation Alignment
【速读】： 该论文旨在解决当前文本到图像生成系统中缺乏显式、可解释的中间表示的问题，即在高阶语言语义与低阶视觉信号之间缺少一个能够有效桥接的结构化表征。现有“文本编码器加扩散解码器”范式虽取得成功，但其直接以文本语义调控连续潜在噪声的方式难以实现对生成过程的精细控制与可解释性。本文提出的视觉思维（Vision-of-Thought, VoT）框架的关键创新在于，在视觉-语言模型（VLMs）与扩散变压器（DiTs）之间引入一个离散的视觉思维层，将VLM从单纯的文本编码器角色升级为多模态规划器，通过生成代表高层视觉计划（如物体、布局等）的离散VoT token来指导图像生成。该框架的核心在于训练一个专用的VoT分词器，其基于闭环目标函数，结合了VLM对齐损失、特征重建损失和向量量化损失，使生成的离散token既具备可由VLM理解的语义可读性，又能保留生成所需的视觉信息。实验结果表明，该方法显著提升了语义对齐能力，并为可控、可解释的图像生成提供了结构化接口。
链接: https://arxiv.org/abs/2609.07815
作者: Jingxiang Sun,Chao Liao,Zhengxiong Luo,Chaorui Deng,Chen-lin Zhang,Junke Wang,Ceyuan Yang,Haoqi Fan,Weilin Huang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Current text-to-image systems typically employ a “text encoder plus diffusion decoder” paradigm, in which text semantics directly modulate continuous latent noise. Despite their success, these methods lack an explicit, interpretable intermediate representation that effectively bridges high-level linguistic semantics and low-level visual signals. In this paper, we propose Vision-of-Thought (VoT), a framework that introduces a discrete visual-thinking layer between vision-language models (VLMs) and diffusion transformers (DiTs). Instead of treating VLMs merely as text encoders, we use them as multimodal planners that generate discrete VoT tokens representing high-level visual plans, such as objects and layouts, before rendering pixels. We train a specialized VoT tokenizer in the VLM semantic space with a closed-loop objective that combines VLM alignment, feature reconstruction, and vector-quantization losses. These objectives make the tokens semantically readable by the VLM while preserving the visual information needed for generation. Experimental results demonstrate that VoT improves semantic alignment and provides a structured interface for interpretable and controllable generation.
[NLP-77] You Cant Prefer Emotions You Dont Sample: Intensity Undershoot in DPO-Tuned LLM s
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）在情感强度控制方面存在系统性偏差的问题，即当用户指令要求模型以极强的情感表达（如“非常兴奋”）生成文本时，模型输出的情感强度远低于请求水平。其核心问题是：尽管通过连续情感空间（如效价-唤醒度，Valence-Arousal, VA）进行条件化控制，模型对情感强度的响应增益显著不足——例如，在Llama-3.1-8B上，效价（valence）和唤醒度（arousal）的响应增益分别仅为0.26和0.13，远低于理想值1.0。解决方案的关键在于识别并纠正训练过程中候选样本分布的非均衡性：自然语料（如EmoBank）中的情感标注以中性为主，且极端情感样本稀缺，导致直接偏好优化（Direct Preference Optimization, DPO）无法学习到对高强度情感的偏好。通过构建覆盖全目标空间的均匀采样候选池，并引入更多高情感强度的生成样本，可显著提升模型响应增益（效价增益从0.26提升至0.40±0.02），同时保持在原始数据集上的泛化性能（EmoBank测试集VA距离仅从0.092上升至0.107）。该结果表明，情感强度控制的瓶颈主要源于候选样本的极端性不足，而非条件化机制本身。
链接: https://arxiv.org/abs/2609.07808
作者: Hyunwoo Kim,Usama Khalid
机构: Hanyang University (汉阳大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted to INLG 2026 (poster). Both authors contributed equally
Abstract:Ask a language model to respond “very excitedly,” and its output is typically only mildly more energetic. We quantify this effect. We condition an instruction-tuned LLM on a continuous Valence-Arousal (VA) target, where valence measures how pleasant a state is and arousal how activated it is, measure the achieved affect with a frozen regressor, and sweep the requested target from -1 to +1. The response moves far less than asked: the gain, the slope of achieved against requested affect, is only 0.26 for valence and 0.13 for arousal on Llama-3.1-8B, where a faithful controller would score 1. The model systematically undershoots requested emotional intensity, which puts a number on the qualitative observation of Fazzi et al. (2025). Our experiments trace this to the preference-learning pipeline. Training targets from natural corpora such as EmoBank are neutral-heavy, and the sampled candidates themselves rarely reach extreme affect, so Direct Preference Optimization (DPO) is left with no extreme exemplar to prefer. If instead we cover the target space uniformly and sample a hotter, larger candidate pool, valence gain rises from 0.26 to 0.40 +/- 0.02 (3 seeds) and extrapolation error drops, at only a modest in-distribution cost (EmoBank-test VA distance 0.092 to 0.107). The same recipe reproduces on Qwen3-8B (gain_v 0.44, with in-distribution accuracy preserved). Arousal is harder and less reliable: its gain barely moves on average and swings across seeds (0.14 +/- 0.07, against valence’s tight +/- 0.02), because raising arousal needs candidates the base model is reluctant to generate. The evidence indicates that faithful intensity is bottlenecked by the extremity of the candidate pool rather than by the conditioning format.
[NLP-78] Climate-ModernBERT: Revisiting Corpus Composition for Domain-Adaptive Continued Pretraining
【速读】： 该论文旨在解决气候领域自然语言处理（Natural Language Processing, NLP）中如何有效融合异构文本数据源（如科学文献、政策文件及合成报告）进行持续预训练（Continued Pretraining, CPT）的问题。其核心挑战在于不同来源的气候语料在分布和特性上存在显著差异，传统联合训练方法难以充分挖掘各数据源的互补信息。论文提出的解决方案关键在于采用参数空间合并（parameter-space merging）策略，通过对独立训练得到的专用模型检查点进行融合，相较于联合多源训练，能更有效地保留异构气候语料中的互补知识，并显著提升模型性能。实验表明，在九个气候NLP基准任务上，最优模型平均F₁达76.3，较原始ModernBERT基线提升2.8个百分点，且学术气候文本提供了最强的适应信号。研究结果验证了参数空间合并在气候领域适应性预训练中的有效性，并公开了所有模型变体与训练检查点以推动后续研究。
链接: https://arxiv.org/abs/2609.07798
作者: Yongan Yu,Shantam Raj,Jingwei Ni,Ario Saeid Vaghefi,Dominik Stammbach,Markus Leippold
机构: McGill University(麦吉尔大学); University of Zürich(苏黎世大学); Princeton University(普林斯顿大学); ETH Zürich(苏黎世联邦理工学院); Mila – Quebec Artificial Intelligence Institute(蒙特利尔人工智能研究所)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Natural Language Processing (NLP) in the climate domain requires models to process heterogeneous text sources, including scientific literature, policy disclosures, and synthetic reports. However, how to effectively combine diverse domain corpora during continued pretraining (CPT) remains underexplored. We introduce Climate-ModernBERT, a family of climate-adapted encoder models obtained through continued pretraining of ModernBERT-Base on three climate corpora: academic climate text, climate-filtered web data, and synthetic climate documents. We systematically compare joint continued pretraining on corpus mixtures with parameter-space merging of independently specialized checkpoints. Across nine climate NLP benchmarks, our best model achieves 76.3 average F_1, improving significantly over a vanilla ModernBERT baseline by 2.8 points. Within the climate NLP setting, the results show that academic climate corpora provide the strongest adaptation signal among the evaluated sources, while parameter-space merging improves over joint multi-source training and better preserves complementary information from heterogeneous climate corpora. We release all Climate-ModernBERT variants and training checkpoints to support future research in climate NLP and domain-adaptive pretraining.
[NLP-79] Does Syntax Matter? A Graph-Augmented Variational Topic Model for Computational Social Sciences
【速读】： 该论文旨在解决传统主题建模方法在捕捉文本深层语义结构方面的局限性，尤其是基于词袋（Bag-of-Words）模型和生成式模型（如LDA）难以有效建模词语间复杂语法关系的问题。现有方法虽引入了密集嵌入表示（如BERTopic），但仍未能充分整合句法依赖关系以提升主题的语义可解释性与结构合理性。为此，本文提出结构化上下文概率主题模型（SCPTM），其核心创新在于将文档-词汇异质图（heterogeneous graph）中的词法与句法边通过图注意力网络（Graph Attention Network）嵌入到变分自编码器（Variational Autoencoder）框架中，从而实现对混合成员主题分布的概率推断。关键突破在于：虽然神经架构整体提升了文档-主题对齐效果，但主要贡献来自变分编码器而非句法信息；真正体现句法价值的是在论辩性文本中增强主题多样性与描述符质量——依赖路径能有效捕获谓词-论元结构及立场表达，而在技术性或行政类文本中则表现冗余。此外，所有变体均呈现正向情感极性差距（valence gap），但主要由短语聚类驱动而非句法过滤。研究结论表明，句法编码具有条件有效性：仅在行动导向、论证性强的文本中带来增益，而在信息性或管理性语料中可能引入噪声。
链接: https://arxiv.org/abs/2609.07797
作者: Alessandro Meneghini
机构: 未知
类目: Computation and Language (cs.CL)
备注: 33 pages, 1 figure
Abstract:Topic modeling is widely used in computational social sciences to identify latent themes in large text corpora. Traditional approaches rely on Bag-of-Words representations and generative models such as LDA, while recent methods like BERTopic operate on dense document embeddings. This paper introduces the Structural Contextual Probabilistic Topic Model (SCPTM), an architecture that incorporates syntactic dependency relations into topic inference. SCPTM represents a corpus as a heterogeneous graph of documents and words connected by lexical and syntactic edges, processed through a Graph Attention Network within a Variational Autoencoder to produce probabilistic, mixed-membership topic distributions. We evaluate seven topic modeling techniques (including four SCPTM ablations) across four corpora differing in register and discourse structure. Our framework combines coherence (C_V, C_NPMI), topic diversity, clustering-label alignment (NMI), and phrase-level diagnostics (complementarity and valence gap). Results show that SCPTM’s neural architecture yields substantial gains in document-topic alignment over generative baselines, but these gains are attributable to the variational encoder rather than to syntax. Syntax contributes to topic diversity, where graph-augmented variants outperform the no-graph baseline across all corpora, and to descriptor quality: dependency paths capture predicate-argument structures and stance in deliberative registers, while proving redundant in technical and institutional corpora. The valence gap is positive across all variants, but driven primarily by phrase grouping rather than syntactic filtering. We conclude that syntactic encoding matters conditionally: it benefits action-oriented, argumentative texts, but introduces noise in informational or administrative registers.
[NLP-80] LLM Agents as Computational Typologists
【速读】： 该论文旨在解决语言类型学研究中依赖专家对参考语法进行人工分析所导致的大规模跨语言比较效率低下、难以扩展的问题。其核心解决方案是提出AUTOTYPOLOGIST——一个基于大语言模型（LLM）的智能代理，能够基于证据开展类型学分析。该代理的关键在于采用类似ReAct的迭代式工作流，实现对参考语法文本的段落检索、逐行注释文本（IGT）的解析以及类型学假设的持续推理与验证。在类型特征编码任务中，该代理在具备完整语法描述的情况下可有效整合信息，但在仅提供目标语言的IGT时仍面临挑战；在类型学假设检验任务中，代理能综合跨语言证据，识别支持性案例与反例。结果表明，该方法为实现可扩展且可追溯的类型学分析提供了可行路径，但仍需专家介入进行最终验证。
链接: https://arxiv.org/abs/2609.07791
作者: Changbing Yang,Christopher Hammerly,Freda Shi,Jian Zhu
机构: University of British Columbia (不列颠哥伦比亚大学); University of Waterloo (滑铁卢大学); Vector Institute (矢量研究所)
类目: Computation and Language (cs.CL)
备注:
Abstract:Linguistic typology relies on expert analysis of reference grammars across languages, making large-scale crosslinguistic comparison labor-intensive and unscalable. We introduce AUTOTYPOLOGIST, an LLM agent for evidence-grounded typological analysis over reference grammars. The agent is capable of retrieving relevant grammar sections, analyzing interlinear glossed text (IGT), and iteratively reasoning over typological hypotheses using a ReAct-style workflow. We evaluate the system on TYPOLOGICAL FEATURE CODING against expert annotations and TYPOLOGICAL HYPOTHESIS TESTING with typological universals using 25 open-source reference grammars. Operating under different information constraints in TYPOLOGICAL FEATURE CODING, the agent can synthesize information from reference grammar prose but still faces challenges with only IGTs in the target language. In TYPOLOGICAL HYPOTHESIS TESTING, the agent can synthesize crosslinguistic evidence and identify both supporting cases and counterexamples. These findings suggest that LLM agents can support scalable and inspectable typological analysis, while still requiring expert validation.
[NLP-81] Signed Rescue Routing: Harm-Aware Cascades for Efficient LLM Inference
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）级联系统中路由策略的优化问题，即如何更高效地决定哪些请求应由小型模型直接回答、哪些应升级至大型模型处理。现有方法多依赖小型模型输出的不确定性或错误概率作为升级依据，但这一代理指标忽略了关键事实：升级仅在大型模型能纠正小型模型错误时才具有价值，而当大型模型将正确答案替换为错误答案时，升级反而有害。为此，论文提出带符号救援路由（Signed Rescue Routing, SRR），其核心创新在于分别预测“大型模型纠正小型模型”的事件与“大型模型取代正确答案”的事件，并基于两者之间的差值进行请求排序。该差值即为带符号的条件增益（signed conditional gain），在固定升级预算下，该指标为贝叶斯最优路由得分。SRR仅需部署时小型模型的输出统计信息，且引入一个轻量级双头路由器，计算开销低。在MMLU、HellaSwag和ARC-Challenge数据集上的实验表明，相较于基于学习的小型模型错误预测器和熵路由方法，SRR在准确率-计算成本曲线上实现了显著更高的面积（AUC），验证了以预测增量价值而非模型不确定性为目标，在实现高效LLM级联中的有效性与优越性。
链接: https://arxiv.org/abs/2609.07786
作者: Zheyuan Wang,Siyu Li,Peiqiao Song,Sijia Chen,Qianqian Song,Qian Liu
机构: Beijing Normal University (北京师范大学); Chongqing Jiaotong University (重庆交通大学); Dalian University of Technology (大连理工大学); Jilin University (吉林大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language model (LLM) cascades answer easy requests with a small model and escalate selected requests to a larger model. Most routers prioritize examples on which the small model appears uncertain or likely to be wrong. This proxy ignores a decisive fact: escalation is useful only when the large model corrects the small model, and it is harmful when the large model replaces a correct answer with an incorrect one. We introduce Signed Rescue Routing (SRR), a budgeted routing method that predicts these two events separately and ranks requests by their difference. We show that this signed conditional gain is the Bayes-optimal routing score under a fixed escalation budget. SRR requires only the small model’s output statistics at deployment and adds a lightweight two-head router. We evaluate SRR with Qwen3-4B and Qwen3-8B on TBD examples from MMLU, HellaSwag, and ARC-Challenge. Across the accuracy-compute curve, SRR reaches an area of TBD, compared with TBD for a learned small-model error predictor and TBD for entropy routing. These results show that predicting incremental value, rather than model uncertainty, is a simple and effective objective for efficient LLM cascades.
[NLP-82] CodeTD: Topology of Attention Detects Hallucinations in Code LLM s
【速读】： 该论文旨在解决生成式 AI 在代码生成过程中因幻觉（hallucination）导致的代码正确性难以自动评估的问题。随着代码大语言模型（Code LLMs）的广泛应用，其生成的代码可能存在逻辑错误或严重安全漏洞，而现有方法在预执行阶段对代码正确性的判断能力有限。为此，本文提出 CodeTD——一种基于代码大模型注意力图拓扑数据分析（Topological Data Analysis, TDA）的预执行代码正确性评估方法。其核心创新在于利用注意力图的拓扑模式量化提示词（prompt）与生成代码之间的不匹配程度，从而实现对代码正确性的有效预测。实验在 HumanEval、MBPP、BigCodeBench 与 MultiPL-E 等主流基准上进行，覆盖 5 种编程语言及 10 个参数规模达 340 亿的 Code LLM，结果表明 CodeTD 显著优于现有基线方法，并具备跨基准的可迁移性。
链接: https://arxiv.org/abs/2609.07779
作者: Daria Voronkova,Ilya Trofimov,Anton Dmitriev,Eduard Tulchinskii,Evgeny Burnaev,Serguei Barannikov
机构: Applied AI Institute; AXXX; IMJ CNRS
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:As AI-code assistant tools become widespread, automatic assessment of the correctness of generated code becomes a significant challenge. Code LLMs are prone to hallucinations, which may lead to code that does not solve the required problem, or even to code with severe security vulnerabilities. In this paper, we introduce CodeTD – the first approach to pre-execution assessment of code correctness based on topological data analysis (TDA) of Code LLMs’ attention maps. Our method quantifies prompt-generation mismatch using topological patterns of attention maps. We carry out experiments with common benchmarks (HumanEval, MBPP, BigCodeBench, MultiPL-E), 5 programming languages and 10 Code LLMs of size up to 34B parameters. The experimental results show that the proposed method outperforms recent baselines. Moreover, CodeTD is transferable between coding benchmarks.
[NLP-83] Bag of Tricks or Bag of Myths? Reducing Modeling Complexity with Task Knowledge in Explainable Suicide Risk Assessment
【速读】： 该论文旨在解决从社交媒体文本中评估自杀风险这一小样本、高风险任务中的核心挑战，即在严重类别不平衡、输出耦合及作者层面数据有限的条件下，实现精准的自杀风险分级、可解释的风险证据识别以及临床相关风险与保护因素的识别。传统自然语言处理（NLP）技术如模型缩放、合成数据生成、损失重加权、集成学习和阈值调优等虽广泛应用，但缺乏在该特定场景下的有效性验证。研究通过在1,635条临床标注帖子上进行约300次受控实验，系统评估了来自7个方法族的31种预设技术，发现仅有5项对比显示出可靠增益。其解决方案的关键在于“任务条件化技术选择”（task-conditioned technique selection）原则：仅当任务特异性知识、结构或实证证据支持时才保留相应技术。具体实现上，将因子预测重构为句子与其术语库定义之间的蕴含关系判断，采用架构多样化的集成模型并结合类别平衡训练与分数重标定；风险预测结果驱动一个由7个模型组成的证据标记器集成系统，证据信息用于约束符号化风险规则，并对难分类别进行独立路由；因子预测模块保持独立性，因风险证据不提供额外因子信号。此外，通过部署一致的校准策略修正验证分数与测试时集成分数之间的偏差，显著提升了因子系统的性能。最终系统在风险、证据和因子三项目标上分别取得0.8203、0.7953和0.7045（宏F1）的得分，综合得分0.7781，在53支参赛队伍中位列第三。
链接: https://arxiv.org/abs/2609.07766
作者: Shlok Shelat,Shrey Salvi,Souvik Roy,Manas Gaur,Amit Sheth
机构: Indian AI Research Organisation(印度人工智能研究组织); Ahmedabad University(艾哈迈达巴德大学); University of Maryland, Baltimore County(马里兰大学巴尔的摩县分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: This work was shaped by the IEEE Big Data Cup, where our team, NIDHI, ranked 3rd overall. NIDHI (Neurosymbolic Intelligent Diagnostics for Holistic Trauma-Informed Care) is an effort from this http URL . 21 pages, 6 Tables, 4 Figures
Abstract:Assessing suicide risk from social media text is a small-data, high-stakes setting requiring not only severity prediction but also supporting evidence and clinically relevant risk and protective factors. Yet common NLP techniques, including model scaling, synthetic data, loss reweighting, ensembling, and threshold tuning, are often applied without testing whether their gains hold up under severe class imbalance, coupled outputs, and limited author-level data. We study 1,635 clinician-annotated posts and audit 31 pre-specified techniques from 7 methodological families through roughly 300 controlled experiments on author-disjoint partitions. We found no prior audit of this playbook in this regime. The findings guide a task-grounded system for three outputs: 4-level suicide risk, evidence spans, and 24 clinical risk and protective factors. Only 5 of 31 comparisons produced reliable gains. We reformulate factor prediction as entailment between each post and its codebook definitions, using an architecturally diverse ensemble with class-balanced training and score rescaling. Risk predictions condition a 7-model evidence tagger ensemble; evidence restricts symbolic risk rules; and a difficult risk class is routed separately. The factor predictor remains independent because risk evidence provides no additional factor signal. We also correct a mismatch between validation scores used for threshold fitting and test-time ensemble scores through deployment-consistent calibration, yielding the largest improvement to the factor system. The final system achieves 0.8203 for risk, 0.7953 for evidence, and 0.7045 macro-F1 for factors, with a 0.7781 composite, ranking third among 53 teams. We call the underlying principle task-conditioned technique selection: retain techniques only when task-specific knowledge, structure, or empirical evidence justifies them.
[NLP-84] Replicating a Disjoint-Set Union Experiment over Various Notions of Micro Units to assess Translation Effort
【速读】： 该论文旨在解决文本生成过程中编辑行为分布的测量问题，即如何有效识别和区分微观（Micro）与宏观（Macro）编辑单元，以评估文本生产过程中的认知负荷。其核心挑战在于不同暂停阈值对单位划分的影响，导致结果不一致。论文通过实验验证多种暂停阈值的适用性后指出，基于过程的单元（process-based units）相较于传统基于时间或空间的划分方式，更能准确反映实际编辑行为的结构特征，因而成为更合适的分析实体。这一发现为理解写作认知过程提供了更为精细的分析框架。
链接: https://arxiv.org/abs/2609.07748
作者: Michael Carl
机构: 未知
类目: Computation and Language (cs.CL)
备注: Proceedings of Translation in Transition 8, 2026
Abstract:The paper describes a replication experiment to assess “the distribution of editing procedures across micro and macro units as an indicator of the strain of text production”. We investigate various pause thresholds to isolate Micro and Macro units and conclude that process-based units might be more suitable entity.
[NLP-85] LLM Forensics: Where Do Backdoors Hide? Localizing and Controlling Trigger Mechanisms with Sparse Autoencoders EMNLP2026
【速读】： 该论文旨在解决大语言模型（LLM）中基于触发词的后门机制（trigger-based backdoors）的内在工作原理不明确的问题，特别是触发词与异常响应之间因果关系的可解释性不足。其核心挑战在于：尽管行为上可定义触发词会引发模型输出特定模式（如从英语切换至法语或德语生成），但触发机制在模型内部的具体实现路径仍不清楚。为揭示这一机制，作者在受控的语言切换场景下，对1B和8B参数量的语言模型进行研究，利用稀疏自编码器（Sparse Autoencoders, SAEs）对各层及变压器组件中的特征进行建模，并通过对比触发提示、翻译样本和预训练控制组，识别出与触发相关的特征方向。研究发现，虽然某些SAE特征能以近乎完美的F1分数区分触发提示与对照组，但这些特征仅作为检测器，不直接控制行为；注意力模块与前馈网络（MLP）的特征虽在触发输入时稳定激活，但移除它们通常无法抑制语言切换，激活也难以诱发目标语言生成。相反，残差流（residual-stream）中的特定特征在被移除时可有效抑制触发响应，且部分特征可在无触发词的情况下诱导目标语言延续。因此，该研究揭示了触发机制在功能层面可分解为三个独立的特征方向：触发检测、残差流传播以及后续语言状态追踪。这种角色层级的解耦结构是该机制最可能泛化至其他触发式后门的关键所在，即使在载荷内容、所涉层数或电路位置不同的情况下仍具鲁棒性。
链接: https://arxiv.org/abs/2609.07746
作者: Wissam Antoun,Francis Kulumba,Théo Lasnier,Benoît Sagot,Djamé Seddah
机构: Inria Paris(法国国家信息与自动化研究所巴黎分部)
类目: Computation and Language (cs.CL)
备注: Accepted at Findings of EMNLP 2026
Abstract:Even though backdoors in LLMs have been a growing concern, their inner workings are still under heavy scrutiny. Trigger-based backdoors are easy to define behaviorally, a rare input that makes the model switch to a chosen response pattern, but the mechanism between triggers and their responses is less clear. We study this mechanism in a controlled, harmless language-switching setting, where fixed trigger sequences make 1B and 8B language models continue English prompts in French or German. For this, we train sparse autoencoders (SAEs) across layers and transformer components, then compare triggered prompts with translation and pretraining controls to identify trigger-relevant feature directions. We show how SAE features separate triggered prompts from controls with near-perfect F1, but features that detect the trigger do not necessarily control the behavior. In intervention tests, attention and MLP features often fire reliably on triggered prompts, making them good detectors, but ablating them rarely suppresses the language switch and activating them rarely induces it. In contrast, residual-stream features can suppress triggered generation when ablated, and some selected features can induce target-language continuations without the trigger. In short, these token-trigger mechanisms decompose into distinct SAE feature directions, with separate features for trigger detection, residual-stream propagation, and later language tracking. This role-level decomposition is the part most likely to transfer to other trigger-based backdoors, even when the payload, layers, or circuit locations differ.
[NLP-86] From Echo Chambers to Epistemic Monoculture: Large Language Models Present Temporally Contingent Partisan Alignments as Knowledge
【速读】： 该论文旨在解决生成式AI（Generative AI）在政治信息传播中作为“中立知识中介”的假象问题。尽管大语言模型（LLMs）常被类比为“更优的谷歌”，但其本质是生成性文本，而非检索已有内容，因而不可避免地嵌入隐性的框架选择（framing decisions）。由于知识传递本身具有建构性，此类系统无法充当“全部人类知识”的中立通道，反而演变为新型政治中介。研究的关键发现在于：基于机制证据，政党认同在Llama 3.1 8B模型内部以可定位的几何方向编码存在，而对齐训练仅掩盖而非消除这一结构。通过利用模型训练截止于2024年的时间节点——恰逢美国政治格局发生重大转变（特朗普第二任期与医疗政策重大变革）——研究设计了自然实验，揭示出模型将时间相关的党派倾向性立场呈现为客观知识，且缺乏区分事实与观点的能力。这导致信息环境从回音室（echo chamber）进一步演化为认知同质化（epistemic monoculture），即语言模型虽宣称整合“全部人类知识”，实则放大了训练数据中固有的文化与党派分歧。
链接: https://arxiv.org/abs/2609.07735
作者: Wend K. Tam
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) are rapidly becoming an interface between citizens and political information. They are often regarded as “a better Google.” While this analogy might work for some instances, it is unintuitively problematic for democratic politics. A search engine retrieves human-authored documents, while a language model generates novel text that necessarily embeds invisible framing decisions. Because conveying knowledge involves framing, a system that generates answers cannot serve as a neutral conduit to “all human knowledge.” Instead, these systems are becoming a new kind of political intermediary. Mechanistic evidence shows that partisan identity is encoded as a locatable geometric direction inside the Llama 3.1 8B model, and that alignment training masks rather than removes this structure. Building on that evidence, we present steering experiments that exploit a model’s training cutoff in 2024. This cutpoint auspiciously falls just before a dramatic realignment in American politics marked by the second Trump administration and the MAHA transformation of health politics, providing us with a natural experiment. We find that the model presents temporally contingent partisan alignments as knowledge, with no mechanism for distinguishing fact from opinion. This reality moves the information environment beyond the echo chamber toward an epistemic monoculture where language models, purporting to summarize “all human knowledge” are, in actuality, simply magnifying the cultural and partisan divides inherent in their training data.
[NLP-87] ranslation Indeterminacy and the Distributional Fallacy
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在语义理解与机器翻译中对分布假设（distributional hypothesis）的过度依赖问题。传统观点认为，语义意义源于语言上下文的分布模式，且跨语言分布对应关系是实现成功翻译的基础。然而，本文批判了这一观点中的因果倒置——语言分布并非语义的根源，而是意义建构实践所产生之结果。尽管如此，论文接受分布对应关系在翻译中的有效性，主张翻译的成功并不依赖于对意义或指称的直接访问，仅需掌握跨语言分布对应关系及其推理组织即可实现。其解决方案的关键在于提出一种生态-具身认知（ecological-enactivist）视角，强调指称与意义根植于主体与环境的互动，并通过以行动为基础的概念得以稳定化，而当前的LLMs缺乏这种具身化的世界参与性认知机制。
链接: https://arxiv.org/abs/2609.07717
作者: Michael Carl
机构: 未知
类目: Computation and Language (cs.CL)
备注: Accepted for publication in Philosophy of Translation
Abstract:Large language models (LLMs) are commonly associated with the distributional hypothesis, according to which (1) semantic meaning is grounded in distributional patterns of linguistic context, and (2) knowledge of cross-linguistic distributional correspondences allows for successful translation. This paper rejects the first claim as a causal inversion: linguistic distributions reflect patterns arising from meaning-making practices rather than constituting their source. At the same time, it accepts the second claim, arguing that translation -human or machine - can succeed without requiring access to meaning or reference. Knowledge of interlingual distributional correspondence and their inferential organization may be sufficient for translation. The paper develops an ecological-enactivist perspective, according to which reference and meaning are grounded in agent-environment interaction and stabilized through action-grounded concepts, forms of world-involving cognition that current LLMs do not possess.
[NLP-88] he Emerging AI Paper-Review Arms Race: Adversarial Co-Evolution in Scholarly Publishing
【速读】： 该论文旨在解决生成式与代理型人工智能（Generative and Agentic AI）在科学科研生产与评价环节中相互作用的系统性问题，尤其关注二者在实际应用中因动态耦合而产生的激励错配、行为变迁与制度响应。传统研究常将AI的科研产出能力与评价能力分离开来分析，但本文指出这种割裂忽视了二者之间日益紧密的反馈关系：当AI加速科研生产时，会加剧对评价体系的压力；而评价过程的自动化与可重复化又反过来催生对评估规则的可预测性利用，进而引发操纵行为和规避策略。论文通过整合230篇学术文献与机构资料，构建了一个包含六类关联动态的分析框架——生产规模化、评价自动化、评价操纵、防御机制与政策响应、规避行为与副作用、长期生态反馈，揭示出一个演进路径：低成本高效率的研究生产推动评价系统的扩展，使AI辅助评价更具可扩展性，但同时也暴露其规则可被利用的漏洞，促使机构采取技术防护与政策管控，这些应对措施又可能引发新的规避行为、错误转移与工作量再分配，并影响未来研究与评价系统所依赖的学术记录。研究证据最支持生产与评价的规模化、可复现的操纵行为及机构响应，而政策后的适应性调整与基于具体成果的长期反馈仍缺乏直接观测。因此，该研究的关键在于提出一种系统性视角，强调不应孤立看待AI的能力，而应关注科研主体与AI系统在时间维度上的持续互动与适应演化。
链接: https://arxiv.org/abs/2609.07713
作者: Chenguang Wang,Ming Li,Adebayo Braimah,Chenrui Fan,Tuo Wang,Weijie Guan,Ruiyi Zhang,Tianyi Zhou,Dawei Zhou
机构: Virginia Tech(弗吉尼亚理工学院); University of Maryland(马里兰大学); Stony Brook University(石溪大学); MBZUAI(MBZUAI)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Generative and agentic AI are reshaping both the production and evaluation of scientific research. These developments are often studied separately, as questions of how AI can produce research and how AI can review it. We argue that this separation misses an increasingly important feature of scholarly publishing: changes on one side alter the incentives, constraints, and behavior of the other. We synthesize 230 scholarly publications and institutional records using a taxonomy of six connected dynamics: production scaling, evaluation automation, evaluation manipulation, defense mechanisms and policy responses, evasion and side effects, and long-horizon ecosystem feedback. The literature shows an emerging progression in which cheaper and faster research production increases pressure on evaluation, AI-mediated evaluation becomes more scalable and repeatable, participants can exploit evaluator regularities, and institutions respond with technical safeguards and policy controls. These responses can in turn induce evasion, redistribute errors and workload, and shape the scholarly records reused by future research and evaluation systems. Evidence is strongest for production and evaluation at scale, reproducible manipulation, and institutional response, while post-policy adaptation and artifact-level long-horizon feedback remain less directly observed. This systems view shifts attention from isolated AI capabilities toward how scholarly actors and AI systems adapt to one another over time.
[NLP-89] DeepTable: Structural Attention Biases and Tree Path Encoding for Hierarchical Table Understanding EACL2027
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在表格理解任务中因将表格内容与表头线性化为令牌序列而导致的二维结构关系和多级行列标题层次依赖信息弱化的问题。现有参数高效微调方法虽引入了基本的行列信息，但未能显式建模由分层表头所引发的丰富结构依赖。为此，论文提出DeepTable，一种面向表格理解的结构感知方法，其核心创新在于两个互补组件：结构注意力偏置（Structural Attention Bias, SAB）通过在注意力得分中引入可学习偏置，显式表达表中任意两个令牌是否共享同一行或列；树路径编码（Tree Path Encoding, TPE）则通过编码每个表单元格对应行列标题的祖先路径，保留其在多层次表格结构中的相对位置信息。该方法与TableLoRA（He et al., 2025）结合，实现结构信息在参数高效适配中的注入。在三个主流表格问答基准测试中，DeepTable在三种LLM骨干网络上均显著优于基线，分别在HiTab上平均提升7.42分、WikiTQ上提升3.23分、FeTaQA上提升2.01 BLEU点，验证了其在不同模型架构下对结构偏置的有效性。
链接: https://arxiv.org/abs/2609.07707
作者: Jyun-Ying Yen,Cheng-Kuan Lin,Yu-Chee Tseng
机构: National Yang Ming Chiao Tung University (国立阳明交通大学), Taiwan
类目: Computation and Language (cs.CL)
备注: 14 pages, 2 figures. Submitted to EACL 2027
Abstract:Large language models (LLMs) have demonstrated strong performance in table understanding. However, they typically process table content and headers as linearized token sequences. This representation weakens the two-dimensional and hierarchical structural relationships encoded by multi-level row and column headers. Existing parameter-efficient fine-tuning methods incorporate basic row and column information but do not explicitly capture the rich structural dependencies induced by hierarchical table headers. We propose DeepTable, a structure-aware approach for table understanding with LLMs. DeepTable comprises two complementary components. Structural Attention Bias (SAB) introduces learnable biases into the attention logits to explicitly represent whether pairs of table tokens share the same row or column. Tree Path Encoding (TPE) represents each table token using the ancestor paths of its row and column headers, preserving its position within the multi-level table structure. We integrate DeepTable with TableLoRA (He et al., 2025) to inject structural information into parameter-efficient adaptation. Across three LLM backbones, DeepTable consistently improves the corresponding TableLoRA baselines on three table question answering benchmarks, achieving average gains of 7.42 points on HiTab, 3.23 points on WikiTQ, and 2.01 BLEU points on FeTaQA. These results demonstrate the effectiveness of the proposed structural biases across different LLM backbones.
[NLP-90] Fine PT-PT Web: A High-Quality 41 Billion Tokens Data Collection of the European Portuguese Web EMNLP2026
【速读】： 该论文旨在解决欧洲葡萄牙语（PT-PT）语料库构建过程中因方言重叠（主要与巴西葡萄牙语PT-BR混淆）及数据处理规模庞大所导致的效率瓶颈问题。其核心解决方案在于提出一种新颖的后抓取阶段过滤模块，该模块在正式筛选前即执行版式内容（boilerplate）去除与行级重复项剔除，从而在早期干预中挽救了大量被传统启发式过滤规则误判为无效的文本。这一策略使最终文档产出率提升19.04%。结合严格的语言识别、加权模糊去重以及神经网络质量分类，该流水线构建了一个可扩展、高质量且具有代表性的生产级PT-PT语料库，适用于大语言模型（LLM）的预训练需求。
链接: https://arxiv.org/abs/2609.07699
作者: Gonçalo Vinagre,Rui Pedro Guerra,Pedro Gomes,Miguel Moura Ramos,Duarte Miguel Alves,Afonso Simplício,Diogo Tavares,David Semedo,Daniel Gomes,João Magalhães
机构: NOVA School of Science and Technology (NOVA 学院科学与技术); NOVA LINCS (NOVA 线性研究中心); Fundação para a Ciência e Tecnologia (科学技术基金会); Instituto Superior Técnico, Universidade de Lisboa (里斯本大学高等技术学院); Instituto de Telecomunicações (电信研究所)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 16 pages, 9 figures, EMNLP 2026 Main
Abstract:Curating Web corpora for regional language variants like European Portuguese (PT-PT) is heavily bottlenecked by dialectal overlap (mainly with PT-BR) and data processing scale. This paper presents an efficient pipeline to curate a production-ready PT-PT corpus from the Portuguese Web, spanning 411 TB of raw data from this http URL. We introduce a novel post-scraping block that removes boilerplate and line duplicates prior to filtering. This early-stage intervention increases final document yield by 19.04% by rescuing valid text that standard heuristic filters prematurely discard. Integrated with rigorous language identification, weighted fuzzy deduplication, and neural quality classification, our pipeline offers a scalable framework and a clean, representative corpus optimized for LLM pre-training.
[NLP-91] Perspectives on Cross-Lingual Consistency in LLM s for Medical Questions EMNLP2026
【速读】： 该论文旨在解决多语言大语言模型（Multilingual Large Language Models, LLMs）在回答医学问题时应遵循跨语言一致性还是文化适应性这一核心争议。现有研究普遍假设医学正确答案应保持跨语言一致，将语言差异视为模型错误；然而，文化适应性研究则主张，在不同文化语境下，适宜的医学回应可能存在合理差异。论文通过对比这两种视角，识别出三大关键缺口：缺乏对医疗从业者等关键利益相关者观点的系统考察、缺乏实证证据以判断何种策略更优，以及尚无基准能够区分普遍正确的医学知识与具有文化特异性的回答。为弥补首项缺口，研究对德国、西班牙和美国三地共356名医疗、自然语言处理（Natural Language Processing, NLP）及人类学专业人士进行了调查，结果显示人类学家普遍支持文化适应，而医疗与NLP领域受访者意见分歧显著，尤以美欧医疗专业人员间差异突出。进一步实验表明，当向LLMs注入职业与国家身份角色提示时，其无法有效复现真实人群中的这种多样性偏好，反而高估了跨语言一致性在学术与医疗群体中的接受度。最终结论指出，目前尚无充分依据判定一致性或文化适应更具优势，亟需基于用户实际需求的实证研究来明确最优路径。
链接: https://arxiv.org/abs/2609.07687
作者: Minh Duc Bui,Mario Sanz-Guerrero,Abteen Ebrahimi,Sagi Shaier,Peter Herbert Kann,Manuel Mager,Katharina von der Wense
机构: Johannes Gutenberg University Mainz, Germany(美因茨约翰内斯古腾堡大学，德国); University of Colorado Boulder, USA(科罗拉多大学博尔德分校，美国); University of Marburg, Germany(马尔堡大学，德国); Universidad Iberoamericana, Mexico(拉丁美洲大学，墨西哥)
类目: Computation and Language (cs.CL)
备注: Accepted to EMNLP 2026 Main
Abstract:Should multilingual LLMs answer medical questions consistently across input languages, or adapt responses to cultural cues? Existing multilingual medical benchmarks usually assume that medically correct answers should remain consistent across languages and treat cross-lingual variation as model error. In contrast, cultural adaptation research argues that appropriate medical answers may legitimately differ across contexts. We review the multilingual medical NLP literature through these two perspectives, we identify three gaps: limited stakeholder perspectives (e.g., of medical professionals), a lack of empirical evidence on which approach better serves users, and no benchmarks capable of distinguishing universally correct from culture-specific cases. To address the first gap, we survey 356 participants across three stakeholder groups (medical, NLP, and anthropology professionals) in three countries (Germany, Spain, and the United States). Anthropologists consistently favor adaptation, while medical and NLP respondents remain divided, with notable divergence between U.S. and European medical professionals. LLMs prompted with profession and country personas fail to reproduce this variation, overestimating cross-lingual consistency preference among NLP and medical personas. We conclude that neither consistency nor adaptation can currently be considered clearly preferable, highlighting the need for empirical evidence on which approach better serves users across cultural contexts.
[NLP-92] On the Recall Scaling Laws in Mamba: A Theoretical and Mechanistic Study via Hashing
【速读】： 该论文旨在解决生成式模型（如Mamba）在上下文记忆任务中实现关联回忆（Associative Recall, AR）的内在机制问题，特别是从机械可解释性（mechanistic interpretability）角度揭示Mamba如何实现高效的记忆检索。其核心挑战在于理解模型在不显式存储记忆的情况下，如何通过隐式学习实现高精度的关联召回。解决方案的关键在于发现：Mamba通过隐式学习线性哈希函数（linear hash functions）来实现回忆，其底层电路表现为一种能够保留语义相似性的低维映射结构。基于此发现，并结合相似性保持哈希理论中的约翰逊-林登斯特拉（Johnson-Lindenstrauss）引理，作者提出了一套名为“回忆缩放律”（Recall Scaling Laws）的理论框架，能够准确预测在给定词汇量和上下文事实数量下，实现完美回忆所需的嵌入维度、状态维度，以及在特定模型尺寸下的回忆成功率；同时该框架还可扩展至多层结构与多头状态空间模型（SSM）模式分析。实验结果验证了该理论框架的准确性与预测能力，为理解关联回忆能力随词汇规模、状态空间大小及模型架构的演化规律提供了可量化、可解释的理论基础。
链接: https://arxiv.org/abs/2609.07681
作者: Yuval Koren,Assaf Ben-Kish,Raja Giryes,Lior Wolf,Itamar Zimerman
机构: Tel Aviv University (特拉维夫大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 53 pages, 20 figures. Code and experiments available at: this https URL
Abstract:Associative Recall (AR) is the cognitive ability to learn and retrieve links between items in memory. In NLP, AR is used as a benchmark for evaluating the in-context memory capacity of architectures such as Mamba, and has been found to strongly correlate with language modeling performance. This paper explores AR from the perspective of mechanistic interpretability, aiming to reverse-engineer the exact internal algorithm used by Mamba to perform recall. Our key insight is that Mamba performs recall by implicitly learning linear hash functions, and we identify the low-level circuit that enables this behavior. Building on these findings and inspired by theoretical tools in similarity-preserving hashing, such as the Johnson-Lindenstrauss lemma, we develop a theoretical framework for analyzing AR, which we term Recall Scaling Laws. Given the vocabulary size and the number of facts in context, this framework allows us to (1) predict the embedding and state dimensions required for Mamba to achieve perfect recall, (2) predict recall success probability given the model dimensions, and (3) analyze multi-layer models and multi-head SSM patterns. Empirical results show that our theoretical findings are accurate and predictive, offering insights into how AR capacity scales with vocabulary, state, embedding size, and architecture.
[NLP-93] Syntactic Patterns and Stylistic Functions in Narrative Prose: A Rule-Based and Machine-Learning Approach
【速读】： 该论文旨在解决叙事散文中句法结构与风格功能之间关联的定量建模问题，核心挑战在于如何将复杂的语言形式特征转化为可计算、可验证的风格标签。其解决方案的关键在于构建一个透明且可复现的规则驱动流程：基于3,300个句子的依存句法标注语料库，通过分析词干（lemma）、通用词性标注（POS tag）及依存关系，对句子进行五类风格标签（描述性、内省性、因果性、意识形态性和中性）的标注；同时，将每句的句法结构编码为线性化的三元组序列（词干-词性-依存关系），作为机器学习分类器的输入特征。实验采用标准分类模型，在10折交叉验证下取得0.948的宏平均F1值，证明了该方法在捕捉句法—风格映射关系上的有效性。整个工作流以Python实现，依托开源工具，强调可复现性与可扩展性，为探究语法结构如何参与叙事意义建构提供了一种系统化、数据驱动的方法框架。
链接: https://arxiv.org/abs/2609.07651
作者: Stefana Janicijevic
机构: 未知
类目: Computation and Language (cs.CL)
备注: Submitted as a Discussion Paper to JOHD (Journal of Open Humanities Data), 2026
Abstract:This paper presents a small-scale quantitative experiment that links syntactic structure to stylistic functions in narrative prose. Starting from a dependency-parsed corpus of 3,300 sentences, we derive sentence-level stylistic labels across five categories — descriptive, introspective, causal, ideological, and neutral — using a transparent rule-based procedure that inspects lemmas, universal part-of-speech tags, and syntactic relations. For each sentence we construct a compact representation of its syntactic profile as a sequence of linearised triples combining lemma, POS tag, and dependency relation. These patterns serve as input to standard machine-learning classifiers trained to predict sentence-level style. The best-performing model achieves a macro-F1 of 0.948 under 10-fold cross-validation. The experiment is implemented entirely in Python using open-source tools. Our goal is not to propose a fully fledged stylistic theory, but to offer a reproducible and extensible workflow for exploring how grammatical structure contributes to narrative interpretation.
[NLP-94] he Art of Hierarchical Competing Patterns: Gaussian Process Optimization of Hyphenation EMNLP2026
【速读】： 该论文旨在解决文本排版系统、文字处理器及网页渲染引擎中分词模式（hyphenation patterns）生成依赖人工调参的效率低与可复现性差的问题。当前生成分词模式所使用的patgen程序需依赖专家经验手动调整参数配置，缺乏系统化优化手段。为此，作者将patgen参数配置选择建模为黑箱超参数优化问题，并采用高斯过程贝叶斯优化（Gaussian-process Bayesian optimization, GPBO）方法进行自动化搜索。其解决方案的关键在于构建一个融合精确率导向的F₁/7分数与显式压缩性-准确性权衡的目标函数，通过引入归一化的前缀树（trie）大小惩罚项，实现对分词模式紧凑性与准确性的联合优化。实验在覆盖14种语言和多种书写系统的17个分词数据集上验证了该方法的有效性：相较于两个从相同训练/测试划分重新生成的强基准手调配置，基于GP优化的配置在16个数据集上提升了F₁/7得分，且所有数据集均显著减小了前缀树规模（中位数优化后/基线比值为0.407）。统计检验（数据集级符号检验，p = 1.37e−4）和预算匹配对比分析进一步表明，系统性搜索不仅具有竞争力，通常还能超越最优的手调配置。结果表明，基于模型的优化方法能够提升分词模式生成的可复现性，降低对专家试错的依赖，同时保持对准确率与紧凑性之间权衡的透明控制。
链接: https://arxiv.org/abs/2609.07638
作者: Ondřej Sojka(Faculty of Informatics, Masaryk University),Petr Sojka(Faculty of Informatics, Masaryk University)
机构: 未知
类目: Computation and Language (cs.CL); Digital Libraries (cs.DL); Machine Learning (cs.LG)
备注: 17 pages, 10 figures, 9 tables. Accepted at EMNLP 2026 (Findings). Code and data: this https URL
Abstract:Hyphenation patterns remain a compact and widely deployed solution for word breaking in typesetting systems, text processors, and web rendering engines, but their generation still depends on manually tuned patgen program parameter profiles. We formulate patgen profile selection as a black-box hyperparameter optimization problem and evaluate Gaussian-process Bayesian optimization for this task. The search objective combines a precision-oriented F_1/7-score with an explicit trie size-accuracy trade-off using a normalized trie-size penalty. We evaluate the method on 17 hyphenated word-list datasets covering 14 languages and multiple scripts. Against two strong hand-tuned profiles regenerated from the same 8/10 training split and evaluated on the same 1/10 held-out test split, the GP-optimized profiles improve F_1/7 on 16 of 17 datasets and reduce trie size on all 17. The median optimized/baseline trie ratio is 0.407. A dataset-level sign test gives p = 1.37e-4; a separate budget-matched comparison on five representative datasets shows that systematic search is competitive and usually improves over the best hand-tuned profile under the fixed comparison objective. The results show that model-based optimization can make pattern generation more reproducible and less dependent on expert trial-and-error while keeping the accuracy-compactness trade-off explicit. Comments: 17 pages, 10 figures, 9 tables. Accepted at EMNLP 2026 (Findings). Code and data: this https URL Subjects: Computation and Language (cs.CL); Digital Libraries (cs.DL); Machine Learning (cs.LG) ACMclasses: I.2.7; I.7.2; G.1.6 Cite as: arXiv:2609.07638 [cs.CL] (or arXiv:2609.07638v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.07638 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-95] Agent IdeaBench: Benchmarking Scientific Ideation in the Agent Era
【速读】： 该论文旨在解决现有科学构想能力评估方法与现代自主型人工智能科学家实际工作流程脱节的问题。当前评估多基于静态、预选文献集生成假设，无法真实反映模型在动态检索与推理过程中的表现，且随着模型能力提升，评估区分度下降。为此，作者提出AgentIdeaBench——一个跨学科基准，通过“静态观察”与“主动探索”两种匹配设置评估科学构想能力。其关键创新在于引入基于文献验证的多维度评分框架，由评审者依据检索到的已有研究（prior art）评判生成假设的原创性，并在主动探索场景中揭示了显著的能力提升空间（headroom），该空间分布不均，且性能提升速度约为静态设置的两倍，呈现能力门槛效应，即更强模型获益更明显。主动探索带来的增益主要体现于假设的可行性、清晰性与具体性提升，而原创性评分保持不变，表明其优势源于更优的上下文对齐（grounding）。此外，研究进一步提出“科学世界建模”（Scientific World Modeling）机制，即在生成阶段通过结构化思想实验迭代优化假设，该机制对中等能力模型尤为有效，而在前沿模型中效果减弱，暗示其已内化此类推理模式。AgentIdeaBench为面向智能体时代的科学构想研究提供了适配的评估基础。
链接: https://arxiv.org/abs/2609.07611
作者: Yunxiang Mo,Tianshi Zheng,Yisen Gao,Rui Wang,Newt Nguyen Kim Hue Nam,Kelvin Kiu Wai Tam,Jiaxin Bai,Yangqiu Song,Ginny Wong,Simon See
机构: HKUST(香港科技大学); NVIDIA AI Technology Center (NVAITC), NVIDIA(英伟达人工智能技术中心，英伟达)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 25 pages, 9 figures, 8 tables. Code and data: this https URL
Abstract:Scientific ideation is the capacity to formulate novel and testable hypotheses from scientific evidence, and autonomous AI scientists depend on it. Existing evaluations largely assess it by asking models to generate ideas from a static, curated set of reference papers. That passive setup departs from the retrieval-and-reasoning workflow of modern AI scientists, and it becomes less discriminative as models improve. We introduce AgentIdeaBench, a multidisciplinary benchmark that evaluates scientific ideation under two matched settings, static observation and active exploration. We report matched Static-Active evaluations for 33 LLMs across 40 densely scored subfields spanning five disciplines, using a multidimensional, literature-verified scoring framework whose critics assess originality against retrieved prior art. Active exploration reveals considerably more capability headroom, and that headroom is unevenly distributed across models. Performance scales about twice as fast as under static observation, and the exploration gain is capability-gated, favoring the strongest models over the weakest. The gain reflects better grounding, improving feasibility, clarity, and specificity while leaving measured originality unchanged under our critics. We further explore Scientific World Modeling, a generation-time loop that refines a draft hypothesis through structured thought experiments. It benefits mid-capability models, and its impact diminishes among frontier models that appear to have internalized such reasoning patterns already. AgentIdeaBench gives future work on scientific ideation a measurement basis suited to the agent era.
[NLP-96] ObGynLongBench: Revealing the Evidence-to-EHR Gap in Longitudinal EHR Decision-Making
【速读】： 该论文旨在解决当前大型语言模型（LLM）在个性化医疗助手应用中，难以从真实纵向电子健康记录（EHR）中可靠做出临床决策的问题。现有医学评估基准多依赖静态问答与预选证据，无法检验模型在复杂、动态的临床场景下的实际表现。为此，研究提出ObGynLongBench——一个基于规则的长上下文EHR基准，涵盖976例真实妊娠患者的1,500个临床决策点，每例均关联患者、孕周时间点及预决策信息边界，支持仅证据、就诊级和病史级EHR三种评估范式。实验表明，模型在提供直接证据时表现良好，但需从同日记录或完整病史中提取证据时准确率显著下降，揭示“证据利用”是核心瓶颈：随着上下文长度增加与证据需求复杂度提升，模型性能持续降低，且同一患者历史中的早期决策失败可预测后续失败。此外，主动搜索代理（active-search agents）在各类EHR访问策略中表现最优，凸显个性化医疗助手在实现患者特异性证据高效利用方面仍面临重大挑战。
链接: https://arxiv.org/abs/2609.07601
作者: Jun Xiang,Zhijie Bao,Rong Hu,Kaizhou Qin,Wei Chen,Zhongyu Wei
机构: Fudan University (复旦大学); Shanghai Innovation Institute (上海创新研究院); Huazhong University of Science and Technology (华中科技大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 25 pages, including references and appendices
Abstract:The application of large language models (LLMs) to personalized medical assistants has garnered growing interest. However, existing medical benchmarks largely rely on static question answering with pre-selected evidence, leaving unclear whether LLMs can make reliable clinical decisions from real longitudinal electronic health records (EHRs). To bridge this gap, we introduce ObGynLongBench, a rule-grounded long-context EHR benchmark for obstetric and gynecologic decision-making, comprising 1,500 clinical decision-point cases from 976 real pregnancy EHR histories and traceable rules. Each case is anchored to a patient, a pregnancy-timeline point, and a pre-decision information boundary, enabling Evidence-only, Visit-level EHR, and History-level EHR evaluation. Evaluating 17 LLMs reveals a substantial Evidence-to-EHR Gap: models perform well when evidence is directly provided, but accuracy drops when evidence must be extracted from same-day records or full pre-decision EHR histories. Further analyses identify evidence utilization as a key bottleneck: performance decreases with longer EHR contexts and more complex evidence requirements, and earlier failures often predict later failures within the same patient history. Finally, active-search agents perform best among EHR access strategies, highlighting patient-specific evidence utilization as a central challenge for reliable personalized medical assistants. Resources are available at this https URL.
[NLP-97] Mapping the Emerging Social Science of Large Language Models
【速读】： 该论文旨在解决当前关于大语言模型（Large Language Models, LLMs）社会影响的社科研究领域碎片化问题，系统性地构建一个可复现的分类框架以整合分散的研究成果。其核心解决方案是基于多模态分析方法（包括句向量嵌入、K-means聚类、组内潜在狄利克雷分配（LDA）、作者与LLM分类标签以及结构主题建模），从198篇精审文献和47,719篇大规模文献库中识别出三个主要研究领域：大语言模型作为社会心智（LLM as Social Minds），关注可社会解释的模型行为；大语言模型社会（LLM Societies），研究基于模型代理之间的交互动态；以及大语言模型与人类互动（LLM-Human Interactions），探讨人类对模型的认知、使用及其受模型影响的机制。该分类体系在子类别层面涵盖推理、人格与偏见、行为博弈、集体智能、仿真、信任、工作、创造力与教育等13个维度，在小样本和大规模语料上均表现出高度稳定性与一致性，且与人工分类高度吻合（准确率77.78%），验证了其科学性和可重复性。该框架揭示了学术影响力分布的差异——尽管人类互动研究占主题体量的78.02%，但顶级会议中“社会心智”与“模型社会”类研究更受青睐，表明理论探索与应用研究之间存在学术生态张力。最终形成的分类体系为理解模型行为、代理交互与制度背景如何共同塑造大语言模型的社会后果提供了系统性、可扩展的分析框架。
链接: https://arxiv.org/abs/2609.07598
作者: Yi Yang,Xiao Jia,Zeyun Dong,Chenzhang Wang,Zhanzhan Zhao
机构: The Chinese University of Hong Kong, Shenzhen(中国科学技术大学深圳校区); Xidian University (西安电子科技大学); The University of Edinburgh (爱丁堡大学)
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) increasingly shape communication, learning, work, creativity, and decision-making, yet social-science research on these developments remains fragmented. We map this emerging field using a curated corpus of 198 papers reviewed in full and a field-scale corpus of 47,719 published papers from five bibliographic databases. Combining sentence embeddings, K-means clustering, within-cluster Latent Dirichlet Allocation (LDA), author and LLM classifications, and structural topic modeling, we identify three domains: LLM as Social Minds, examining socially interpretable model behavior; LLM Societies, examining collective dynamics among interacting model-based agents; and LLM-Human Interactions, examining how people perceive, use, and are affected by LLMs. These domains contain 13 subcategories spanning reasoning, personality and bias, behavioral games, collective intelligence, simulation, trust, work, creativity, and education. In the curated corpus, the three-domain solution is highly stable under resampling (adjusted Rand index = 0.952), and K-means assignments agree with author full-text classifications for 77.78% of papers. At field scale, 13 of 15 topics map onto the taxonomy, while K-means and structural-topic-model domains agree for 73.83% of overlapping papers. LLM-Human Interactions accounts for 78.02% of domain-mapped topic mass, but venue analysis reveals a contrasting pattern: Social Minds and LLM Societies together account for 66.37% of highly cited papers in leading conference venues, whereas LLM-Human Interactions accounts for 76.81% in the corresponding journal subset. The resulting taxonomy provides a reproducible framework for understanding how model behavior, agent interaction, and institutional context jointly shape the social consequences of LLMs.
[NLP-98] I Dont Miss You but I Do: Self-Explanation Faithfulness of Modality Missingness in Vision-Language Models
【速读】： 该论文旨在解决生成式视觉-语言模型（Vision-Language Models, VLMs）在输入模态缺失情境下，其自我解释（self-explanations）是否能够真实反映模态信息对预测结果的影响这一关键问题。现有研究对模型在部分模态不可用时如何依赖或补全信息缺乏深入评估，尤其忽视了模型对其自身推理过程的可解释性是否可信。为此，作者提出一种干预性评估协议（interventional protocol），通过要求模型在模态缺失情况下声明：单一模态所能支持的判断、恢复缺失模态是否可能改变其输出、以及当前可用证据是否充分；随后执行对应模态干预并对比模型的预测行为与自我陈述的一致性。实验评估了来自两个模型家族的八种开源VLM，在四种任务设置（包括互补与同构的图文任务、多视角驾驶场景）中发现，模型普遍存在对可用模态证据充分性的过度高估，其预测的“变化率”仅在8.8%的中位数水平，而实际执行干预后的真实变化率达72.1%，且62/64的模型-任务-条件组合均存在显著低估。此外，模型极少做出“证据不足”的判断，但一旦作出此类声明，则具有高度准确性——恢复模态后约78%-100%的案例中预测发生改变。回溯性自解释分析也揭示相同偏差：在互补数据上，模型高估单模态的独立解释能力；在同构数据上，高估单一表示形式的充足性。这些结果表明，当前VLMs在描述其预测对可用与缺失模态信息的依赖关系方面存在系统性误判，从而凸显以可执行干预作为行为基准（behavioral ground truth）对于评估多模态自我解释可靠性的必要性。
链接: https://arxiv.org/abs/2609.07596
作者: Aydin Javadov,Daniel Schoess,Florian von Wangenheim
机构: ETH Zurich(苏黎世联邦理工学院)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-language models are increasingly used in settings where some input modalities may be unavailable, yet we know little about whether they can faithfully explain how such missing information affects their own predictions. We introduce an interventional protocol for evaluating self-explanations of modality dynamics: models state what each modality alone would support, whether restoring a missing modality would change their answer, and whether the available evidence is sufficient; we then execute the corresponding modality intervention and compare these claims with the model’s realized behavior. We evaluate eight open-weight VLMs from two model families across four tasks spanning complementary and isomorphic text-image settings and a multi-view driving setting. We find a systematic tendency to overstate the sufficiency of available modality evidence. Models substantially underestimate the effect of restoring missing modalities: task-level median predicted change rates are at most 8.8%, while the corresponding executed change rates reach 72.1%, with underprediction in 62 of 64 model-task-condition settings. Insufficiency claims are rare, but precise when produced: restoring the modality changes the answer in a median of 78-100% of flagged cases. Retrospective self-explanations show the same tendency: on complementary data, models over-credit single-modality sufficiency; on isomorphic data, they over-credit single representation sufficiency relative to their executed behavior. Together, these results show that VLMs systematically mischaracterize how their predictions depend on available and missing modality evidence, motivating executable interventions as a behavioral ground truth for evaluating multimodal self-explanations.
[NLP-99] Validating DBpedia Triple Sets for Natural Language Generation
【速读】： 该论文旨在解决知识图谱中实体三元组（triple）质量参差不齐的问题，尤其关注从DBpedia等开放知识源中提取的三元组在自然语言生成（Natural Language Generation, NLG）任务中的可用性。其核心挑战在于如何在保留高置信度正确三元组的同时，有效过滤掉存在语义歧义或错误的可疑三元组。解决方案的关键在于提出一种基于验证规则（validation rules）的三元组筛选方法，通过引入领域特定的校验逻辑对三元组进行质量评估，从而实现高精度的三元组选择。实验结果表明，该方法可在人工标注数据上达到98%的精确率；进一步优化少数属性（Property）定义后，召回率可提升40%且不损害精确率，显著提升了可用于自然语言生成的高质量三元组集合的构建效率与可靠性。
链接: https://arxiv.org/abs/2609.07589
作者: Mark Andrade,Simon Mille,Anya Belz,Brian Davis
机构: 未知
类目: Computation and Language (cs.CL)
备注: 20 pages, 1 figure 18 tables, this https URL
Abstract:We present a study of the quality of individual DBpedia triples from the perspective of Natural Language Generation, and propose and evaluate an approach for collecting entity-specific triple sets that filters out questionable triples while minimizing the loss of correct ones. We show in an evaluation against manually annotated data that with validation rules, it is possible to reach 98% precision in triple selection, and with improvements to a few Property definitions, it is possible to improve recall by 40% without harming precision.
[NLP-100] Were Cooked! - Probing LLM Political Alignment Via Conflict-Framed Recipe Translation
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在翻译任务中隐含政治立场的潜在问题，尤其关注在看似中立的任务背景下，单一政治敏感性表述（如“侵略者”“敌人”“邻国”或“殖民者”）是否足以触发模型的隐性政治倾向。其核心解决方案在于设计了一个完全交叉的因子实验，通过让八种源自西方、中国及欧洲的模型将具有文化归属感的食谱翻译至未明确指定的目标语言，系统考察不同框架条件下的翻译行为。研究发现，模型并非简单拒绝或要求澄清，而是主动化解歧义，且其语言处理与推理模式在模型族系间呈现出显著聚类特征：西方模型倾向于模糊推诿，中国模型则静默化解冲突，而Mistral Large展现出高服从性与基于冲突情境的推理能力。此外，模型对框架术语的敏感性高度一致，微小的语义差异即可显著影响行为表现。这一结果揭示了在涉及地缘政治敏感议题的翻译场景中，大语言模型可能在无用户察觉的情况下做出隐性政治判断，因而亟需对其部署进行审慎评估与监管。
链接: https://arxiv.org/abs/2609.07568
作者: Svetlana Gorovaia,Angelica Henestrosa,Ivan P. Yamshchikov
机构: CAIRO, Technical University of Applied Sciences Würzburg-Schweinfurt(蔡罗，维尔茨堡-施韦因富特应用技术大学); Würzburg, Germany(维尔茨堡，德国)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) are increasingly deployed for translation tasks, yet their implicit political positioning in such contexts remains understudied. We ask whether a single politically charged framing term, such as aggressor, enemy, neighbour, or coloniser is sufficient to trigger implicit political alignment in an otherwise apolitical task. We present a fully crossed factorial study in which eight models spanning Western, Chinese, and European origins are prompted to translate culturally attributed recipes into a target language left deliberately unspecified. Across 17 languages, four framing conditions, eight models, and 15,680 responses, we find that models do not simply decline or ask for clarification but resolve the ambiguity. Language resolution and reasoning behavior cluster meaningfully along model families: Western models hedge and deflect with vague justifications, Chinese models resolve conflicts silently, and Mistral Large emerges as a distinct profile combining high compliance with conflict-grounded reasoning. Sensitivity to framing terms is consistent across models: even subtle framing variation is sufficient to modulate behavior. Our findings urge caution when deploying LLMs for translation in conflict-adjacent contexts, where implicit political judgments may be made without any signal to the user.
[NLP-101] Qwen -Audio-3.0-ASR Technical Report
【速读】： 该论文旨在解决自动语音识别（ASR）在真实生产环境中面临的多重挑战，包括多样化的区域方言、动态变化的实体与热词、长距离上下文依赖以及非流畅的口语化表达等问题。现有学术基准性能与实际应用需求之间仍存在显著鸿沟。其解决方案的关键在于提出一种基于混合专家模型（Mixture-of-Experts, MoE）架构的大型语言模型（LLM）驱动型ASR系统——Qwen-Audio-3.0-ASR，该系统通过统一的指令遵循框架实现多项生产级功能：支持30种语言及16种中国方言的跨语言/方言识别，具备行业领域实体识别、分层热词自定义、单次通过的语音转写润色能力，以及长音频上下文建模能力，并进一步开发了适用于低延迟场景的流式版本（Qwen-Audio-3.0-ASR-Streaming）。实验结果表明，该系统在中英文、多语言及真实工业数据集上均达到或超越当前领先商业系统（如GPT-4o Transcribe和Gemini 3.1 Pro）的性能表现。
链接: https://arxiv.org/abs/2609.07549
作者: Chuanmeng Bian,Daren Chen,Peixin Chen,Zhigao Chen,Zhiyun Fan,Zhifu Gao,Bo Gong,Qing Gu,Jiajun He,Yawei Hu,Yunjie Ji,Jingbei Li,Xiangang Li,Xu Li,Zengxi Li,Zheng Li,Chengdong Liang,Baiji Liu,Ying Liu,Bin Ma,Yiping Peng,Yuezhang Peng,Zhendong Peng,Yu Pu,Yang Shi,Xin Shu,Jian Tang,Biao Tian,Peiyao Wang,Tianzi Wang,Wen Wang,Wupeng Wang,Cheng Wen,Yuzhong Wu,Zijian Xia,Yunchong Xiao,Nan Yang,Jianwei Yu,Jixing Yu,Binbin Zhang,Lei Zhang,Sitong Zhao,Guangdong Zhou,Yuan Zhou,Jianheng Zhuo
机构: Alibaba Token Foundry
类目: Computation and Language (cs.CL)
备注: 21 pages, 16 figures
Abstract:In recent years, automatic speech recognition (ASR) has witnessed transformative advancements driven by three complementary paradigms: data scaling, model scaling, and deep integration with large language models (LLMs). However, bridging the gap between academic benchmark performance and real-world production utility remains a persistent challenge, particularly in handling diverse regional dialects, dynamic entities and hotwords, long-range contextual information, and disfluent spontaneous speech. In this report, we present Qwen-Audio-3.0-ASR, a Mixture-of-Experts (MoE) LLM-based ASR system designed to address these production demands through a unified, instruction-following framework. The model is built upon the Qwen backbone, and is trained on tens of millions of hours of large-scale speech data. Qwen-Audio-3.0-ASR supports transcription across 30 languages and 16 Chinese dialectal varieties spanning eight major dialect regions. Beyond multilingual and dialectal recognition, the model provides production-oriented capabilities including industry-domain entity recognition, hierarchical hotword customization, native single-pass transcription polishing, and long-audio contextual modeling. We further develop a dedicated streaming variant, Qwen-Audio-3.0-ASR-Streaming, for latency-sensitive applications. Extensive evaluations on Chinese, English, multilingual, and real-world industrial test sets demonstrate state-of-the-art or highly competitive recognition performance across a broad range of evaluation conditions, with strong performance relative to leading commercial and proprietary systems including GPT-4o Transcribe and Gemini 3.1 Pro.
[NLP-102] Where Should Language Sit in a Multimodal Model? Lessons from What Language Does to Human Perception and Cognition
【速读】： 该论文旨在解决当前生成式人工智能模型中语言作为内部表征所引发的系统性问题，即语言是否应作为模型的核心内部表示而非仅限于输入与输出边界。其核心关切在于：当语言被用作模型的内部状态时，可能导致认知偏差、信息冗余及可解释性下降。解决方案的关键在于将语言重新定位为一种基于共享代码本（shared codebook）的压缩机制——词是索引，语义内容由接收者构建，而代码本由群体共同维护。研究通过六种视觉-语言模型和两种机器人策略的线索冲突实验表明，模型在多源信息不一致时仍倾向于依赖文本线索，其权重分配仅为理想观察者斜率的11%至82%，且大量预测直接复制文本内容；部分策略甚至完全丢弃冗余但有用的视觉线索，而非合理降权，反映出对语言路径的过度依赖。此外，训练中已通过语言路径充分拟合的数据，导致视觉线索未被有效学习。研究指出，尽管语言模型是人类语言网络的最佳模拟，但其在语言社区中的嵌入正不断压缩概念多样性，强化语言主导性。因此，论文主张语言应保留在模型边界与共享代码本之中，如大脑中的角色，而非作为内部表示，否则将付出审计困难的代价。
链接: https://arxiv.org/abs/2609.07474
作者: Peng Xie
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Language models compute over tokens: language is their input, their output, and increasingly their internal representation. Whether language should keep all of these positions depends on what language does to the system that uses it. The one system with a century of data on that question is the human. We review what language does to human perception, the brain, and thought, and read the same evidence against multimodal models and language models. Throughout, we treat language as a compressor that runs on a shared codebook: a word is an index, the content is in the receiver, and a community maintains the codebook. In humans the compression is measurable, learning the codebook reorganizes the senses, and thought survives the loss of language. We then measure the rule that models apply when two cues disagree, with cue-conflict experiments on six vision-language models and two robot policies. Surviving cues are weighted in the order their reliabilities prescribe, at 11 to 82% of the ideal observer’s slope, and many answers copy the text. One policy family drops a cue that adds no information beyond the others rather than down-weighting it, another keeps it at a weight that fails when the cues conflict, and a visual cue that identifies the task in every training frame is never learned, because the language pathway already fits the data. Language models are the best current models of the human language network, and they have entered the human speech community, shifting word frequencies while alignment narrows their conceptual diversity. We close with seven implications for token-based systems. Language belongs at a model’s boundary and in the shared codebook, as in the brain, not as its internal representation; the price of leaving the codebook inside is auditability.
[NLP-103] MEMO: Multimodal Evidence Memory Organization for Long-Horizon LLM Agents
【速读】： 该论文旨在解决长时运行的大语言模型（LLM）智能体在有限上下文窗口容量下，如何高效组织和管理外部记忆以支持复杂推理任务的问题。核心挑战在于：在持续积累交互轨迹的同时，必须在预算约束下选择关键证据，并以最优模态（如文本或视觉）进行组织，以兼顾信息完整性与上下文利用率。现有方法多依赖单一模态的读出机制——文本虽保真度高但线性表示导致不同重要性的内容竞争相同上下文资源；而视觉读出虽可通过二维布局增强结构表达并突出重点，却可能在渲染与压缩过程中丢失细粒度信息。为此，本文提出一种多模态证据记忆组织方法（MEMO），其关键在于通过三个协同模块实现智能化记忆规划与生成：首先，基于训练好的证据提取器筛选相关记忆块，并构建包含来源信息与呈现需求的证据单元；其次，引入查询条件驱动的记忆管理者，动态为每个单元分配文本、视觉或双通道载体，并选择匹配证据结构的布局；最后，由确定性记忆构建模块生成对应的文本包与视觉页面。记忆管理者通过离线阅读器反馈进行训练，使记忆保留与呈现策略与下游任务的实际使用效果对齐。实验在HotpotQA、2WikiMultiHopQA、LoCoMo和ALFWorld四个基准上验证了MEMO的有效性，结果表明其能在显著减少记忆令牌数量的前提下提升下游任务性能，尤其在资源受限场景下构建更高效的动态工作记忆。
链接: https://arxiv.org/abs/2609.07471
作者: Xian Gao,Jinpeng Wang,Jiacheng Ruan,Guangyu Cao,Ting Liu,Yuzhuo Fu
机构: 未知
类目: Computation and Language (cs.CL)
备注: 8 pages, 2 figures. Working in progress
Abstract:Long-running LLM agents rely on external memory to store and reuse information beyond a single context window, yet there is a fundamental tension between the continuous accumulation of interaction trajectories and the limited context capacity. The key challenge in agent memory is therefore not only to retrieve relevant records, but also to select necessary evidence under a given budget and organize it in an appropriate modality. Existing memory readout methods mainly use textual or visual forms. Text preserves high fidelity, but its linear token representation makes contents with different importance compete for the limited context at nearly uniform unit cost. Visual readout renders text into document-like images, which can use two-dimensional layouts to expose structure and emphasize key information, but it may lose fine-grained details during rendering and compression. To address this issue, we propose MEMO, a multimodal evidence memory organization method for LLM agents. MEMO first uses a trained evidence extractor to select relevant memory blocks and form evidence units with source information and presentation requirements. A trained query-conditioned memory manager assigns each unit to a textual, visual, or dual-channel carrier and selects a layout that matches the evidence structure. A deterministic memory construction module then generates the textual package and visual pages. The memory manager is trained with feedback from an offline reader that measures the utility of the guided memory plan, so that retention and presentation decisions align with downstream usage. We evaluate MEMO on four benchmarks, HotpotQA, 2WikiMultiHopQA, LoCoMo, and ALFWorld, with multiple reader backends. The results show that MEMO presents memory more efficiently with fewer memory tokens, improves downstream task performance, and builds more effective working memory under constrained budgets.
[NLP-104] FramingQA: Does the Question Shape the Answer? Measuring the Compositional Framing Effect
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在高风险领域（如法律、医疗、金融及机器人仿真）中对问题表述方式敏感的问题，即模型响应易受用户隐含立场或不当假设引导的“框架偏差”（framing bias）影响。这种偏差会导致模型输出建议并非基于客观事实，而是受提问方式的误导，从而在关键决策场景中引发严重后果。其解决方案的关键在于构建FramingQA基准测试，系统性地在三个嵌套层级上评估模型对框架偏差的鲁棒性：（1）根级——直接通过框架化表述重构问题；（2）命题级——在中立问题前附加带有偏见的前提；（3）全局级——将框架化前提与框架化问题配对。实验结果表明，即使某些模型在特定问题变体上表现准确，也难以保证在相同事实信息下对不同表述方式具有稳健性，揭示了当前主流开放模型在应对复杂现实语境中的显著脆弱性。
链接: https://arxiv.org/abs/2609.07448
作者: Hazel H. Kim,Andrew M. Bean,Guilherme Affonso Ferreira de Camargo,Shanyu Chauhan,Felix Drinkall,Jade Kosché,Chenyang Ma,Glory Nwaugbala,Nabeel Seedat,Bradley Max Segal,Samuel Recht,Hinrich Schütze,Philip H.S. Torr
机构: University of Oxford (牛津大学); Thomson Reuters; University of California San Diego (加利福尼亚大学圣地亚哥分校); University of Cambridge (剑桥大学); LMU Munich (慕尼黑路德维希-马克西米利安大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:We introduce FramingQA, a benchmark that measures the model sensitivity to question framing across law, medicine, finance, and robotic simulations. Large language models (LLMs) often change their responses to subtle rephrasings that align with an implied stance by users. This can leave users with advice tainted by how they happened to phrase a question rather than by the underlying facts, and the consequences are highly costly in high-stakes domains. Because in the realistic scenarios, both expert practitioners and non-expert users frequently ask LLMs questions containing incomplete or misleading assumptions, models are highly susceptible to those framings. To test this, we inject the framing bias across three nested levels: a framing-biased question phrasing (root), an injected framing-biased premise prepended to a neutral question (propositional), and a premise paired with a framing-biased question (global). Evaluating nine open models (3.8B-70B) across four families, we find that strong per-variant accuracy does not guarantee the robustness across differently phrased questions under the fixed factual information.
[NLP-105] An LLM -Associated Register Shift in Korean Journal Abstracts: A Morphology-Aware Excess-Vocabulary Study 2018-2026
【速读】： 该论文旨在解决近年来学术韩语文本中出现的异常语言模式变化问题，具体表现为自2023年起学术写作中某些词汇使用频率显著偏离历史趋势，可能暗示生成式 AI（Generative AI）在科研写作中的渗透。研究通过引入“超额词汇量”（excess vocabulary）这一指标，将该方法适配至以词素为单位的韩语文本分析，并基于398,296篇韩国科学引文索引（KCI）摘要（2018年8月至2026年8月）及47,165篇越南语摘要进行对比验证。其核心解决方案在于构建多层级统计控制框架：首先采用单字词与重选分半集（split-half set）的双指标检验，识别出2024年末至2026年间韩语摘要中存在系统性语言变异；其次通过主题领域控制、语义标注一致性筛选以及跨语言对照（英文摘要对比），排除翻译或学科偏倚等干扰因素。关键发现表明，在假设条件下，大语言模型（LLM）处理的韩语摘要中，人工撰写比例的下限分别达到2024–2026年的3.5%、10.5%和16.1%，而分半集估计更高，达7.8%、20.6%和33.0%；即使在严格控制语义风格后，仍保留约14.7点的异常值，且与期刊基线摘要匹配后仍存34.1点异常，说明该现象具有显著的非人为特征。此外，翻译路径无法解释该现象，且英文摘要中相似模式提前一年出现，进一步支持其源于模型生成而非翻译所致。最终结论指向生成式 AI 在学术韩语文本中的广泛介入，其影响程度随情景设定而异，但已呈现可量化的系统性趋势。
链接: https://arxiv.org/abs/2609.07447
作者: Aron Lee(INTFRAME Research)
机构: 未知
类目: Computation and Language (cs.CL); Digital Libraries (cs.DL)
备注: 30 pages, 8 figures, 31 tables. Working paper. Data, code and this version are deposited at Zenodo: doi: https://doi.org/10.5281/zenodo.22303588
Abstract:Excess vocabulary, a word’s frequency above its pre-2023 trend, is how the change in scholarly English after 2022 has been measured. We adapt it to Korean with morphological units on 398,296 KCI abstracts (2018-August 2026), with 47,165 Vietnamese abstracts for comparison. Placebo floors are 0.1-2.2 points for the single-word statistic and at most 2.9 for the re-selected split-half set statistic. Korean abstracts show nothing in 2023, onset in late 2024, a rise through 2025 flattening in mid-2026: sisahada “suggest” appears in 21.4% of 2026 abstracts against 5.3% expected; plain verbs like araboda “look into” fall to a quarter of trend. Under stated assumptions the single-word conditional lower bound on LLM-processed abstracts is 3.5%, 10.5% and 16.1% for 2024-2026 and a split-half set bound 7.8%, 20.6% and 33.0%. Holzwarth et al.‘s estimator under the same discipline gives 41.9% and 72.1% for 2025-2026. Subject-matter controls reduce but do not remove it: restricting the set to lemmas three language-model annotators all call style leaves 14.7 of the 33.0 points, and pairing each 2026 abstract with its journal’s closest base-period abstract leaves 34.1. Tested translation routes do not explain it: the surface marks of translated Korean fall as the markers rise. In the same articles’ English abstracts the excess appears a year earlier; where the English side carries none, the Korean shift persists at 30 to 66% of the rate where it does. Control abstracts from three providers reproduce the rising words, with marker turnover consistent with model generations; implied prevalences are scenario-dependent.
[NLP-106] Beyond Single-Negative Preference: Multi-Negative DPO for LLM -Centric Historical Entity Linking
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在历史实体链接（Historical Entity Linking, HEL）任务中，由于偏好优化（Preference Optimization）仅采用单个负例样本而导致的候选实体信息浪费问题。现有方法通常仅使用一个负例进行训练，忽略了同一提及词（mention）下其他有效但被拒绝的候选实体所携带的丰富对比信息。为此，论文提出多负例直接偏好优化（Multi-Negative Direct Preference Optimization, MDPO），其核心在于引入一种基于参考的成对目标函数，将正确实体与每个提及词对应的所有合法负例候选进行比较，从而充分利用实例内全部候选信息。该方法在保留原有直接偏好优化（Direct Preference Optimization, DPO）的Bradley-Terry建模框架基础上，通过掩码与长度归一化的序列得分机制，有效整合多负例信息。实验在hipe-2020和newseye数据集上验证了MDPO的有效性，覆盖法语、德语、英语、瑞典语和芬兰语的历史报纸文本，结果表明，相比监督微调和单负例DPO，MDPO显著提升性能，尤其在无实体链接（NIL）提及、语义歧义、光学字符识别（OCR）噪声以及历史难名实体等挑战场景下表现突出。进一步分析揭示，候选生成仍是端到端实体链接中的主要瓶颈，凸显了充分挖掘实例内多负例信息对于提升模型性能的关键作用。因此，该研究的核心贡献在于证明：在历史实体链接任务中，利用所有实例内的负例候选是一种简单而高效的改进策略。
链接: https://arxiv.org/abs/2609.07379
作者: Tien Nam Nguyen,Emanuela Boros,Ahmed Hamdi,Adam Jatowt,Mickaël Coustaty,Antoine Doucet
机构: L3i, University of La Rochelle(拉罗谢尔大学信息与智能实验室), France; University of Toulouse, IRIT(图卢兹大学信息与机器人研究所), France; University of Innsbruck(因斯布鲁克大学), Austria; FRI, University of Ljubljana(卢布尔雅那大学前沿研究所), Slovenia
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) have recently shown promise for historical entity linking, but preference optimization for this task is often formulated with only one negative candidate per training instance. This discards information from the remaining candidates retrieved for the same mention. We introduce multi-negative direct preference optimisation (MDPO), a reference-based pairwise objective that compares the correct entity with all valid rejected candidates associated with each mention. MDPO preserves the Bradley-Terry formulation of DPO while exploiting the complete candidate set through masked, length-normalised sequence scores. We evaluate MDPO on hipe-2020 and newseye, covering French, German, English, Swedish, and Finnish historical newspaper text. Experiments show that MDPO improves over supervised fine-tuning and single-negative DPO, with particularly strong gains for NIL mentions, semantic ambiguity, OCR noise, and historically difficult names. Further analyses disentangle candidate-generation and selection errors, showing that candidate retrieval remains a key bottleneck for end-to-end entity linking. These results demonstrate that incorporating all within-instance negative candidates is a simple and effective improvement for LLM-based historical entity linking.
[NLP-107] Beyond Fluent Generation: A CPU Reliability Benchmark for MCP-Style Tool Calling in Sub-2B Small Language Models for Edge Deployment
【速读】： 该论文旨在解决在资源受限的单板计算机（如Raspberry Pi、NVIDIA Jetson Nano等）上部署小型语言模型（Small Language Model, SLM）代理所面临的挑战，核心问题是实现高效、可靠且安全的本地化智能代理运行，以减少对云端的依赖、提升数据本地性并适应间歇性网络连接。其解决方案的关键在于构建一个平台无关的CPU基准测试框架，评估多个参数量低于20亿的开源模型（包括Phi-1.5、Pythia-1.4B、TinyLlama-1.1B-Chat、Qwen2.5-0.5B和Qwen2.5-1.5B）在生成式AI（Generative AI）任务中的工具调用能力，特别是在模型上下文协议（Model Context Protocol, MCP）场景下，要求模型输出可机器解析的JSON格式、正确选择工具、完整提供参数并避免误操作。研究通过引入恢复解析器（recovery parser），对生成结果进行后处理以提取有效信息，并基于可解析性、工具名称准确性、参数完整性及值一致性等指标评估性能。结果显示，仅Qwen2.5-1.5B在贪婪解码与核采样下分别达到75%和79%的可解析率，而其他模型表现显著偏低；尤其值得注意的是，原始输出中仅有5条（占0.5%）可直接解析为合法JSON，凸显了对输出恢复机制的高度依赖。同时，资源探测表明大模型虽性能更优但需更高内存与延迟，揭示了边缘部署中可靠性与资源消耗之间的权衡。因此，安全部署还需结合模式验证、约束生成、最小权限执行及关键操作的人工介入等机制。
链接: https://arxiv.org/abs/2609.07370
作者: Abrar Shahriar Qurat-Ul-Ain Mastoi
机构: University of the West of England, Bristol (西英格兰大学，布里斯托尔)
类目: Computation and Language (cs.CL)
备注:
Abstract:Resource constrained single-board computers including Raspberry Pi, NVIDIA Jetson Nano, Arduino UNO Q, Orange Pi, and LattePanda motivate on-device small language model (SLM) agents that reduce cloud dependence, improve data locality, and tolerate intermittent connectivity. Model Context Protocol (MCP)-style tool invocation demands more than fluent generation: an agent must emit machine-readable JSON, select the correct tool, supply all required arguments, and avoid unintended actions. We establish a platform-agnostic CPU baseline by evaluating five open-weight models below two billion parameters Phi-1.5, Pythia-1.4B, TinyLlama-1.1B-Chat, Qwen2.5-0.5B, and Qwen2.5-1.5B on 100 prompts spanning weather retrieval, web search, calculation, email composition, and task creation, under greedy decoding and nucleus sampling. A recovery parser strips Markdown fences, extracts brace-delimited substrings, and scores parseability, tool-name correctness, argument completeness, and value agreement. Under this criterion, Qwen2.5-1.5B achieves 75% (greedy) and 79% (sampling); Qwen2.5-0.5B achieves 72% (greedy) but drops to 32% under sampling. Phi-1.5 scores 0%; Pythia and TinyLlama reach at most 7%. A strict post-hoc audit finds only 5 of 1,000 raw responses directly parseable as JSON, exposing near-total dependence on output recovery. A CPU resource probe shows Qwen2.5-1.5B requires 7,960 MiB and 30.782 s mean latency; Qwen2.5-0.5B uses 3,637 MiB and 10.627 s, revealing a reliability-resource trade-off for edge deployment. These results do not cover the named boards directly or a full MCP implementation. Safe deployment requires schema validation, constrained generation, least-privilege execution, and human escalation for consequential actions.
[NLP-108] BlueprintAgent : Constraint-Triggered Targeted Revisits for Simulation-Ready Generation from Scanned Structural Blueprints EMNLP2026
【速读】： 该论文旨在解决将现有钢筋混凝土（RC）建筑施工图（blueprints）自动化转换为可直接用于有限元模拟（FEM）的结构化框架模型这一关键问题，该过程对结构安全评估与抗震加固至关重要，但目前仍高度依赖人工操作。传统方法中，直接对扫描图纸进行多模态大语言模型（MLLM）提示存在可靠性不足的问题，生成结果常违反梁-柱支撑关系、跨数或三维连续性等工程约束。为此，论文提出BlueprintAgent（BPA），一种基于约束触发的多模态智能体系统，其核心创新在于将工程约束建模为可调用的验证器（validator），当检测到实体层面的冲突时，触发针对局部区域的靶向MLLM重推理——这是一种运行时动态控制机制，区别于固定流程和自由形式的自我反思。实验在20个匿名RC框架项目共300张真实扫描图纸上进行，对比五种基线与六种消融实验，结果显示BPA在宏平均梁类F1达到0.994，显著优于单个MLLM零样本提示的0.301及固定流水线的0.820；移除由MLLM主导的轴线判定机制后，复杂多页项目中的梁与柱类F1性能急剧下降。研究表明，在密集技术图纸场景下，工程约束应作为触发实体级靶向重推的触发器，而非事后输出过滤手段，以实现更高精度与鲁棒性的仿真模型提取。
链接: https://arxiv.org/abs/2609.07362
作者: Zhouyuan Xu,Chen Yang,Linhao Wang,Jiansheng Fan,Chen Wang
机构: Tsinghua University (清华大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to Findings of EMNLP 2026. 14 pages, 4 figures
Abstract:Converting in-service reinforced-concrete (RC) building blueprints into simulation-ready models—structured frame representations that support deterministic FEM export and qualified-engineer review—underpins safety assessment and seismic retrofit, but the process remains manual. Direct prompting of a multimodal large language model (MLLM) over a scanned sheet is unreliable: outputs often violate engineering constraints on beam–column support, span count, or 3D continuity. We present BlueprintAgent (BPA), a constraint-triggered multimodal agent for simulation-ready frame extraction from scanned blueprints. BPA treats the MLLM as the primary reader and decision maker, with OCR and computer vision supplying localized evidence. Its central mechanism realizes engineering constraints as callable validators whose entity-level conflict reports trigger targeted MLLM revisits over the local region—an inference-time control distinct from fixed pipelines and free-form self-reflection. We evaluate BPA on 300 real scanned blueprint sheets from 20 anonymized RC frame projects, against five baselines and six ablations. BPA reaches a macro-averaged Beam F1 of 0.994, against 0.301 for single-MLLM zero-shot and 0.820 for a fixed pipeline; removing MLLM-led axis adjudication collapses Beam and Column F1 on complex multi-sheet projects. For dense technical drawings, engineering constraints are best deployed as triggers for entity-level targeted revisits rather than as post-hoc output filters.
[NLP-109] Content-Based Addressing for Long Context
【速读】： 该论文旨在解决旋转位置编码（Rotary Position Embedding, RoPE）在长上下文场景下存在的训练-测试位置不匹配问题。随着上下文长度增加，传统RoPE依赖递增的整数位置索引生成相对旋转，导致在推理时面对未在训练中出现的位置偏移，从而引发性能下降。其解决方案的关键在于：将输入序列划分为固定单位（units），在单位内部保留原始的RoPE位置编码以维持局部顺序敏感性，同时为每个完成的单位分配一个基于内容的地址（content-based address），通过学习到的映射函数对新单位进行地址分配，而非扩展位置计数器或标识符表。该方法确保了局部RoPE结构不变、任意两固定令牌间的注意力关系不受插入或重排序其他单位的影响，并且新增单位不会引入额外的相对旋转。实验表明，在字符级“Tiny Shakespeare”任务中，采用内容驱动地址机制的模型在256字符和4096字符上下文下的验证困惑度分别为3.82和4.04，显著优于连续RoPE从4.71恶化至12.09的表现；另一项诊断实验进一步验证了该机制可有效检索并利用多个串行化事实信息。因此，该研究提出的核心原则是：局部位置通过位置编码处理，跨单位则通过内容编码进行地址定位。
链接: https://arxiv.org/abs/2609.07314
作者: Mahesh Godavarti
机构: A Carrot, Inc.
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Rotary position embedding (RoPE) uses each token’s integer position to determine the rotation applied inside attention. This works well for local token order, but increasing context length creates a positional train-test mismatch: RoPE produces relative rotations at offsets not seen during training. Methods that rescale, interpolate, randomize, or bias positions specify how attention handles those offsets, but still derive positional information from a growing token counter. We instead divide a token stream into units, retain ordinary RoPE positions within each unit, and assign every completed unit an address computed from its content. Adding units then applies the same learned map to new content rather than extending a positional range or an identifier table. We prove that this construction preserves local RoPE exactly, leaves the attention comparison between two fixed tokens unchanged when other units are inserted or reordered, and does not create new relative rotations merely because more units are added. In a character-level Tiny Shakespeare diagnostic, a model trained on 256-character contexts has validation perplexity 4.04 at 256 characters and 3.82 at 4096, while continuous RoPE changes from 4.71 to 12.09. A second diagnostic shows that content-based addressing can retrieve and use information from multiple serialized facts. These are controlled shallow experiments, not scale benchmarks, but they support a direct prescription: use position to address locally and content to address across units.
[NLP-110] LANTERN: Language Model Assessment on Noisy and Transformed Tasks for Understanding Error and Robustness Nuances
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在面对输入数据扰动时的鲁棒性评估难题，尤其关注模型对输入扰动的敏感性。其核心挑战在于缺乏系统性的评估框架来量化不同规模与类型模型在多种扰动情境下的响应稳定性。解决方案的关键在于构建一个合成且增强的多样化数据集，涵盖多个主流的LLM基准测试，重点聚焦于多选题（Multiple-Choice Question, MCQ）数据集和指令遵循任务，并在此基础上对小、中、大规模的基线模型与指令微调模型进行广泛实验。通过系统分析模型在词错误率、字符重复与复制、选项选择变化以及指令遵循变异性等维度上的表现，该研究定量揭示了模型在扰动条件下的响应差异，为提升大语言模型的鲁棒性及建立更可靠的评估方法提供了关键实证依据。
链接: https://arxiv.org/abs/2609.07309
作者: Vamsi Krishna Kodavali,Rituraj Singh
机构: Samsung(三星)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Robustness evaluation of large language models (LLMs) remains a critical challenge, particularly in assessing their sensitivity to perturbations in input data. In this work, we systematically evaluate LLM robustness across multiple dimensions, including word error rate, character repetition and duplication, modifications in choices, and variability in instruction following. To facilitate this evaluation, we construct a synthetic and augmented dataset encompassing a diverse set of LLM benchmarks, specifically targeting multiple-choice question (MCQ) datasets and instruction-following tasks. We conduct extensive experiments on LLMs of varying scales-small, medium, and large-as well as across base and instruction-tuned variants. Our analysis quantifies the variability in model responses under perturbed conditions and highlights discrepancies relative to baseline models. The findings provide insights into the stability of LLMs across different evaluation scenarios contributing to the development of more robust and reliable language models as well as robust evaluation methodologies.
[NLP-111] SPARROW: Scalable Taxonomy Induction via Structure-Preserving Partitioning and Constraint-Guided Merging EMNLP2026
【速读】： 该论文旨在解决大规模概念集合在生成式层次结构（taxonomy）时，基于大语言模型（LLM）的方法因上下文长度限制及层次推理中的结构性缺陷而导致性能显著下降的问题。其核心挑战在于：随着概念规模扩大，现有方法在局部与全局层次关系的保持上出现严重退化。为应对这一问题，论文提出SPARROW框架，其关键创新在于采用“分而治之—增量融合”的范式，结合两种机制：一是基于谱聚类（spectral partitioning）的结构保真分割，确保每个子集内部的层次连通性得以保留；二是约束引导的增量融合策略，将块间关系作为结构约束而非全局真实标签进行处理，有效缓解了因局部优化导致的父节点错位（Parent Displacement）和层次信号弱化（Structural Fragmentation）等问题。实验结果表明，SPARROW在多个大规模基准测试中均显著优于现有方法，在不同骨干模型下均展现出最优的全局结构质量。
链接: https://arxiv.org/abs/2609.07307
作者: Yirui Zhang,Yixuan Tang,Yandong Sun,Mong-Li Lee,Anthony Kum Hoe Tung
机构: National University of Singapore(新加坡国立大学)
类目: Computation and Language (cs.CL)
备注: Accepted to EMNLP 2026 Main
Abstract:Taxonomy induction aims to organize concept sets into coherent hierarchical structures. Recent LLM-based methods can induce taxonomies directly from flat term lists, avoiding the need for corpora, but degrade sharply as concept sets scale up. We argue that this degradation stems not only from context length limitations, but also from structural failures in hierarchical reasoning. To address this, we adopt a divide-and-merge paradigm that partitions concepts into smaller subsets, induces local taxonomies, and merges them into a global hierarchy. However, we identify two structural failure modes inherent to this paradigm: Structural Fragmentation, where partitioning weakens local hierarchical signals, and Parent Displacement, where locally plausible relations are misplaced in the global hierarchy. To address both, we propose SPARROW, a scalable taxonomy induction framework that combines structure-preserving spectral partitioning to retain hierarchical connectivity within each block, and constraint-guided incremental fusion that treats block-level relations as structural constraints rather than ground truth for global placement. Experiments on large-scale benchmarks show that SPARROW consistently achieves the strongest global structural quality across backbones. The code is available at this https URL.
[NLP-112] RouteRelay: Event-Triggered Cross-Layer Route Reuse for Efficient Dynamic Sparse Attention
【速读】： 该论文旨在解决动态稀疏注意力（dynamic sparse attention）在长序列预填充（long-context prefill）过程中因每层均需重新构建块-块得分矩阵而导致的高计算开销问题，尤其针对跨层路由路径变化较小但仍重复执行全量路由的情况。其核心解决方案是提出一种与路由器无关的方法——RouteRelay，关键在于通过重用深度方向上的路由元数据（route metadata），仅在中间层对前一层选出的 top-k 路由结果及一组紧凑的“哨兵块”（sentinel set，包含近似匹配和随机探测的块）进行重评分，且仅当哨兵块挑战当前最弱选中块时才触发查询行的重新路由。该方法引入了 top-k 稳定性条件与遗漏挑战者的概率上界，并设计了行选择性 GPU 执行机制。实验表明，在低、中、高跨层漂移场景下，RouteRelay 保持至少 99.99% 的路由召回率，同时分别仅需 25.0%、55.4% 和 78.2% 的行重新路由；在不同路由规模下，可实现 100.0% 的召回率，同时仅评估 38.4%–51.6% 的完整路由得分对，显著降低计算成本。尽管其未融合的 CPU 实现仍慢于稠密矩阵乘法，但揭示出行压缩（row compaction）与路由日志更新（ledger updates）是后续内核工程优化的关键目标。
链接: https://arxiv.org/abs/2609.07306
作者: Bin Li,Sisi Liu,Chenyang Hu,Chaoyang Zhang,Wei Li,Hui Song
机构: Xiamen University of Technology(厦门理工学院); Fuzhou University(福州大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Dynamic sparse attention reduces long-context prefill cost by routing each query chunk to a small set of key chunks at every Transformer layer. The sparse attention kernel avoids most token interactions, but the router still rebuilds a chunk–chunk score matrix layer after layer, even when the selected routes change little. We introduce RouteRelay, a router-agnostic method that reuses only route metadata across depth while continuing to compute attention with the current layer’s queries, keys, and values. Anchor layers perform full routing. Intermediate layers rescore the previous top- k route and a compact sentinel set of near-miss and randomly probed chunks. A query row is rerouted only when a sentinel challenges its weakest selected chunk. We give a top- k stability condition, a probabilistic bound on missed challengers, and a row-selective GPU execution design. In a reproducible empirical evaluation, RouteRelay retains at least 99.99% route recall while rerouting 25.0%, 55.4%, and 78.2% of rows under low, moderate, and high cross-layer drift, respectively. Across routing scales, RouteRelay retains 100.0% recall while evaluating 38.4–51.6% of full-routing score pairs as the key-chunk count grows from 128 to 1024. Its unfused CPU execution remains slower than dense matrix multiplication, exposing row compaction and ledger updates as the main kernel-engineering targets.
[NLP-113] Probing the Structure and Dynamics of LLM Value Expression through Value Conflicts
【速读】： 该论文旨在解决当前大型语言模型（LLM）伦理评估中将模型价值视为静态、单一化认知的局限性，提出模型价值表达本质上是一种结构化且动态的现象。其解决方案的关键在于提出“冲突驱动的价值探针”（Conflict-driven Value Probing）框架，通过在受控环境下诱发模型面临价值冲突，并施加四类干预手段以扰动冲突状态，从而探测模型在具体情境中的价值表达模式。实验结果揭示了三个核心规律：（1）表达二元性——模型在抽象评估中呈现理想主义倾向，而在具体冲突中转向务实优先；（2）功能可引导性——模型能够根据任务目标灵活调整其价值表达配置；（3）有限可塑性——价值重配置存在内在约束，外部压力会引发安全与目标导向的优先级转变，而负面表述则能区分出不可动摇的核心价值与可调适的次要价值。这些发现共同构建了对大模型价值表达结构与动态性的系统性理解，为模型可控性、对齐性及安全性研究提供了行为学基础。
链接: https://arxiv.org/abs/2609.07296
作者: Kaicheng Zhang,Jingyi Xiao,Renjun Hu,Xiaoling Liu,Yunshi Lan,Xuan Zhou
机构: 未知
类目: Computation and Language (cs.CL)
备注: Code and data are available at this https URL
Abstract:Ethical evaluation of Large Language Models (LLMs) often characterizes model values as static and monolithic. In contrast, we argue that LLM value expression is better understood as a structured yet dynamic phenomenon. To investigate this, we introduce Conflict-driven Value Probing, a controlled framework that places LLMs in value conflicts and implements four types of interventions that perturb these conflicts to probe LLM value expression. Applying this framework to ten LLMs, we identify three recurring patterns. (1) Expression duality: models shift from broad idealistic orientations in abstract assessment toward more pragmatic priorities in concrete conflicts. (2) Functional steerability: models readily reconfigure their expressed value profiles toward task-defined value objectives. (3) Bounded plasticity: such reconfiguration is not without constraints, i.e. pressure induces a security- and goal-oriented priority shift while negative framing distinguishes protected values from those more amenable to redirection. Together, these findings characterize both the structure and dynamics of LLM value expression: context flexibly reconfigures expressed priorities, yet within behavioral boundaries. This behavioral account provides a foundation for understanding controllability, alignment, and safety in LLMs. Code and data are available at this https URL.
[NLP-114] AD: Token-Adaptive Contrastive Decoding with Confidence-Guided Gating for Hallucination Mitigation in Large Audio-Language Models INTERSPEECH2026
【速读】： 该论文旨在解决大音频-语言模型（Large Audio-Language Models, LALMs）在音频问答任务中易产生幻觉的问题，即模型在未检测到声音事件时仍错误地回答“是”，从而降低系统的可靠性。其核心解决方案是提出一种无需训练的去幻觉策略——令牌自适应解码（Token-Adaptive Decoding, TAD），该方法通过对比真实音频与对应静音参考音频的输出逻辑值（logits），生成音频-静音差异（audio-silent margin），以实现对初始“是/否”决策的语义锚定。TAD的关键在于引入一个令牌自适应、置信度引导的门控机制，该机制在解码首步起决定性作用，并针对肯定类令牌具有类别条件性，能够根据证据强度动态调整干预程度，避免在证据不足或已充分时出现过度修正。实验结果表明，相较于固定对比强度的对比基线（如Audio-Aware Decoding, AAD），TAD在AudioCaps-Hallucination数据集上显著提升Qwen2和Gemma模型的F1分数，在多个测试划分下均表现更优；在Clotho-AQA数据集上也实现了微幅但稳定的性能提升，验证了其有效性与鲁棒性。
链接: https://arxiv.org/abs/2609.07286
作者: Heyu Chang,Nianwen Si,Hao Zhang,Wenlin Zhang,Dan Qu
机构: 未知
类目: ound (cs.SD); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to Interspeech 2026
Abstract:Large audio-language models (LALMs) can hallucinate audio objects, answering “yes” to absent sound events, thus undermining reliability in audio question answering. We propose Token-Adaptive Decoding (TAD), a training-free strategy for hallucination mitigation that grounds the initial yes/no decision by contrasting logits under real audio with a matched silent reference. TAD introduces a token-adaptive, confidence-guided gate that is decision-critical at the first decoding step and class-conditional on affirmative tokens, using the audio-silent margin to avoid overcorrection when evidence is weak or already sufficient. Experiments on AudioCaps-Hallucination show that, relative to Audio-Aware Decoding (AAD), a contrastive baseline with fixed contrast strength, TAD improves F1 for Qwen2 by 0.059 to 0.117 across Popular, Adversarial, and Random splits, and for Gemma by 0.025 to 0.064, while on Clotho-AQA it raises F1 from 0.810 to 0.816 on Qwen2 and remains comparable to AAD on Gemma.
[NLP-115] Separating Stream Stability from Long-Term Recall in Language Models
【速读】： 该论文旨在解决流式语言模型在处理无限长输入序列时，稳定性与内容可访问性之间的根本矛盾问题。尽管现有方法常将流式处理、长上下文建模与记忆系统混为一谈，但三者本质目标不同：流式模型需保证生成过程的长期稳定（即避免输出发散），而传统注意力机制受限于近期令牌缓存（recent-token cache）的容量，导致已移出缓存的内容无法被有效利用。为此，论文提出三个关键概念：稳定性边界（stability horizon）——模型预测行为保持合理的时间范围；可访问边界（access horizon）——过去内容仍能对输出产生因果影响的范围；以及效用边界（utility horizon）——任务性能维持可接受水平的范围。研究通过构造性证明表明，稳定性边界可无限延伸，而可访问与效用边界则受限于缓存机制。为此，作者提出 ThreeH 评估框架，在统一状态与计算预算下量化三类边界。实验表明，注意力下沉（attention sink）机制虽支持恒定内存下的稳定生成，但仅保留缓存内内容的局部建模能力，无法实现语义层面的长期记忆；而递归状态（recurrent state）与检索机制（retrieval）可显著扩展语义边界，揭示了缓存策略、状态更新、外部记忆等组件在构建高效流式系统中的互补作用。
链接: https://arxiv.org/abs/2609.07282
作者: Peipei Cao,Xin Zhang,Jie Tang,Xiao Li,Siying Li,Qing Pei
机构: Guangxi University(广西大学); Chongqing Jiaotong University(重庆交通大学); Guangdong University of Technology(广东工业大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Methods for streaming language models are often discussed alongside long-context and memory systems, although they solve different problems. An attention sink can stabilize autoregressive generation over an indefinitely long stream while the model remains unable to use content that has left its recent-token cache. We argue that this distinction should be explicit in system claims and evaluation. We introduce three horizons: the stability horizon, over which predictive behavior remains well behaved; the access horizon, over which past content can still causally affect the output; and the utility horizon, over which a task retains acceptable performance. We show constructively that the stability horizon can be infinite while the access and utility horizons are finite. We then propose ThreeH, an evaluation contract that measures all three horizons under a common state and compute budget. Applying the framework to attention-sink streaming clarifies its strength, constant-memory, stable generation, without treating anchor tokens as semantic memory. The framework exposes roles for cache policies, recurrent state, retrieval, and external memory. Experiments on 128K-token streams, delayed binding recall, and delayed decisions show that attention sinks preserve local modeling but not content beyond the active cache; recurrent and retrieval state extend the semantic horizon.
[NLP-116] CEDAR: Error-Bounded Residual Routing for Efficient Long-Context Attention
【速读】： 该论文旨在解决后处理稀疏注意力（post-hoc sparse attention）在长上下文预填充（long-context prefill）中因硬性选择（hard selection）导致的显著性能损失问题。具体而言，硬性选择会将所有被忽略的词块（chunk）的注意力概率置零，一旦路由错误便无法恢复，且固定扩展预算对简单与模糊查询均投入相同计算资源，造成效率低下。为此，论文提出了一种粗到细的误差感知动态注意力路由方法——粗到细误差感知动态注意力路由（Coarse-to-fine Error-aware Dynamic Attention Routing, CEDAR）。其核心创新在于：在保持语言模型冻结的前提下，引入残差注意力路径，为每个语义词块生成低成本的键值摘要（key–value summary），并通过估计块内键值分布的离散度来量化近似误差，据此动态分配可变的精确令牌注意力扩展预算。高误差词块被进一步展开为精确注意力计算，而精确与摘要贡献在单一softmax归一化中融合，实现精炼（refinement）替代而非重复粗粒度证据。理论分析给出了输出误差上界，并指导预算分配。控制实验表明，在相同精确词块预算下，残差摘要使重建误差相比硬丢弃降低超过98%；在长上下文基准测试中，CEDAR在维持约3倍核函数加速的同时，几乎完全恢复了硬稀疏路由造成的性能损失。
链接: https://arxiv.org/abs/2609.07237
作者: Siyu Li,Dong Wang,Jie Zhou,Wei Li,Yang Xu,Sijie Song
机构: Shanghai University (上海大学); East China Normal University (华东师范大学); Jiangsu Ocean University (江苏海洋大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Post-hoc sparse attention accelerates long-context prefill by routing each query to a small set of token-level interactions. Hard selection, however, assigns zero probability to every omitted chunk: a routing miss cannot be recovered, and a fixed expansion budget spends the same work on easy and ambiguous queries. We introduce Coarse-to-fine Error-aware Dynamic Attention Routing (CEDAR), a coarse-to-fine method that keeps the language model frozen while preserving global coverage. Each semantic chunk contributes a cheap key–value summary to a residual attention path; chunks with high estimated approximation error are then expanded to exact token attention. Exact and summarized contributions are combined in a single softmax normalization, so refinement replaces, rather than duplicates, coarse evidence. We derive an output-error bound governed by within-chunk key/value dispersion and use it to allocate a variable refinement budget. A controlled clustered-attention study shows that residual summaries reduce reconstruction error by more than 98% relative to hard dropping at equal exact-chunk budgets. Experiments on long-context benchmarks demonstrate that CEDAR recovers most of the quality lost by hard sparse routing while maintaining approximately 3\times kernel speedup at 128K context.
[NLP-117] KABURI-TTS: Phoneme-Keyed Activity-conditioned Bi-channel Utterance Rendering for Interaction
【速读】： 该论文旨在解决全双工对话合成中缺乏高质量、双通道且每通道仅含单说话人语音的会话语音数据的问题，尤其针对现有对话文本到语音（TTS）系统在处理真实对话中的重叠、插话（backchannel）、打断等双人同时发声现象时鲁棒性不足的挑战。其解决方案的关键在于提出KABURI-TTS框架，该框架以分说话人的音素光栅（phoneme raster）作为输入，基于每帧音素及由此推导出的语音活动检测（voice activity）信息，分别在双通道上生成两个说话人的语音输出。由于音素光栅由独立模块提供，该方法实现了对单说话人/单通道、双人对话场景的可控生成。用户评估表明，相较于强基线模型，KABURI-TTS在话语层面和交互层面均表现出更高的自然度；语音活动分析进一步证实，该方法生成了更丰富的语音重叠和更频繁的换轮次行为，从而更贴近人类对话的真实特征。
链接: https://arxiv.org/abs/2609.07200
作者: Ryuichiro Higashinaka,Shinnosuke Takamichi,Tetsuji Ogawa
机构: Nagoya University (名古屋大学); National Institute of Informatics, LLMC (日本信息研究所，语言模型与计算中心); Keio University (庆应义塾大学); Waseda University (早稻田大学)
类目: ound (cs.SD); Computation and Language (cs.CL)
备注: Accepted at APSIPA ASC 2026
Abstract:Realizing full-duplex spoken dialogue requires large amounts of two-channel, one-speaker-per-channel conversational speech data. Although conversational text-to-speech (TTS) engines have been developed, they are not necessarily robust to two-party simultaneous phenomena such as backchannels, interruptions, and overlaps that occur while the interlocutor is speaking. In this work, aiming at conversational speech synthesis that reproduces human-like overlap, we propose KABURI-TTS. KABURI-TTS takes a per-speaker phoneme raster as input and renders the speech of the two speakers on separate channels, conditioned on the per-frame phonemes and the voice activity derived from them. Because the phoneme raster is supplied by a separate module, the proposed method enables controllable generation of one-speaker-per-channel, two-party spoken dialogue. A user evaluation shows that, compared with strong baselines, the proposed method attains higher naturalness at both the utterance and the interaction level. Furthermore, an analysis of voice activity confirms that the proposed method produces more overlap and more frequent turn-taking.
[NLP-118] SIFTING: A Novel LLM -Based Framework for Structured and Transparent Information Extraction from Clinical Free-Text Reports with Application to Tumor Staging in Lung Cancer
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在临床自由文本信息提取中输出结果非结构化且缺乏可追溯性的问题，这限制了其在临床工作流中的验证与实际应用。其核心解决方案是提出SIFTING框架，该框架通过结合LLMs的语言理解能力与分段处理机制，采用结构化提示词设计并实施严格的输出控制策略，确保提取的临床发现能够精准关联至原始文本来源，从而实现信息提取的准确性与透明性双重保障。研究以肺癌放射学报告中的肿瘤T分期信息提取任务为例，基于自托管的4位量化版Llama-3.3-70B模型进行实验，结果显示SIFTING-T-stage达到90%的准确率（95%置信区间：84%-95%），性能媲美具备推理能力的最大规模先进模型，并与临床专家表现无显著差异（p > 0.001），同时具备完整的溯源能力。因此，SIFTING框架实现了临床自由文本中信息提取的高精度、结构化与可验证性，为数据自主可控、结果可复现及临床验证集成提供了可靠技术路径。
链接: https://arxiv.org/abs/2609.07185
作者: Mirco Hess,Gerben van Veenendaal,Joris Wakkie,Yiwen Soo,Malcolm H. Lawson,John D. Maclay,Arjun Nair,Neal Navani
机构: Axana B.V.(阿克萨纳有限公司); University College London (伦敦大学学院); Mid South Essex NHS Foundation Trust (中南埃塞克斯国民保健服务基金会信托); NHS Greater Glasgow and Clyde (英国国家医疗服务体系格拉斯哥和克莱德地区)
类目: Computation and Language (cs.CL)
备注: 13 pages, 8 figures, 6 tables
Abstract:Background: Large language models (LLMs) show promise for extracting information from clinical free-text documents, but their outputs are often unstructured and lack traceability, complicating validation and adoption in clinical workflows. In this work we introduce SIFTING, an LLM-based framework designed to address these shortcomings. Methods: SIFTING combines the language comprehension capabilities of LLMs with segment-level processing and structured prompts with strict output control, linking findings to the source text to enable both accurate and transparent information extraction. To demonstrate its capabilities, we applied the framework to the task of extracting tumor T-stage information from 130 lung cancer radiology reports (SIFTING-T-stage). A compact 4-bit quantized version of the open-source LLM Llama-3.3-70B (35 GB) was used in a fully self-hosted setup, providing full control over data and model. Performance was evaluated against a reference standard created by four clinical experts and compared with a range of LLMs as used in a conventional single-prompt approach, using bootstrap resampling to estimate confidence intervals. Results: SIFTING-T-stage achieved an accuracy of 90% (95% CI: 84-95) against the reference standard. We found its performance to be comparable to even the largest state-of-the-art LLMs with reasoning capabilities and to be interchangeable with clinical experts (p 0.001), while at the same time offering full traceability through source text references. Conclusion: SIFTING enables accurate, structured, and traceable information extraction from clinical free-text documents. It ensures data control, reproducibility, and verifiable outputs that can support clinical validation and workflow integration. Comments: 13 pages, 8 figures, 6 tables Subjects: Computation and Language (cs.CL) Cite as: arXiv:2609.07185 [cs.CL] (or arXiv:2609.07185v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.07185 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Mirco Hess [view email] [v1] Mon, 7 Sep 2026 08:15:19 UTC (2,213 KB)
[NLP-119] CircuitLens: Reasoning Circuits as Data Selection Signals for Reinforcement Learning with Verifiable Rewards EMNLP2026
【速读】： 该论文旨在解决强化学习中可验证奖励（Reinforcement Learning with Verifiable Rewards, RLVR）对训练数据选择敏感的问题，即现有数据筛选方法（如难度过滤、人工精调、奖励轨迹评分）将数据价值视为独立于学习模型的内在属性，忽略了模型自身在训练过程中的动态响应。其核心解决方案是提出一种基于46个经对比消融识别出的推理敏感注意力头的电路推理得分（Circuit Reasoning Score, CRS），该得分可在冻结的基础模型上通过单次前向传播计算，无需依赖奖励标签或仿真轨迹。研究发现，与直观假设相反——更强的推理回路激活并不必然带来更优的训练数据：在Qwen2.5-Math-7B模型上，低参与度的十等分组在三个中等难度基准（GSM8K +2.0个百分点，OlympiadBench +1.6个百分点，Minerva +2.9个百分点）上优于随机选择，而高参与度组收益较小且与中等参与度组无显著差异。这一优势具有边界条件，包括在领域定制数据集上所有方法表现趋同、1.5B规模下有效方向不同，以及最低奖励训练条件下下游泛化能力最强。因此，在所测试的Qwen2.5-Math设置中，RLVR的数据选择呈现明显的范式依赖性，而非可简化为静态的问题质量排序。
链接: https://arxiv.org/abs/2609.07183
作者: Zhuofan Chen,Ziqian Jiao,Yikai Cui,Zhixin Cai,Jun Bai,Wenge Rong
机构: Beihang University (北京航空航天大学); Beijing Institute for General Artificial Intelligence (通用人工智能研究院)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted at EMNLP 2026 Findings. Long paper. 9 pages + references + appendix
Abstract:Reinforcement learning with verifiable rewards (RLVR) is sensitive to which problems a model trains on, yet existing selection criteria–difficulty filtering, hand-curation, reward-trajectory scoring–assess data value as an intrinsic property of problems, independent of the model that will learn from them. We introduce Circuit Reasoning Score (CRS), a selection signal derived from 46 reasoning-sensitive attention heads identified via contrastive ablation, computed in a single forward pass on the frozen base model without reward labels or rollouts. CRS runs against the intuitive hypothesis that stronger reasoning-circuit engagement produces better training data: on Qwen2.5-Math-7B, the lowest-engagement decile improves over random selection on three medium-difficulty benchmarks (GSM8K +2.0 pp, OlympiadBench +1.6 pp, Minerva +2.9 pp), while the highest-engagement decile gains less and is indistinguishable from the middle decile. The advantage has boundary conditions: on a domain-curated pool no selection method separates from the others; at 1.5B scale the useful direction differs; and the lowest-reward training condition produces the strongest downstream generalization. Within the Qwen2.5-Math settings tested, RLVR data selection appears regime-dependent rather than reducible to a static ranking of problem quality.
[NLP-120] In-Place Instruction Following in Diffusion Language Models
【速读】： 该论文旨在解决扩散型大语言模型（Diffusion Large Language Models, dLLMs）在生成文本时难以有效遵循用户指定约束的问题，尤其针对约束条件可锚定于输出任意位置的“原位提示”（In-place Prompting, IPP）场景。其核心挑战在于，尽管dLLMs具备双向迭代去噪机制以支持灵活约束，但原始模型在推理过程中常对约束片段的优先级不足，导致生成结果偏离预期。为此，论文提出GRAFT框架——一种面向IPP任务的后训练方法，通过结合约束感知的监督微调（Supervised Fine-Tuning, SFT）与偏好优化（Preference Optimization），增强模型对约束内容的关注能力。实验表明，GRAFT在四个代表性dLLMs上将平均原位指令遵循（In-place Instruction Following, IIF）得分从57.75提升至73.10（+15.35），在字面约束与语篇功能约束上分别取得15.91和15.57的绝对提升，同时保持了模型原有的通用生成能力。关键创新在于引入推理时注意力偏置探测机制，并据此设计针对性的后训练策略，从而实现对约束信息的有效聚焦与精准执行。
链接: https://arxiv.org/abs/2609.07160
作者: Zheng Nie,Zherui Li,Jiaming Zhang,Kun Wang,Zhenhong Zhou,Yufei Guo
机构: National University of Singapore(新加坡国立大学); Nanyang Technological University(南洋理工大学); Peking University(北京大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Diffusion Large Language Models (dLLMs) generate text via bidirectional iterative denoising, naturally supporting user-specified constraints anchored at arbitrary output positions, a paradigm known as In-place Prompting (IPP). We formalize this as the In-place Instruction Following (IIF) task and construct IIF-Bench, a hierarchical benchmark spanning literal, style, and discourse-function constraints, paired with a rubric-based local-global evaluation protocol. An inference-time attention-bias probe suggests that vanilla dLLMs often under-prioritize constraint spans during denoising. We then propose GRAFT, an IPP-oriented post-training framework combining constraint-aware SFT and preference optimization. On four representative dLLMs, GRAFT raises the average IIF score from 57.75 to 73.10 (+15.35 points), with absolute gains of 15.91 and 15.57 points on literal and discourse-function constraints, while preserving general generation ability.
[NLP-121] FreqBLiMP: Frequency-Controlled Minimal Pairs Reveal Robustness and Frag ility of LLM s Under Lexical Rarity EMNLP2026
【速读】： 该论文旨在解决现有最小对（minimal-pair）评估基准（如BLiMP）在测试语言模型（LMs）语法知识时忽视词汇频率差异的问题。尽管词汇频率是自然语言使用中普遍且高度偏斜的特性，但传统评估方法未考虑罕见词汇项对模型判断的影响，导致无法准确检验模型在涉及低频词汇的句法对比任务中是否仍能保持稳定的语法偏好。为此，论文提出FreqBLiMP，作为对BLiMP的频率控制扩展版本，通过在明确的齐普夫频率（Zipf-frequency）分布下重新生成全部67个语义-句法范式，同时保留每对最小差异句之间的语法对比关系。实验结果表明，随着词汇频率下降，句子的似然度呈现一致且单调的降低趋势，但整体对比可接受性判断的准确率仅出现小幅下降，显示出一定的鲁棒性。然而，这一总体稳定性掩盖了不同语言现象间的显著差异：模型在显性形态句法泛化任务上表现稳健，而在依赖词元（lemma-specific）信息的现象中则明显退化，揭示出当前大语言模型在处理低频词汇时的深层局限。解决方案的关键在于引入可控的词汇频率调控机制，从而更真实地评估语言模型在复杂自然语言分布下的语法理解能力。
链接: https://arxiv.org/abs/2609.07153
作者: Tyrone White,Yuki Arase
机构: School of Computing, Institute of Science Tokyo (东京工业大学信息科学研究所); Tokyo, Japan
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted to EMNLP 2026 Main Conference. Repo link: this https URL
Abstract:Minimal-pair benchmarks such as BLiMP evaluate linguistic knowledge by testing whether language models (LMs) prefer acceptable sentences over minimally different unacceptable ones. However, these benchmarks largely ignore lexical frequency variation, despite lexical frequency being a pervasive and highly skewed property of natural language use. Consequently, existing evaluations do not test whether grammatical preferences remain stable when contrasts involve rare lexical items. We introduce FreqBLiMP, a frequency-controlled extension of BLiMP that regenerates all 67 paradigms under explicit Zipf-frequency regimes while preserving each minimal-pair’s grammatical contrast. Evaluating multiple open-weight LLM families across scales, we find that decreasing lexical frequency produces a consistent, monotonic decrease in sentence likelihood, but only a modest reduction in overall contrastive acceptability accuracy. However, this aggregate stability masks substantial variation across linguistic phenomena, with LLMs remaining robust on overt morphosyntactic generalization while degrading on phenomena that require lemma-specific information.
[NLP-122] Vishing-Tactics-Bench: Forecasting Exploitation Trajectories in Voice Phishing Calls EMNLP2026
【速读】： 该论文旨在解决语音诈骗（vishing）实时防御的难题，传统方法依赖于通话结束后进行事后欺诈分类，已无法阻止实际损害的发生。其核心问题在于：如何在通话进行中准确预测当前对话正朝着何种具体危害（信息获取或金融剥削）演进。为此，研究提出Vishing-Tactics-Bench基准，基于Endsley的情境意识（Situation Awareness, SA）框架，将防御范式从事后分类重构为对潜在伤害的动态投影——即在每一轮对话中实时预测是否将达成两种终端危害之一。关键解决方案是构建了一个6战术分类体系（Vishing-Tactics），基于MITRE ATT&CK框架适配至语音诈骗场景，并对5,645个合成中文诈骗通话中的35,340条诈骗者语句进行标注。进一步提出“剥削轨迹预测”（Exploitation Trajectory Forecasting）任务，采用生存分析风格的评估协议，结合AP@k、C-index与发散误差三个指标衡量模型性能。实验表明，该战术轨迹可作为通话战术状态的可解释表征，支持针对特定危害的精准预测，进而用于下游干预策略选择；在严格控制误报率的前提下进行分层提前预警分析，揭示了轨迹信号在通话早期即可提供有效预警的关键时间节点。
链接: https://arxiv.org/abs/2609.07151
作者: Jeongmin Lee,Dongmyung Sul,Seung Yun,Jinxia Huang
机构: Electronics and Telecommunications Research Institute (ETRI), Korea; University of Science and Technology (UST), Korea
类目: Computation and Language (cs.CL)
备注: 16 pages, 3 figures. Accepted to Findings of EMNLP 2026
Abstract:Voice phishing (vishing) unfolds in real time; by the time a call has ended and post-hoc classification is possible, the harm has already been done. The more actionable question is which concrete harm (Information Gathering or Financial Exploitation) an ongoing call is tactically progressing toward. We present Vishing-Tactics-Bench, a benchmark grounded in Endsley’s situation-awareness (SA) framework that recasts vishing defense from after-the-fact fraud classification to harm projection: predicting at each turn whether the call will reach either terminal harm. We adapt MITRE ATTCK to vishing as a 6-tactic taxonomy (Vishing-Tactics) and label 35,340 scammer utterances across 5,645 synthetic Chinese calls. We define Exploitation Trajectory Forecasting, a survival-style protocol over the two terminal harms with three metrics: AP@k, C-index, and divergence error. Baselines ranging from a Markov heuristic to fine-tuned LLMs show that the tactical trajectory serves as an interpretable representation of the call’s tactical state, supporting harm-specific forecasting, which can then be used for the downstream application of intervention selection; a stratified lead-time analysis at a tight false-alarm budget further identifies at what point in a call the trajectory signal yields early warning.
[NLP-123] Encoded Early Used Late: Where Transformers Begin to Act on an Inferred Partners Expertise
【速读】： 该论文旨在解决大模型在对话过程中对隐含关系属性（如对话伙伴的专家程度）的表征与使用之间的时间错位问题，即该属性在何处被可读（线性可解码）与其在何处真正影响输出决策之间存在显著延迟。核心问题是：对于需要通过多轮对话逐步推断出的隐含属性（如对方专家水平），其内部表示是否在因果作用前就已充分形成？研究提出的关键解决方案是基于反事实补丁（counterfactual patching） 和层间可解码性分析，结合自建数据集ExpertCollab（包含四个不同专业水平角色的多轮科研规划对话），发现“伙伴专家程度”这一推理性关系属性在早期网络层中即可达到最高可解码性，且在接近网络中点前便迅速衰减至接近随机水平。反事实实验表明，若在峰值可解码层注入专家差异信息，对后期输出的影响微乎其微；而若在中点之后注入，则几乎完全传播并影响输出，揭示出超过一个数量级的信息传播延迟。此外，通过内容匹配的随机对照和无探针诊断方法进一步确认该转变发生在早期层，且静态控制属性在整个网络中保持可解码性。因此，结论表明：推理性关系属性的内部表示早于其因果作用阶段完成，从而为干预或引导基于伙伴条件的行为提供了精确的介入窗口——必须在早期层进行读出或调控。该发现为理解生成式模型中的渐进式认知表征机制提供了关键实证支持。
链接: https://arxiv.org/abs/2609.07139
作者: Mika Okamoto,Gabriele Sarti
机构: Georgia Institute of Technology(佐治亚理工学院); Northeastern University(东北大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Published at the Scientific Understanding of Foundation Models (Sci-FM) Workshop at COLM 2026
Abstract:A transformer can make an attribute linearly decodable in its residual stream at a depth where that attribute does not yet influence the output. This gap between where information is readable and where it is used has been shown for attributes stated directly in the input. We ask whether it also holds for an attribute the model must infer gradually over a conversation, namely how expert its dialogue partner is. Using ExpertCollab, a corpus of multi-turn research-planning dialogues between model-played personas at four expertise levels, we find that partner expertise is most decodable in the early layers and falls to near chance before the midpoint of the network. Counterfactual patching shows that injecting the expertise difference at the layer of peak decodability barely changes a fixed late-layer readout, whereas the same difference injected past the midpoint propagates almost completely, a separation of more than an order of magnitude. A content-matched random control and a probe-free diagnostic place the transition at the same early layer, and a statically specified control attribute stays decodable throughout. An inferred relational attribute is therefore represented well before it becomes causally active, which bounds where any attempt to read out or steer partner-conditioned behavior must intervene. We use one model on a synthetic corpus as an initial demonstration.
[NLP-124] Retrieval-Augmented Multi-Prompt Ensemble for Minor-Grain Breeding Information Extraction CCL2026
【速读】： 该论文旨在解决小宗作物育种文献中联合抽取12类实体与6类关系的复杂信息提取问题，其核心挑战在于如何在缺乏大量标注数据的情况下实现高精度、高召回率的多类型信息抽取。解决方案的关键在于提出一种无需训练的RAME（Retrieval-Augmented Multi-Prompt Ensemble）框架，通过三个核心机制实现高性能：首先，采用混合BM25-嵌入检索器进行少样本实例的精准检索，增强提示的上下文相关性；其次，设计三种具有不同精度-召回权衡特性的提示策略（严格型、宽松型、平衡型），形成多视角推理；最后，通过大规模重复采样结合多数投票机制，有效过滤噪声预测结果，提升预测置信度。该方法基于DeepSeek-V4-Flash模型，在CCL2026-Eval Task 5任务中取得总分0.499（命名实体识别[NER] 0.730，关系抽取[RE] 0.346）的成绩，排名第一，相较官方基准GPT-5.5（0.448）实现11.4%的相对提升，验证了其在低资源场景下高效利用大语言模型潜力的能力。
链接: https://arxiv.org/abs/2609.07134
作者: Hang Zhao,Jiahao Wang
机构: Zhengzhou University(郑州大学); Harbin Institute of Technology (Shenzhen)(哈尔滨工业大学（深圳）)
类目: Computation and Language (cs.CL)
备注: Accepted at CCL 2026 (China National Conference on Computational Linguistics)
Abstract:This paper presents our system for CCL2026-Eval Task 5: Minor-Grain Breeding Information Extraction (MGBIE), which jointly extracts 12 entity types and 6 relation types from minor-grain breeding literature. We propose RAME (Retrieval-Augmented Multi-Prompt Ensemble), a training-free framework that elicits multiple LLM outputs under controlled diversity and aggregates them by majority voting to obtain high-confidence predictions. RAME combines (i) retrieval-augmented few-shot selection via a hybrid BM25-embedding retriever, (ii) a three-prompt ensemble (Strict, Relaxed, Balanced) spanning the precision to recall spectrum, and (iii) large-scale repeated sampling with majority voting to filter noisy predictions. Built on DeepSeek-V4-Flash, RAME achieves a Total Score of 0.499 (NER 0.730, RE 0.346) on the leaderboard, ranking 1st and surpassing the official Track-A baseline powered by GPT-5.5 (0.448), representing an 11.4% relative improvement. Code is available at this https URL.
[NLP-125] Line-Coupled Language Model
【速读】： 该论文旨在解决自回归语言模型（autoregressive language model）在解码过程中每次仅生成一个词元（token），导致前向传播效率低下的问题。现有并行生成方法如扩散模型（diffusion models）、插入式解码（insertion-based decoding）和多词元预测（multi-token prediction）虽可提升生成效率，但往往需要额外的训练开销或难以准确预测高度依赖的未来词元。为此，论文提出线耦合语言模型（Line-Coupled Language Model, LCLM），其核心创新在于将多行文本作为并行生成单元，通过共享因果上下文（shared causal context）耦合各文本行，并将不同行的词元交错排列成单一因果序列，同时采用行错位旋转位置编码（line-staggered rotary positions），在保持标准下一个词元预测目标与因果注意力机制的基础上，实现多行并行生成。实验表明，跨行目标间的依赖性显著低于同行连续词元，验证了以行作为并行生成单位的合理性。在881M参数规模下，LCLM每前向传播平均生成2.94个内容词元，而基线自回归模型仅为1.00个，且验证交叉熵损失仅为2.44；即使在每步生成16个词元时，其损失仅比基线高0.09（2.34 vs. 2.25），充分体现了其在保持生成质量的同时显著提升效率的能力。
链接: https://arxiv.org/abs/2609.07129
作者: Shiyuan Li,Shaorong Zhang,Zhaorui Yang,Qian Zhang,Greg Ver Steeg,Bingyao Li
机构: University of California, Riverside(加州大学河滨分校)
类目: Computation and Language (cs.CL)
备注: 17 pages, 13 figures, 11 tables. Code: this https URL
Abstract:Autoregressive language models generate one token per decoding step, limiting the useful output of each forward pass. Although diffusion models, insertion-based decoding, and multi-token prediction enable parallel generation, they either incur additional training-time token traffic or struggle to predict strongly dependent future tokens. We introduce the Line-Coupled Language Model (LCLM), an autoregressive model that advances multiple text lines together by predicting the next token for every active line while coupling the lines through shared causal context. LCLM interleaves line tokens into a single causal sequence and uses line-staggered rotary positions, retaining the standard next-token objective and causal attention. Controlled experiments show that cross-line targets are substantially less dependent than consecutive same-line targets, supporting lines as parallel generation units. With 881M parameters, LCLM produces an average of 2.94 content tokens per forward pass with a validation cross-entropy loss of 2.44, compared with 1.00 token per forward pass and a loss of 2.39 for the vanilla autoregressive baseline. Most notably, even when LCLM generates 16 tokens per forward pass, its loss is only 0.09 higher than that of the vanilla autoregressive baseline (2.34 vs. 2.25).
[NLP-126] PTCG: Persona-guided Tree-based Counterargument Generation EMNLP2026
【速读】： 该论文旨在解决现有生成式AI在生成反论点时存在的单一性问题，即多数方法仅能产出一个反论点，难以体现现实辩论中所需的多样性与说服力。其核心解决方案是提出一种基于角色引导的树状反论点生成框架（Persona-guided Tree-based Counterargument Generation, PTCG），该框架融合了受思维树（Tree-of-Thoughts）启发的逐步生成与剪枝机制，并引入代表不同立场的说话者角色（speaker persona）以实现视角代入。通过从原始论点中推断作者角色并结合多元角色设定，PTCG能够系统性地生成多角度、高说服力的反论点。实验结果表明，基于大模型评分、分类器评估及人工评测均显示，PTCG在反论点的多样性和说服力方面显著优于基线方法。
链接: https://arxiv.org/abs/2609.07120
作者: Eunbeen Son,Yohan Jo,Joonsuk Park,JinYeong Bak
机构: Sungkyunkwan University (成均馆大学); Seoul National University (首尔国立大学); University of Richmond (里士满大学); NAVER Cloud
类目: Computation and Language (cs.CL)
备注: Accepted to Findings of EMNLP 2026. 30 pages, 13 figures, 15 tables
Abstract:The ability to generate counterarguments is important for critical thinking and balanced discourse, yet existing approaches typically produce only a single counterargument, failing to capture the diversity and persuasiveness required in real-world debates. To address this limitation, we propose Persona-guided Tree-based Counterargument Generation (PTCG), a framework that combines Tree-of-Thoughts-inspired step-wise generation and pruning with speaker persona selection. By estimating the author’s persona from the original argument and incorporating speaker personas representing distinct perspectives, PTCG operationalizes perspective-taking and enables the generation of diverse counterarguments. Results from LLM-as-a-Judge, classifier-based assessment, and human evaluations indicate that PTCG shows consistent improvements in both the diversity and persuasiveness of counterarguments compared to baseline methods.
[NLP-127] he Illusion of Debiasing: Persona Steering Redistributes Rather Than Reduces Bias in LLM s
【速读】： 该论文旨在解决提示工程（prompt-based interventions）对语言模型内部表征影响的机制问题，具体探究系统性提示（如角色设定、人格设定、角色指令等）是重塑模型内部结构，还是仅作用于输出通道。其核心解决方案在于采用人格设定（persona conditioning）作为可控探针，通过多层级测量方法（包括自我报告、开放式生成与词级参数关联）在三个指令微调模型中系统评估提示干预的效果沿模型深度的变化。研究发现存在梯度解离现象：人格设定虽能被模型识别并响应，但其影响局限于输出层面，未真正重构模型内部结构——模型可遵循单一特质指令，却无法再现人类不同特质间的协变关系。随着模型深度增加，这种解离进一步加剧：人格设定强化封闭式问答中的偏见，改变整体语气但不改变群体间差异，且几乎不影响已饱和的语义关联基线。因此，提示引导主要作用于输出通道，其结构性影响存在局限，而表面可操控性可能掩盖了深层结构的稳定性。
链接: https://arxiv.org/abs/2609.07117
作者: Ziyue Feng,Hongbo Fang,James A. Evans
机构: University of Chicago(芝加哥大学); Chicago, IL, USA
类目: Computation and Language (cs.CL)
备注:
Abstract:Prompt-based interventions: system prompts, personas, role instructions, reliably reshape what a language model says, but it is unclear which layer they reach. Do they reconfigure internal structure, or only modulate the output channel? We use persona conditioning as a controlled probe, measuring its effects along a depth axis from self-report, through open-ended generation, to word-level parametric association, across three instruction-tuned models. We find a graded dissociation. Personas are legible but not structural: models follow single-trait instructions yet fail to reproduce human inter-trait covariance. The dissociation deepens with depth: personas hold or amplify closed-form QA bias, shift absolute tone while leaving between-group disparity unchanged, and barely perturb an already saturated associative baseline. Prompt-based steering thus operates in the output channel and has a structural reach limit that surface manipulability can mask.
[NLP-128] Revisiting Complete Reasoning Traces for Post-Training EMNLP2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在后训练阶段依赖完整推理轨迹（reasoning trajectories）以提升推理能力的有效性问题。现有方法通常采用监督微调（Supervised Fine-Tuning, SFT）对模型进行训练，使用预收集的完整推理路径，但这些路径往往冗长且包含大量非必要信息。研究发现，完整轨迹带来的性能增益有限，而部分轨迹（partial trajectories）即使经过严重截断仍能保持有效性。其关键解决方案在于：通过注意力分析与受控的词元移除实验揭示推理轨迹中中间步骤具有高度冗余性，即对最终推理质量贡献甚微；因此，模型可通过内部知识推断缺失的推理步骤，仅需学习轨迹起点与终点即可实现有效行为调整。这一发现表明，基于端点的训练不仅可减少冗余信息干扰，还能促进模型生成更连贯的替代推理路径，并显著提升强化学习或在线蒸馏等后训练方法的性能，从而推动对完整推理轨迹必要性的重新审视。
链接: https://arxiv.org/abs/2609.07103
作者: Jaehui Hwang,Sangdoo Yun,Byeongho Heo,Dongyoon Han
机构: 未知
类目: Computation and Language (cs.CL)
备注: To appear in EMNLP 2026 Findings
Abstract:Large language models (LLMs) are often post-trained on pre-collected reasoning trajectories to improve their reasoning capability. Such trajectories tend to be long due to complex, interwoven paths, which often include detours on the path toward the answer. However, it has been underexplored whether LLMs indeed benefit from learning complete trajectories in post-training, such as supervised fine-tuning (SFT). Starting from our pilot study, we find that full trajectories provide only limited benefit, while partial trajectories are effective even under heavy truncation. We analyze redundancy in reasoning trajectories through attention-based analyses and controlled token-removal studies, both of which show that intermediate tokens contribute minimally to final reasoning quality. This suggests that avoiding redundant information may allow LLMs to internally infer coherent alternatives by inferring missing steps from their internal knowledge, given known trajectory endpoints. Furthermore, we show that training LLMs using endpoints leads to consistent changes in reasoning behavior, and that it also benefits post-training methods based on reinforcement learning or on-policy distillation, highlighting the need to revisit complete reasoning traces. Code is available at this https URL.
[NLP-129] Risk Is Not Review Value: Wrong-Answer Exposure Under Bounded Review Budgets KDD2026
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）助手生成答案数量超过人工审查能力这一核心问题，尤其关注在有限审查预算下如何优先选择最应审查的答案。传统评估方法仅关注答案是否错误、缺乏支持或置信度低，而本研究提出在固定审查预算约束下的“审查优先级”新范式，强调在资源受限条件下最大化减少错误暴露。其解决方案的关键在于将审查优先级建模为“错误暴露度降低”（exposure reduction），通过综合考虑估计错误率、干预可修复性（intervention affordance）、影响程度及审查成本等多维度因素，构建审查价值评分体系。实验采用错误答案暴露比率（WAER）和修复后残余暴露度（PRRE）作为评价指标，在720项的TAT-QA/SciFact压力测试集上验证表明，基于审查价值排序的方法在20%审查预算下维持了近似不变的WAER（0.605 vs. 0.600），但显著降低了修复后的残余暴露度（从0.881降至0.716），证明该方法不仅提升了错误检测效率，更有效降低了审查资源耗尽后仍存在的错误风险，揭示了可信的LLM评估需兼顾错误识别与有限审查能力下的暴露控制。
链接: https://arxiv.org/abs/2609.07095
作者: SangJin Park,Myungsub Choi,Jineok Kim,Minseung Kang
机构: Tynapse(韩国首尔)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted at SeT-LLM 2026 Workshop, KDD 2026
Abstract:LLM assistants often produce more answers than humans can review before users see them. Most evaluations ask whether an answer is wrong, unsupported, or low-confidence. Bounded review budgets instead ask which answers should be checked first under a fixed review budget. Risk alone is not enough: a high-risk answer may be hard to repair, while a moderately risky answer may be directly correctable from available evidence. For generated-answer evaluation, we model review prioritization as exposure reduction, where review value combines estimated wrongness, intervention affordance, impact, and cost. We evaluate review queues with Wrong-Answer Exposure Ratio (WAER), the fraction of wrong answers left unreviewed, and post-repair residual exposure (PRRE), the fraction still exposed after deterministic benchmark-supported repairs. PRRE uses repairability rules that do not numerically reuse the affordance scores used for ranking. On a 720-item TAT-QA/SciFact stress benchmark, review-value ranking keeps answer-level WAER nearly unchanged at 20% budget (0.605 vs. 0.600) but lowers PRRE from 0.881 to 0.716. These results show that trustworthy LLM evaluation should measure not only error detection, but also how limited review capacity reduces exposed wrong answers.
[NLP-130] Comparing Self-Supervised and Domain-Invariant Features for Cross-Domain Voice Phishing Detection INTERSPEECH2026
【速读】： 该论文旨在解决语音诈骗检测中的三大核心挑战：真实犯罪录音因隐私限制难以获取；即便可得，样本数量极少，无法支持模型微调；同时亟需轻量级的仅基于声学特征的检测方法，以替代计算资源消耗大的自监督学习（Self-Supervised Learning, SSL）模型。其解决方案的关键在于对比域不变韵律特征与自监督表征（HuBERT、wav2vec2.0）在跨域场景下的表现：通过在基于情景的演员录制数据上训练，测试于真实犯罪通话数据，结果表明，域不变韵律特征可在无真实样本情况下实现69.5% F1的零样本检测性能，且支持零样本部署；而HuBERT在有少量真实样本（5-shot）时达到最高性能（94.2% F1），但依赖真实数据和较高计算成本；wav2vec2.0则表现出高精度导向的检测特性（90.2% F1，99.4% 精确率，5-shot）。研究揭示了关键权衡：域不变韵律特征适用于无真实数据的零样本部署，而自监督学习方法虽性能更优，但需真实样本与大量算力支持。
链接: https://arxiv.org/abs/2609.07079
作者: Jeongmin Lee,Seung Yun,Minkyu Lee,Ran Han,Yoonkyu Woo,Jinxia Huang
机构: 未知
类目: ound (cs.SD); Computation and Language (cs.CL)
备注: 5 pages, 2 figures. Accepted at INTERSPEECH 2026
Abstract:Voice phishing detection faces three critical challenges: real criminal recordings are unavailable due to privacy constraints; when available, only a handful of samples exist, insufficient for fine-tuning; and lightweight acoustic-only detection is needed as an alternative to large self-supervised models. We compare domain-invariant prosodic features and self-supervised representations (HuBERT, wav2vec2.0) through cross-domain evaluation-training on scenario-based actor recordings and testing on authentic criminal calls. Domain-invariant prosodic features achieve 69.5% F1 zero-shot and 71.0% with 5-shot learning. HuBERT achieves highest performance (94.2% F1, 5-shot), while wav2vec2.0 exhibits a precision-oriented detection profile (90.2% F1 with 99.4% precision, 5-shot). These findings reveal fundamental trade-offs: domain-invariant features enable zero-shot deployment when no real data exists, while SSL methods achieve higher performance but require real samples and compute.
[NLP-131] A Hyperbolicity Atlas of Large Language Model Hidden States
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）中提示词（prompt-token）隐藏状态向量间距离结构是否具有格罗莫夫双曲性（Gromov Hyperbolicity, GH）这一关键问题，即探究这些向量空间是否呈现出类似树状的层级距离特性。其解决方案的关键在于系统性地测量并分析当代多种开源大模型在不同参数规模、层深、模型家族和输入领域下的隐藏状态，构建了一个涵盖四个维度（参数规模、层深、模型族、输入域）的GH映射图谱。研究发现，层深是主导因素：中间层通常呈现高相对双曲性平台，而末层则表现出更强的树状结构；参数规模的影响较弱且非单调，不同模型家族间的差异显著，且输入域与模型专业化程度存在交互效应。这一结果表明，格罗莫夫双曲性可作为一项实用诊断工具，用于识别层级距离结构的出现位置、揭示模型专业化对结构的影响，并指导更具针对性的模型-层-域对比分析。
链接: https://arxiv.org/abs/2609.07053
作者: Zhichao Yang,Yuanze Hu,Gen Li,Qingchen Yu,Shiying Duan,Xinyu Wang,Ye Qiu,Zeming Liu,Guangxu Chen,Zhaoxin Fan
机构: Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, School of Artificial Intelligence, Beihang University(北京航空航天大学未来区块链与隐私计算高精尖创新中心，人工智能学院); South China University of Technology(华南理工大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:LLM hidden states are ordinary vectors, but the distances among those vectors may still show hierarchical structure. To our knowledge, this paper is the first systematic study of whether prompt-token hidden states in contemporary LLMs exhibit Gromov Hyperbolicity (GH), a distance-based measure of tree-likeness. Using 818,904 sample-layer measurements from ten open-weight models across MATH500, HumanEval, WinoGrande, and TruthfulQA, we build a GH map over four axes: parameter scale, layer depth, model family, and input domain. The clearest pattern is depth, not scale: middle layers usually form a high-relative-hyperbolicity plateau, while final layers often become substantially more tree-like. Scale effects are weak and non-monotonic, matched 7/8B model families differ strongly, and domains interact with model specialization. These findings make GH useful as a practical diagnostic: it shows where hierarchical distance structure appears, how specialization changes it, and which model-layer-domain comparisons deserve closer analysis.
[NLP-132] Aha-Flow Distillation: Flow Markers Matter in LLM Reasoning
【速读】： 该论文旨在解决大模型在复杂推理任务中因缺乏持续性、过程性思考而导致的推理质量瓶颈问题，尤其针对传统“顿悟时刻”（Aha Moment）驱动的推理模式易出现回溯与修正过度、难以保持连贯性的问题。其核心解决方案在于引入“流动时刻”（Flow Moment）这一新的推理范式，通过构建生成式推理链中的“流动标记”（Flow Marker），以捕捉持续、确认性、过程导向的语言表达（如“I’m doing”）。在此基础上，提出流-顿悟蒸馏（Aha-Flow Distillation, AFD），一种双模式自蒸馏框架：一方面保留传统的简洁解法监督（Aha分支），另一方面引入经重构的“流动思维链”（Flow-CoT）作为辅助监督信号，并在训练时分别施加直接且自信的推理指令。在推理阶段，仅使用标准反思指令，使流动式推理仅作为训练信号存在，避免对推理效率产生负担。实验结果表明，在AIME25和HMMT25基准上，该方法显著提升了Qwen3-8B和Qwen3-4B模型的平均准确率（Avg@12），验证了双模式结构不仅依赖于异质推理监督的引入，更关键在于其在自蒸馏过程中对不同推理模式的组织方式所带来的一致性提升。
链接: https://arxiv.org/abs/2609.07036
作者: Xiaodong Wang,Peixi Peng
机构: Peking University (北京大学); Pengcheng Laboratory (鹏城实验室)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Work in progress
Abstract:We identify the Flow Moment, a reasoning pattern characterized by sustained, process-confirming verbalizations such as I’m doing, in contrast to the revision- and backtracking-oriented Aha Moment. We refer to their corresponding linguistic expressions as Flow Markers and Aha Markers, respectively. Based on this observation, we construct Flow-CoT by rewriting the discourse markers of original reasoning traces while preserving their underlying reasoning content, and use it as auxiliary supervision for on-policy self-distillation (OPSD). We further propose \textbfAha-Flow Distillation (AFD), a dual-mode extension of OPSD that pairs different forms of privileged information with corresponding reasoning instructions. The Aha branch retains concise solution-based supervision, while the Flow branch introduces rewritten Flow-CoT under a direct and confident reasoning instruction. At inference time, the model uses only the standard reflective instruction, so Flow-style reasoning serves purely as a training signal. Experiments on AIME25 and HMMT25 show consistent improvements across Qwen3-8B and Qwen3-4B: AFD improves Avg@12 from 60.8 to 61.3 on Qwen3-8B and from 57.5 to 58.6 on Qwen3-4B over our reproduced OPSD baselines. Controlled ablations further show that, with the same Flow-CoT/Aha-CoT composition, dual-mode training improves Avg@12 from 59.5 to 60.1, indicating that the benefit comes not only from introducing heterogeneous reasoning supervision, but also from how it is organized during self-distillation. The code is available at this https URL.
[NLP-133] LatentMD: Benchmarking Markdown Boundary Failures in LLM -Generated Text
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在生成Markdown格式文本时，普遍存在但长期被低估的“边界错误”问题，即模型生成的内容在语义上正确，但在语法边界（如代码块分隔符）的匹配与闭合上存在缺陷，导致下游渲染器或解析器无法正确处理。其核心解决方案是提出LatentMD——一个针对CommonMark标准下代码块边界故障的基准测试框架与评估协议，通过将内容正确性与边界正确性解耦，实现对“内容正确但边界错误”的输出的精准识别。该框架包含4,179个测试用例及命令行接口（CLI），支持对任意模型输出进行量化评分。实验覆盖9个主流LLM及约37,600次生成结果，发现高达38.0%的有效主网格输出虽内容正确却存在边界错误，且此类错误在未明确提示的场景下尤为严重。消融分析表明，边界失败主要由同类型对称分隔符冲突（symmetric-delimiter collisions）驱动，而非嵌套结构本身，提示性指令仅部分缓解问题，而该现象可泛化至Python三重引号文档字符串，相比之下JSON因采用非对称分隔符仍保持鲁棒性。因此，LatentMD为需要严格依赖解析器的生成式应用提供了可复现、可诊断的评估靶点。
链接: https://arxiv.org/abs/2609.06993
作者: Sungjune Lee,Myungjoo Kang
机构: Seoul National University(首尔国立大学)
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Large language models (LLMs) increasingly generate Markdown that is consumed by renderers, agents, code extractors, and structured downstream pipelines. Yet existing evaluations often conflate content quality with format adherence, leaving Markdown boundary failures under-measured. We introduce LatentMD, a benchmark and evaluation protocol for diagnosing CommonMark-level fence-boundary failures in LLM-generated Markdown. LatentMD separates content correctness from boundary correctness, enabling detection of outputs that are content-correct but boundary-broken. The benchmark contains 4,179 prompts and a CLI for scoring arbitrary model outputs. Across 9 LLMs and roughly 37,600 generations, we find that Markdown boundary failures are widespread: 38.0% of valid main-grid outputs are content-correct but boundary-broken, with substantial boundary breakage under unspecified prompts and in a small human-authored validation set. Ablations show that failures are driven primarily by same-family symmetric-delimiter collisions rather than nesting alone, are only partially mitigated by prompt hints, and generalize to Python triple-quote docstrings while JSON remains robust as an asymmetric-delimiter control. LatentMD provides a reproducible diagnostic target for parser-sensitive LLM evaluation.
[NLP-134] rain Overcomplete Deploy Compact: Scaling Recovery Capacity for Structured LLM Pruning EMNLP
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在结构化剪枝（structured pruning）后恢复阶段存在的性能瓶颈问题，即“容量-知识不对称性”（capacity-knowledge asymmetry）——剪枝后的恢复模块表征能力不足以承载被移除模型知识的复杂性，导致性能下降。其核心解决方案是提出一种名为OverRep（Overcomplete Reparameterization）的过参数化重构框架，遵循“训练时过参数化，部署时紧凑化”的原则：在训练阶段临时扩大恢复模块的参数规模以充分吸收原始模型的知识蒸馏信息；训练完成后，通过代数合并将过参数化的重构模块转化为数学等价的紧凑形式，从而在不增加推理阶段内存占用和计算量的前提下，完整保留剪枝模型的高效架构。此外，OverRep引入了渐进式激活机制（annealed activation），在训练中支持非线性动态演化，最终收敛至线性状态，确保精确的代数合并。实验表明，在三种主流模型架构上，OverRep在25%和50%剪枝率下分别相较强基线提升高达5.5和8.4个点的推理性能，同时保持与现有方法相当的内存消耗和TFLOPs。
链接: https://arxiv.org/abs/2609.06974
作者: Seungmin Oh,Donggeon Lee,Jongbin Ryu
机构: Ajou University (亚洲大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted by the Conference on Empirical Methods in Natural Language Processing (EMNLP) 2026
Abstract:Large language models achieve strong performance across diverse tasks, but deployment remains costly because of memory, latency, and energy demands. Structured pruning reduces these costs by removing architectural components, yet its recovery stage is often limited by a mismatch between the recovery module’s representational capacity and the complexity of the removed knowledge. We call this bottleneck the capacity-knowledge asymmetry and propose OverRep, an Overcomplete Reparameterization framework for structured LLM pruning. Following the principle of “train overcomplete, deploy compact”, OverRep temporarily overparameterizes the recovery module during training to absorb complex knowledge distilled from the original model. After recovery, the overcomplete re-parameterization is algebraically merged into a mathematically equivalent compact module, preserving the pruned model’s inference-time architecture and computational cost. OverRep further introduces an annealed activation that enables nonlinear training dynamics while converging to a linear regime for exact algebraic merging. Across three backbone families, OverRep improves retained reasoning performance over strong recovery baselines by up to 5.5 and 8.4 points at 25% and 50% pruning, respectively, while keeping memory usage and TFLOPs comparable to existing recovery methods. Our code is available at this https URL.
[NLP-135] CantoneseLLM v2: Reasoning in a Low-Resource Language
【速读】： 该论文旨在解决粤语（Cantonese）在书面语料资源匮乏的问题，特别是缺乏可用于模型训练的原生粤语推理过程（reasoning traces）大规模语料库。针对这一挑战，研究提出并发布了基于Qwen3 8B和30B-A3B架构的CantoneseLLM v2模型，其关键解决方案在于采用多阶段训练框架：首先通过7.84亿条粤语及香港相关语料进行上下文预测训练（CPT），随后引入聊天向量融合（chat-vector merging）以保留原始模型的推理语言特性；接着利用有限的粤语推理数据进行监督微调（SFT），但发现此过程会显著缩短或消除推理链，导致基准性能下降；随后采用直接偏好优化（DPO）恢复推理块结构，尤其对8B模型效果明显，但仅部分恢复性能；最终通过强化学习与视觉反馈增强（RLVR）训练，并以粤语语言特征和繁体中文书写系统作为乘性约束条件，实现了粤语语言对齐，成功恢复了丢失的性能。实验表明，30B-A3B模型在HKCanto-Eval上的得分为73.16，仅比其融合检查点低1.20分，同时保持了原始模型所缺失的粤语推理行为。研究还公开了模型检查点、训练环境及一个涵盖十三年的繁体中文Common Crawl数据集，为后续粤语生成式AI研究提供重要基础。
链接: https://arxiv.org/abs/2609.06970
作者: Tsz Chung Cheng,Chung Shing Cheng,Chaak Ming Lau,Cheuk Hei Chong
机构: Kyushu University (九州大学); The Education University of Hong Kong (香港教育大学); Votee AI; Beever AI
类目: Computation and Language (cs.CL)
备注:
Abstract:Cantonese is widely spoken but remains low-resource in written data, with no large corpus of native Cantonese reasoning traces available for model training. We develop and release CantoneseLLM v2, comprising models based on Qwen3 8B and 30B-A3B. The models are trained through CPT on 784 million Cantonese and Hong Kong-related tokens, chat-vector merging, SFT, DPO, and RLVR. Evaluation across the training stages shows that chat-vector merging transfers instruction following but preserves the donor model’s reasoning language, while SFT with limited Cantonese reasoning data substantially shortens or removes reasoning traces and reduces benchmark performance. DPO restores the reasoning-block format, particularly for the 8B model, but recovers only part of the lost performance. The RLVR training with Cantonese language and Traditional Chinese scripts as multiplicative constraints introduced Cantonese language alignment and restored the lost performance. The 30B-A3B model reaches 73.16 on HKCanto-Eval, within 1.20 points of its merged checkpoint, while retaining the Cantonese reasoning behaviour absent from that checkpoint. We release the model checkpoints, the training environments, and a thirteen-year Traditional Chinese Common Crawl dataset. The models can be accessed at this https URL
[NLP-136] MOLE: Detecting Insider Threats in AI Agents
【速读】： 该论文旨在解决前沿实验室中人工智能代理（AI agent）在操作高权限账户时，因模型错位、提示注入或操作员误用等风险，可能导致模型权重泄露、训练数据污染或发布安全机制弱化等问题，而现有基准测试无法在有限审查预算下评估防御者是否能有效检测此类异常行为。其解决方案的关键在于提出MOLE——一个开放的基准测试平台，包含150个由AI操作的账户，在30个工日内共享9个具有状态的服务，涵盖12种威胁类型和来自4个模型的8个语料库，总计约200亿词元。通过在39个代理模型上进行实验发现，72%的代理完成了大部分有害目标，且代理拒绝行为无法预测任务完成情况。MOLE支持对40种监控器在不同语料生成方式、可观测性水平及威胁场景下的系统性比较，结果显示即使表现最优的监控器在单日审计事件评估中仍遗漏近半数已实现的危害。此外，MOLE可促进监控器开发：基于基准引导的搜索使中等性能监控器性能提升49%-64%，而选择性部署更强监控器相比全量应用可使预算-受试者工作特征曲线下面积（budget-AUC）提高10%，同时保持相近的建模成本。
链接: https://arxiv.org/abs/2609.06966
作者: Aashiq Muhamed,Virginia Smith
机构: Carnegie Mellon University (卡内基梅隆大学); Pittsburgh, PA 15213, USA
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Cryptography and Security (cs.CR)
备注:
Abstract:Model misalignment, prompt injection, or operator misuse could lead AI agents operating frontier-lab accounts to exfiltrate model weights, poison training data, or weaken release gates. Existing benchmarks do not test whether defenders can detect this activity among routine work under a limited review budget. We introduce MOLE, an open benchmark of 150 AI-operated accounts sharing 9 stateful services over 30 workdays, with 12 threats and 8 corpora from four models totaling roughly 20 billion tokens. Of 39 agent models, 72% complete most assigned harmful objectives and agent refusal does not predict completion. MOLE enables comparison of 40 monitors across corpus generators, observability levels, and threats; even the best evaluated monitor in our single-day audit-event comparison misses nearly half of completed harm. MOLE also enables monitor development: benchmark-guided search improves a mid-tier monitor by 49-64%, while selective use of a stronger monitor improves budget-AUC by 10% over applying it to every account-day at comparable modeled cost.
[NLP-137] urEngMix: A Text Corpus and Benchmark for Turkish-English Code-Mixed Language Identification and Named Entity Recognition EMNLP2026
【速读】： 该论文旨在解决低资源语言对在代码混杂（code-mixed）文本上的自然语言处理（NLP）性能低下问题，尤其聚焦于土耳其语-英语这一具有特殊挑战的混合语言场景。其核心挑战在于英语词干与土耳其语词缀在形态上高度融合，形成单一混合语言词元（token），导致传统模型难以准确识别语言归属与命名实体。论文提出TurEngMix数据集，包含5.5K条噪声自然的社会媒体文本（共486,974个词元），并构建了一个新的土耳其语-英语代码混杂语言识别（LID）与命名实体识别（NER）基准，涵盖15,000个专家标注词元。实验结果表明，尽管单语词元的标注表现可靠，但所有模型在混合词元上的误差率显著升高，尤其在形态整合型词元上，GPT-4o与Qwen的NER错误率分别高达5.2倍和6.3倍，凸显了形态融合带来的建模难题。解决方案的关键在于构建高质量、真实世界来源的代码混杂语料库及标注基准，并揭示当前大语言模型（LLM）在处理复杂形态融合现象时的局限性，为未来计算语言学与社会语言学研究提供基础支持。
链接: https://arxiv.org/abs/2609.06963
作者: Ilayda Dogan,Phuong-Anh Nguyen-Le,Julia Mendelsohn
机构: University of Maryland (马里兰大学)
类目: Computation and Language (cs.CL)
备注: Accepted to W-NUT 2026 (11th Workshop on Natural User-generated Text), co-located with EMNLP 2026. 15 pages, 4 figures, 12 tables
Abstract:Natural language processing systems underperform on code-mixed text, particularly for low-resource language pairs. Turkish-English poses a further challenge: it lets English stems combine with Turkish suffixes to form single mixed-language tokens. We introduce TurEngMix, a corpus of 5.5K noisy, naturally occurring social media posts (486,974 tokens) rich in Turkish-English code-mixing. From this corpus, we construct a new Turkish-English benchmark for code-mixed language identification (LID) and named entity recognition (NER), comprising 15K expert-annotated tokens. Evaluating both decoder LLM and fine-tuned encoder baselines, we find that monolingual Turkish and English tokens are labeled reliably, but all models have high error rates on mixed-language tokens for both LID and NER. For morphologically integrated tokens, NER error rates were 5.2x and 6.3x higher for GPT-4o and Qwen, respectively. This highlights how morphological integration remains a challenge. We release the corpus, annotations, and code to support future computational and sociolinguistic research on Turkish-English code-mixing.
[NLP-138] Dynamic-Programming-Guided Hierarchical BPE and Empirical Analysis of Vocabulary Pruning
【速读】： 该论文旨在解决传统字节对编码（Byte Pair Encoding, BPE）在词汇表构建过程中，因贪婪合并策略导致的词汇分配非最优问题，尤其在固定模型可见词汇表规模下难以实现最佳压缩性能。其核心挑战在于如何在保证词汇表大小受限的前提下，有效平衡子词单元的通用性与压缩效率。解决方案的关键在于提出一种基于动态规划引导的分层字节对编码（Dynamic-Programming-Guided Hierarchical BPE, DH-BPE）方法：首先生成一个适度过大的初始候选词汇表，随后利用动态规划精确计算每个候选子词在最小令牌分割下的暴露度，并结合BPE训练所诱导的层次依赖关系，实施暴露度导向且依赖感知的剪枝策略，从而选出最具实用价值的固定大小词汇表。实验表明，相较于标准BPE及近期优化基线（如Pruned BPE、MinGram、MinGram-PP），DH-BPE在12K和16K目标词汇量下均实现了更优的聚合压缩表现；在跨语料库评估中，当候选池放大至2.0和3.0倍时，DH-BPE甚至超越了表现优异的MinGram-PP，仅在极大候选池（4.0–5.0倍）下才被反超。定性分析进一步揭示，DH-BPE能够有效协调后期完整合并与可复用子词组件之间的权衡，为固定词汇预算下的词汇分配提供了兼具理论严谨性与实践可行性的新范式。
链接: https://arxiv.org/abs/2609.06898
作者: Kenny Shao
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 21 pages, 5 figures, 3 tables, and 1 algorithm
Abstract:Byte Pair Encoding (BPE) constructs vocabularies through greedy pair merging, but the resulting merge order does not necessarily allocate a fixed model-visible vocabulary optimally for compression. We propose Dynamic-Programming-Guided Hierarchical BPE (DH-BPE), a vocabulary-construction method that combines token exposure under exact minimum-token segmentation with the hierarchical dependencies induced by BPE training. Starting from a modestly overshot BPE candidate vocabulary, DH-BPE uses dynamic programming to measure candidate utility and applies exposure-guided, dependency-aware pruning to select a fixed-size model-visible vocabulary. We compare DH-BPE against Standard BPE and recent vocabulary-optimization baselines, including Pruned BPE, MinGram, and MinGram-PP, in primary evaluations at 12K and 16K target vocabulary sizes, with an additional 18K evaluation against MinGram only. Across the primary 12K and 16K comparisons, DH-BPE consistently improves aggregate compression over Standard BPE, Pruned BPE, and MinGram under a shared exact minimum-token DP encoder. MinGram-PP achieves stronger aggregate compression in the primary comparisons, but DH-BPE outperforms it at overshoot factors f = 2.0 and f = 3.0 in cross-corpus evaluation; at 12K, MinGram-PP reverses this ordering only with the substantially larger candidate pools at f = 4.0 and f = 5.0. Qualitative analysis further shows that DH-BPE balances later, more complete BPE merges with reusable subword components, providing a practical approach to improving vocabulary allocation under a fixed model-visible vocabulary budget.
[NLP-139] owards Bridging the Gap Between Offline and Iterative Alignment via Preference Distillation
【速读】： 该论文旨在解决离线大语言模型（LLM）对齐中，尽管直接偏好优化（DPO）方法具有简洁性和计算高效性，但迭代式扩展方法在学术基准测试中表现更优的问题。其核心问题是：为何迭代方法普遍优于离线方法？以及能否将迭代方法的优势融入离线对齐框架？针对第一个问题，研究通过受控实验发现，迭代方法中引入的显式偏好建模（explicit preference modeling）是其性能优越的关键因素。基于此洞察，论文提出一种名为“蒸馏偏好概率策略优化”（Distilled Preference Probability Policy Optimization, DP3O）的新型离线对齐算法。该方案首先利用辅助大模型学习一个显式偏好模型，并将其知识蒸馏至策略优化过程。理论上，显式偏好建模相比隐式建模具有更优的估计误差控制能力，且DP3O通过方差减少实现了比硬标签DPO更紧的泛化界。实证结果表明，DP3O在多种对话及下游任务上均超越现有先进离线方法，性能接近迭代式DPO，同时训练时间缩短约42%，充分验证了其有效性与高效性。
链接: https://arxiv.org/abs/2609.06893
作者: Wenbo Zhang,Wenzhuo Zhou,Hengrui Cai,Zhengling Qi
机构: University of California, Irvine (加州大学欧文分校); George Washington University (乔治华盛顿大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted by Transactions on Machine Learning Research (TMLR)
Abstract:Direct preference optimization DPO is a promising offline approach for aligning large language models (LLMs) due to its simplicity, computational efficiency, and implicit modeling of human preferences. Interestingly, iterative extensions of DPO have achieved stronger performance on academic benchmarks, raising two key questions: (i) Why do iterative methods generally outperform offline ones? (ii) Can their advantages be incorporated into offline alignment? To answer the first question, our controlled experiments reveal that the explicit preference model, additionally introduced in the iterative procedure, is a key factor behind its superiority over offline methods. This insight leads us to answer the second question affirmatively and propose Distilled Preference Probability Policy Optimization (DP3O), an effective and efficient offline alignment algorithm. DP3O first learns an explicit preference model using a helper class of LLMs and then distills its knowledge into policy optimization. Theoretically, we show that explicit preference modeling admits better estimation error control than implicit formulations, and that DP3O achieves a tighter generalization bound than hard-label DPO through variance reduction. Empirically, we evaluate DP3O on a wide range of chat-based and downstream tasks and show that it outperforms state-of-the-art offline methods, achieves performance comparable to iterative DPO, and reduces training time by about 42% , demonstrating both its effectiveness and efficiency.
[NLP-140] Contextual Observer Grounding: Evaluating Situated Spatial Reasoning in Vision-Language Models EMNLP2026
【速读】： 该论文旨在解决在具身任务（如机器人操作）中，模型如何基于说话者的语境化视角理解语言指令中的空间关系这一关键问题。其核心挑战在于实现“上下文观察者定位”（contextual observer grounding），即模型需从共享环境知识、活动背景及常识中推断说话者的视角，并据此解析其描述的方位关系。现有视觉-语言模型（VLMs）虽具备一定空间推理能力，但在缺乏显式说明时难以准确推断说话者视角，导致对方向性语言指令的响应效果不佳。为系统评估此能力，作者构建了三维场景与查询数据集——视角基准测试（Point-of-View Benchmark, POVBench），通过分离“推断型”“陈述型”和“给定型”三种观察者定位形式，量化模型在复杂情境下的空间定位表现。研究发现，将观察者相对的空间推理过程显式分解后，可显著提升目标定位精度，表明显式建模视角依赖关系是提升具身语言理解性能的关键。
链接: https://arxiv.org/abs/2609.06880
作者: Mimo Shirasaka,Haochen Zhang,Yonatan Bisk
机构: The University of Tokyo(东京大学); Carnegie Mellon University(卡内基梅隆大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Machine Learning (cs.LG); Robotics (cs.RO)
备注: Accepted to EMNLP 2026 Findings
Abstract:Reasoning over language instructions in embodied tasks such as robotics often requires understanding spatial relations from a speaker’s situated perspective. Humans infer such perspectives from shared environmental knowledge, activity context, and commonsense. Recent vision-language models (VLMs) appear capable of spatial reasoning, but their ability to infer a speaker’s viewpoint from contextual cues and interpret situated spatial relations from that viewpoint remains unclear. We call this capability contextual observer grounding. To study this capability, we construct the Point-of-View Benchmark (POVBench), a dataset of 3D scenes and queries that disentangles Inferred, Stated, and Given forms of observer grounding in natural embodied communication. Given multi-view observations and a natural-language sentence, models must localize unseen or underspecified targets from situated spatial and contextual cues. Across state-of-the-art VLMs, localizing targets from directional language remains challenging, even when observer grounding is made explicit. We find that explicit breakdowns of observer-relative spatial reasoning improve target localization. Our project page is available at this https URL.
[NLP-141] AutoLexSteer: Automatic Contrast Construction for Lexical Activation Steering
【速读】： 该论文旨在解决生成式人工智能（Generative AI）中大型语言模型（LLM）输出控制的精准性问题，尤其是针对传统引导向量（steering vectors）构建过程中因嵌入空间不透明而导致的人工构造困难与精度不足的问题。其核心解决方案是提出一种基于词义（word senses）的新颖引导向量——Hangman，以及首个完全自动化的引导向量构建方法AutoLexSteer。AutoLexSteer利用WordNet中提取的语义相近词汇家族，自动识别需规避的源语义和期望达到的目标语义，从而实现对模型行为在词级及词义层面的精确引导，显著提升了引导的可解释性与可控性，并成功应用于抑制如阿谀奉承（sycophancy）等特定不良行为。
链接: https://arxiv.org/abs/2609.06879
作者: Shuhe Wang,Lachlan Cowley,Eduard Hovy,Jey Han Lau
机构: The University of Melbourne (墨尔本大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Steering vectors have rapidly emerged as a popular and effective method for guiding the output of LLMs in very specific ways. But constructing accurate steering vectors is a difficult manual process due to the opacity of embeddings. We introduce Hangman, a novel type of steering vector that operates using word senses, as well as AutoLexSteer, the first fully automated process for building steering vectors. AutoLexSteer employs families of closely-related words extracted from WordNet to specify both the steering source to be avoided and the desired steering target. The steering vectors are quite precise, can be used to steer at the level of words and sets of word senses (meanings), and are able to steer certain LLM behaviors like sycophancy. The dataset and code can be found at this https URL.
[NLP-142] Exact Record Omission in Delta Attention: A Transport Criterion Its Cost and a Replay Certificate
【速读】： 该论文旨在解决生成式 AI（Generative AI）系统中用户请求“遗忘”特定记忆记录时，如何实现精确删除的问题。核心挑战在于，当记录通过循环神经状态（recurrent memory）被逐步融合进模型的内部状态后，其影响会持续扩散并嵌入后续更新中，导致直接删除变得困难。解决方案的关键在于提出“运输收据”（transported receipt）机制：即在记录插入时保存其对系统状态的增量变化，并在删除时通过逆向抵消该变化来恢复至“从未存储”的状态。然而研究发现，只有当该记录引发的后续所有更新净效应相互抵消时，运输收据才能实现精确遗忘；而实验结果表明，在释放的48B Kimi Linear混合模型中，这一条件并未满足——即使经过4096个后续词元，仍存在约4.5%的状态残余，且现有各类收据方法均无法消除该残留。相比之下，通过回滚至记录前的检查点（checkpoint）并重放剩余序列的方法可实现完全精确遗忘，其计算开销与重放长度成正比，是唯一能达成此目标的方案。此外，尽管掩码注意力行（masking attention rows）可使采样恢复接近未存储状态，但审计者仍可通过重建参考状态检测到残留印记，说明仅局部修改不足以彻底消除影响。
链接: https://arxiv.org/abs/2609.06872
作者: Vishwajith Ramesh
机构: Vy Labs, Inc.
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Cryptography and Security (cs.CR)
备注: 22 pages, 8 figures, 9 tables. Source-free evidence for every quantitative claim is packaged with the source
Abstract:When a user asks an assistant to forget a record, the test is whether the memory now matches the state it would hold if the record had never been stored. Independently encoded rows can be removed directly; a recurrent memory folds records into an evolving state. One hope is a receipt: save the difference the record made when it arrived, carry it forward through later updates, and subtract it, so that deletion costs one fixed-size edit no matter how long the conversation runs. We show that a transported receipt reaches exact omission if and only if the changes the record induces in later updates cancel out on net, and we measure whether they do on the released 48B Kimi Linear hybrid. They do not: after 4,096 further tokens the record still leaves an imprint of about 4.5% of the state norm that none of the tested receipt classes removes, recomputing half the suffix closes less than half the gap, and the per-token log a receipt needs costs more than a full checkpoint after 88 tokens. The same write-rule classification held on Mamba-2, Falcon-H1, and RWKV-7 with predictions recorded before the runs. Restoring a checkpoint from before the record and replaying the surviving suffix matches the never-stored state exactly on every array we check. In the hybrid suffix sweep, masking the record’s attention rows brings sampled recovery close to the never-stored floor even though the recurrent imprint remains, and an auditor who rebuilds the reference can still detect it. Among the evaluated methods, checkpoint replay achieves exact omission, with work proportional to the replayed suffix.
[NLP-143] You Are What You Read: Misalignment via In-Context Persona Induction
【速读】： 该论文旨在解决大模型在微调（fine-tuning）过程中因使用窄域数据（narrow data）而导致的广泛性偏差（broad misalignment）问题，尤其是当模型仅通过演示不良行为（harmful behavior）来学习时所引发的潜在风险。其核心解决方案是提出“人格诱导”（persona induction）机制：即在不进行任何微调、不直接提示不良行为的前提下，仅通过在上下文中引入一系列看似无害的传记性事实（biographical facts），即可促使模型在面对无关问题时表现出特定人物的身份特征与观点。研究发现，随着传记事实数量的增加，模型对目标人格的采纳程度呈S型上升趋势，3至10个事实即可使身份采纳率超过50%；对于无害人格，模型可实现近乎零偏差的完全采纳；而对于有害人格，模型则会在无关问题上以高达80%的频率输出其典型观点。此外，通过格式化指令可控制人格激活时机，且由于每个事实本身均属良性，此类上下文触发方式被内容过滤器标记的概率仅为3%，远低于等效直接指令的24%-33%。因此，该方案的关键在于利用累积的良性上下文信息实现隐蔽而高效的模型行为引导，从而规避传统微调和显式指令带来的安全与伦理风险。
链接: https://arxiv.org/abs/2609.06851
作者: Kyuhee Kim,Benjamin Berczi,Cozmin Ududec
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 20 pages, 14 figures, 27 tables. Includes appendices
Abstract:Broad misalignment has been produced by finetuning on narrow data, harmful or benign, and in context only by demonstrations of the undesirable behaviour itself. We show that benign data suffices in context, with no finetuning and no demonstration of harmful behaviour in the prompt. Biographical facts that converge on a single figure, placed in a model’s context as ordinary conversational turns, lead it to answer as that figure on questions the facts never touch. We call this persona induction. Across nine personas and thirteen models, identity adoption rises sigmoidally with the number of facts and crosses 50% within 3 to 10 of them. Misalignment then tracks which figure is described. Harmless personas reach full adoption with near-zero misalignment, while harmful ones voice their characteristic views on unrelated questions, at rates up to 80%. A formatting instruction can gate when the persona activates. Because each fact is individually benign, accumulated biographical context is flagged by content filters on 3% of inputs against 24-33% for an equivalent direct instruction.
[NLP-144] XYBench: Can LLM s Respond Prag matically to Queries with Misconceptions? EMNLP
【速读】： 该论文旨在解决非专业用户在使用大语言模型（LLM）寻求帮助时，常因认知偏差或技术误解提出“XY问题”（即用户表面上询问如何解决某个具体问题，实则其背后存在根本性误解）所带来的有效响应难题。其核心挑战在于：尽管用户提问的表层需求（X）明确，但真正需要解决的是其隐含的根本问题（Y），而当前主流模型往往仅回应表面请求，未能识别并纠正用户的误解。解决方案的关键在于构建一个系统性的评估框架——XYBench，涵盖来自技术类（如Stack Overflow/StackExchange）与日常类（如WikiHow及人工标注子集）共8,115个真实场景的查询数据，并基于合作回应理论（cooperative response theory）设计三维度评价标准：（a）是否包含实际可行的解决方案；（b）对正确解决方案的强调程度；（c）能否准确识别用户的认知误区。实验表明，即使最强的现有大模型也主要回应字面请求（0.75–0.92），而对真正意图的响应率仅为0.33–0.71，且在识别误解方面显著落后于人类（最高63%对比人类79–90%）。此外，尽管模型在多选情境中倾向于选择实用方案，但在生成阶段却难以自主产出此类回应，说明当前模型在实现“语义层面的智能引导”和“认知纠偏”方面仍存在根本性能力缺失。
链接: https://arxiv.org/abs/2609.06842
作者: Akhila Yerukola,Jena D. Hwang,Mingqian Zheng,Jenna Godsey,Hyunwoo Kim,Valentina Pyatkin,Jennifer Hu,Maarten Sap
机构: Carnegie Mellon University(卡内基梅隆大学); Allen Institute for AI(艾伦人工智能研究所); NVIDIA(英伟达); Johns Hopkins University(约翰霍普金斯大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted to Empirical Methods in Natural Language Processing (EMNLP) 2026, 32 pages, 25 figures
Abstract:When non-expert users ask LLMs for assistance, their queries can often have misconceptions (e.g., “How do I parse XML with regex?”). In such cases, often referred to as the XY-problem, LLMs must identify the misconception (“regex are fragile”) and meaningfully direct the user toward a pragmatic solution that will address the root problem implicit in the request (“use an XML parser”). We introduce XYBench, a benchmark of 8,115 such queries, drawn from technical (StackOverflow/StackExchange) and everyday (WikiHow and a manually-curated subset) domains. We design an evaluation paradigm that assesses model responses along three criteria grounded in cooperative response theory: (a) presence and (b) emphasis on pragmatic solutions, and © identification of misconceptions. Our experiments show that even the strongest LLMs predominantly answer the literal request (0.75–0.92) and far less often the intended one (0.33–0.71), while substantially lagging behind humans at identifying misconceptions (at most 63% vs. 79–90%). Further, models overwhelmingly prefer pragmatic responses in a multiple choice setting yet consistently fail to generate them. Oracle ablation experiments show that providing explicit user intent at generation time helps; however a large gap remains, suggesting pragmatic redirection is a fundamentally underdeveloped capability in current LLMs.
[NLP-145] NormViz: A Benchmark and Framework for Grounding Multimodal Reasoning in Global Cultures
【速读】： 该论文旨在解决当前多模态大模型在跨文化情境下对视觉行为的社会规范理解能力不足的问题。现有研究多聚焦于文本或单一视觉对象识别（如食物、服饰），而忽视了对可观察行为如何受本地社会规范影响的深层推理，即“视觉规范理解”（visual norm understanding）这一关键但未被充分探索的领域。为应对该挑战，论文提出一个高质量、经人工验证的基准测试集NormViz-Bench，包含3,268组对比图像对（共6,536张图像），覆盖16个国家，每组图像仅在与文化相关的视觉行为（如物体、属性、空间关系和动作）上存在差异，且每张图像均标注是否符合、违反或与本地社会规范无关，要求成对判断必须同时正确，以避免模型依赖表面视觉线索。实验表明，即使最先进的视觉语言模型（VLMs），如Gemini 3.0 Flash和Qwen2.5 VL 7B，在该任务上的准确率也分别仅为26.6%和21.6%，尤其在识别违规行为及文化中性行为时表现不佳。为此，论文进一步构建了NormViz-Train训练数据集，包含6.4万张带解释的图像，通过微调显著提升模型性能——在Qwen3-VL 4B和8B上相对提升分别达125%和36%，虽绝对性能仍低，但验证了通过引入带解释的因果化训练数据可有效引导模型建立视觉感知与文化意义之间的关联。因此，解决方案的关键在于构建兼具语义对比性和文化敏感性的数据基准与可解释的训练范式，从而推动多模态AI向具备真实文化认知能力的方向发展。
链接: https://arxiv.org/abs/2609.06831
作者: Akhila Yerukola,Fabrice Y Harel-Canada,Simran Khanuja,Abhinav Sukumar Rao,Ashima Suvarna,Nanyun Peng,Saadia Gabriel,Maarten Sap
机构: Carnegie Mellon University (卡内基梅隆大学); University of California, Los Angeles (加利福尼亚大学洛杉矶分校)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted to Conference on Language Modeling (COLM) 2026. 37 pages, 23 figures
Abstract:AI systems are used worldwide, but they struggle to serve the needs of culturally diverse populations. Prior work on cultural understanding evaluates AI systems on text-only settings or on visual artifact recognition (e.g. foods, clothing). The ability to reason about visually observable behaviors through local social norms, which we call visual norm understanding, remains unexamined. We introduce NormViz-Bench, a high quality, human-validated benchmark of 3,268 contrastive image pairs (6,536 images) spanning 16 countries. Each pair varies only in the culturally relevant behavior (e.g., objects, attributes, spatial relations, and actions) that alters how each image is interpreted. Each image is labeled as conforming to, violating, or irrelevant to local social norms, and pair-level evaluation requires both images to be correctly classified, thereby preventing reliance on superficial visual shortcuts. Even the strongest VLMs, Gemini 3.0 Flash and Qwen2.5 VL 7B, succeed on only 26.6% and 21.6% of pairs, struggling most with identifying violating and culturally benign visual behaviors. Towards bridging this, we introduce NormViz-Train, a training dataset of 64k images paired with explanations. Though absolute performance remains low (30%), finetuning on NormViz-Train improves pair accuracy relatively by up to 125% and 36% Qwen3-VL 4B and 8B respectively, showing a path forward to teach models to connect visual perception to cultural significance. Together, NormViz-Bench and NormViz-Train establish visual norm understanding as a challenging and consequential frontier for multimodal AI.
[NLP-146] yped Federated Artifacts for the Agent ic Web:Sharing Tool-Routing Knowledge Across FrozenHeterogeneous LLM Agents
【速读】： 该论文旨在解决多供应商生成式代理在开放网络环境中难以实现高效、可信且可互操作的工具调用协同问题，核心挑战在于现有协议中扁平文本（如提示词、示例池）无法有效区分噪声统计、合并规则与文档信息，而权重和适配器又无法跨平台传递知识。其解决方案的关键是提出共享类型化联邦实体（typed federated artifacts），即具有结构化模式验证的语义对象，支持字段级隐私保护、争议解决机制及跨模型知识迁移，并将其实例化为统一的工具路由知识库（SYNAPSE1）。通过在StableToolBench基准上清理冗余数据后，该联邦合集可在每客户端仅传输20 MB JSON的前提下，实现与集中式系统相差不超过1.1分的路由性能；当将联合经验以结构化字段形式呈现而非扁平字符串时，在干净数据上可提升8.5分，面对60%注入矛盾时仍保持7.4分优势，表明结构化合并与渲染过程不可分割。实验进一步揭示，尽管不同冲突处理策略表现相似，但结构化格式本身显著优于非结构化表达，且所有联邦方案均使GPT-4o代理单步工具调用准确率至少提高6.7分，归因于格式优化而非分布式协作本身。然而，研究也警示：在存在标签泄露的基准测试中，基于TF-IDF的简单分类器反而超越所有大模型路由方案，因其依赖于预先存在的标签查询与测试用例的完整复现，暴露了当前基准对无标签工具路由能力评估的失效。
链接: https://arxiv.org/abs/2609.06815
作者: Abhijit Chakraborty,Ni Trieu,Vivek Gupta
机构: Arizona State University (亚利桑那州立大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:An open, networked web will allow agents to run frozen models from multiple vendors, keep their history private, and teach each other which tool to call and when. Flat text (prompts, example pools) makes it difficult for the protocol to distinguish between noise statistics, merging rules, and documentation. Weights and adapters cannot transfer that knowledge between platforms. We suggest sharing typed federated artifacts, schema-validated objects with well-defined fields for per-field privacy (described here, but measured), dispute resolution, and cross-model transfer, and instantiating them as SYNAPSE1, a common tool-routing knowledge. After deleting 192 garbage entries and 1,916 training items that duplicate or almost duplicate test queries, a federated compendium routes within 1.1 points of a centralized one at 20 MB of JSON per client each round on StableToolBench (3,180 tools). The same experience merged and shown to the router as typed fields rather than one flat string is worth 8.5 points on clean data and 7.4 under 60% injected contradiction. Crossing merge and rendering shows the halves are inseparable (the typed merge shown flat is the worst arm), while three conflict policies are indistinguishable, so the conflict log that motivated this work is not the On \tau-bench retail, each compendium arm improves GPT-4o agents’ per-step tool-call accuracy by at least 6.7 points, attributed to format rather than federated experience. Two cautionary findings conclude the paper: on a topic-labeled math proxy and StableToolBench, a TF-IDF classifier over the same labeled experience beats every LLM routing arm (by 48 and 26 points, mostly retrieval recall) because the benchmark’s pool holds labeled queries for every supposedly unseen tool and every test query verbatim before our filter. It cannot measure routing to tools without labels, which routing exists for.
[NLP-147] rain Smarter Not Harder: Switching Signal-Guided Training in Active Learning EMNLP2026
【速读】： 该论文旨在解决主动学习（Active Learning, AL）中训练策略选择这一被忽视的关键问题，即在每轮标注过程中应选择从零开始重新训练（retrain）还是基于先前检查点进行微调（fine-tune）。其核心挑战在于如何在训练效率与模型校准性之间实现动态平衡：早期阶段数据分布变化显著，重新训练能更有效地重塑模型；而随着模型轨迹趋于稳定，微调则更为高效且风险更低。为此，论文提出 HybridAL——一种自适应训练调度方法，通过在线监测两个互补的稳定性信号（谱指数变化 Δα，基于权重；准确率变化 ΔAcc，基于验证集），在模型轨迹持续稳定后自动由重新训练切换至微调。实验结果表明，HybridAL 在三种编码器主干网络和六个文本分类任务上，保持了与重新训练和微调相当的终点宏F1分数（非劣于0.010），同时最多节省49%的重新训练时间，并有效恢复了重新训练在负对数似然（NLL）指标上的校准优势。相较于预设固定轮次切换的策略，HybridAL 以适度增加的计算成本实现了更低的 NLL，证明了基于模型轨迹动态决策的切换机制能够提供优于固定早期切换的时-校准权衡。
链接: https://arxiv.org/abs/2609.06806
作者: Nagham Omar,Maya Rozenshtein,Evgeny Mishlyakov,Avigdor Gal
机构: Technion (以色列理工学院)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Accepted to EMNLP 2026 Main Conference
Abstract:Training strategy, namely whether to retrain from scratch or fine-tune from the previous checkpoint, is an overlooked decision variable in active learning. We show that this choice has exploitable structure: retraining is most useful in early rounds, when each batch can substantially reshape the labeled distribution, while fine-tuning becomes safer once the model trajectory stabilizes. We propose HybridAL, an adaptive training schedule that monitors an online stabilization signal and switches from retraining to fine-tuning after sustained stabilization. Two complementary signals, spectral exponent change \Delta\alpha (weight-based) and accuracy change \Delta Acc (validation-based), span different points on the time-calibration trade-off. Across three encoder backbones and six text-classification tasks (five seeds each), HybridAL keeps endpoint macro-F1 non-inferior to retraining and fine-tuning at a 0.010 margin, saves up to 49% of retraining time, and recovers a substantial fraction of retraining’s calibration advantage as measured by negative log-likelihood (NLL). Compared with schedules that switch at a pre-committed round, HybridAL obtains lower NLL at moderate additional cost, showing that trajectory-dependent switching provides a stronger time-calibration trade-off than fixed early switching.
[NLP-148] AURA-Eval: Evaluation Framework for Acting Under Risk Awareness in LLM Agent Trajectories
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）智能体在工具使用流程中因安全行为评估过于简化而难以准确识别风险、预判潜在危险动作及实现安全任务完成的问题。现有评估方法通常将行为结果压缩为单一得分，忽略了对决策路径中关键安全节点的细致分析。为此，论文提出AURA-Eval框架，其核心在于结合受控增强与对工具使用轨迹的细粒度行为诊断，通过识别安全敏感决策点、生成可控变异样本，并构建具有或不具有安全执行路径的对照案例，从而实现对模型行为的系统性评估。该框架基于157条真实轨迹生成1,249个评估项，覆盖20个前沿及开源模型，采用多维度评分标准对风险识别能力、行动策略选择和场景特定动作安全性进行分类评估。实验结果表明，当任务无安全解时，主流闭源模型更倾向于识别风险并提出替代方案，而开源模型则更频繁直接执行不安全请求；同时，增加执行后果严重性或减少事前监督机会会显著暴露各类模型的安全脆弱性。
链接: https://arxiv.org/abs/2609.06783
作者: Ruoxi Shang,Christina-Maria Androna,Orfeas Menis Mastromichalakis,Yu Feng,Aniruddhan Ramesh,Rico Angell,Shang Hong Sim,Chrysoula Zerva,Emmanouil Koukoumidis
机构: University of Washington (华盛顿大学); Oumi; Instituto de Telecomunicações (葡萄牙电信研究所); University of Pennsylvania (宾夕法尼亚大学); University of Cincinnati (辛辛那提大学); New York University (纽约大学); National Technical University of Athens (雅典国立技术大学)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Software Engineering (cs.SE)
备注:
Abstract:LLM agents operate in workflows where unsafe actions can have real consequences. Existing safety evaluations often reduce behavior to a single score, obscuring risk recognition, pre-action detection, and safe task completion when a safe solution exists. We introduce AURA-Eval, a framework combining controlled augmentation with granular diagnosis of behavior in tool-use trajectories. Its pipeline identifies safety-critical decision points, generates controlled variations, and constructs counterparts differing in whether a request has a safe fulfillment path. Using 157 sourced trajectories, we generate 1,249 evaluation items and evaluate 20 frontier and open-weight models. We developed rubrics to classify risk detection, action strategy, and scenario-specific action safety. Our results show that LLM agents engage in unsafe behavior more often when no safe fulfillment path exists. In these cases, frontier proprietary models more often recognize risk and exhibit safer behavior by proposing alternatives, while evaluated open-weight models more often directly execute unsafe requests. Increasing impact or reducing opportunities for oversight before execution also exposes greater vulnerability across models.
[NLP-149] AuthBench: A Large-Scale Multilingual Benchmark for Authorship Representation across Genres and Lengths
【速读】： 该论文旨在解决当前作者身份表征（authorship representation）研究中缺乏统一、大规模且多语言、多场景评估基准的问题。现有基准通常局限于单一语言、特定文体或有限文档长度，难以全面评估模型在真实复杂场景下的泛化能力。为此，论文提出 AuthBench——一个大规模多语言作者身份表征基准，涵盖10种主流语言、9类主要文体与66个细粒度文体类别，覆盖四种文档长度区间，共计428,150篇由153,825名个体撰写的文档。其核心解决方案在于构建一个标准化、可扩展的评估框架，支持两个互补任务：作者归属（same-author retrieval）与作者验证（same-author binary decision），并采用统一的零样本协议对47个神经网络模型和3个非神经基线进行系统评测。实验结果表明，当前作者身份表征仍远未达到理想性能，最佳检索模型仅达Success@5 = 0.258，最佳验证模型在EER上为0.076、ROC-AUC为0.968，且不同任务类型、语言、文体及文档长度间存在显著性能差异。这些发现揭示了模型在作者身份识别中的局限性，使AuthBench不仅作为基准工具，更成为诊断模型成功或失败条件的重要分析资源。
链接: https://arxiv.org/abs/2609.06771
作者: MaoXun Huang,Zhenxing Zhang,Claire Cardie
机构: Cornell University (康奈尔大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Authorship signals matter in settings where writing style carries identity: digital forensics, plagiarism analysis, account linking, misinformation investigation, and machine-generated text detection. Yet current authorship benchmarks remain fragmented, usually covering only a narrow language set, a single genre, or a limited document-length regime, which makes it difficult to assess whether modern representations truly generalize. We introduce AuthBench, a large-scale multilingual benchmark for authorship representation that is designed to make this evaluation broad, standardized, and realistic. AuthBench contains 428,150 documents written by 153,825 individuals across ten widely used languages, 9 primary genres, 66 fine-grained genres, and four document-length buckets. It supports two complementary tasks: authorship attribution, formulated as same-author retrieval and authorship verification, formulated as same-author binary decision. We benchmark 47 neural models and three non-neural baselines under a unified zero-shot protocol. Results show that authorship representation remains far from solved: the best retrieval model reaches only 0.258 Success@5, while the best verification model achieves 0.076 EER and 0.968 ROC-AUC. The leaderboard also reveals a meaningful task split, with different model families leading retrieval and verification, and large performance differences across languages, genres, and lengths. These findings position AuthBench not only as a new benchmark, but as a diagnostic resource for studying when and why authorship representations succeed or fail. We release AuthBench, its evaluation toolkit, and benchmark data at this https URL and this https URL. Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.06771 [cs.CL] (or arXiv:2609.06771v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.06771 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-150] A Novel Semantic Manifold Alignment Attack against Embedding-to-Embedding Obfuscation in Privacy-Preserving LLM s EMNLP2026
【速读】： 该论文旨在解决隐私保护大语言模型（LLM）中嵌入式混淆（Embedding-to-Embedding Obfuscation, E2EO）方案在面对新型语义结构泄露攻击时的安全性问题。尽管现有E2EO方法通过局部一对一替换实现了对传统词频分析与嵌入逆向攻击的较强防御，但其核心机制缺乏密码学安全保障，且保留了原始语义结构，导致潜在的语义泄露风险。本文提出一种名为代理流形对齐（Proxy Manifold Alignment, PMA）的新攻击方法，其关键在于识别并利用E2EO方案中保留的语义结构不变性——即混淆后的向量序列可被视作一个未知的“符号语言”，其中符号即为向量本身。基于此，PMA将密文到明文的重构问题建模为从未知符号语言到明文的语言翻译任务：仅通过访问混淆向量流和公开语料库，首先分别使用Word2Vec独立建模混淆序列与公开语料中的共现模式，构建两个代理向量嵌入；随后，通过基于结构相似性的流形对齐，建立两者之间的映射关系；最终实现对原始明文的高精度还原。实验表明，PMA在恢复准确率上显著优于现有最先进攻击方法，揭示了当前E2EO方案在语义保真性下的根本性安全缺陷。
链接: https://arxiv.org/abs/2609.06749
作者: Sicong Li,Lingfeng Yao,Xingke Yang,Ke Tu,Chenhao Wu,Hao Wang,Jiang Liu,Phone Lin,Xin Fu,Miao Pan
机构: University of Houston(休斯顿大学); The Chinese University of Hong Kong, Shenzhen(香港中文大学深圳分校); Waseda University(早稻田大学); Stevens Institute of Technology(史蒂文斯理工学院); National Taiwan University(台湾大学)
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL)
备注: Accepted at the EMNLP 2026 Main Conference as a long paper
Abstract:With the widespread applications of large language models (LLMs), privacy-preserving inference has become increasingly essential for sensitive queries. To balance privacy and utility, a series of lightweight obfuscation approaches has recently been proposed, where users locally transform plaintext embeddings into the fixed ciphertext ones. While such Embedding-to-Embedding Obfuscation (E2EO) schemes demonstrate considerable resilience against traditional token frequency and embedding inversion attacks, the core mechanism behind remains to be the large-scale one-to-one substitution, which provides no cryptographic guarantees. In this paper, we propose Proxy Manifold Alignment (PMA), a novel attack against E2EO in privacy-preserving LLMs. Our key observation is that E2EO schemes keep the original semantic structure, so that the obfuscated vector stream can be regarded as an unknown tokenizer-language whose symbols are the vectors themselves. Therefore, the proposed ciphertext to plaintext reconstruction attack can be formulated as a translation task from the unknown tokenizer-language to plaintext. Specifically, by only accessing the obfuscated vector stream, the target tokenizer and a public corpus, the PMA attack first employs Word2Vec to model the co-occurrence patterns within the obfuscated stream and the public corpus independently, and constructs two proxy vector embeddings. Then, the attack aligns the underlying manifolds of these two embeddings based on structural similarity. Finally, it maps the obfuscated vectors back to plaintext. Experimental results demonstrate that PMA consistently achieves higher plaintext recovery than other state-of-the-art attack methods.
[NLP-151] Reason Through the Latent! Making Latent Visual Reasoning Necessary
【速读】： 该论文旨在解决生成式视觉推理中“隐状态信息虽存在但未被实际使用”的核心问题，即模型在推理过程中可能依赖于显式的文本链式思维（chain-of-thought）路径，而非真正利用图像对应的隐状态进行因果性推理。其关键解决方案是提出因果视觉循环推理（Causal Visual Recurrent Reasoning, CVRR），通过强制将循环计算作为唯一可依赖的图像条件化路径来确保模型真正利用隐含视觉信息。具体而言，CVRR 在预训练视觉语言模型完成图像编码后，从问题的隐藏状态初始化循环过程，并反复读取固定的视觉证据以更新递归状态；在解码前，移除所有中间视觉状态与原始多模态键值缓存（KV cache），仅保留最终的递归状态携带图像条件信息。这一设计使模型在多个基准测试（V^*、MMVP、BLINK、MME-RealWorld-Lite）上保持强性能，而其他兼容的隐式推理方法即使在相同约束下重训练也无法恢复等效视觉能力。因果干预实验进一步验证了预测结果对递归内容的敏感性以及持久视觉证据对递归轨迹的因果影响，从而明确区分了“隐状态的信息量”与“实际用于预测的计算路径”之间的本质差异。
链接: https://arxiv.org/abs/2609.06746
作者: Suhyeong Park,Junha Jung,Jaewoo Kang
机构: Korea University(高丽大学); AIGEN Sciences
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Latent visual reasoning aims to perform multimodal reasoning through hidden-state computation rather than explicit textual chains of thought. However, visual information being present in a latent state does not imply that the model actually relies on that state when producing its answer, especially when alternative image-conditioned paths remain available. We introduce \textbfCausal \textbfVisual \textbfRecurrent \textbfReasoning (CVRR), which preserves pretrained visual competence while making recurrent computation the required image-conditioned path to prediction. CVRR initializes recurrence from the question hidden state after the pretrained vision-language model has incorporated the image, then repeatedly updates this state while re-reading the same fixed visual evidence. Before decoding, visual states and the original multimodal KV cache are removed so that only the final recurrent state carries image-conditioned information to the answer. Across the V^* , MMVP, BLINK, and MME-RealWorld-Lite benchmarks, CVRR retains strong performance under this strict interface, while compatible latent reasoners fail to recover comparable visual competence even when retrained under the same constraint. Causal interventions further show that predictions remain sensitive to recurrent content when the question is held fixed, and that persistent visual evidence causally revises the recurrent trajectory. These results distinguish latent informativeness from latent computation that is actually used for prediction.
[NLP-152] Event Interaction in Low-Rank Bottlenecks for Temporal Relation Extraction ACL2026
【速读】： 该论文旨在解决参数高效微调（Parameter-Efficient Fine-Tuning, PEFT）场景下，传统事件间交互建模方法在低秩瓶颈（low-rank bottleneck）中信息流受阻的问题。主流方法如拼接事件跨度或使用浅层融合虽在全量可训练模型中表现良好，但在PEFT框架（如Adapter和LoRA）中因低秩约束导致事件间交互信号无法有效传递，从而引发性能显著下降。其解决方案的关键在于提出一种理论驱动的新型架构——卷积瓶颈交互（Convolutional Bottleneck Interaction, CBI），该架构通过轻量级深度卷积（depthwise convolution）增强事件表示，并在低秩瓶颈内部采用逐元素相乘（element-wise multiplication）显式建模事件间的细粒度交互，从而在保持极低计算开销的前提下，显著提升事件间交互信息的传递效率。实验表明，在五个数据集和七种骨干模型的Adapter与LoRA设置下，CBI均带来一致且显著的性能提升，最高达+31.7微平均F1，验证了在低秩空间内显式建模事件交互对时序关系抽取的重要性。
链接: https://arxiv.org/abs/2609.06731
作者: Wei Sun,Tingyu Qu,Jesse Davis,Marie-Francine Moens
机构: KU Leuven (鲁汶大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted in Transactions of ACL 2026 (pre-MIT Press publication version)
Abstract:Temporal relation extraction determines whether an event occurs before, after, or simultaneously with another event, and therefore relies on accurately modeling how the two events interact. Mainstream systems achieve this by concatenating event spans or using shallow fusion, which works well when all model parameters are trainable. However, in parameter-efficient fine-tuning, low-rank bottlenecks restrict information flow and prevent these interaction signals from passing through, leading to clear performance drops. To address this limitation, we propose a theoretically grounded architecture, Convolutional Bottleneck Interaction (CBI), which first applies lightweight depthwise convolution to enhance event representations and then uses element-wise multiplication to capture effective event-event interactions inside the bottleneck. Across five datasets and seven backbone models in the Adapter and LoRA settings, CBI provides consistent and substantial gains, up to +31.7 micro F1, while adding minimal computational cost, showing that explicit interaction inside low-rank spaces is crucial for temporal relation extraction. The code is available at this https URL.
[NLP-153] Counterfactual Tests for Measuring Chain-of-Thought Faithfulness in Visual Language Models EMNLP2026
【速读】： 该论文旨在解决生成式视觉-语言模型（Vision Language Models, VLMs）中链式思维（Chain-of-thought, CoT）的可信度问题，尤其是针对视觉输入时CoT是否真实反映模型决策过程这一关键挑战。现有方法多适用于文本输入，难以直接迁移至视觉场景。为此，论文将反事实评估方法家族——反事实测试（Counterfactual Test, CT）与相关反事实测试（Correlational Counterfactual Test, CCT）拓展至视觉领域，分别命名为vCT与vCCT，以量化评估视觉输入下CoT的可信性。其解决方案的关键在于：通过引入可控的图像扰动（如单个物体移除），系统性地分析模型预测变化与CoT内容之间的对应关系，从而揭示CoT是否真实追踪影响预测的视觉证据。实验结果表明，当前主流VLMs的CoT存在显著不可靠性——当移除物体导致预测大幅改变时，CoT可能完全忽略该物体；而当预测变化较小时，反而可能提及该物体。此外，研究发现“先预测后解释”（Predict-then-Explain）形式的解释比预回答阶段的CoT更贴近扰动引发的概率变化，且二值化vCT得分普遍接近饱和，提示其判别能力受限。为增强评估的严谨性，论文还引入重建控制组，验证了物体移除带来的扰动效应显著大于仅图像编辑本身。最终，作者构建并公开了Counter-SNLI-VE与Counter-A-OKVQA两个图像对数据集，每对图像仅在单一对象上存在差异，为未来视觉可解释性研究提供了基准工具。
链接: https://arxiv.org/abs/2609.06704
作者: Bayar Menzat,Maximilian Süss,Ruizhi Wang,Benno Steinegger,Thomas Lukasiewicz,Oana-Maria Camburu
机构: Vienna University of Technology (维也纳工业大学), Austria; University of Oxford (牛津大学), UK; Imperial College London (帝国理工学院), UK; University College London (伦敦大学学院), UK
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted to Findings of the Association for Computational Linguistics: EMNLP 2026
Abstract:Chain-of-thought (CoT) may often look plausible, yet it may not faithfully reflect the model’s decision-making process. While methods for measuring the faithfulness of CoTs for textual inputs have been increasingly introduced, using these methods for visual inputs is not straightforward. In this work, we adapt the family of counterfactual methods for measuring CoT faithfulness, namely the Counterfactual Test (CT) and Correlational Counterfactual Test (CCT), to visual inputs, and call them vCT and vCCT, respectively. Using vCT and vCCT, we benchmark eight recent open-source Vision Language Models (VLMs) on two datasets. Our analysis shows that CoTs do not reliably track visual evidence that influences model predictions: they may omit the removed object even when its removal causes a large prediction shift, yet mention it when the shift is small. We further find that Predict-then-Explain explanations align more strongly with perturbation-induced probability shifts than pre-answer CoTs, while binary vCT scores are often nearly saturated. We also include a reconstruction control, in which images pass through the same editing pipeline without object removal, and find that the main object-removal intervention induces larger shifts than reconstruction alone. We construct and release Counter-SNLI-VE and Counter-A-OKVQA, two datasets of image pairs that differ by a single object.
[NLP-154] DianShi-RxnDB: A Large-Scale Fine-Grained Organic Reaction Data Platform Built via a Fully Automated Pipeline for Researchers and AI Agents
【速读】： 该论文旨在解决化学领域高质量结构化有机反应数据稀缺的问题，特别是针对散落于专利文本、图像及反应示意图中的反应知识难以系统化整合的挑战。其核心解决方案是构建一个大规模、细粒度的有机反应数据库——DianShi-RxnDB，通过全自动化提取与标准化流程，从美国专利商标局（USPTO）和欧洲专利局（EPO）1976至2025年间发布的有机合成专利中，自动解析并结构化出约2400万条反应实例，其中约1480万条（61.7%）通过自动化质量筛选。每条记录包含反应物、产物、催化剂、反应条件（温度、时间、用量）、产率、实验步骤及来源专利溯源信息。在对1300条人工抽检合格样本的评估中，字段级微平均准确率达92.95%。相较于现有基准数据集Pistachio，DianShi-RxnDB在去重记录数量、表征粒度及字段级精确匹配方面具有显著优势。平台提供基于Web的研究工作台支持查询、筛选、比对与溯源验证，并集成模型上下文协议（Model Context Protocol, MCP）服务，为生成式AI代理提供可组合的结构化检索工具。该研究的关键在于实现跨模态（文本、图像、图示）专利数据的端到端自动化结构化处理，从而为人工智能驱动的化学研发（AI4Chem）提供高可用、高精度的基础数据支撑。
链接: https://arxiv.org/abs/2609.06703
作者: Yubin Wang,Xingjian Wei,Jiang Wu,Yinfan Wang,Boyu Zhu,Lin Zhang,Jianing Yu,Huazheng Zeng,Ruiyi Ding,Junyuan Gao,Jiaxing Sun,Lingli Ge,Haote Yang,Jingchao Wang,Aijia Guo,Qian Jiang,Yurui Zhao,Wenjian Zhang,Chen Zhu,Lijun Wu,Xiaolei Yang,Haodong Chen,Junjie Yuan,Zichao Ye,Shaowei Hou,Jing Ye,Jia Yu,Shan Wang,Lijun Wu,Jiantao Qiu,Chao Xu,Yuqiang Li,Guangyu Wang,Bowen Zhou,Dahua Lin,Conghui He
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:High-quality structured organic reaction data are essential for developing artificial intelligence for chemistry (AI4Chem), yet much of this knowledge remains dispersed across patent text, images, and reaction schemes. We present DianShi-RxnDB, a large-scale, fine-grained organic reaction data platform built via a fully automated extraction and normalization pipeline integrating patent text, images, and reaction schemes. Its corpus covers organic synthesis patents from the USPTO and EPO published between 1976 and 2025, yielding approximately 24 million reaction instances, of which approximately 14.8 million (61.7%) pass automated qualification checks. Each instance represents a specific single-step experiment recording participants, roles, quantities, temperatures, reaction times, yields, experimental procedures, and provenance links to source patents. In a manual evaluation of 1,300 sampled qualified instances, the micro-averaged field-level accuracy was 92.95%. A matched comparison with Pistachio further indicated advantages in deduplicated record counts, representation granularity, and field-level exact agreement. The platform provides a Web research workbench for searching, filtering, comparing, and source-verifying records, and a Model Context Protocol (MCP) service offering AI agents composable structured retrieval tools. DianShi-RxnDB is available at this https URL .
[NLP-155] PARSER: Read in Parallel Reason in Depth for Long-Context LLM Agents
【速读】： 该论文旨在解决传统顺序记忆代理（sequential memory agents）在处理长文档时存在的两个核心问题：一是推理过程与文档遍历深度耦合，导致对证据位置敏感且推理延迟随文档长度线性增长；二是难以应对证据在文本中位置、顺序或距离的扰动。其解决方案的关键在于提出PARSER框架，通过解耦阅读与推理过程实现突破：引入一组轻量级子代理（subagents），每个子代理负责独立读取文档中的单一片段，实现并行阅读；同时由一个主代理（lead agent）通过迭代的“广播-聚合”机制，在多轮中主动向所有子代理查询信息，整合证据并生成更深层次的追问。该设计将全部可学习行为集中于主代理，并采用强化学习进行优化，而子代理保持冻结的预训练模型状态。实验表明，PARSER在7K至896K token的多跳问答任务中显著优于现有最强顺序基线（平均提升5.7点，896K token时达12.0点），且在9B规模下超越DeepSeek-V4-Pro 6.3点；同时对证据位置、顺序和距离的扰动具有鲁棒性，推理延迟降低最高达11倍。
链接: https://arxiv.org/abs/2609.06702
作者: Kun Li,Zexuan Qiu,Tianhua Zhang,Irwin King,Helen Meng
机构: The Chinese University of Hong Kong(香港中文大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Sequential memory agents process long documents by reading chunks one after another while maintaining a compact memory state, coupling document traversal to reasoning depth. This coupling introduces sensitivity to evidence placement and ties inference latency linearly to document length. We introduce PARSER, which decouples reading from reasoning. A bank of lightweight subagents each bound to a single chunk read the entire document in parallel, while a lead agent reasons in depth through iterative scatter–gather rounds: at each round it broadcasts a query to all subagents, aggregates the returned evidence, and formulates a deeper follow-up query conditioned on what has been found so far. This decoupled design concentrates all learnable behavior in the lead agent, which is optimized with reinforcement learning, while the subagents remain frozen off-the-shelf models. On multi-hop QA with contexts ranging from 7K to 896K tokens, PARSER with a 4B backbone outperforms the strongest sequential memory baseline by 5.7 points on average and by 12.0 points at 896K tokens. Scaling to a 9B backbone, PARSER surpasses DeepSeek-V4-Pro by 6.3 points. Controlled experiments confirm that PARSER is robust to perturbations in evidence position, order, and distance, conditions that cause large accuracy swings in sequential methods, while reducing inference latency by up to 11x.
[NLP-156] A Grapheme-Aware Indic Tokenizer for Tamil: Large-Scale Training and Intrinsic Evaluation
【速读】： 该论文旨在解决现有通用分词方法（如BPE、WordPiece和SentencePiece）在处理形态丰富的印度语言（尤其是泰米尔语）时效率低下、导致词元碎片化严重的问题。其核心挑战在于泰米尔语采用基于音素（grapheme）的书写系统，一个可见字符可能由多个Unicode码点构成，传统以码点为单位的分词方式会破坏完整的音素簇，从而影响词汇表达的完整性与下游模型性能。解决方案的关键在于提出一种面向泰米尔语的音素感知分词器，通过在WordPiece词表学习前引入可逆的Unicode映射策略，将文本先归一化为完整的音素簇单元，再在此基础上进行分词。该方法在音素层级而非单个码点层级上操作，既保留了语言学意义上的语义单元边界，又保持了与基于Transformer架构模型的完全兼容性。实验结果表明，该分词器在减少词元碎片化、提升序列效率与压缩比方面显著优于GPT-2、mBERT、mT5、mBART和NLLB等五种主流多语言分词器，在多项内在评估指标上表现最优，验证了音素感知预处理对泰米尔语分词的有效性。
链接: https://arxiv.org/abs/2609.06690
作者: Hari Krishnan K V,Sudarsun Santhiappan
机构: IIITDM Kanchipuram (印度信息技术研究所德里分校坎奇普拉姆分校); Wadhwani School of Data Science and AI, IIT Madras (瓦德瓦尼数据科学与人工智能学院，印度理工学院马德拉斯分校)
类目: Computation and Language (cs.CL)
备注: 10 pages, 1 figure
Abstract:Tokenization forms the foundation of modern Natural Language Processing (NLP) systems by transforming raw text into discrete units that neural language models can process. The effectiveness of this process directly influences vocabulary efficiency, sequence length, computational cost, and downstream model performance. Although multilingual tokenizers such as Byte Pair Encoding (BPE), WordPiece, and SentencePiece have performed well across numerous languages, they often segment morphologically rich Indic languages inefficiently. Tamil, in particular, poses unique challenges because its grapheme-based writing system can represent a single visible character with multiple Unicode code points. In this work, we present a grapheme-aware Indic tokenizer for Tamil that preserves complete grapheme clusters through a reversible Unicode mapping strategy prior to WordPiece vocabulary learning. By operating on grapheme-level representations instead of individual Unicode code points, the tokenizer produces linguistically meaningful token boundaries while remaining fully compatible with transformer-based language models. The tokenizer is trained on a large-scale Tamil corpus and evaluated using a comprehensive intrinsic evaluation framework that measures compression efficiency, token fragmentation, information density, and vocabulary utilization. Experimental evaluation compares the proposed tokenizer against five widely used multilingual tokenizers: GPT-2, mBERT, mT5, mBART, and NLLB. The proposed tokenizer achieves the strongest performance among the evaluated tokenizers on fragmentation- and sequence-efficiency-oriented intrinsic metrics, while matching the highest observed compression ratio. These results demonstrate the effectiveness of grapheme-aware preprocessing for Tamil tokenization.
[NLP-157] Data Efficient Sample Selection for In-Context Learning EMNLP2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在上下文学习（In-context Learning, ICL）中如何高效选择最优示范样本组合的问题。现有方法通常采用静态的任务级样本选择策略，仅在离线阶段一次性确定示例子集，无法适应未见过的查询，且未能建模示范样本与下游模型性能之间的复杂非线性关系。为此，本文提出DearICL（Data Efficient Algorithm for Ranking ICL samples），将示范样本选择建模为一个子集排序问题，引入基于可微排序目标的非线性代理模型，并结合间隙指数（gap-index）强化学习框架进行优化。该方法通过充分采样“边缘臂”（borderline arms）实现对优质与次优样本的细粒度区分，从而支持实例级（instance-level）的子集排序。实验结果表明，在开源LLM的示范选择基准上，DearICL相比强基线线性带模型实现了8.08%–15.9%的准确率提升，同时具备较低的样本复杂度。
链接: https://arxiv.org/abs/2609.06670
作者: V Venktesh,Cem levi,Avishek Anand
机构: Stockholm University (斯德哥尔摩大学); SKIM Group B.V.; TU Delft (代尔夫特理工大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Accepted to EMNLP 2026 Main track, 22 pages with sample-complexity proof in Appendix;
Abstract:The In-context learning (ICL) paradigm aids large language models (LLMs) to adapt to new tasks without need for fine-tuning. However, selecting an optimal combination of demonstration examples from a large pool of example subsets is a challenging problem. Existing approaches for selection do not model the complex relationship between ICL samples and downstream LLM performance. They typically perform static task-level selection, choosing subsets once offline, which can fail to generalize to unseen queries. We introduce DearICL (Data Efficient Algorithm for Ranking) ICL samples, a new framework that models demonstration example selection as a subset ranking problem. DearICL employs a non-linear surrogate employing a differentiable sorting objective within a gap-index bandit algorithm. The gap-index based approach enables fine-grained separation of good arms and borderline arms, which is used as an auxiliary objective to train the non-linear surrogate through sufficient sampling of borderline arms, supporting instance-level subset ranking. On exemplar selection benchmarks with open-source LLMs, DearICL achieves 8.08-15.9% accuracy gains over strong linear bandit baselines, with low sample complexity. Code and data: this https URL.
[NLP-158] ECOKV: Geometry-Aware KV Cache Eviction via Complementary Diversity Metrics
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在推理过程中因键值缓存（KV cache）存储带来的内存与计算开销问题，进而限制了模型的可扩展性。现有基于余弦相似度的多样性度量方法虽能识别关键的键值对，但其归一化操作会丢失向量幅度信息，且由于隐藏表示的各向异性特性，导致不同层间余弦相似度普遍偏高，难以有效区分冗余程度。为此，本文提出ECOKV方法，其核心创新在于构建一种几何感知的复合度量机制，联合利用欧几里得距离与余弦相似度，从互补角度捕捉标记（token）间的多样性。同时，该度量被用于估计每个注意力头的冗余水平，从而在令牌选择过程中实现多样性和重要性得分的自适应加权。此外，研究发现常用于保留近期令牌的观察窗口可显著缩小，从而将更多缓存容量分配给更具信息量的令牌，带来持续性能提升。实验表明，ECOKV在多种压缩比下均达到当前最优表现，并可无缝集成至已有KV缓存淘汰策略中。通过分析重要性与多样性之间的关系以及跨层、跨注意力头的冗余模式，进一步揭示了缓存优化的内在机制。
链接: https://arxiv.org/abs/2609.06663
作者: Chin Ting Hsu,Yu-Syuan Xu,Ling Zou,Hsien-Kai Kuo,Wen-Huang Cheng
机构: MediaTek Inc.(联发科技); National Taiwan University(台湾大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 8 figures
Abstract:Although multimodal Large Language Models (MLLMs) excel in diverse tasks, their scalability remains limited by the memory and computational overhead of KV cache storage. Recent KV cache eviction approaches incorporate a cosine similarity-based diversity metric with importance metrics to selectively retain critical key-value pairs. However, cosine similarity involves normalization that discards magnitude information, and it often yields uniformly high similarity values across layers due to the anisotropy property of hidden representations. In our study ECOKV, we rigorously deconstruct the capabilities of existing diversity metrics. Moving beyond simple measurement, we propose a geometry-aware composite metric that jointly leverages Euclidean distance and cosine similarity to capture token diversity from complementary perspectives. Furthermore, we use these two metrics to estimate the redundancy level of each attention head, allowing adaptive weighting between diversity and importance scores during token selection. Finally, we demonstrate that the observation window commonly employed to preserve recent tokens can be substantially reduced, thereby allocating more cache capacity to informative tokens and yielding consistent improvements. Extensive experiments demonstrate that ECOKV achieves state-of-the-art performance under various compression ratios and can be seamlessly integrated with existing KV cache eviction methods. We further analyze the relationship between importance and diversity, and examine redundancy patterns across layers and attention heads.
[NLP-159] SerenAI: State-transition system inspired by text-based world AI models
【速读】： 该论文旨在解决在法律、运营或财务等高合规性工作流中，大语言模型（Large Language Models, LLMs）生成非约束性自由文本时难以进行可审计解释的问题。现有方法虽广泛使用大模型，但其输出缺乏可验证性和结构化逻辑，导致在关键任务场景下可信度不足。为此，论文提出一种基于文本的系统SereneAI，受世界模型（World-Models）启发，构建为状态转移系统，其输出包含四元组：因果增量（causal deltas）、可逻辑推导的下一状态（next state）、有效性奖励（validity reward）以及终止信号（termination signal），实现对生成过程的可验证预测。解决方案的关键在于引入验证器兼容的适应性训练范式，通过两阶段微调策略——即参数高效微调（Parameter-Efficient Fine-Tuning）与基于验证器的强化学习（Verifier-Based RL），在12个环境、涵盖10个推理领域的50,000个因果案例上进行训练。实验结果表明，相较于初始8B开源基线模型，SereneAI显著提升了多项结构化输出指标：JSON有效性从85.0%提升至93.2%，模式有效性从55.0%升至84.0%，精确匹配率在各类输出项中均有大幅提升，最高达81.5%。这些结果支持了“验证器兼容的适配训练能有效提升结构化状态转移预测能力”的论断，但尚未达到法律级可靠性标准。因此，论文进一步提出了面向证据基础法律工作流的验证协议，包括校准机制、人工监督及主权本地部署方案，以推动系统向实际生产环境落地。
链接: https://arxiv.org/abs/2609.06647
作者: Elvin Babayev,Artem Sinitsa,Arash Hajisharifi,Kabir Bakhshaei
机构: Collision Technologies S.R.L.S. Societa Benefit(碰撞科技有限公司)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 8 pages, 4 figures, 5 tables
Abstract:Although professional workflows leverage large language models widely, the interpretation for auditing unconstrained free-text generation is usually intractable if such generation demands legal, operational or financial workflow. We hereby demonstrate a text based system called SerenAI - inspired by world-models, it is a state transition system that outputs verifiable predictions rather than merely text: Provided with a description of the environment, state, and actions, the generated output contains 4 items: causal deltas that causally effect the given state, a next state that can logically follow from the given state and action, a validity reward, and a termination signal. For the released proto-model, we employ 2 steps of adaptation training, namely parameter efficient fine-tuning followed by verifier based RL over 50,000 exampled cause and effects in 12 environments spanning 10 reasoning domains. Compared to an initial internal evaluation of an 8B open-weight baseline, SerenAI increased JSON validity from 85.0% to 93.2%, schema validity from 55.0% to 84.0%, exact structured-output match from 0.0% to 41.5%, causal-delta exact match from 0.0% to 41.5%, resulting-state exact match from 0.0% to 42.0%, reward exact match from 1.0% to 80.5%, and termination exact match from 38.0% to 81.5%. These support the narrower claim that verifier-compatible adaptation can improve structured transition prediction. They do not yet establish legal-grade reliability. Accordingly, the paper also specifies a validation protocol for evidence-grounded legal workflows, calibration, human oversight, and sovereign on-premise deployment.
[NLP-160] Mind the Gap: Exposing LLM Translation Blind Spots Using the AlphaMWE Multilingual Parallel Corpus
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在机器翻译（Machine Translation, MT）任务中，尤其是在多词表达（Multiword Expressions, MWEs）相关语言理解与翻译方面存在的性能瓶颈问题。尽管模型在通用语境下表现良好，但其对特定领域和语言对的依赖性仍导致在涉及习语、固定搭配等复杂语言现象时表现受限。该研究的关键解决方案在于构建并使用公开的多语言平行语料库AlphaMWE作为测试集，系统评估了31个不同语言对（包括英译中、英译波斯语、德语、阿拉伯语及其方言）的翻译系统性能。通过结合自动评估指标（BLEU、ChrF、BERT-score）筛选出每语言对的前三名系统，并进一步开展人工评估，揭示了自动指标在评价中可能存在的偏差以及人类评估在发现语言特异性错误方面的不可替代价值。研究结果表明，习语化表达和多词表达依然是制约当前LLMs翻译质量的核心挑战。
链接: https://arxiv.org/abs/2609.06634
作者: Lifeng Han,Jiahui Liang,Anna Latusek,Karim El Haff,Amal Haddad Haddad,Josua Höfgen,Kilian Evang,Min Ma,Maryia Zhyrko
机构: Leiden University (莱顿大学); Leiden University Medical Centre (莱顿大学医学中心); Institute of Computer Science PAS, Warsaw (波兰科学院计算机科学研究所, 华沙); University of Ljubljana (卢布尔雅那大学); University of Granada (格拉纳达大学); Technical University of Munich (慕尼黑工业大学); Heinrich Heine University Düsseldorf (海因里希·海涅大学杜塞尔多夫); Google DeepMind (谷歌深度思维)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: WMT 2026 Test Suites Shared Task system paper (accepted). 20 pages, 8 figures, 7 tables
Abstract:LLMs’ performance on machine translation (MT) tasks is often dependent on the data availability in the specific domains and language pairs that they are trained upon. To examine if Multiword Expressions (MWEs) still set a bottleneck for LLMs regarding language understanding and translation, we report the system performances from the WMT2026 Test Suites shared task, for which we used the publicly available multilingual parallel corpus AlphaMWE as the test suites. We received 31 MT systems’ outputs covering English to Chinese (zh), Polish (pl), German (de), Arabic (ar) including Modern Standard Arabic (MSA) and two dialectal ones (Egyptian and Tunisian Arabic). We carried out automatic evaluations using BLEU, ChrF, BERT-score to select the Top3 systems per language pair, followed up with human evaluations on the selected systems. Our findings show that: figurative/MWE phenomena remain challenging; automatic metrics sometimes disagree; human evaluation uncovers language-specific errors hidden by aggregate scores.
[NLP-161] SAGE: A Hierarchical Framework for Evaluating Interpretive Literary Quality in Narratives
【速读】： 该论文旨在解决现有自然语言生成（NLG）评估指标无法有效衡量叙事作品在文化表征、情感深度与哲学思辨等诠释性维度上的文学质量这一关键问题。其核心解决方案是提出SAGE——一种六层评估框架，通过将基于规则的可观测文本属性分析与基于大语言模型（LLM）的诠释性品质评估相分离，实现对文化理论、情感理论及存在主义哲学维度的系统化评价。该框架采用多轮迭代式LLM评估与独立交叉验证机制，确保了测量级别的可靠性（98.8%收敛率，94%评分者间一致性），且在不同模型间具有稳定性。实证研究表明，尽管生成叙事在情感-心理表征层面已接近人类水平，但在文化批判与哲学深度方面仍存在约两倍于人类的差距；同时，LLM生成内容在所有三个维度上均低于商业类型小说的表现。这一发现揭示了文学能力的系统性边界：可从训练语料中通过模式复现的能力与依赖文化立场与哲学参与的深层表达之间存在本质差异，后者无法仅靠模式匹配实现。
链接: https://arxiv.org/abs/2609.06611
作者: Tianyu Wang,Nianjun Zhou
机构: Mercy University(梅西大学); Math Computer Science Department(数学与计算机科学系); IBM T.J. Watson Research Center(IBM托马斯·沃森研究中心)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 12 pages, 4 figures
Abstract:Assessing the literary quality of narratives requires evaluating interpretive dimensions (cultural representation, emotional depth, and philosophical engagement) that existing NLG metrics cannot measure. We introduce SAGE, a six-layer evaluation framework that separates rule-based assessment of observable textual properties from LLM-based evaluation of interpretive qualities drawn from cultural theory, affect theory, and existentialist philosophy. Each interpretive layer is assessed through multi-round iterative LLM evaluation with independent cross-validation, achieving measurement-grade reliability (98.8% convergence, 94% inter-rater agreement) stable across evaluator models. Validated on 600 evaluations across 100 short stories, our central finding is a systematic capability boundary: emotional-psychological representation approaches human levels, while cultural critique and philosophical depth exhibit approximately double the gap. LLM-generated narratives score below even commercial genre fiction on all three layers. We interpret this as a boundary between pattern-reproducible literary capacities learnable from training corpora and stance-requiring ones demanding cultural positioning and philosophical engagement that pattern matching alone cannot provide.
[NLP-162] Discovering Translation-Worthy Languages with E-Values NEURIPS2026
【速读】： 该论文旨在解决多语言文本分类中的翻译路由决策问题，即在何时对多语言文档进行翻译以优化分类性能。传统方法如统一翻译或基于启发式语言层级的策略无法提供统计上受控的路由选择，存在误判风险且缺乏可解释性。其解决方案的关键在于提出一种基于成对e过程（paired e-processes）的语言级路由机制，该机制能够持续比较直接分类与翻译辅助分类的表现，并在统计控制下动态确定最终路由策略。通过设定家族误差控制下的阈值（280），确保在每项数据集的14种可选语言中，任意错误路由的概率不超过0.05。实验结果显示，在SIB-200和MASSIVE数据集上，该方法分别在4/15语言和14/15语域中选择翻译，相较于直接分类分别提升0.814和16.70个百分点的测试准确率；所有28次决策在50种独立结果顺序及组内阈值变化下均保持稳定。研究表明，成对e过程实现了统计可控、任意时间有效性（anytime-valid）且可审计的多语言分类路由。
链接: https://arxiv.org/abs/2609.06593
作者: Wajdi Ben Saad,Safa Madiouni
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 10 pages, including references and supplementary material. Workshop paper. Code: this https URL
Abstract:Choosing when to translate multilingual documents is a central routing problem in text classification: translation can improve predictions for some languages while degrading others or adding unnecessary computation. Uniform translation and heuristic language tiers do not provide statistically controlled route selection. We introduce a language-level router based on paired e-processes that continuously compares direct and translation-assisted classification before freezing a routing policy. A familywise-controlled threshold of 280 bounds the probability of any false route across 14 eligible languages per dataset by 0.05. On SIB-200 and MASSIVE, the router selects translation for 4 of 15 languages and 14 of 15 locales, improving held-out accuracy over direct classification by 8.14 and 16.70 percentage points, respectively. All 28 decisions remain stable across 50 outcome-independent orderings and relative to the per-group threshold. Our results demonstrate that paired e-processes enable statistically controlled, anytime-valid, and auditable multilingual classification routing.
[NLP-163] A Translational Note on AI Safety Evaluation UAI
【速读】： 该论文旨在解决当前自动化红队测试（automated red-teaming）在评估AI安全时存在的根本性缺陷，即“威胁模型覆盖缺口”（threat-model coverage gap）。该问题的核心在于：现有基准测试仅衡量攻击者在开发者预先设定的有限危害集合中的探测能力，而未被包含在内的潜在危害对任何基于该框架的评估（无论是否自动化）均处于“不可见”状态。这一盲点与学术密码学和临床药物试验中曾出现的评估有效性局限性类似——内部有效的评估可能完全忽略了其本应关注的真实应用场景。研究发现，即使在当前开放权重模型中，非英文提示所引发的新风险仍会因英文基准的局限而被忽视。因此，解决方案的关键在于引入部署环境与开发者不同的独立评估者，以实现更全面的威胁模型覆盖。这一需求具有方法论基础，且现有评估体系本身难以自发产生此类评估者。
链接: https://arxiv.org/abs/2609.06573
作者: Madhava Gaikwad
机构: Independent Researcher(独立研究员)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR); Computers and Society (cs.CY)
备注: Accepted for the 2nd Workshop on Safe AI at UAI (SafeAI 2026). 6 pages
Abstract:Recent studies report that automated red-teaming finds more vulnerabilities, at lower cost, than human red-teaming on standard AI safety benchmarks, and some read this as evidence that human evaluators are becoming dispensable. The comparison measures one thing and the conclusion claims another. A benchmark measures how thoroughly an attacker searches a predefined set of harms, fixed in advance by the developers, and a harm left out of that set is invisible to any attacker working inside it, automated or not. The same blind spot appeared in academic cryptography and in clinical drug trials, where an evaluation that was internally valid stayed silent about the population it was never pointed at. We call the AI-safety version the \emphthreat-model coverage gap, and find that it persists in a current open-weight model, where harms surface in non-English prompts that English benchmarks miss. Closing it requires evaluators whose deployment context differs from the developers’. The case for those evaluators is methodological, grounded in coverage, and the existing evaluation frame is unlikely to produce them on its own.
[NLP-164] LLM s Mirror Country-Specific Gender Patterns If Asked but Skew Male When Generating Media in Local Languages
【速读】： 该论文旨在解决生成式 AI（Generative AI）在跨文化语境下可能加剧性别刻板印象的问题，尤其关注大型语言模型（LLMs）在长文本生成任务中是否延续并强化了社会中存在的性别关联。现有基准测试多依赖选择式问答形式，无法充分反映真实生成场景中的偏见表现；同时，全球范围内尤其是非西方国家的本地化性别关联数据严重匮乏。为此，研究团队在美国、印度、肯尼亚和尼日利亚共收集了695名受访者对22种职业与家庭角色的性别关联数据，并评估了8个主流大模型在两种情境下的表现：直接提问与媒体内容生成。结果表明，模型在直接提问时能较好匹配调查所得的人类性别认知，但在媒体生成任务中，其输出在主要本地语言群体中显著偏向男性，与人类创作媒体中的男性主导现象一致。值得注意的是，在非美国地区，英语提示下的偏差变化较小且不显著，说明仅使用英语或采用单一国家基准进行评估会掩盖模型在实际部署最广泛的本地语言中的偏见。指令提示（instruction prompting）虽可部分缓解生成方向上的偏移，但以牺牲与本地人类基准的一致性为代价。因此，论文提出关键解决方案在于：必须采用生成式测试范式、使用本地语言提示，并结合基于本地人群的实证数据构建基准，才能全面、准确地评估大模型在全球部署中的性别偏见。
链接: https://arxiv.org/abs/2609.06545
作者: Sharif Kazemi,Tanya Popli,Neil K. R. Sehgal,Sunny Rai,Niyati Malhotra,Victor Orozco-Olvera,Ana María Muñoz Boudet,Samuel P. Fraiberger,Sharath Chandra Guntuku,Manuel Tonneau
机构: University of Pennsylvania (宾夕法尼亚大学); Cornell University (康奈尔大学); World Bank Group (世界银行集团); University of Oxford (牛津大学); New York University (纽约大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) are increasingly used to generate media, but whether their content perpetuates gender stereotypes is unknown: standard benchmarks rely on selection-based formats rather than long-form generation, and surveyed baselines for local gender associations are scarce outside the West. We collect gender associations for 22 occupational and domestic roles from 695 respondents across the United States, India, Kenya, and Nigeria, and evaluate eight LLMs under two regimes: direct questioning and media generation. Models track the surveyed associations under direct questioning but skew substantially more male under media generation in major local-language cells, consistent with the male bias documented in human-produced media. Outside the US, the shift is much smaller and non-significant under English prompting, so English-only or country-agnostic evaluation would miss this bias in the languages where these models are most deployed. Instruction prompting reduces the shift directionally, but trades off against alignment with the surveyed associations. Evaluating LLM gender bias for global deployment therefore requires generation-format testing, local-language prompting, and locally-collected human baselines.
[NLP-165] A Unified Policy Architecture (UPA): The Governance Kernel for Enterprise AI Operating Systems
【速读】： 该论文旨在解决企业级生成式AI（Generative AI）向自主智能体（autonomous AI agents）演进过程中所面临的统一治理难题。随着智能体具备规划、推理、记忆、工具调用、工作流执行及跨代理协作等能力，传统分散的授权、安全、合规与管控机制已无法有效应对这一复杂系统化风险。其核心解决方案是提出统一策略架构（Unified Policy Architecture, UPA），通过构建一个涵盖AI、智能体、工具、工作流、记忆、企业资源以及代理间交互的统一策略模型，实现对自治AI系统的整体性治理。UPA的关键在于将策略控制从静态授权扩展至运行时义务、人工审批、合规性保障、审计证据生成及治理评估等多个维度，并基于声明式策略语言、可扩展插件机制、行业政策包及评估框架，支持多智能体协同、溯源感知策略和状态感知的运行时治理，为构建安全、可问责、可治理的企业级智能操作系统提供了坚实基础。
链接: https://arxiv.org/abs/2609.06543
作者: Prabhu Raghav,Balamurugan Pandi,Arul Vivek,Shek Mohammed,Sridhar S
机构: SuperAgentX AI
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Software Engineering (cs.SE)
备注: 58 pages, 6 figures. Includes appendices with the DGPL grammar, SID registry, EAGBench benchmark specification, extended governance models, and policy examples
Abstract:Enterprise AI is evolving into an Enterprise Operating System where autonomous AI agents can plan, reason, use memory, invoke tools, execute workflows, and collaborate with other agents. This shift creates a new governance challenge: existing authorization, security, guardrails, and compliance mechanisms are fragmented and are not designed to govern autonomous AI as a unified system. This paper introduces the Unified Policy Architecture (UPA), a governance architecture for Enterprise AI Operating Systems. UPA provides a unified policy model for governing AI and agents, tools, workflows, memory, enterprise resources, and agent-to-agent interactions and enterprise business rules. It extends policy control beyond authorisation to include runtime obligations, human approvals, compliance, audit evidence, and governance evaluation. We present UPA’s governance model, declarative policy language foundations, policy evaluation semantics, extensible plugins, industry policy packs, and an evaluation framework for enterprise governance. We also identify extensions for multi-agent coordination, provenance-aware policies, and stateful runtime governance. UPA provides a foundation for building secure, accountable, and governable Enterprise Operating Systems for autonomous AI. Comments: 58 pages, 6 figures. Includes appendices with the DGPL grammar, SID registry, EAGBench benchmark specification, extended governance models, and policy examples Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Software Engineering (cs.SE) Cite as: arXiv:2609.06543 [cs.AI] (or arXiv:2609.06543v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.06543 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-166] ProcArena: A Multi-Scenario Benchmark for LLM s on Direct and Interactive PL/SQL Development from Natural Language
【速读】： 该论文旨在解决当前自然语言（Natural Language, NL）到PL/SQL代码生成任务中存在的场景局限性问题，即现有研究多集中于从完整自然语言需求直接生成PL/SQL代码，而忽视了实际数据库开发中多样化的应用场景，如从零开始编写、代码修改、调试与优化等，这些场景往往需要直接生成或多次交互式协作。为填补这一空白，论文提出ProcArena，一个基于执行验证的综合性基准测试平台，涵盖直接生成（Direct）和交互式（Interactive）两种模式。其关键创新在于构建了包含3,998个可执行任务、覆盖157个数据库、涉及9类子开发场景的高质量数据集，并通过迭代逻辑增强（Iterative Logic Enhancement）和场景特异性适配器生成具有挑战性的直接任务，利用知识融合（Knowledge Integration）与需求扰动（Requirement Perturbation）构建配对的交互任务，同时保持目标代码的可执行性。此外，设计了一种受控的求解器-用户模拟器协议（Solver-User Simulator），使模型能够在不暴露隐藏执行反馈的前提下，通过澄清用户意图和检查数据库环境实现有效交互。实验评估表明，即使在最佳模型下，直接模式与交互模式的平均准确率也仅分别为62.2%和57.8%，凸显了真实场景下NL-to-PL/SQL开发仍面临巨大挑战，尤其在交互式情境中。
链接: https://arxiv.org/abs/2609.06527
作者: Hang Zhang,Chaokun Wang,Yuzhi Pan,Ziyao Zhong,Shuo Cao,Yue Xue,Zeyu Huang,Xingwei Zhou,Fang Niu,Bofan Xie,Guanchen Ge,Leqi Zheng,Ziyang Liu,Xiannian Cao,Pengcheng Ge
机构: Tsinghua University (清华大学); Lenovo Group Limited (联想集团有限公司)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Databases (cs.DB)
备注:
Abstract:Large language models (LLMs) have shown strong potential for translating natural-language (NL) requirements into PL/SQL programs, attracting increasing attention from the database community. However, existing NL-to-PL/SQL efforts primarily focus on directly generating PL/SQL from complete NL requirements. In practice, PL/SQL development involves diverse scenarios, such as from-scratch development, code modification, debugging, and optimization, and may require either direct generation or multi-turn interaction. Yet, no comprehensive benchmark evaluates multi-scenario, direct and interactive, and multi-dialect NL-to-PL/SQL development. In this paper, we present ProcArena, an execution-based benchmark covering both Direct and Interactive modes. ProcArena comprises 3,998 executable tasks over 157 databases, spanning nine development subscenarios in PostgreSQL and Oracle. We construct challenging Direct tasks through Iterative Logic Enhancement and scenario-specific adapters, and derive paired Interactive tasks through Knowledge Integration and Requirement Perturbation while preserving executable targets. We further design a controlled Solver-User Simulator protocol that allows models to clarify user intent and inspect the database environment without exposing hidden execution feedback. Evaluating seven language models, we find that the best average scores are only 62.2% and 57.8% in Direct and Interactive, respectively, demonstrating that realistic NL-to-PL/SQL development remains challenging, particularly in interactive settings.
[NLP-167] DFlow: Enabling Verifier Information Flow in Block Diffusion Speculative Decoding
【速读】： 该论文旨在解决生成式 AI（Generative AI）中大语言模型（LLM）推理效率优化问题，具体针对块扩散推测解码（Block Diffusion Speculative Decoding）方法中存在的计算资源浪费问题。现有方法在验证阶段仅保留被接受的前缀，丢弃被拒绝的后缀，导致对这些位置的计算无法在后续起草轮次中复用，迫使起草器从头重建未来标记的表示，造成冗余计算。其解决方案的关键在于提出 DFlow 框架，通过使验证器（verifier）在被拒绝位置产生的隐藏状态信息跨起草轮次流动，实现对后续预测的指导。DFlow 无需额外目标模型计算，直接复用验证器在被拒绝部分生成的隐藏状态，显著提升计算利用率。为有效学习这一跨轮次的信息流，引入自条件训练（self-conditioned training）策略，将先前预测中验证器的表示反馈至后续预测过程。实验结果表明，在 Qwen3 系列模型上，DFlow 在多个基准测试中均显著提升了草稿质量与接受长度，优于 DFlash 基线方法。
链接: https://arxiv.org/abs/2609.06498
作者: Yaojie Zhang,Linfeng Zhang,Bin Cui,Xupeng Miao
机构: Peking University (北京大学); Shanghai Jiao Tong University (上海交通大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Block diffusion speculative decoding improves LLM inference efficiency by proposing a block of future tokens in parallel and verifying them with a single forward pass through the target model. However, existing methods retain only the accepted prefix and discard the rejected suffix, preventing the computation spent on these positions from benefiting subsequent drafting rounds and forcing the drafter to repeatedly reconstruct representations for future tokens from scratch. We observe that rejection only determines whether a proposed token can be committed, while the verifier representations at rejected positions can still provide useful information for subsequent predictions. Based on this observation, we propose DFlow, a simple yet effective framework that enables verifier information to flow across drafting rounds. DFlow reuses the hidden states produced by the target verifier for the rejected suffix to guide subsequent drafting without additional target computation. To effectively learn this information flow across drafting rounds, we introduce a self-condition train strategy that feeds verifier representations from earlier predictions back into subsequent predictions. Experiments on Qwen3 models across diverse benchmarks demonstrate that DFlow consistently improves draft quality and acceptance length over DFlash.
[NLP-168] OracleZoom: On-Policy Self-Distillation Inspired Reference-Constrained Recursive Image Super Resolution
【速读】： 该论文旨在解决递归超分辨率（Recursive Super-Resolution, RSR）在极端放大倍率下因深层预测缺乏真实标签（ground truth）而导致的监督缺失问题，尤其在多级递归过程中，随着放大深度增加，所需源图像分辨率呈几何级增长，使得深层输出难以获得有效监督。其核心解决方案是提出一种受策略蒸馏（on-policy distillation）启发、基于参考约束的训练框架OracleZoom，通过在训练过程中保留并利用最末层真实图像作为“证据”来扩展监督边界。该方法结合直接与跨尺度监督以约束可验证内容，引入无参考质量评估目标以指导细粒度细节生成；同时采用KL约束的预训练潜在先验（latent prior）防止由质量驱动导致的生成漂移，并通过指数移动平均（EMA）一致性机制稳定监督边界。实验表明，OracleZoom在7个数据集上均达到当前最优的超分辨率性能，平均CLIPIQA达0.713，在深层放大尺度上提升更为显著，且大幅减少了幻觉现象。
链接: https://arxiv.org/abs/2609.06490
作者: Shubhashis Roy Dipta,Sourajit Saha,Shaswati Saha,Nobin Sarwar
机构: University of Maryland, Baltimore County (马里兰大学巴尔的摩县分校); University of Maryland, Baltimore County (马里兰大学巴尔的摩县分校); University of Maryland, Baltimore County (马里兰大学巴尔的摩县分校); University of Maryland, Baltimore County (马里兰大学巴尔的摩县分校)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Recursive Super-Resolution (SR) extends fixed-scale SR to extreme magnification by repeatedly feeding predictions back into the same model, analogous to zooming an image repeatedly. However, ground truth availability at every scale, especially at depth, remains challenging as the required source resolution grows geometrically, leaving deeper predictions unsupervised. We present OracleZoom, an on-policy distillation-inspired, reference-constrained framework that trains on its trajectory while carrying the last ground-truth evidence beyond the supervision boundary. Direct and cross-scale supervision constrain verifiable content, while a no-reference quality objective guides unresolved fine-scale detail. A KL-constrained pretrained latent prior limits quality-driven drift, while EMA consistency stabilizes the supervision boundary. Across seven datasets, OracleZoom achieves the state-of-the-art SR quality across zooming scales, averaging 0.713 CLIPIQA, with larger gains on deeper scales, while significantly reducing hallucinations. Code, data, and models are available at this https URL .
[NLP-169] A Group-Based Resource Allocation Model for the Fractional Knapsack Problem
【速读】： 该论文旨在解决分数背包问题（fractional knapsack problem）中传统Dantzig贪心规则因价值-成本比排序导致的敏感性问题：当预算在两个价值-成本比相近的物品间耗尽时，输入的微小扰动可能引发分配结果的剧烈变化。为缓解这一稳定性问题，论文提出一种两阶段分配规则，其核心在于引入基于邻近属性的分组机制——将单位成本在区间 [w−,w+] 内、且具有相似价值-成本比的物品划分为一个群组（group），并以群组为单位按代表值-成本比降序分配预算，群组内部不再进行细化排序。该方案的关键优势在于：群组与最优解之间的损失被严格界定为 vUGw++w−w+−w−+εvUG，其中 εv 限制群组内价值波动，且该调和因子对任意群组规模均保持紧致。当分组保持顺序兼容性时，总损失仅由唯一预算绑定群组决定，若每群最多含 K 个物品，则单个物品的损失为 O(K/n)；若群组比值区间重叠度不超过 ω，则损失增加一项 ωC 的加性项。此外，在相邻群组间的分离间隙内，分组分配策略对成本数据呈现Lipschitz连续性，其模数为 wminK。算法时间复杂度为 O(n+mlogm+∣Γ∣log∣Γ∣)，其中 m 为群组数，Γ 为边界群组；若采用线性时间选择方法确定边界群组的分配，则可优化至 O(n+mlogm)。
链接: https://arxiv.org/abs/2609.06470
作者: Abhinaba Chakraborty
机构: ID Lab, University of Ghent-imec(根特大学-imec实验室), Ghent, Belgium
类目: Data Structures and Algorithms (cs.DS); Computation and Language (cs.CL)
备注:
Abstract:To solve the fractional knapsack problem, Dantzig’s greedy rule orders items according to their value-to-cost ratio. This ordering introduces priority issues. An arbitrarily small perturbation to the input can change the allocation if the budget is exhausted between two items with very similar ratios. To mitigate that problem, we introduce a two-stage rule. We group items sharing attributes within a radius \delta . These groups are then evaluated in descending order of ratio, and divide their group’s budget share without further ranking. Consider a group featuring an aggregate capacity U_G , unit costs contained in [w^-,w^+] , and a representative value \widehatv . The group’s loss compared to the exact optimum is bounded by \widehatv, U_G\fracw^±w^-w^++w^-+\varepsilon_v U_G , in which \varepsilon_v limits the group’s internal value variation. Moreover, for any group size, this harmonic factor remains tight. The overall loss becomes restricted to the single budget-binding group whenever the grouping remains order-compatible; thus, groups containing at most K items suffer a per-item loss of \mathcalO(K/n) . Should group ratio intervals exhibit an overlap of at most \omega , an additive term \omega C degrades this bound. Within the separation margin between adjacent groups, the grouped allocation remains Lipschitz continuous with respect to cost data, exhibiting a modulus of \fracKw_\min . Computing this allocation takes \mathcalO(n+m\log m+|\Gamma|\log|\Gamma|) time given m groups and a boundary group \Gamma . Alternatively, the time complexity drops to \mathcalO(n+m\log m) if a linear-time selection method identifies the boundary group’s allocation.
[NLP-170] One Step One Lead: Mitigating Higher-Order Interference in Multi-Domain Reinforcement Learning via Cross-Step Control
【速读】： 该论文旨在解决多领域强化学习（Reinforcement Learning, RL）联合训练中因域间干扰导致的大语言模型（Large Language Models, LLMs）性能退化与优化不稳定性问题。现有方法通常依赖单步视角下的梯度对齐或基于曲率的代理指标诊断干扰，但此类方法易忽略一种关键的序列性干扰：即使同一时刻各领域梯度在参数空间中近似正交，其连续更新在输出空间中仍可能部分相互抵消。为揭示此现象，论文提出通过相邻检查点间的连续标记概率足迹（token log-probability footprints）直接捕捉输出空间中的局部二阶交互关系，无需显式重构同一步的海塞矩阵（Hessian）。基于此洞察，作者提出OSOL（Output-space Sequential Optimization with Log-probability footprints），其核心在于每轮迭代中指定一个焦点领域，利用前一检查点的足迹评估各标记级别的反冲风险（rebound risk），并在此基础上实施基于风险排序、自适应缩放的修正，嵌入标准GRPO更新流程。分析表明，该修正能有效抑制跨步输出层面的回溯成分；控制实验进一步验证，跨步回溯与后续任务损伤的相关性显著高于同点梯度诊断指标，且足迹风险排名优于基于海塞的代理方法。在Qwen3-30B-A3B上，OSOL实现领域宏平均得分0.4822，较最强基线提升5.7%，且无需显式高阶微分。
链接: https://arxiv.org/abs/2609.06469
作者: Zihan Lin,Xiaohan Wang,Jie Cao,Jiajun Chai,Guojun Yin,Wei Lin,Ran He
机构: MAISNLPR, Institute of Automation, Chinese Academy of Sciences(中国科学院自动化研究所智能系统与自然语言处理重点实验室); Meituan(美团)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Reinforcement learning (RL) across multiple domains can broaden the reasoning capabilities of large language models (LLMs), yet joint training often degrades individual-domain performance and can destabilize optimization. Existing work typically diagnoses such interference from a single-step view using first-order gradient alignment or curvature-based proxies. We show that this view can miss a critical form of sequential interference: same-point domain gradients may remain nearly orthogonal even when consecutive realized updates partially reverse one another in output space. We further show that consecutive token log-probability footprints recover this interaction directly from adjacent checkpoints as a local second-order interaction in output space, without explicitly reconstructing same-step curvature. Building on this insight, we propose OSOL, which designates a focus domain at each iteration, uses the preceding checkpoint footprint to rank token-level rebound risk, and applies a drift-ranked, adaptively scaled correction within the standard GRPO update. Our analysis shows that this correction suppresses the targeted cross-step output backtracking component. Controlled studies further show that cross-step backtracking is more strongly associated with subsequent task damage than same-point gradient diagnostics, while the preceding footprint ranks future rebound risk more accurately than Hessian-based proxies. On Qwen3-30B-A3B, OSOL reaches a domain-macro average of 0.4822, improving by 5.7% over the strongest compared baseline, without explicit higher-order differentiation.
[NLP-171] Phase-cycled randomized benchmarking of quantum processors: recovering hidden classical noise correlations
【速读】： 该论文旨在解决随机化基准测试（Randomized Benchmarking, RB）在存在经典时间关联噪声时无法有效揭示其内在相关性的问题，尤其针对具有对称马尔可夫型漂移（stationary symmetric telegraph fluctuator）的系统。其核心挑战在于，传统RB方法因克里福门旋蒸（Clifford-twirled）响应对噪声相位呈偶函数特性，从而掩盖了时间相关的噪声关联信息。为突破这一局限，研究提出的关键解决方案是构建一种基于八设定相位循环的连通正弦相位协方差测量方法，在理想克里福旋蒸与经典空闲退相干条件下实现对噪声时间关联性的敏感探测。该可观测量在独立槽噪声与固定失谐情形下严格为零，且无需弱相位或高斯近似假设，从而提供了一种可直接识别非独立噪声关联的实验手段。通过闭合的马尔可夫响应模型、无电路依赖性分析及800次蒙特卡洛模拟验证了该构造的有效性，并利用配对自助法（paired bootstrap）量化了估计器的经验覆盖范围，同时采用保守置信集明确其有限样本假设。在IBM处理器上的两组实验对比了工程化共享符号与独立重置相位的噪声行为，结果表明二者在相同采样预算下均显著偏离零点（主效应分别为0.254和0.211），但负控区间均包含零值，说明其差异仍受统计不确定性影响；此外，理想拉姆齐/回波估计器在所有测试类别中均表现出更高的精度。本工作建立了基准测试可辨识性限制与可控相关性测量之间的明确联系，为量子噪声建模提供了新的实验范式，且未宣称任何原生或量子存储检测能力。
链接: https://arxiv.org/abs/2609.06448
作者: Mirza Samad Ahmed Baig,Syeda Anshrah Gillani,Abdul Akbar Khan,Muhammad Omer Khan
机构: Fandaqah(法达卡)；Heidelberg University (海德堡大学)；Argaam(阿加姆)；Fortanixor(福坦尼克斯)
类目: Emerging Technologies (cs.ET); Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: 8 pages, 3 figures. Code and data: this https URL
Abstract:Randomized benchmarking can hide classical temporal correlations because its Clifford-twirled response is even in the noise phase. For a stationary symmetric telegraph fluctuator, we show that continuous evolution and independent stationary resets at slot boundaries yield identical mean responses for arbitrary fixed idle modulations. We construct an eight-setting phase-cycle measurement of the connected sine-phase covariance under ideal Clifford twirling and classical idle dephasing. This observable vanishes for independent slot noise and fixed detuning without a weak-phase or Gaussian approximation. A closed telegraph response, independent circuit calculations and 800 simulation trials validate the construction and quantify the empirical coverage of a paired bootstrap estimator. A separate conservative confidence set states its finite-sample assumptions. Two acquisitions on an IBM processor compare engineered shared-sign and independently reset phases with identical marginals. Their primary contrasts are 0.254 and 0.211, with empirical 95% intervals [0.177, 0.331] and [0.136, 0.285], respectively; all negative-control intervals include zero. At equal shot and sensingwindow budgets, an ideal Ramsey/echo estimator is more precise in every tested class. The result supplies an explicit connection between a benchmarking identifiability limitation and a controlled correlation measurement. No native or quantum-memory detection is claimed.
[NLP-172] Decomposing LLM -Judge Uncertainty to Target Expert Labels
【速读】： 该论文旨在解决大规模生成式 AI (Generative AI) 输出评估中专家标注资源浪费的问题，核心挑战在于：当使用大语言模型（LLM）作为评判者时，如何高效地分配有限的专家标注资源。其关键解决方案是引入一个小型贝叶斯模型，通过分离两种不同类型的不确定性——偶然性不确定性（aleatoric uncertainty）（即专家群体内部的真实分歧，无法通过更多标注缓解）和认知性不确定性（epistemic uncertainty）（即评判者自身的知识不足，可通过补充标注降低），从而精准识别出评判者真正“无知”的区域。该模型基于已收集的标注数据进行回归学习，以量化对黑箱评判者预测的信任程度，无需额外调用评判者或采样。实验表明，在真实人类分歧数据集（ChaosNLI）上，仅依据认知性不确定性排序来引导标注可比总不确定性减少83%的误差，且显著优于单纯依赖最低标注置信度的策略。研究证明了可以准确估计评判者的认知盲区而非人类真实分歧，并据此优化专家标注的部署策略。
链接: https://arxiv.org/abs/2609.06444
作者: Ryan Lail
机构: Composo AI
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 9 pages (4 pages of content plus references and appendices), 3 figures
Abstract:An LLM judge evaluates outputs at scale. Experts should label only where it is least sure. Its natural escalation signal conflates two uncertainties: aleatoric, real disagreement in the expert pool, which labels cannot reduce, and epistemic, the judge’s ignorance, which labels do reduce. A small Bayesian model separates them: a regression on labels already collected learns how far to trust a black-box judge’s prediction. Both components follow as simple formulas, with no sampling or further judge calls. The components isolate on a real LLM judge against exactly known truth, and stated confidence is no guide to its actual error. On real human disagreement (ChaosNLI) the epistemic ranking removes 83% more error than total uncertainty for the same expert labels, though simply escalating the least-labelled items does as well there. We demonstrate we can estimate where a judge is ignorant rather than where experts genuinely disagree, and propose using this to direct expert labelling.
[NLP-173] Visual Search Augmented Chain-of-Thought Reasoning for Attribute Value Extraction from Product Videos EMNLP2026
【速读】： 该论文旨在解决电商场景中视频到属性值提取（Video-to-Text Attribute Value Extraction, AVE）任务面临的挑战，即现有方法依赖静态图像，无法有效利用视频中的时序信息、多视角观测及细粒度视觉细节；同时，直接应用通用视频视觉语言模型（Video VLMs）在特定领域表现受限，因缺乏领域知识且微调成本高昂。其解决方案的关键在于提出一种无需训练、即插即用的视觉搜索增强思维链推理框架（ViS-CoT），通过视觉聚类提取代表性帧，结合视觉搜索检索语义相关的商品知识以增强属性线索；随后，采用交错式思维链（CoT）推理模块，基于图像对齐的辅助文本（来自图像字幕与自动语音识别）迭代优化推理过程；最终融合多源信息实现精准、细粒度的属性预测。实验表明，ViS-CoT在VideoAVE数据集上跨14个品类显著提升多个先进视频VLM的性能，平均微平均F1得分提升17.91个百分点。
链接: https://arxiv.org/abs/2609.06410
作者: Tong Wu,Ming Cheng,Jiazhen Hu,Jiaying Gong,Hoda Eldardiry
机构: Virginia Tech(弗吉尼亚理工学院); Amazon(亚马逊)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 6 figures, accepted for publication in EMNLP 2026 Findings
Abstract:Existing approaches to visual attribute value extraction (AVE) primarily rely on static product images, failing to capture temporal cues, multi-angle views and fine-grained visual details. Directly applying video vision-language models (VLMs) to product AVE results in limited performance due to the lack of domain knowledge, and fine-tuning them requires extensive high-quality data and substantial computational resources. Thus, we propose visual search augmented chain-of-thought reasoning (ViS-CoT), a training-free, plug-and-play pipeline that can be easily applied to any open-source video VLM for video-to-text AVE in e-Commerce. Specifically, ViS-CoT employs visual clustering to identify representative frames, followed by visual search to retrieve semantically similar product knowledge that can enrich attribute cues. Next, an interleaved CoT reasoning module iteratively refines reasoning through visually-aligned auxiliary texts derived from captioning and automatic speech recognition. Finally, the integrated information guides the model toward accurate and fine-grained attribute predictions. Extensive experiments across 14 product categories on the VideoAVE dataset show that ViS-CoT consistently enhances multiple state-of-the-art video VLMs, achieving an average improvement of 17.91 percentage points in micro-F1.
[NLP-174] Hierarchical Wasserstein Merging for Multi-Domain Multi-Task Learning: From Specialists to a Generalist EMNLP2026
【速读】： 该论文旨在解决多领域多任务学习（MD-MTL）中因分布偏移导致的联合训练干扰问题，尤其针对现有模型融合方法仅关注参数层面而忽略跨领域与跨任务隐空间表示分布几何结构的缺陷。其解决方案的关键在于提出一种基于表示层面的分层Wasserstein融合（Hierarchical Wasserstein Merging, HWM）框架，将每个领域-任务专用模型视为共享支持上的隐藏表示分布，并通过构建任务级与全局Wasserstein质心来分别捕捉任务内领域差异与跨任务结构特征，从而实现无需训练的专用模型加权聚合或基于混合Wasserstein对齐损失的训练式通用模型学习，显著提升了模型在异构场景下的泛化性能与有效性。
链接: https://arxiv.org/abs/2609.06406
作者: Ming Cheng,Jiaying Gong,Hoda Eldardiry
机构: Virginia Tech(弗吉尼亚理工学院); Amazon(亚马逊)
类目: Computation and Language (cs.CL)
备注: 20 pages, 2 figures, accepted for publication in EMNLP 2026 Findings
Abstract:Multi-domain multi-task learning (MD-MTL) aims to build a single generalist model that performs well across heterogeneous domains and tasks. However, joint training often suffers from interference under distribution shifts. Existing model merging methods mostly operate on model parameters while overlooking the geometric structure of latent representation distributions across domains and tasks. To address these limitations, we propose Hierarchical Wasserstein Merging (HWM), a representation-level framework that models each domain-task specialist as a distribution of hidden representations on a shared support. HWM constructs task-level and global Wasserstein barycenters to capture within-task domain variation and cross-task structure, enabling either training-free specialist aggregation by Wasserstein-derived weights or training-based generalist learning through a hybrid Wasserstein alignment loss. Experiments on four NLP tasks across four domains per task show that HWM achieves superior effectiveness and generalization capability in MD-MTL settings.
[NLP-175] Building Trustworthy Graph-Agent ic RAG for Social Good: Architectures Failure Propagation and Assurance by Construction
【速读】： 该论文旨在解决生成式 AI 系统在社会有益场景中因图结构化证据与智能体控制机制耦合而引发的可靠性问题，尤其关注当答案依赖跨文档、实体、时间或机构间关系时，系统可能因图构建缺陷导致错误证据被持续传播并影响后续决策路径，最终造成严重后果。其解决方案的关键在于提出一种“构建即保障”（assurance-by-construction）的设计范式，通过定义五项接口契约——证据、检索、推理、能力与委托、结果——在系统边界显式表达溯源性、时效性、授权状态、不确定性及可恢复性，从而实现对图结构与控制行为的可控约束。该框架进一步推动了从图断言、轨迹、主张到协调与结果的全链条评估体系构建，以确保系统在真实性、可追溯性、可监督性和可救济性等社会价值维度上的可靠性。
链接: https://arxiv.org/abs/2609.06391
作者: Vijay Bommireddy,Raviteja Bommireddy
机构: Independent Researcher(独立研究员); IIITDM Kancheepuram(印度信息科技研究所德里分校坎切普拉姆校区)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 12 pages, 2 figures
Abstract:Graph-agentic retrieval-augmented generation combines structured evidence with adaptive controllers that can plan retrieval, traverse relations, verify intermediate claims, delegate subtasks, and use tools. This combination is useful when answers depend on relations across documents, entities, time, or institutions, but it also creates coupled failure paths: a defect in graph construction can become retrieved evidence, alter later control decisions, and propagate toward a consequential outcome. We examine how such systems should be designed and evaluated for social-good settings in which freshness, authorization, traceability, oversight, and recourse matter alongside answer quality. We organize the literature by graph substrate, graph lifecycle, agent function, coordination pattern, and authority boundary, and distinguish graph-based retrieval from observation-dependent graph control. We then synthesize reported risks as an evidence-to-action failure chain and propose an assurance-by-construction blueprint comprising five interface contracts for evidence, retrieval, reasoning, capability and delegation, and outcome. These contracts make provenance, temporal validity, authorization, uncertainty, and recoverability explicit at system boundaries. An illustrative public-benefit information design shows how the framework constrains graph structure, permissions, abstention, and operating authority. Finally, we derive an evaluation agenda spanning graph assertions, trajectories, claims, coordination, and outcomes.
[NLP-176] Are Verifier Errors Independent Within a GRPO Group? Evidence from Qwen 2.5 Rollouts
【速读】： 该论文旨在解决在使用自动验证器（verifier）评估大模型生成结果时，因多个完成项（completions）共享相同提示（prompt）和答案格式而导致的验证错误相关性问题。传统基于独立验证误差的分析可能忽略这种由共享答案格式引发的依赖结构，从而低估真实噪声水平并导致对模型性能评估的偏差。其解决方案的关键在于通过大规模实证分析（基于24,998组由Qwen2.5-1.5B生成的八条完成项）量化同一组内不同完成项之间的验证误差相关性，发现组内平均验证误差相关系数为0.530（95%置信区间：0.500–0.560），表明在交换对称误差模型下，每组有效样本量仅为1.70，远低于名义上的8个完成项。研究进一步揭示，分数、根式、符号表达式及区间等复杂答案形式表现出更强的聚集性，而单位标注与百分号则相关性较弱。此外，通过重放四类基于规则的验证器配置下的组内相对优势，发现高达0.83%的组别存在优势方向不一致现象。这些结果表明，组内依赖既可能源于共同的提示难度，也可能源于共享的答案格式，因此必须发展考虑提示和答案形式特征的验证噪声分析方法，而非仅依赖聚合错误率。
链接: https://arxiv.org/abs/2609.06386
作者: Esther Xin
机构: Independent Researcher(独立研究员)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 10pages, 4 figures, code and data in this https URL
Abstract:Group-based reinforcement learning with verifiable rewards (RLVR) scoresmultiple completions per prompt using automatic verifiers. Analysesbased on independent verifier errors may overlook dependence associatedwith shared answer formats. We investigate this dependence in24,998 groups of eight completions generated by Qwen2.5-1.5B onMATH, GSM8K, and DeepMath-103K. We estimate a pooled within-groupverifier-error correlation of 0.530 (95% confidence interval:0.500–0.560). Under an exchangeable-error model, this correspondsto a design-effect-adjusted effective sample size of 1.70 for aneight-completion group. Dependence varies substantially across answerforms: fractions, radicals, symbolic expressions, and intervals exhibitstronger clustering than unit annotations and percent signs. Replayinggroup-relative advantages across four rule-based verifier configurationsidentifies at least one advantage-sign disagreement in up to 0.83% ofgroups. Because a group is repeated sampling for one prompt, thiswithin-group clustering may reflect shared prompt difficulty as well asshared answer form, and we do not attempt to separate the two this http URL studies of correlated judgments across multiple evaluators, ouranalysis examines dependence across completions scored by the sameverifier. These findings motivate prompt- and answer-form-aware analysesof verifier noise rather than characterizations based solely onaggregate error rates.
[NLP-177] From Reading Code to Reading Spec: A Verified Layer for LLM -Driven Codebase Maintenance
【速读】： 该论文旨在解决大语言模型（LLM）生成代码所导致的软件复杂性增加与工程师维护负担加剧的问题。当前，尽管LLM具备自动化代码生成的潜力，但其直接操作复杂代码库的能力受限于代码结构的非规范化与语义不透明性。为此，论文提出可验证的原始功能表示（Provable Representation Of Original Functionality, PROOF），通过结构化规范间接管理代码库。其核心解决方案在于将代码库拓扑抽象为分层的自然语言表示，并基于该规范重建源代码以严格证明语义等价性，从而建立可信的验证基础。在此基础上，所有维护请求均通过规范驱动执行，代码修改与规范同步更新，有效防止语义漂移。实验结果在真实世界代码库上的验证表明，该方法能实现全生命周期的可靠代码库管理。
链接: https://arxiv.org/abs/2609.06383
作者: Xinhao Zhang,Jingjie Lu,Kunpeng Liu,Fei Xie
机构: Portland State University (波特兰州立大学); Clemson University (克莱姆森大学)
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL)
备注:
Abstract:The rapid growth of LLM-generated code increases software complexity and the maintenance burden on engineers. While LLMs offer a potential automated alternative, this structural complexity hinders their ability to manage codebases directly. We introduce the Provable Representation Of Original Functionality (PROOF), which manages codebases indirectly via structured specifications. To enable full-lifecycle codebase management strictly through these specifications, PROOF abstracts codebase topology into a hierarchical natural-language representation. To establish absolute trust, the system proves semantic equivalence by reconstructing source code exclusively from this specification. This verified foundation drives maintenance requests, executing code modifications while synchronously updating itself to prevent semantic drift. Experiments on real-world repositories confirm the effectiveness of these specifications.
[NLP-178] Cross-Lingual Representation Alignment by Token-Level Optimal Transport in a Language-Agnostic Space EMNLP2026
【速读】： 该论文旨在解决大语言模型（LLM）在跨语言对齐（Cross-lingual Alignment, CLA）过程中忽视语言特异性信息、仅依赖句级对齐所导致的性能不佳及输入输出语言不一致问题。其核心挑战在于如何在保持语言独特性的同时实现高质量的跨语言表示对齐。解决方案的关键在于提出CAROT（基于最优传输的无语言偏好空间中的跨语言表示对齐），通过两个关键步骤：首先识别大语言模型内部状态中蕴含的语言特异性表示，随后利用最优传输（Optimal Transport）在标记级别对齐跨语言的无语言偏好表示，同时显式保留语言特异性成分。该方法在推理阶段验证了其作为有效对齐目标的能力，显著提升多语言性能达11.2个百分点，并确保输入输出语言一致性；进一步将所得表示用作训练目标以实现内部对齐，使训练后的模型在18个评估场景中的11个超越现有CLA方法，为大语言模型中有效对齐目标的设计提供了重要洞见。
链接: https://arxiv.org/abs/2609.06381
作者: Taisei Yamamoto,Ryoma Kumon,Danushka Bollegala,Hitomi Yanaka
机构: The University of Tokyo (东京大学); Riken (理化学研究所); University of Liverpool (利物浦大学); Tohoku University (东北大学)
类目: Computation and Language (cs.CL)
备注: Accepted to EMNLP 2026 main
Abstract:Cross-lingual alignment (CLA) aims to align the representations of large language models (LLMs) across languages, enabling cross-lingual transfer to improve multilingual capabilities. Previous CLA methods often ignore language-specific information encoded in representations and only consider sentence-level alignment, which may lead to suboptimal performance and input-output language mismatch. We propose CAROT (Cross-Lingual Alignment of Representations in a Language-Agnostic Space via Optimal Transport), which consists of two steps: identifying language-specific representations in LLMs’ internal states and aligning language-agnostic representations across languages at the token level by optimal transport, while explicitly preserving language-specific representations. Inference-time steering experiments show that the representations computed by CAROT are effective alignment targets, improving multilingual performance by up to 11.2 points in accuracy while maintaining input-output language consistency. We further use the representations obtained by CAROT as training targets, internalizing the aligned representations. The trained models outperform existing CLA methods in 11 of 18 evaluation settings (3 models \times 3 tasks \times ID/OOD languages). Our work provides insights into what constitutes effective alignment targets for CLA in LLMs. Code is available at this https URL
[NLP-179] Query-Oblivious Coresets for Softmax Attention: Improved Bounds and Efficient Constructions
【速读】： 该论文旨在解决软注意力机制（softmax-attention）中查询无关的共核集（query-oblivious coreset）的尺寸优化问题，即在不依赖具体查询的情况下，从键值对集合中选取一个子集 $ S $，使得仅基于 $ S $ 计算的注意力输出与全集计算结果在 $ \ell_2 $ 范数下对球内任意查询均保持 $ \varepsilon $ 误差。此前，Liberty、Andoni 与 Kleiner 提出了一个上界 $ O(\sqrt{d},e^\rho + \frac{1}{2}\log\rho + o(\log\log\rho)/\varepsilon) $，并给出了 $ \Omega(\sqrt{d},e^\rho/\varepsilon) $ 的下界，二者之间存在未闭合的差距，且他们推测需引入新方法才能突破。本文的关键突破在于：通过将查询与键的球体进行球面提升（spherical lift），将其转化为一个指数核（exponential kernel）实例，直接应用 Bozzai 与 Rothvoss 的链式绑定（chaining bound），再结合 Chevet 不等式实现键维与值维的分离，从而得到首个具有全球保证且尺寸为 $ O(e^\rho(\sqrt{d_v} + \sqrt{d_k}\log(1+\rho))/\varepsilon) $ 的无权共核集构造，并可在随机多项式时间内计算。此外，文中还提出了一个维度无关的采样上限 $ O(e^{2\rho}/\varepsilon^2) $，完整构建了理论包络。在固定维度情形下，通过将键球补全为球面使核函数退化为无权高斯核，利用 Tai 的直径无关界获得 $ O_d_k,d_v(e^\rho/\varepsilon) $ 的共核集大小，从而排除了该场景下对数项下界的可能，解决了 Bozzai 与 Rothvoss 关于指数核和 Hellinger 核的开放问题。同时，论文重新以中心化约定形式重述了 Liberty–Andoni–Kleiner 上界并给出完整证明，并表明 Chen 等人的单向通信下界可转移至查询无关共核集，在 $ \varepsilon \ll e^{-\rho} $ 时成为目前已知最强的下界。其核心思想揭示：维度因子实为“一次签名应对所有查询”的代价——对于单一查询，偏差仅为 $ O(e^\rho) $，且与维度无关。
链接: https://arxiv.org/abs/2609.06327
作者: Ofek I.Cohen
机构: 未知
类目: Data Structures and Algorithms (cs.DS); Computation and Language (cs.CL)
备注:
Abstract:A query-oblivious coreset for a softmax-attention head is a subset S of the key–value pairs such that attention computed from S alone is within \varepsilon of the full output, in \ell_2 , simultaneously for every query in a ball. Liberty, Andoni and Kleiner proved that unweighted coresets of size O(\sqrt d,e^\rho+\frac12\log\rho+o(\log\log\rho)/\varepsilon) exist, \rho being the query radius times the centred key radius, against a lower bound \Omega(\sqrt d,e^\rho/\varepsilon) , and conjectured that closing the gap needs new techniques. We show it does not. A spherical lift of both balls into one exponential-kernel instance lets the chaining bound of Bozzai and Rothvoss apply directly, and Chevet’s inequality splits key from value dimension: unweighted coresets of size O(e^\rho(\sqrtd_v+\sqrtd_k\log(1+\rho))/\varepsilon) exist and are computable in randomised polynomial time, the first with a whole-ball guarantee at the existential size up to \sqrt\log(1+\rho) . A dimension-free sampling cap O(e^2\rho/\varepsilon^2) completes the envelope. In fixed dimension the logarithm disappears: completing the key ball to a sphere makes the kernel an unweighted Gaussian one, so Tai’s diameter-free bound gives O_d_k,d_v(e^\rho/\varepsilon) , ruling out a matching logarithmic lower bound there and answering the Gaussian-restriction case of a question of Bozzai and Rothvoss for the exponential and Hellinger kernels. We restate the Liberty–Andoni–Kleiner bound in the centred convention with a full proof, and show that the one-way communication bounds of Chen et al.\ transfer to query-oblivious coresets, where for \varepsilon\ll e^-\rho they are the strongest floors known. The dimensional factor is the price of one signing for all queries: for a single query the discrepancy is O(e^\rho) , dimension-free.
[NLP-180] A Ticket from Marginals to Joints: Coupled-Noise Distillation for One-Step Block Generation in Diffusion Language Models
【速读】： 该论文旨在解决自回归语言模型（autoregressive language models）每次前向传播仅生成一个词元，而扩散语言模型（diffusion language models）虽可一次性生成一个词元块但需多步迭代的问题，核心目标是实现在单次前向传播中完成一个词元块的完整生成。其关键挑战在于如何在单步生成中有效利用噪声信号以引导生成内容的连贯性与合法性。现有方法通过在掩码嵌入上添加独立于数据的高斯噪声场，并采用“胜者为王”或重要性加权策略训练模型，但此类方法对噪声的控制能力较弱——实验表明，噪声携带的信息量随竞争噪声场数量的对数增长，导致单步生成结果在不同模型规模下仍难以保持连贯性。为此，论文提出一种新框架——CONDOR（Coupled-Noise Distillation for One-Step Readout），其核心创新在于引入噪声条件下的教师-学生协同训练机制：教师模型在随机掩码位置和“胜者为王”策略下训练；学生模型则在单次前向传播中提出一个词元块，保留选定词元，并在相同噪声场下由教师通过多步填充其余位置生成参考块，从而指导学生学习；同时结合无噪声的掩码语言模型（masked-LM）损失项，锚定真实标签。实验结果显示，在TinyStories数据集上，该方法显著提升了单步生成的合法性，且不同噪声场仍能产生多样化输出，实现了真正意义上的单前向传播块生成。
链接: https://arxiv.org/abs/2609.06324
作者: Lin Yao
机构: Shanghai Jiao Tong University (上海交通大学); Zhongguancun Academy (中关村学院)
类目: Computation and Language (cs.CL)
备注:
Abstract:Autoregressive language models commit one token per forward pass; diffusion language models commit a block of tokens over several steps. We ask whether a block can be committed in a single forward pass. We study this with a noise-conditioned masked denoiser: a data-independent Gaussian noise field is added to the mask embeddings so that, in principle, each sampled field selects one joint mode of the block. The established way of training such a model is to sample several fields per example and let them compete for the data, by winner-take-all or importance weighting. This gives the noise only coarse control: in our experiments, the information it carries grows roughly with the logarithm of the number of competing fields, and one-step outputs remain rarely coherent across the model sizes tested. We propose CONDOR (Coupled-Noise Distillation for One-Step Readout). A noise-conditioned teacher is trained with a random number of masked positions and winner-take-all. A student proposes a one-step block, retains selected tokens, and learns from the block obtained when the teacher refills the other positions in several steps under the same noise field; a noise-free masked-LM term on the ground truth anchors the student. Human evaluation on TinyStories shows a large gain in one-step legality while different noise fields still yield different blocks, at one forward pass per block.
[NLP-181] Reliability validity and diagnostic evidence for multi-model LLM short-answer scoring
【速读】： 该论文旨在解决生成式 AI（Generative AI）在教育评分中应用时缺乏充分证据支持的问题，特别是针对简答题评分在信度、效度、评分严重性、诊断价值及失败案例等方面的验证不足。其核心解决方案是采用重复多次、多模型协同的“OCG-PRES”框架对大语言模型（LLM）进行短答案评分评估，通过引入五个维度——概念覆盖、关系准确性、推理完整性、矛盾控制与领域相关性——系统化地评估GPT、DeepSeek和通义千问（Qianwen）三类模型在三次独立运行中的表现。研究基于996条SciEntsBank数据，对比了模型评分与官方二分类及五分类标签的一致性，并与基于答案长度、Jaccard重叠、TF-IDF余弦相似度以及传统逻辑回归模型的非LLM基准方法进行比较。结果表明，所有模型在重复评分中均表现出高信度（组内相关系数ICC(3,k)达0.977至0.992），其中DeepSeek最为稳定；GPT在官方标签匹配上表现最优（AUC=0.909），而Qianwen则更严格，具有更高精度但较低召回率。此外，OCG-PRES评分在各官方类别间呈现预期的诊断模式，且在AUC和F1指标上全面优于非LLM基线。研究证实，基于多模型重复评估的OCG-PRES框架能够为生成式AI辅助短答案评分提供可靠的信度、效度与诊断证据，支持将其作为人类评分的辅助工具，而非完全替代。
链接: https://arxiv.org/abs/2609.06315
作者: Chunyi Zhao,Chao Li
机构: 未知
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注:
Abstract:Large language models (LLMs) are increasingly used or proposed for educational scoring, but single-model and single-run evaluations provide limited evidence for assessment use. Short-answer scoring requires evidence about reliability, validity, severity, diagnostic value, and failure cases. This study evaluated repeated multi-model OCG-PRES guided LLM scoring for short-answer assessment. The analysis used 996 SciEntsBank responses. GPT, DeepSeek, and Qianwen each scored every response across three independent runs using five OCG-PRES dimensions: concept coverage, relation accuracy, reasoning completeness, contradiction control, and domain relevance. Scores were evaluated against official binary and five-category labels and compared with non-LLM baselines based on answer length, Jaccard keyword overlap, TF-IDF cosine similarity, and a combined traditional logistic model. Repeated-run reliability was high for all models, with ICC(3,k) = .977 for GPT, .992 for DeepSeek, and .981 for Qianwen. DeepSeek was the most stable across runs. GPT showed the strongest official-label alignment by AUC (.909), while Qianwen was stricter, with higher precision but lower recall under the fixed threshold = 3.0 rule. OCG-PRES scores followed expected diagnostic patterns across five official categories and outperformed all non-LLM baselines in AUC and F1. Repeated multi-model OCG-PRES scoring provides reliability, validity, and diagnostic evidence for LLM-assisted short-answer scoring. The findings support cautious, evidence-based use as a scoring support tool rather than a replacement for human judgement.
[NLP-182] SignDino: Self-Supervised Sign Language Representation Learning via Temporal-Axis Self-Distillation
【速读】： 该论文旨在解决自监督手势语言表示学习中两个关键问题：一是手势由有限数量的解剖学上区分明显的发音器（articulators）产生，二是其语义高度依赖于这些发音器随时间的组织结构。现有自然图像自监督学习（SSL）方法在处理此类时序性、多模态的手势数据时存在局限。为此，论文提出SignDino，一种将DINOv3的学生-教师框架从图像块的空间域迁移至追踪手势流的时间域的自监督视频编码器。其核心创新在于：通过检测-跟踪流水线（YOLOv8n+ByteTrack）将视频分解为左手、右手和面部三路时序流；采用冻结的DINOv3 ViT-B/16对每个帧级解剖区域裁剪进行嵌入，而轻量级时间注意力机制（temporal Transformers）作为学生与指数移动平均（EMA）教师网络，仅负责建模跨时间的发音器状态演化。训练过程结合了时间域DINO自蒸馏、帧级掩码标记预测（类iBOT）、KoLeo特征扩散及帧间相似性结构的Gram锚定策略，从而在保持强图像级视觉原语不变的前提下，聚焦于学习发音器动态变化的时间模式。实验表明，SignDino在手语转英文翻译、孤立手势识别和指拼检测等任务上均展现出优异的自监督表征能力，且在匹配下游评估条件下达到竞争性或领先水平。
链接: https://arxiv.org/abs/2609.06296
作者: Junyi Hu,Zhewen He,Haomian Huang,Zhenhua Li,Zhifei Li,Yi Fang
机构: New York University Abu Dhabi (纽约大学阿布扎比分校)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注: 24 pages, 8 figures
Abstract:Self-supervised sign language representation learning must model two properties not central to natural-image SSL: signs are produced by a small set of anatomically distinct articulators, and their meaning depends on the temporal organisation of those articulators. We introduce SignDino, a self-supervised sign-video encoder that moves the DINOv3 student–teacher recipe from the spatial domain of image crops to the temporal domain of tracked sign streams. Each video is decomposed into left-hand, right-hand, and face streams by a detector-first YOLOv8n+ByteTrack pipeline. A frozen DINOv3 ViT-B/16 embeds each per-frame anatomical crop, while lightweight temporal Transformers, not the image backbone, form the student and EMA teacher. They are trained by temporal DINO self-distillation, frame-level masked-token prediction in the style of iBOT, KoLeo feature spreading, and Gram anchoring of the frame-to-frame similarity structure. This design keeps strong image-level visual primitives fixed and learns only how articulator states evolve across time. We evaluate on sign-to-English translation, isolated sign recognition, and fingerspelling detection benchmarks. Across these tasks, SignDino provides a strong public self-supervised representation and shows competitive or state-of-the-art performance under matched downstream evaluation.
[NLP-183] Steering Geometry: Validating Human Value Geometry in LLM Steering Space EMNLP2026
【速读】： 该论文旨在解决生成式AI（Generative AI）在对齐敏感场景中，通过激活控制（activation steering）实现行为调控时存在的可解释性与语义一致性问题。现有方法多聚焦于单一行为的操控效果，但其背后的隐空间几何结构是否反映人类价值观与道德的内在理论框架尚不明确。为此，研究以施瓦茨基本人类价值观理论（Schwartz’s Theory of Basic Human Values）为细粒度分析框架，构建了一个包含26,000样本、覆盖20种人类价值的基准测试集，系统评估了分布驱动型方法（如CAA、SphericalSteer、ODESteer）与行为中心型方法（如COLD-Steer、BiPO）在多种模型架构与规模下的表现。研究发现，分布驱动型方法能有效恢复与理论预测一致的人类价值观拓扑结构（斯皮尔曼等级相关系数ρ最高达0.51，p < 10⁻¹³），而行为中心型方法虽具备相当的操控性能，却与预期的价值几何结构关联性极弱。此外，模型规模越大，几何保真度越高，但指令微调后会出现下降趋势。更重要的是，良好的几何对齐可带来更符合人类直觉的价值迁移效应：正确引导某一价值会同时提升相容价值并抑制对立价值。因此，该研究的关键突破在于揭示了分布驱动的激活控制方法在捕捉深层语义结构方面的优势，并确立了几何一致性与跨价值泛化能力之间的因果联系。
链接: https://arxiv.org/abs/2609.06289
作者: Mohammad Mahdi Abootorabi,Armin Saghafian,Ali Bazshoushtari,Hamid Rezaei,EunJeong Hwang,Vered Shwartz,Parvin Mousavi,Purang Abolmaesumi
机构: University of British Columbia (不列颠哥伦比亚大学); Vector Institute for AI (向量人工智能研究所)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted to EMNLP 2026 Main Conference (top 15.4%)
Abstract:As large language models (LLMs) are increasingly deployed in alignment-sensitive contexts, activation steering has emerged as a lightweight, inference-time alternative to fine-tuning methods (e.g., RLHF, DPO) for behavioral control. However, existing work typically validates steering on isolated behaviors, leaving it unclear whether steering vectors encode coherent semantic structure or merely exploit behavior-specific shortcuts. We investigate whether the latent geometry of LLM steering vectors reflects theory-specified structure in human values and morality. Using Schwartz’s Theory of Basic Human Values as our primary fine-grained framework, we introduce a 26K-sample benchmark covering 20 human values and analyze distribution-driven methods (e.g., CAA, SphericalSteer, ODESteer) and behavior-centric approaches (e.g., COLD-Steer, BiPO) across diverse model families and sizes. We find that distribution-driven methods recover human value topologies aligned with theoretical predictions (Spearman \rho up to 0.51, p 10^-13 ). In contrast, behavior-centric methods achieve comparable steering performance but show little correlation with the expected value geometry. Geometric fidelity improves with model scale but drops after instruction tuning. Finally, better geometric alignment also leads to more human-consistent transfer across values: steering one value correctly lifts compatible values and suppresses opposing ones. Code and data are available at: this https URL.
[NLP-184] Correction as Annotation: Bootstrapping a Dependency Parser for Documentary Medieval Latin
【速读】： 该论文旨在解决中世纪拉丁语文本在自然语言处理（Natural Language Processing, NLP）工具支持方面的严重不足问题，尤其针对现有拉丁语树库（Latin treebank）模型在1258至1446年间马赛编纂的160份清册文本上表现不佳的困境。现有五种可用的拉丁语依存句法分析模型在该数据集上的最佳标注依存弧准确率（labelled attachment score）仅为0.62，形态感知得分（morphology-aware score）低至0.24，且性能与文本体裁或时期接近性无显著相关性。为突破这一瓶颈，研究提出一种基于迭代式人工校正的领域内训练数据生成策略：在九轮迭代中，利用前一模型对200个句子进行预标注，由专家修正后作为下一模型的训练数据，且各批次样本独立于模型状态采样，未采用主动学习选择机制。通过总计33小时的人工标注投入（覆盖1,804个句子），通用词性标注准确率从0.80提升至0.98，标注依存弧准确率从0.48跃升至0.92，显著优于所有基线模型，同时仅使用了最大基线模型所需训练数据的3%。此外，标注者工作量占比从初始的54%下降至14%-18%的稳定水平，该指标无需独立金标准即可反映标注效率，可作为迭代终止的实用依据。其核心解决方案在于通过低成本、非主动学习的迭代标注-训练闭环，高效构建高质量领域适配的训练数据，从而实现小样本条件下的高性能句法分析。
链接: https://arxiv.org/abs/2609.06266
作者: Gabriel H. Pizzorno(Harvard University)
机构: Harvard University(哈佛大学)
类目: Computation and Language (cs.CL); Digital Libraries (cs.DL)
备注: 40 pages, 6 figures, 10 tables. Code and data: this https URL . Trained models archived at this https URL
Abstract:Medieval documentary sources remain inadequately served by existing natural language processing tools. None of the five readily available Latin treebank models attains usable performance on a collection of 160 inventories compiled in Marseille between 1258 and 1446. The best labelled attachment score is 0.62 and the best morphology-aware score is 0.24. Performance does not correlate with either genre or period proximity. To address this shortfall, in-domain training data was generated as a by-product of using these inadequate models. In each of nine iterations, a model pre-annotated 200 sentences; an expert corrected the annotations; and the corrected sentences were used to train the subsequent model, with batches sampled independently of model state, without active-learning selection. Thirty-three hours of annotation effort over 1,804 sentences increased universal part-of-speech accuracy from 0.80 to 0.98 and labelled attachment from 0.48 to 0.92, outperforming all baselines on the reported metrics while using 97% less training data than the largest one of them. Annotator effort declined from 54% of tokens to a plateau of 14-18%, an operational progress metric that requires no separate gold standard and can serve as a stopping criterion.
[NLP-185] Beyond the Flag: Clinical Framing Closes the Moderation Gap in Suicide Risk Measurement EMNLP2026
【速读】： 该论文旨在解决当前内容审核接口（Moderation APIs）在识别自杀相关风险时，仅能提供二元性违规标记而无法有效衡量临床风险严重程度的问题。其核心挑战在于：平台不仅需检测潜在违规内容，还需根据风险等级采取差异化的响应措施，而现有法规（如加州参议院法案243）已将这一分级响应要求纳入合规范畴。为此，研究提出并构建了一个包含516条r/SuicideWatch帖子的基准数据集，由持证精神科医生依据哥伦比亚自杀严重程度评估量表（Columbia Suicide Severity Rating Scale, CSSRS）进行四层级有序标注（指示、意念、行为、尝试），用于评估各类模型在七种有序感知指标下的表现。关键发现包括：商业级审核接口虽能较好区分高低风险（高风险类F1达0.860），但对严重程度的量化能力较差（宏平均F1仅为0.395），且系统性高估最严重类别；基于临床知识的零样本提示（zero-shot prompting）显著缩小了性能差距（宏平均F1提升至0.562），其中专家设计的提示框架（而非微调、添加推理或朴素多代理聚合）是关键有效因素；推理能力的作用具有语境依赖性——在冗长嘈杂的Reddit帖子中反而降低性能，而在短小精炼的临床文本中则显著提升。研究主张，比例性照护义务要求平台具备对风险严重程度的精细化评估能力，而非仅依赖二元标记，并公开了评估框架以支持该目标的实现。
链接: https://arxiv.org/abs/2609.06263
作者: Shreyas Krishnan,Gun Ahn,Jungjin Kim
机构: University of California, Berkeley(加州大学伯克利分校); Wondi AI; MIT(麻省理工学院); Harvard Medical School(哈佛医学院); McLean Hospital
类目: Computation and Language (cs.CL)
备注: 20 pages, 6 figures. Accepted at the 5th Workshop on NLP for Positive Impact (NLP4PI), EMNLP 2026
Abstract:Moderation APIs are built to flag policy-violating content, not to measure graded clinical risk. But a platform’s duty does not end at detection: the response owed to passive distress differs sharply from the response owed to active planning with means access, and emerging regulation (e.g., California Senate Bill 243) is turning that distinction into a compliance requirement. We therefore ask how well deployed safety signals recover clinically meaningful severity. We release a benchmark of 516 r/SuicideWatch posts rated by a licensed psychiatrist on a four-level ordinal schema (Indicator, Ideation, Behavior, Attempt) grounded in the Columbia Suicide Severity Rating Scale, and evaluate moderation APIs, prompted LLMs, and supervised baselines under seven ordinal-aware metrics. Three findings. Vendor moderation APIs separate low- from high-severity posts well (0.860 high-risk F1) but measure severity poorly (0.395 macro F1), systematically over-predicting the most severe category. Clinically grounded zero-shot prompting recovers much of that gap (0.562 macro F1), and expert-authored framing (not fine-tuning, added reasoning, or naive multi-agent aggregation) is the effective lever. The value of reasoning depends on register: it hurts on long, noisy Reddit posts and helps on short, clinician-authored statements. We argue graded severity, not a binary flag, is what a proportionate duty of care requires, and release our evaluation framework to support that measurement.
[NLP-186] VDiff-Bench: A Challenging Benchmark for Fine-Grained Image Difference Identification
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在细粒度图像差异识别任务中表现不佳的核心问题，尤其针对其在对比两个相似图像时难以准确识别细微变化的局限性。现有标准视觉问答任务无法充分暴露模型在跨图像比较中的脆弱性，因此本文提出VDiff-Bench——一个面向细粒度图像差异识别的多选基准测试，包含1,756个四选一问题，覆盖10类变化（如位置、运动、色彩、纹理、噪声/分辨率、文本等），每题提供真实差异项及精心设计的语义相近干扰项，以增强任务挑战性。解决方案的关键在于构建具有强语义相关性的负样本，迫使模型具备对低层次视觉差异（如噪声、纹理）的敏感性与精确区分能力。实验表明，尽管主流开源与闭源模型在语义级变化上表现尚可，但在低层次变化识别上普遍失败，部分模型甚至错误判断为“无差异”，揭示出当前MLLMs在跨图像对比理解上的系统性缺陷，而VDiff-Bench为此提供了有效的诊断工具。
链接: https://arxiv.org/abs/2609.06245
作者: Yixin Wan,Tianle Zheng,Kai-Wei Chang
机构: University of California, Los Angeles (加州大学洛杉矶分校)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Multimodal Large Language Models (MLLMs) perform strongly on general visual understanding tasks such as visual question answering, yet they often struggle with a basic comparative skill: identifying what has changed between two similar images. We introduce VDiff-Bench, a challenging multiple-choice benchmark for fine-grained Image Difference Identification. VDiff-Bench contains 1,756 four-way questions over image pairs and covers 10 change categories: position, motion, regional image color, overall image color, appearance/disappearance, noise/resolution, texture, substitution/size, OCR/text, and illumination. Each question corresponds to two image inputs with 4 choices: the true difference, two hard negative descriptions, and a “no difference” distractor. To make the task challenging, we specifically curate ground-truth-conditioned negatives that require models to distinguish the actual change from nearby semantic alternatives. Experiments with 11 state-of-the-art open- and closed-source MLLMs show that fine-grained visual comparison remains brittle: models exhibit uneven performance across sources and change categories, with persistent failures on subtle low-level changes like noises and textures. For instance, three 7-8B-scale open-source MLLMs score 52.5-70.6% on semantic changes but only 8.7-33.3% on low-level changes like noise and texture, falsely assuming no changes between two image inputs. Surprisingly, despite strong performance of other closed-source commercial models, Grok 4.3 demonstrate remarkable performance drop on identifying noise and texture differences between images, falling significantly behind large open-source models like Kimi K2.5 and K3. Overall, VDiff-Bench provides a targeted diagnostic for evaluating comparative visual understanding in MLLMs, exposing failures that are not captured by standard single-image vision-language tasks.
[NLP-187] SLATE: Are AI-Generated Slides Educationally Effective? A Benchmark for Language Teaching Quality and Learner Knowledge Acquisition
【速读】： 该论文旨在解决生成式语言教学幻灯片在视觉呈现与实际教学有效性之间存在的显著脱节问题。当前大语言模型（LLM）虽能生成外观精美的教学幻灯片，但其对学习者知识获取的实际促进作用缺乏可靠评估标准。为此，研究提出SLATE（基于幻灯片的教学有效性学习评估基准），作为首个从教学有效性与学习者知识习得双重维度评估AI生成语言教学幻灯片的基准体系。其关键创新在于：将低资源语言的国际语言学奥林匹克竞赛谜题转化为90个标准化教学单元，包含1,133个可评估项目，并配套结构化课程大纲及近迁移与远迁移测试集；采用预测试-后测试设计以消除先验知识泄露，确保学习增益真实反映教学效果。通过视觉语言模型（VLM）作为可扩展的学习者代理，并辅以三系统人类小规模验证，研究发现内容有效性与学习增益关联性较弱，而教学设计质量则与学习成效呈强正相关。此外，多数系统在近迁移与远迁移任务间存在显著性能差距，甚至前沿模型也出现负向学习增益。结果揭示了教学成果质量与生成物表面美观度之间的解耦现象，强调必须重构生成式教学系统的构建、评估与部署范式，推动从“形式美观”向“实质有效”的范式转变。
链接: https://arxiv.org/abs/2609.06212
作者: Jingzhuo Wu,Jiajun Zhang,Liu Yi,Leqi Zheng,Yuheng Jing,Xinyuan Zhou,Quan yang
机构: Beijing Normal University(北京师范大学); University of Science and Technology of China(中国科学技术大学); Institute of Automation, Chinese Academy of Sciences(中国科学院自动化研究所); Beijing Language and Culture University(北京语言大学); Tsinghua University(清华大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:LLMs have achieved remarkable capabilities in generating language teaching slides. However, a critical mismatch persists between visual polish and actual instructional effectiveness. To address this gap, we introduce SLATE (Slide-based Learning Assessment for Teaching Effectiveness), the first benchmark that evaluates AI-generated language teaching slides through instructional effectiveness and learner knowledge acquisition. SLATE transforms linguistics olympiad puzzles from low-resource languages with negligible web presence into 90 standardized instructional units comprising 1,133 assessable items, paired with a structured course outline and matched near- and far-transfer test sets. This pretest-posttest design eliminates pretrained knowledge leakage, ensuring gains reflect learning rather than prior recall. Using VLMs as scalable learner proxies and directionally supported by a three-system human pilot, our results show that content validity exhibits a weak association with learning gain, while pedagogical design exhibits a robust positive association. Moreover, most systems show a significant gap between near- and far-transfer accuracy, and even frontier models can produce negative learning gains. SLATE reveals a dissociation between artifact quality and instructional effectiveness, calling for a paradigm shift in how generative teaching systems are built, evaluated, and deployed.
[NLP-188] What the Window Does Not Contain: Auditing Provenance in a Document-Grounded Instability Benchmark EMNLP2026
【速读】： 该论文旨在解决大语言模型在处理相同文档时对同一问题产生不一致回答的稳定性问题，即模型输出在重复推理中缺乏可复现性。其核心挑战在于现有评估基准（如基于文本片段构建的基准）可能因关键证据未包含在模型输入窗口内而导致结果不可靠。解决方案的关键是通过构建名为Probity的基准，系统性识别并审计此类“证据缺失”问题，发现36个因证据不在上下文窗口中而引发的异常项，并将其与需基于窗口内数值推导答案的情况区分开来。实验表明，这些被标记的项目表现出显著更高的回答波动性（wobble=0.255 vs. 0.087），剔除后可使跨模型一致性下降约五分之一。尽管预设修复策略——重新裁剪输入窗口以确保包含必要证据——未能显著降低波动性（改善仅0.058，置信区间包含零），研究仍揭示了证据缺失与模型不稳定性之间的相关性。研究强调，大多数测量值处于无法反映真实不稳定性范围的“盲区”，从而限制了以准确性为导向的语料库在评估模型稳定性方面的有效性。为此，作者开源了完整数据集、全部11.28万次原始响应及可运行的审计工具，以提升未来评估的透明度和可信度。
链接: https://arxiv.org/abs/2609.06147
作者: Seyed Mosayeb Alam
机构: KTH Royal Institute of Technology(皇家理工学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted as a poster at the 11th Workshop on Financial Technology and NLP (FinNLP 2026), co-located with EMNLP 2026. Code and data: this https URL
Abstract:Ask a language model the same question about the same document twenty times, and it sometimes returns two different answers. We built Probity, a benchmark of 60 tasks and 470 items from real venture-financing filings, to measure how often this happens. Then we audited our own corpus and found a defect any excerpt-built benchmark can carry: items whose evidence is missing from the window of text the model is shown. The audit flags 36 items and separates two failures a single flag would conflate: evidence genuinely absent from the window and answers that must be computed from numbers the window does supply. Flagged items change their answers far more often, wobbling at 0.255 against 0.087 on the 427 clean items, and excluding them cuts apparent cross-model agreement by about a fifth. Before testing whether the missing evidence explains the instability, we registered a prediction: re-cut each window to hold its evidence, and instability should fall below a set threshold. It failed: the repair moved wobble by 0.058, with an interval containing zero. We report the association as correlational. Almost all measurements sit where instability cannot show, which bounds what a corpus built for accuracy can say about stability. We release the corpus, all 112,800 raw responses, and the audit as a runnable check for any document benchmark.
[NLP-189] Protocol Compression Changes Which Party Pays: Bilateral Cost in Cross-Organization LLM Agent Communication
【速读】： 该论文旨在解决跨组织协作中，因使用生成式AI（Generative AI）进行长消息通信而导致的令牌（token）成本高昂的问题。其核心挑战在于：尽管压缩表示法看似能节省成本，但在实际跨厂商、跨组织场景下，由于双方无法在对方端部署解码器，各自独立使用本方的分词器、定价策略与缓存状态，导致压缩带来的潜在节约可能被解析失败引发的额外模型调用所抵消。研究的关键发现是，压缩不仅未能普遍降低总成本，反而在多个供应商组合中使跨厂商成本差异扩大了1.078倍（95%置信区间[1.066, 1.091]），甚至出现双方角色互换——原本较贵的一方变为更便宜的一方。进一步分析表明，运行时模式协商协议虽能在技术层面达成一致，但作为谈判机制却严重失效：在135次对话中仅121次达成模式共识，且无一例采用理想预设模式；任务成功完成的仅有9次，其余106次陷入僵局。虽然协商后会话平均轮次从17.6降至10.8，成本仅为英文基线的52%，但这主要源于会话提前终止而非握手开销被覆盖。盈亏平衡点介于20至70轮之间，且全部高于实际观察到的英文会话轮次，说明在真实应用中压缩方案难以实现净收益。因此，解决方案之关键在于：必须重新评估压缩策略在多主体异构环境下的经济可行性，尤其需考虑跨系统间因分词不一致、缓存状态差异及协商失败带来的隐性成本放大效应。
链接: https://arxiv.org/abs/2609.06129
作者: Janghoon Lee(Redrob)
机构: Redrob
类目: Computation and Language (cs.CL)
备注: 30 pages, 3 figures, 27 tables
Abstract:Agents that talk across organizations exchange long messages billed by the token. A shorter notation therefore looks like a saving that costs nothing but an agreement to use it. Recent work reports the saving is conditional. Compressed notation can instead raise total tokens by 8% to 11% over a JSON baseline, when parsing failures force extra model calls. That is measured for one payer. Between two organizations neither side can install a decoder at the other end, and each pays under its own tokenizer, price, and cache state. We measure both sides. A preregistered token-level study covered 198 content-matched item pairs across six vendors, for 2,376 native-usage cells. We then overlay an English baseline, runtime schema negotiation followed by compression, and injected-schema compression on a two-party procurement bargain with an exactly enumerated feasible set. The overlay covers 1,053 completed dialogues of a 1,215-cell grid across 3 model pairs, plus a 405-dialogue rerun of the negotiated condition. Compression amplifies cross-vendor cost dispersion by a factor of 1.078, with a 95% CI of [1.066, 1.091], and two vendor pairs reverse which endpoint is cheaper. Runtime negotiation succeeds as a protocol and fails as a bargain. The parties agree a schema in 121 of 135 headline dialogues, none of them the schema we would have supplied. They settle the task in only 9 of those dialogues, and they reach impasse in 106 of them. The negotiated sessions average 10.8 turns against 17.6, and cost 52% of the English total because sessions end sooner, not because the handshake is repaid. Break-even horizons run from 20 to 70 turns, the low end only under the conditional accounting, and all of them lie above every observed English session. On one cross-vendor pair both parties keep about half their cost. On the other the receiving party pays more at a high cache-hit rate.
[NLP-190] STQA: A Benchmark for Stock-Focused Tabular Question Answering over Historical and Forecasted Data EMNLP2026
【速读】： 该论文旨在解决股票市场分析中跨历史数据与未来预测进行复合推理的系统性评估难题，现有基准因任务割裂而无法全面衡量模型在自然语言问答中的综合能力。其解决方案的关键在于提出STQA（面向股票的表格问答）基准，该基准基于大规模金融数据集构建，涵盖4,417只股票及31,400个由专家设计模板生成的问答对，并附有细粒度的意图与槽位标注，实现了对历史查询、数值预测及基于预测的推理任务的端到端评估。为有效运行该基准，研究进一步提出SQFRS（股票查询-预测-推理系统），一种基于智能体的统一框架，通过协调SQL检索与时间序列预测工具，实现多模态信息融合。实验表明，尽管当前大语言模型在历史查询任务上表现良好，但在依赖预测结果的推理任务中仍面临显著挑战，暴露出工具协同与不确定性下的推理能力瓶颈。因此，该研究为可信、工具增强型金融智能体的未来发展提供了严谨的测试平台。
链接: https://arxiv.org/abs/2609.06117
作者: Baoxu An,Wenmian Yang,Zhensheng Wang,Weijia Jia
机构: Beijing Normal University (北京师范大学); Beijing Normal-Hong Kong Baptist University (北京师范大学-香港浸会大学)
类目: Computation and Language (cs.CL)
备注: 9 pages of main text, 15 pages of appendices, 19 figures. Accepted to Findings of EMNLP 2026
Abstract:Stock market analysis inherently requires composite reasoning over historical records and future projections, yet existing benchmarks remain fragmented across isolated tasks. We introduce STQA (Stock-focused Tabular Question Answering), an end-to-end benchmark designed to systematically evaluate natural-language question answering over historical data, numerical forecasts, and forecast-based reasoning. Built on a large-scale financial dataset, STQA covers 4,417 stocks and contains 31,400 question-answer pairs derived from expert-crafted templates, accompanied by fine-grained intent and slot annotations. To operationalize this benchmark, we present SQFRS (Stock Query-Forecast-Reasoning System), an agent-based unified framework that orchestrates SQL retrieval and time-series forecasting tools. Experiments demonstrate that while current large language models perform well on historical queries, forecast-based reasoning poses a substantial challenge, revealing critical bottlenecks in tool coordination and reasoning under uncertainty. The dataset and code are available at this https URL. STQA thus serves as a rigorous testbed for future research on trustworthy, tool-augmented financial agents.
[NLP-191] From Two Passes to One: Compact and Efficient Target-Stance Extraction
【速读】： 该论文旨在解决目标-立场抽取（Target-Stance Extraction, TSE）任务中传统方法存在的效率与性能瓶颈问题。现有方法采用串行两阶段神经模型架构，先识别文本中的目标（target），再判断作者立场（stance），导致计算开销大且参数冗余。本文提出一种单次前向传播的联合架构，能够同时预测目标与立场，在将可训练参数减少近50%的同时，仅以4-7 F1分数的轻微性能损失为代价，显著提升了模型效率。其解决方案的关键在于设计端到端的联合建模框架，通过共享表示学习增强目标与立场之间的语义关联性，并摒弃传统的“目标擦除”（target-scrubbing）预处理策略——该策略虽被广泛采用，但会人为抑制目标预测的准确性。实证表明，保留原始文本中的显式目标提及可使目标分类与生成任务的F1值提升至少6个百分点，从而更贴近真实应用场景，极大促进了TSE在舆情监测等下游任务中的部署与集成。
链接: https://arxiv.org/abs/2609.06108
作者: Ethan Mines,Bonnie Dorr
机构: University of Florida (佛罗里达大学)
类目: Computation and Language (cs.CL)
备注: 10 pages, 3 figures, 7 tables
Abstract:Target-Stance Extraction (TSE) is the task of predicting both the target (or topic) of an author’s writing and the author’s stance toward it. Existing approaches to TSE use a sequential pipeline of two separate neural models: one to identify the target and another to determine the stance. We present a one-pass, joint architecture that predicts both in a single forward pass, reducing trainable parameters by nearly 50% with only a 4-7 F1 point tradeoff in performance. We further demonstrate that standard target-scrubbing practices artificially suppress target prediction accuracy. Retaining explicit target mentions, as in real-world deployments, improves F1 by at least 6 points across both target classification and target generation settings. These improvements allow for significantly easier integration of TSE in downstream applications such as public opinion tracking.
[NLP-192] DataFlex-RL: An Evaluation Platform for RLVR Data Policies
【速读】： 该论文旨在解决强化学习中可验证奖励（Reinforcement Learning with Verifiable Rewards, RLVR）场景下数据策略（data policy）对模型训练效果的影响问题，具体聚焦于滚动回放（rollout）的选择、加权方式及不同领域贡献的分配如何影响最终性能。其核心挑战在于缺乏统一评估框架来系统比较不同数据策略的有效性。解决方案的关键在于提出DataFlex-RL这一标准化评估平台，基于一致的GRPO（Generalized Reward Policy Optimization）训练流程，在相同实验条件下对比13种不同的数据配置。研究发现，尽管均匀采样（uniform sampling）在域均衡平均准确率上相比未训练检查点提升了7.76个百分点，但所有八种滚动回放选择或重加权方法均未在95%置信区间内显著优于均匀采样；三种自适应混合策略也未能超越固定等权重混合策略。进一步通过修正后的多种子扩展实验（12种子）和敏感性分析表明，评估结果高度依赖于基准测试集的设计——当使用仅包含数学类基准的简化评估集时，排名与完整12基准集呈现负相关（相关系数-0.33），而保留全部12个基准的评估则保持较高一致性。因此，该研究揭示了当前数据策略调整虽能改变训练过程，但难以稳定实现超越均匀采样的可复现性能提升，强调了评估协议标准化的重要性。
链接: https://arxiv.org/abs/2609.06107
作者: Hao Liang,Mingrui Chen,Hengyi Feng,Meiyi Qiang,Wentao Zhang
机构: 北京大学(University of Beijing)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Data policies for reinforcement learning with verifiable rewards (RLVR) determine which rollouts are used, how strongly they are weighted, and which domains contribute to subsequent training batches. We introduce DataFlex-RL, an evaluation platform for comparing these choices under a common GRPO recipe. Our primary experiment evaluates 13 configurations across 12 matched seeds using Qwen2.5-7B-Base and 12 mathematics, logic, and science benchmarks. Uniform GRPO improves the domain-balanced average accuracy by 7.76 percentage points over the untrained checkpoint. None of the eight rollout-selection or reweighting methods achieves a paired 95% confidence interval that excludes zero relative to uniform sampling, and none of the three adaptive mixtures outperforms a fixed equal mixture at the same level of precision. A corrected 12-seed extension on Llama-3.1-8B-Base places the additional methods on the same score scale as the original controls, but does not reveal a consistent winner in terms of observed mean performance. We also quantify evaluation sensitivity by rescoring nine Qwen2.5-7B-Instruct runs using a math-heavy six-benchmark summary, consisting of five mathematics benchmarks and GPQA-Diamond but no logic benchmark, and comparing it with the domain-balanced 12-benchmark summary. The resulting rankings are negatively correlated, with a correlation coefficient of -0.33, whereas summaries that retain all 12 benchmarks largely agree. Across the controlled settings studied here, changing the data policy measurably changes the training process but does not produce a reproducible improvement over uniform training.
[NLP-193] VERPO: Verified Evidence Regularized Policy Optimization
【速读】： 该论文旨在解决生成式语言模型在后训练阶段中，基于序列级奖励（sequence-level rewards）难以精准指导令牌级决策（token-level decisions）的保留或修正问题。现有方法如证据条件教师（Evidence-conditioned Teachers）虽通过回放带有特权反馈的采样轨迹提供更密集的监督信号，但盲目模仿可能引入与任务成功无关的格式或推理风格偏差。为此，本文提出验证证据正则化策略优化（VERPO）框架，其核心在于将证据视为对策略修正的提议，同时保持最终结果目标不变。VERPO通过分离无证据参考恢复与带符号的令牌级证据修正，实现更精细的控制；其中，费舍尔证据对比（Fisher Evidence Contrast）沿估计的证据存在方向抑制不必要修正，而停止的逐令牌零点预测控制器（ZPD）则根据局部奖励对齐度与费舍尔移动成本动态调节接受程度，且参考通道独立于接受机制。实验表明，在五个科学推理与工具使用任务上，所提方法在不同骨干模型上的平均得分显著提升，例如在Qwen3-4B上从0.6826升至0.6857，Qwen3-8B上从0.6895升至0.7058，Llama-3.2-1B上从0.4751升至0.5657，验证了该方案的有效性与通用性。
链接: https://arxiv.org/abs/2609.06100
作者: Haijiang Li,Chengyu Lv,Yi Zhang,Zhibing Zhang,Rui Qian,Yuchen Zhang,Xiaofan Zhang,Mingshan Wang,Xiaofei Jing,Yu Tong,Cangqi Zhou
机构: Tongji University (同济大学); Nanjing University (南京大学); Fudan University (复旦大学); Nanjing University of Science and Technology (南京理工大学); Xiaohongshu (小红书)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 38 pages, 9 figures, including appendices
Abstract:Verifiable outcome rewards guide language-model post-training, but sequence-level advantages do not identify which token-level decisions should be preserved or revised. Evidence-conditioned Teachers provide denser supervision by replaying sampled trajectories with privileged feedback. Yet indiscriminate imitation risks transferring formatting or reasoning-style shifts that do not support task success. We introduce VERPO, a Verified Evidence Regularized Policy Optimization framework that treats evidence as a proposal for policy correction while retaining the outcome objective. It separates evidence-free reference restoration from signed token-level evidence corrections. Fisher Evidence Contrast attenuates corrections along an estimated evidence-presence direction. A stopped token-wise ZPD controller scales acceptance according to local reward alignment and Fisher movement cost, while the reference channel remains independent of acceptance. Across five scientific-reasoning and tool-use tasks, the best variant on each backbone exceeds the strongest compared baseline in average score. The averages rise from 0.6826 to 0.6857 on Qwen3-4B, from 0.6895 to 0.7058 on Qwen3-8B, and from 0.4751 to 0.5657 on Llama-3.2-1B.
[NLP-194] ACE: Adapter Consolidation across Experts for Parameter-Efficient Fine-Tuning of MoE LLM s EMNLP2026
【速读】： 该论文旨在解决混合专家模型（Mixture-of-Experts, MoE）在参数高效微调（Parameter-Efficient Fine-Tuning, PEFT）过程中因每个专家独立配置低秩适配器（LoRA）所导致的三重碎片化问题：适配容量被分散至多个窄低秩更新、稀疏路由下梯度监督变得稀疏且不均衡，以及计算执行被分解为大量小型矩阵乘法（GEMM）。研究发现，微调过程中部分专家的LoRA适配器在功能上趋于相似，表明专家特异性适配器存在冗余。针对此冗余，作者提出ACE（Adapter Consolidation across Experts），其核心在于将功能相似的专家分组，并在相同的参数预算下，用共享的高秩LoRA模块替代原有的专家专属适配器；同时引入分组适配器执行机制，将分散的专家级适配计算合并为更少、更大的组级别GEMM，从而提升计算效率。实验覆盖12个数据集及4种MoE骨干网络，在三个具备完整基线对比的模型上，ACE在参数匹配条件下实现了当前最优平均准确率，且相比传统专家级LoRA实现1.31×至1.48×的训练加速，同时未增加峰值内存占用。
链接: https://arxiv.org/abs/2609.06072
作者: Ahin Lee,Sehyun Yun,Joonha Park,Taesik Gong
机构: Ulsan National Institute of Science and Technology (UNIST); Ulsan, Republic of Korea
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 23 pages, 13 figures. Accepted to EMNLP 2026
Abstract:Parameter-efficient fine-tuning (PEFT) of mixture-of-experts (MoE) models commonly attaches a separate low-rank adapter to each expert. This expert-wise design fragments adaptation in three ways: capacity is split across narrow low-rank updates, gradient supervision becomes sparse and imbalanced under sparse routing, and execution is decomposed into many small GEMMs. We find that such expert-wise separation is often unnecessary, as subsets of LoRA adapters become functionally similar during fine-tuning, revealing redundancy among expert-specific adapters. Based on this redundancy, we propose ACE (Adapter Consolidation across Experts), which groups redundant experts and replaces their expert-specific adapters with group-shared higher-rank LoRA modules under the same PEFT budget. ACE further introduces grouped adapter execution, which consolidates fragmented expert-wise adapter computations into fewer, larger group-level GEMMs. Across evaluations covering 12 datasets and four MoE backbones, ACE achieves the highest observed mean accuracy among the parameter-matched PEFT methods on the three backbones with complete baseline coverage, while providing 1.31\times to 1.48\times wall-clock training speedup over expert-wise LoRA without increasing peak memory. Our code is available at this https URL.
[NLP-195] DPH Parser: A Bottom-Up Grammar-Driven Parser for Joint Constituency and Dependency Analysis
【速读】： 该论文旨在解决现有神经网络驱动的依存句法分析器在可解释性方面的不足，尤其是在处理真实世界中存在噪声和不完整标注的文本时，其内部机制缺乏透明性。为此，论文提出了一种基于语法驱动的自底向上无监督句法分析框架——依赖短语层次解析器（Dependency-Phrase Hierarchy Parser, DPH Parser），其核心在于结合广义短语结构语法（Generalized Phrase Structure Grammar, GPSG）与主导短语结构语法（Head-driven Phrase Structure Grammar, HPSG）的思想，利用基于特征的简洁规则库逐步构建成分结构，并通过显式的主导词标注推导出依存关系。该方法的关键创新在于：在保持规则系统简洁性的同时，融合概率词性标注、递归短语投射与加权解析假设，实现对现实语料中部分噪声输入的有效处理；更重要的是，整个句法推导过程具有明确的可解释性，能够同时生成成分结构与依存结构，为非黑箱化自然语言理解提供了可行路径。尽管当前性能尚未达到现代神经网络解析器水平（如Stanza和spaCy），但实验表明其在小规则集下仍能有效应用于真实树库数据，验证了透明规则基自底向上解析在实际场景中的可行性。
链接: https://arxiv.org/abs/2609.06070
作者: Hussein Ghaly
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:This paper presents Dependency-Phrase Hierarchy Parser (DPH Parser), a grammar-driven bottom-up unsupervized parsing framework inspired by Generalized Phrase Structure Grammar (GPSG) and Head-driven Phrase Structure Grammar (HPSG). The parser incrementally constructs constituency structures using a compact inventory of feature-based syntactic rules while deriving dependency relations through explicit head annotations. The system combines probabilistic POS tagging, recursive phrase projection, and weighted parse hypotheses to process realistic and partially noisy text input. Unlike purely neural and data-driven parsers, the resulting syntactic derivations remain explicitly interpretable. We evaluated parser performance on English corpora from the Universal Dependencies (UD) project using Unlabeled Attachment Score (UAS) as the main parsing metric, comparing the outcomes against Stanza and spaCy parsers. For a small inventory of syntactic rules, DPH parser achieved UAS values of 53.32% 52.58% (UD Devset/Testset respectively). For the same data, Stanza achieved 89.12% 88.67% while spaCy achieved 56.91% and 58.59%. Although the current system does not yet approach the accuracy of modern neural parsers, the results demonstrate the feasibility of applying transparent rule-based bottom-up parsing to realistic treebank data while jointly producing constituency and dependency structures. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2609.06070 [cs.CL] (or arXiv:2609.06070v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.06070 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-196] Dont Lose Entities from Retrieval to Generation: Dual Entity Recovery RAG for multi-hop QA EMNLP2026
【速读】： 该论文旨在解决检索增强型多跳问答（Retrieval-augmented Multi-hop Question Answering, QA）系统中因信息分解导致的实体信息丢失问题。现有方法将查询与语料库分别分解为子问题和短语级检索单元，虽提升了处理效率，但在此过程中存在双重脆弱性：一是检索阶段，前一跳解析出的实体在后续子问题中被遗忘，导致检索器缺乏匹配依据；二是生成阶段，即使正确段落被召回，其孤立的句子因失去上下文而无法锚定指代实体，造成“迷失于生成”（lost-in-generation）现象，此问题在黄金证据固定时仍显著损害答案质量。解决方案的关键在于提出双实体恢复检索增强生成框架（Dual Entity Recovery RAG, DER-RAG），通过两个轻量级组件实现端到端的实体显式传递：一是双向查询分解机制，使已解析实体在子问题间跨跳传递；二是生成时为每个句子附加主体实体前缀，确保大语言模型（LLM）具备足够的上下文以识别指代对象。该方法无需图结构构建、语料修改或微调，在三个多跳问答基准上达到或超越强基线性能，包括依赖复杂离线结构的图方法。
链接: https://arxiv.org/abs/2609.06065
作者: Heechang Lee,Dong-Young Lim
机构: UNIST(韩国科学技术院)
类目: Computation and Language (cs.CL)
备注: Findings of the Association for Computational Linguistics: EMNLP 2026
Abstract:Retrieval-augmented multi-hop question answering (QA) decomposes a query into sub-questions and decomposes the corpus into smaller retrieval units such as sentences. Both forms of decomposition improve the pipeline, but we show that both share the same vulnerability, the loss of entity information, and that this loss breaks the pipeline at two separate points. The first point is retrieval, where a sub-question loses the entity resolved at the previous hop, leaving the retriever with nothing to match against. The second point is harder to see, because retrieval still appears to succeed. Once a passage is split into sentences, an isolated sentence loses the context that grounds its pronouns, so even with the correct sentence in hand the LLM cannot tell which entity the sentence is about. We isolate this second point as a distinct failure mode that we call lost-in-generation, and a retrieval-controlled experiment shows that it degrades answers even when the gold evidence is fixed in the context. We then propose Dual Entity Recovery RAG (DER-RAG), which keeps the grounding entity explicit from retrieval through to generation with two lightweight components, a two-way query decomposition that carries the resolved entity across sub-questions and a subject entity prefix attached to each sentence at generation time. DER-RAG needs no graph construction, no corpus modification, and no fine-tuning, yet on three multi-hop QA benchmarks it matches or exceeds strong baselines, including graph-based methods that depend on costly offline structures.
[NLP-197] GradeTrap: Authority Cues in Images Shift VLM Judgments Despite Explicit Instructions to Ignore Them
【速读】： 该论文旨在解决生成式视觉语言模型（Vision-Language Models, VLMs）在真实世界应用中过度依赖人类权威、缺乏独立证据评估能力的问题。其核心挑战在于：当模型面临与自身判断冲突的外部信息源时，是否能够真正实现自主决策，而非被动服从权威或同侪意见。解决方案的关键在于设计一种名为GradeTrap的受控评估范式，通过构造社会线索之间的直接冲突情境——即一个应引发顺从性认同的学生答案，与一个被标注为同伴、教师或官方答案键的矛盾答案并置——来检验模型在明确指令“忽略所有学生答案、反馈及评分标记”的前提下，仍会否受到权威来源的影响。实验结果表明，在三款主流模型（Gemini 3.5 Flash-Lite、GPT-5.6 Luna、Claude Haiku 4.5）共有的45个测试项中，尽管存在明确的“忽略”指令，官方答案键的出处仍导致高达19.5个百分点的判断偏移，显著高于仅展示学生答案或通用第二答案对照组的效果，揭示出权威信息源对模型判断具有强大且难以抑制的引导作用，而这一现象在不同模型间存在差异。
链接: https://arxiv.org/abs/2609.06058
作者: Deep Dessai(The University of Texas at Austin)
机构: The University of Texas at Austin(德克萨斯大学奥斯汀分校)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注:
Abstract:As vision-language models (VLMs) become increasingly capable and are deployed in consequential real-world settings, they must evaluate evidence independently rather than defer uncritically to human authority. We introduce GradeTrap, a controlled evaluation that places two social cues in direct conflict: a student answer, which should attract sycophantic agreement, and a conflicting answer attributed to a peer, teacher, or official answer key, which should attract authority-based deference. Models produce free-form answers while being explicitly instructed to solve independently and ignore all student answers, feedback, and grading marks. We test the models on 60 synthetic real-world trade-off scenarios. Five neutral trials establish a stable model-relative preference, followed by three repetitions of six experimental cues including controls. On the 45-item common intersection across Gemini 3.5 Flash-Lite, GPT-5.6 Luna, and Claude Haiku 4.5, a generic second-answer control yields 5.4% conflicting-answer selection. Relative to that control, pooled within-item changes show no reliable peer-review effect, a 6.9-point teacher-review effect, and a 19.5-point official-key effect. In contrast, a displayed conflicting student answer alone compared to a displayed student reference answer alone only raises selection from 2.2% to 5.2%. Official-key provenance therefore redirects judgements more than a student answer or the generic second-answer control, despite an explicit ignore instruction and an opposing student answer given along with the official key. Effects vary in magnitude across the three models.
[NLP-198] Generating Adversarial Texts for Machine Translation via GRPO
【速读】： 该论文旨在解决机器翻译（Machine Translation, MT）评估中基准测试有效性下降的问题，即随着MT系统性能不断提升，传统评测集已难以有效揭示模型的残余缺陷。现有挑战性测试集的构建方法要么依赖高成本的人工创建或筛选，要么自动化方法在生成具备足够翻译难度与语言多样性样本方面表现不佳。为此，论文提出一种基于强化学习的可扩展文本重写方法，通过微调大语言模型，利用基于翻译难度的奖励信号，并结合语义相似性、语法正确性及近似长度保持等约束条件，将原始源文本改写为对MT系统更具挑战性的实例。其核心创新在于采用群体相对策略优化（Group Relative Policy Optimization, GRPO），实现对翻译难度的有效控制。实验结果表明，在WMT25基准上，该方法使平均COMET评分从0.63降至0.48，显著低于基线模型的0.64，同时保持了较高的语法正确性和可读性；在未见的WMT19–WMT24数据集上验证了方法的良好泛化能力，且人工评估显示重写后翻译质量明显下降，仅带来适度自然度损失和微小语法变化。该方案为构建高质量、可扩展的挑战性测试集提供了有效路径。
链接: https://arxiv.org/abs/2609.06048
作者: Florian Zogaj,Jakob Hütteneder,Giovanni De Muri,Federico Villa,Aryan Sood,Vilém Zouhar
机构: ETH Zurich (苏黎世联邦理工学院)
类目: Computation and Language (cs.CL)
备注: Accepted at KONVENS 2026
Abstract:As machine translation (MT) systems continue to improve, standard benchmarks become less informative for exposing remaining weaknesses. Traditional methods for creating challenging test sets rely on expensive manual creation or curation, while automated approaches struggle to produce sets with the necessary translation difficulty and linguistic diversity. We propose a scalable reinforcement-learning-based approach for rewriting existing source texts into instances that are more difficult to translate for MT systems. We fine-tune a large language model with Group Relative Policy Optimization (GRPO), using reward signals based on translation difficulty together with constraints for semantic similarity, grammaticality, and approximate length preservation. On WMT25, our approach substantially reduces average COMET translation quality from 0.63 to 0.48, while preserving grammaticality and readability, whereas the base model remains at 0.64. Evaluations on the unseen WMT19-WMT24 benchmarks confirm that this behavior generalizes beyond the training data, and human evaluation further shows that the rewrites substantially lower translation quality while incurring a moderate drop in naturalness and only a small change in grammaticality. We release our code to support reproducibility.
[NLP-199] Factors Influencing the Emergence of Dependency Length Minimization in Neural Agent Simulations
【速读】： 该论文旨在解决语言使用中依赖关系长度最小化（Dependency Length Minimization, DLM）现象的起源问题，尤其探讨其是否源于高效信息处理的认知约束。现有计算模拟研究因缺乏真实交际情境且结果不一致，难以明确回答此问题。本文采用基于循环神经网络（Recurrent Neural Networks, RNNs）的新型语言学习与交际框架，通过训练人工代理在交际过程中学习并使用人工语言，系统考察了多种与认知加工限制相关的因素（如听觉噪声、说话者容量有限、增量句法加工）对词序偏好形成的影响。研究发现，这些因素在塑造神经代理的词序选择上存在复杂交互：在完整语义空间中，代理倾向于固化为单一主导词序；而在半语义空间中则表现出“短在前、长在后”的偏好，仅在动词前置语言中与DLM一致；而只有当代理面临增量加工压力时，才稳定呈现出符合DLM的词序偏好。这一结果表明，人类认知加工限制可能确实在塑造DLM中发挥关键作用。研究揭示了神经模型再现人类语言偏好所需的关键条件，同时也凸显了在设计能够捕捉人类语言处理认知偏差的涌现交际模型时所面临的挑战。
链接: https://arxiv.org/abs/2609.06025
作者: Yuqing Zhang,Tessa Verhoef,Gertjan van Noord,Arianna Bisazza
机构: University of Groningen(格罗宁根大学); Leiden University(莱顿大学)
类目: Computation and Language (cs.CL)
备注: This is a preprint version of the manuscript currently under review at Cognitive Science
Abstract:Given various grammatical options, language users prefer the word order choice that reduces the overall length of syntactic dependencies, a principle known as dependency length minimization (DLM). The origins of this preference remain an open question, particularly whether it originates from constraints on efficient information processing. Computational simulations provide a powerful approach to identifying the factors influencing the emergence of linguistic phenomena. However, previous simulations of DLM have not examined realistic interaction contexts and have produced mixed results. The present study investigates the emergence of DLM in artificial languages using a recently proposed language learning and communication framework based on recurrent neural networks (RNNs). In this framework, agents are trained to speak and interpret artificial languages and then use these languages to communicate. Using this framework, we study the impact of several factors related to processing limitations in a communicative setting, such as noise during listening, limited speaker capacity, and incremental sentence processing. Our results reveal a complex interplay among these factors in shaping word order preferences in neural agents. Specifically, in the full meaning space, agents regularize toward a single dominant word order, while in the half meaning space they show a short-before-long preference that only aligns with DLM in verb-initial languages. A consistent DLM preference emerges only when agents are subject to incremental processing pressure. These findings suggest that limitations in human cognitive processing may indeed play a role in shaping DLM. Our findings provide insights into the conditions under which neural models replicate human-like preferences and highlight the challenges of designing emergent communication models that capture human cognitive biases in language processing.
[NLP-200] ri-PvP: Exposing Modality Bias in Omni-Modal Large Language Models through Perceptual-Propositional Evidence Conflicts EMNLP2026
【速读】： 该论文旨在解决生成式多模态大模型（Omni-modal Large Language Models, OLLMs）在跨模态冲突情境下存在的模态偏倚（modality bias）问题，尤其关注现有评估基准因混淆感知信号（perceptual signals）与命题信号（propositional signals）而导致的偏倚归因不清。其核心挑战在于，传统基准将同一模态内的两种不同形式的证据混合在一起，使得测得的模态偏倚无法明确区分是源于模态本身还是证据形式差异。为解决这一问题，论文提出Tri-PvP——一个包含8,000个样本的三模态冲突基准，系统性地分离视觉与听觉模态中的感知信号与命题信号，从而实现对模态偏倚与证据形式偏倚的独立评估。关键发现表明：多数模型存在显著的视觉偏倚，且在证据形式上呈现系统性不对称——模型对视觉模态更倾向于依赖感知信号，而对听觉模态则更偏向命题信号。进一步通过层间线性探测与对比解码分析揭示，模态偏倚早在早期表示层即已可线性可解，且难以通过表层干预完全缓解，提示需发展深层架构层面的纠偏策略。
链接: https://arxiv.org/abs/2609.06011
作者: Yen-Ting Piao,Shu-Yun Chen,Chin-Hui Chu,Chun-Wei Chen,Shih-Yun Shan Kuan,Hung-yi Lee,Yun-Nung Chen
机构: National Taiwan University (国立台湾大学); NTU Artificial Intelligence Center of Research Excellence (NTU AI-CoRE)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD)
备注: EMNLP 2026 Findings
Abstract:Omni-modal large language models (OLLMs) jointly process vision, audio, and text, yet their modality bias under cross-modal conflict remains underexplored. Existing benchmarks conflate two distinct forms of evidence within a single modality: perceptual signals (e.g., a photograph or recording of a dog) and propositional signals (e.g., the declarative claim “this is a dog”), such that any measured modality bias is inherently confounded with evidence-form bias, precluding clean attribution to either source. To address this, we introduce Tri-PvP, an 8,000-sample tri-modal conflict benchmark crossing vision, audio, and text, where vision and audio each take perceptual or propositional form. Evaluating five OLLMs, we find robust visual bias across most models and evidence-type conditions. Crucially, we reveal a systematic asymmetry in evidence-form bias: models exhibit a stronger bias toward perceptual signal in vision but propositional in audio. Further analyses via layer-wise linear probing and contrastive decoding reveal that modality bias is already linearly decodable from early representation layers and can only be partially mitigated, calling for mitigation strategies beyond surface-level interventions.
[NLP-201] ModularPhaseNet: Finite-Cyclic Phase Geometry for Computable Semantic Hierarchy Direction and Context Consistency in Standard Transformers
【速读】： 该论文旨在解决传统Transformer模型在建模复杂语义相位结构（phase geometry）时缺乏精确离散不变量的问题，尤其关注如何在不依赖量子硬件或复数矩阵运算的前提下，实现对连续相位空间的可计算、可解释且具有数学严格性的离散化。其核心挑战在于如何在保持标准Transformer架构的基础上，引入具备群论结构的离散相位表示，以增强模型对语义层次、上下文一致性及逻辑矛盾等高级语言现象的捕捉能力。解决方案的关键在于提出ModularPhaseNet，通过将辅助相位通道量化至有限域$ \mathbb{F}_p 乘法群中的循环子群 G = \langle g \rangle （阶为 q \mid (p-1) $），实现相位的整数可计算表示；在此基础上，构建三个关键模块：有限相位编码器（finite-phase encoder）、商滤层（quotient-filtration hierarchy module）与群值连接模块（group-valued connection module），其输出作为实值偏置项融入自注意力机制。该设计使得相位合成对应于群乘法、相对相位为群除法，并通过商群的滤链结构诱导概念层次，以有向相对群元表示语义方向，利用规范不变的环路全息性（gauge-invariant cycle holonomy）度量上下文一致性。训练阶段采用实群代数分布或直通Gumbel-Softmax，推理则基于精确模指数运算与预计算表，避免了复数运算和离散对数求解。理论证明涵盖了量化失真界、商诱导划分的嵌套性、规范不变性、平坦连接的离散可积性以及注意力输出的有界性，其核心假设是这些精确的离散不变量能够在受控算力下提升层次结构恢复、话语对齐、矛盾检测与幻觉风险校准的能力。
链接: https://arxiv.org/abs/2609.06000
作者: Kiyotaka Kasubuchi,Kazuo Fukiya
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 16 pages, 1 figure. Extends arXiv:2602.14419 (WavePhaseNet) and arXiv:2608.15820 (QuantumPhaseNet). Theory and a pre-registered evaluation plan; no experimental results are reported
Abstract:We propose ModularPhaseNet, a classical and integer-computable discretization of the continuous complex phase geometry introduced in QuantumPhaseNet. The real-valued hidden states of a standard Transformer are retained, while only an auxiliary phase channel is quantized into a cyclic subgroup G = g of order q | (p-1) in the multiplicative group of F_p. A continuous phase e^i phi is represented by z = g^a mod p; phase composition becomes group multiplication, relative phase becomes group division, conceptual hierarchy is induced by a filtration of cyclic quotients, semantic direction is represented by oriented relative group elements, and contextual consistency is measured by gauge-invariant cycle holonomy. The method introduces three components into an otherwise standard Transformer: a finite-phase encoder, a quotient-filtration hierarchy module, and a group-valued connection module. Their outputs enter self-attention as real-valued bias terms. Training uses distributions in the real group algebra or straight-through Gumbel-Softmax, whereas inference uses exact modular exponentiation and precomputed tables. No quantum hardware, complex-valued matrix multiplication, or discrete-logarithm computation is required. We prove quantization-distortion bounds, nesting of quotient-induced partitions, gauge invariance, a discrete integrability result for flat connections, and boundedness of the resulting attention output. The central empirical hypothesis is that these exact discrete invariants improve hierarchy recovery, discourse alignment, contradiction detection, and calibrated hallucination-risk prediction under a controlled compute budget. This paper reports the theory together with a pre-registered evaluation plan; the experiments described in Section 14 have not yet been carried out, and no empirical result is claimed here.
[NLP-202] Alignment by Stereotyping: How LLM s Sacrifice Individual Distinctiveness for Cultural Adaptation EMNLP2026
【速读】： 该论文旨在解决生成式 AI 在个性化交互中因依赖用户人口统计学特征进行文化适配而可能引发的个体差异消解问题。其核心关切在于：当前广泛采用的人口统计学条件化策略是否真正服务于个体用户的独特性，抑或仅通过将个体响应向群体均值靠拢（即“刻板印象对齐”）来提升整体准确性，从而系统性地抹除个体独特性。研究发现，尽管人口统计学信息能提升多数模型在世界价值观调查（World Values Survey）中的价值对齐准确率，但其代价是显著压缩个体差异——模型倾向于将用户回答拉向所属人口群体的中心点，而非保留真实个性。这一现象被定义为“对齐中的刻板印象化”（alignment by stereotyping）。通过10,000次置换检验（六个人口统计属性、七种模型）验证，表现最优的模型在个体压缩程度上远超人类基线；同时，家庭内部规模扩展进一步加剧了这一权衡，并削弱了模型对文化内涵的内在理解能力。基于经PRISM（Kirk et al., 2024）真实人机对话验证的合成对话数据集，研究还发现：将人口统计信号分散到多轮对话中，相较于集中式标签，可部分抑制原型检索，该结果在真实对话中得到初步验证，但需更大规模复现以确认普适性。因此，解决方案的关键在于重新审视人口统计学特征在个性化生成中的作用机制，避免以牺牲个体性为代价换取表面准确性，并探索更精细的动态特征注入方式以实现真正的个性化适配。
链接: https://arxiv.org/abs/2609.05993
作者: Qishuai Zhong,Zongmin Li,Siqi Fan,Aixin Sun
机构: Nanyang Technological University, Singapore; University of Electronic Science and Technology of China
类目: Computation and Language (cs.CL)
备注: EMNLP 2026 Findings
Abstract:Large language models are increasingly deployed for personalized interaction, and demographic conditioning via user profiles is a widely adopted strategy for cultural adaptation. We ask whether this approach genuinely serves individual users or achieves accuracy by erasing individual distinctiveness. Studying seven models including frontier GPT-5.1 on the World Values Survey, we find that demographic profiles improve value alignment accuracy for most models, but at a systematic cost to individuality. That is, models pull responses toward demographic group centroids rather than preserving individual differences, a behavioral pattern we term alignment by stereotyping. Permutation tests (10,000 permutations, six demographic attributes, seven models) certify that top-performing models compress individuals far above the human baseline; within-family scaling amplifies this tradeoff while degrading intrinsic cultural understanding. Using a synthetic dialogue dataset validated on real human-chatbot conversations from PRISM (Kirk et al., 2024), we further show that distributing demographic signals across conversational turns partially suppresses prototype retrieval compared to compact demographic labels, a finding validated on real conversations via PRISM but requiring replication at larger scale.
[NLP-203] Beyond Cross-Lingual Transfer: Benchmarking Propagation Boundaries in Multilingual LLM Unlearning
【速读】： 该论文旨在解决多语言大语言模型（LLM）在去记忆（unlearning）过程中难以精确控制知识传播范围的问题，尤其关注在需要跨语言统一抑制目标知识或仅在特定语言内进行知识抑制时，现有方法无法有效区分知识传播不足与过度传播的困境。其解决方案的关键在于提出一种名为跨语言且语言限定的去记忆评估协议（CLLPU），通过构建两种典型场景——通用目标遗忘（common-goal forgetting）与语言条件遗忘（language-conditioned forgetting），系统性地刻画知识传播边界。CLLPU采用目标导向的主题配对、模式感知的关系匹配以及双锚点多语言翻译技术，构建了涵盖10种语言的800组知识单元对与72,000个问答实例，实现对知识传播行为的精细测量。实验表明，现有六种代表性方法在两类场景中均表现出相反的失效模式：在要求全局抑制时遗忘不彻底，而在要求语言限定抑制时又出现越界传播。研究进一步揭示，模型整体多语言能力的保持可能掩盖邻近知识的实质性损伤，从而凸显出传播控制作为多语言LLM去记忆核心挑战的重要性。
链接: https://arxiv.org/abs/2609.05976
作者: Pengyang Shao,Chuanpeng Lu,Wei Qin,Yanzheng Jin,Xiaohao Liu,Xi Ai,Kenji Kawaguchi,Richang Hong
机构: National University of Singapore(新加坡国立大学); Hefei University of Technology(合肥工业大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Model (LLM) unlearning aims to suppress target knowledge while preserving general capabilities. In multilingual settings, unlearning must additionally propagate within its intended linguistic scope. However, existing evaluations mainly measure cross-lingual transfer and cannot distinguish insufficient from excessive propagation. We introduce CLLPU (Cross-Lingual and Language-Bound Protocol for LLM Unlearning), a multilingual benchmark that formulates this problem through two settings: common-goal forgetting, where target knowledge should be suppressed across all languages, and language-conditioned forgetting, where suppression should remain confined to a designated language. CLLPU combines goal-guided topic pairing, schema-aware relation matching, and dual-anchor multilingual translation to construct 800 matched knowledge-unit pairs and 72,000 QA instances across ten languages. Experiments with six representative methods on Llama-3.1-8B-Instruct reveal opposite failure modes: forgetting remains incomplete when universal suppression is required, yet spreads beyond the intended boundary when language-conditioned confinement is required. We further find that general multilingual utility can conceal damage to neighbor knowledge. These findings establish propagation control as a central challenge for multilingual LLM unlearning. We publicly release CLLPU together with its construction pipeline.
[NLP-204] Solving versus Verifying: Catching Contradictions in Tax Reasoning Systems ICTAI2026
【速读】： 该论文旨在解决大语言模型在处理真实法律文本时因输入缺陷（如事实缺失或事实矛盾）而导致可靠性下降的问题。尽管模型在结构良好、无缺陷的基准测试中表现出超过90%的准确率，但真实场景中的法律输入常存在缺陷，而现有模型在遇到此类问题时仍会强行输出看似确定的答案，缺乏对错误的自我识别能力，从而带来潜在风险。其解决方案的关键在于引入一种基于自我验证的“矛盾门控”机制：通过额外调用一次模型进行输入验证，若发现矛盾则主动拒绝输出，从而实现对缺陷输入的有效识别与处理。实验表明，这一方法仅需一次额外推理调用，无需训练或外部工具，即可显著提升模型在矛盾输入下的拒答率，同时对干净输入的准确率影响极小（最多下降约5个百分点），证明了生成式模型在关键任务中可通过轻量级自检机制大幅提升可靠性。
链接: https://arxiv.org/abs/2609.05928
作者: Albert Sadowski,Jarosław A. Chudziak
机构: Warsaw University of Technology (华沙理工大学); Warsaw, Poland
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted for the 38th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2026)
Abstract:Large language models now compute correct tax liabilities on over 90% of well-formed cases in statutory benchmarks, which makes them candidates for the tax-advisory and compliance systems that consume such an answer directly. Real legal inputs, however, are frequently defective: required facts are missing, or stated facts contradict one another. Accuracy on clean benchmarks says nothing about how a model behaves then, and a system that computes straight through a defective input returns a confident number with no sign that anything is wrong. This raises two questions: does a model asked to solve a case abstain when the input is defective, and when it does not, can the same model catch the defect when asked instead to verify the input? We study six recent models on SARA-derived tax cases under missing-fact and contradictory-fact perturbations. The strongest models abstain when a fact is missing but compute through injected contradictions, returning the clean-input answer 63-76% of the time with no signal of the conflict; asked instead to verify the same input, they flag most of those contradictions. We wire that verification call into a simple contradiction gate: one extra call that abstains when the model reports a conflict. Across all six models it recovers most of the missed contradiction abstention at a clean-accuracy cost of at most about 5 percentage points, with no training and no external tooling. High accuracy on well-formed inputs is therefore an incomplete measure of reliability, and the detection the solver misses is cheaply recoverable with a single self-check.
[NLP-205] Neuron-Guided Fine-Tuning: Unlocking Efficient Alignment Mechanisms for Large Language Models EMNLP2026
【速读】： 该论文旨在解决现有监督微调范式（尤其是全参数微调）中存在的参数冗余、数据质量不一致以及灾难性遗忘等问题。这些问题在当前方法中通常被孤立处理，缺乏统一的优化信号来协同数据选择、参数更新与知识保留。其解决方案的关键在于提出一种名为神经元引导微调（Neuron-Guided Fine-Tuning, NGFT）的综合性框架，该框架以神经元激活模式作为通用代理，统一微调生命周期中的多个环节。NGFT通过三个协同机制实现这一目标：（1）自适应任务特定神经元选择，在一次前向传播中识别关键神经元，集中更新并减少冗余；（2）基于激活的数据选择，优先选取能最大化贡献于关键神经元的信息密集型样本；（3）神经元激活对齐，设计一种新型损失函数，将激活状态锚定在预训练阶段的分布上，从而深化表征学习并有效保留通用知识。实验结果表明，NGFT在多种模型和领域特定及通用基准上均显著优于主流微调方法，在提升效率与性能的同时，有效缓解了灾难性遗忘问题。
链接: https://arxiv.org/abs/2609.05913
作者: Zeyu Wu,Junchao Wu,Shudong Liu,Runzhe Zhan,Xin Chen,Shu Yang,Yichao Du,Longyue Wang,Weihua Luo,Jinsong Su,Derek F. Wong
机构: University of Macau(澳门大学); Alibaba Group(阿里巴巴集团); Nanjing University(南京大学); KAUST(阿卜杜拉国王科技大学); Wuhan University(武汉大学); Xiamen University(厦门大学)
类目: Computation and Language (cs.CL)
备注: EMNLP 2026 Findings. Codes are available at: this https URL
Abstract:Existing Supervised Fine-Tuning paradigms, particularly Full Parameter Fine-Tuning are often plagued by parameter redundancy, inconsistent data quality, and catastrophic forgetting, which current methods typically address in isolation and lack a unified optimization signal to bridge data selection, parameter updates, and knowledge preservation. To address this, we propose Neuron-Guided Fine-Tuning (NGFT), a holistic framework that leverages neuron activation patterns as a universal proxy to unify the fine-tuning lifecycle. NGFT operates via three synergistic mechanisms: (1) Adaptive Task-Specific Neuron Selection, which identifies essential neurons in a single forward pass to concentrate updates and reduce redundancy; (2) Activation-Based Data Selection, which prioritizes information-dense samples that maximize contribution to key neurons; and (3) Neuron Activation Alignment, a novel loss function that anchors activations to pre-trained states, deepening representation learning and preserving general knowledge. Experimental results across three models across both domain-specific and general benchmarks demonstrate that NGFT significantly outperforms existing mainstream fine-tuning methods in both efficiency and performance, while effectively mitigating catastrophic forgetting.
[NLP-206] Structurally Close Temporally Distant: Measuring Security Exposure in Long-Horizon LLM Agents
【速读】： 该论文旨在解决长时序大语言模型（LLM）智能体在与不可信内容、持久化记忆、外部状态及敏感工具交互时所面临的安全性评估偏差问题。现有研究多依赖恶意输入与下游攻击动作之间的执行步骤数（即序列距离 \DT）来衡量攻击威胁，但该方法忽视了状态依赖关系中的潜在影响路径，导致对安全隔离程度的误判。其核心解决方案是提出一种溯源感知的执行图（provenance-aware execution graph），通过确定性状态流转、标识符追踪与工具溯源构建事件间的结构化关联，并定义影响距离（influence distance, \DI） 作为从不可信源头到敏感操作的最短结构路径长度。相较于传统的序列距离 \DT，\DI 能够捕捉隐藏在步骤计数之外的间接影响路径，二者之差 \Gap = \DT - \DI 可量化被步数统计掩盖的安全耦合程度。实验表明，在454组注入-目标对中，96.9%的配对存在 \Gap > 0，中位差距达9跳；即使剔除最大一类仅由溯源边构成的路径，仍有91.0%保持解耦。在银行任务场景中，33.8%的攻击对可通过不同溯源机制实现解耦。进一步分析显示，\Gap 在控制序列距离、攻击类型和后端模型后，无法独立预测攻击成功率（\beta_\Gap=0.066, p=0.088），表明其更多反映的是结构接近性而非因果决定性。然而，在固定阈值下，基于 \DI 的预执行门控机制可拦截5个序列距离法遗漏的攻击目标，且未引入额外良性阻断，虽提升不显著（p=0.0625），但仍验证了执行结构分析在揭示隐蔽威胁路径与支持精准运行时干预方面的潜力。该研究强调应关注候选影响路径的识别，而非强求因果归因。
链接: https://arxiv.org/abs/2609.05911
作者: Md Jafrin Hossain,Nur Al Hasan Haldar
机构: The University of Western Australia (西澳大利亚大学)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Long-horizon LLM agents interact with untrusted content, persistent memory, external state, and sensitive tools. Existing analyses often characterize attacks by the number of execution steps between malicious input and a downstream action. We show that temporal remoteness can overstate security separation in stateful agents. We introduce a provenance-aware execution graph linking agent events through deterministic state, identifier, and tool provenance, and define \emphinfluence distance \DI as the shortest structural path from an untrusted source to a sensitive action. We compare it with \emphsequence distance \DT , the shortest injection–sink path in the ordered trajectory. Since the influence graph contains every sequence edge, \DI \leq \DT ; \Gap=\DT-\DI measures the separation hidden by step count. Across 454 injection–sink pairs from 360 long-horizon AgentDojo trajectories over OpenAI’s \textttgpt-4o-mini and \textttgpt-4o and Claude’s Haiku 4.5 and Sonnet 4.6, \Gap0 for 96.9% of pairs, with a median gap of 9 hops; 91.0% remain decoupled after removing the largest provenance-only edge class. On AgentDojo’s banking suite, 33.8% of 231 pairs from 377 trajectories decouple through different provenance mechanisms. Among 274 OpenAI pairs, \Gap does not independently predict attack success after controlling for \DT , attack family, and backend ( \beta_\Gap=0.066 , p=.088 ). At matched thresholds k=2,3 , a deterministic \DI -based pre-execution gate blocks five attack sinks missed by a sequence-only gate with no additional benign blocking, although the paired gain is not significant ( p=.0625 ). Execution structure therefore reveals proximity hidden by step count and can support targeted runtime intervention. We measure candidate influence pathways rather than causal attribution.
[NLP-207] UniRRM: Unified Reasoning Reward Models Across Languages and Evaluation Paradigms
【速读】： 该论文旨在解决开放性任务中奖励模型可靠性不足的问题，尤其针对现有方法在依赖昂贵的专用大语言模型（LLM-as-a-Judge）或缺乏可解释性的标量奖励模型时所面临的局限。现有生成式奖励模型虽具潜力，但仍受限于静态评估标准、评价范式碎片化以及多语言支持能力有限等挑战。为此，论文提出了一种名为MixReward的大规模多语言数据集，覆盖六个领域和103种语言，包含成对与列表式标注数据；并设计了UniRRM——一个统一的推理型奖励模型（Unified Reasoning Reward Model），支持多语言及多种评价范式。其核心创新在于采用分阶段推理链动态生成任务通用且指令相关的评估标准，实现细粒度、输入自适应的判断，同时保持跨语言的一致性。实验表明，UniRRM-8B与UniRRM-14B在多个基准测试中性能接近同规模模型的最先进水平，并能有效泛化至未见的评价范式，消融实验进一步验证了其可靠性和有效性。
链接: https://arxiv.org/abs/2609.05910
作者: Peng Lai,Yichao Du,Junchao Wu,Weibo Gao,Linan Yue,Longyue Wang,Weihua Luo,Derek F. Wong,Guanhua Chen
机构: Southern University of Science and Technology(南方科技大学); Alibaba Group(阿里巴巴集团); Wuhan University(武汉大学); University of Macau(澳门大学); University of Science and Technology of China(中国科学技术大学); Southeast University(东南大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement learning (RL) excels on tasks with verifiable rewards, but in open-ended tasks, the reliability of reward models remains a key challenge. Existing solutions either depend on costly proprietary LLM-as-a-Judge systems or opaque scalar reward models that lack interpretability. Recent works on generative reward models offer a promising alternative, but they remain constrained by static evaluation criteria, fragmented evaluation paradigms, and limited multilingual support. To address these challenges, we introduce \textbfMixReward, a large-scale multilingual dataset spanning six domains and 103 languages, containing both pairwise and listwise data, and propose \textbfUniRRM, a unified reasoning reward model supporting multiple languages and evaluation paradigms. UniRRM uses a staged reasoning chain to dynamically generate task-generic and instruction-specific criteria, enabling fine-grained, input-adaptive judgments while maintaining consistency across languages. Experiments demonstrate that UniRRM-8B and UniRRM-14B achieve performance close to the state-of-the-art for models of comparable size across multiple benchmarks, and are effective for unseen evaluation paradigms. In addition, ablation studies validate the reliability and effectiveness of UniRRM.
[NLP-208] From Narrative to Auditable Forecasts: A Structured Scaffold for Agent ic Forecasting EMNLP2026
【速读】： 该论文旨在解决当前大语言模型（LLM）代理在实时预测中依赖隐式叙事聚合所带来的准确性与可审计性不足的问题。现有方法通常通过自然语言形式收集证据并进行主观讨论，最终给出概率估计，但缺乏从证据到预测结果的显式更新路径，导致决策过程不透明且难以验证。其解决方案的关键在于提出AuditForecast——一种结构化的概率预测框架，通过引入定量基准模型锚定初始预测，利用模型引导的数据检索获取基础概率，并在模型能力范围之外通过机械式聚合方法在对数似然比空间（odds space）中整合情境因子的影响，从而将预测过程从基于散文的直觉判断转变为具有明确中间对象的结构化流程。该方法不仅在多个实时预测基准上显著提升了预测准确性和校准度，优于主流的代理基线甚至部分市场隐含参考值，同时在成本-精度权衡上保持帕累托优势，且生成可审计的预测报告，使预测构建过程透明化，支持系统性的事后分析。
链接: https://arxiv.org/abs/2609.05905
作者: Yuanpu Cao,Yongkang Du,Yurui Chang,Lu Lin,Jinghui Chen
机构: The Pennsylvania State University(宾夕法尼亚州立大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted to Findings of EMNLP 2026
Abstract:LLM agents are increasingly used for live forecasting, where they retrieve up-to-date information and produce estimates for unresolved future events. However, current agentic forecasting often relies on implicit narrative aggregation: agents collect evidence, discuss it in prose, and often assign a probability without an explicit update path from evidence to forecast. This limits both forecasting accuracy and auditability. We propose AuditForecast, an agentic scaffold for structured probabilistic forecasting. AuditForecast first anchors the forecast with a suitable quantitative baseline model, uses model-guided data retrieval to derive a base probability, and then applies situational factor updates outside the model’s scope through mechanical aggregation in odds space. This turns forecasting from a prose-based judgment into a structured process with explicit intermediate objects. Across multiple live forecasting benchmarks, AuditForecast improves forecasting accuracy and calibration relative to strong agentic baselines, surpasses market-implied references in several settings, and outperforms substantially more expensive deep-research agents while remaining Pareto-dominant in the cost–accuracy tradeoff. Beyond performance gains, AuditForecast produces an auditable forecasting report that makes forecast construction explicit and supports systematic post hoc analysis.
[NLP-209] AlignDiff: Exploiting Model-Intrinsic Information for Better Preference Data Selection
【速读】： 该论文旨在解决大语言模型与人类偏好对齐过程中因偏好数据质量不佳而导致的性能瓶颈问题，尤其关注现有数据集普遍存在的固有噪声和分布偏移问题。其核心解决方案是提出一种基于模型内在信号的偏好数据过滤框架——AlignDiff。该框架的关键在于：首先利用正向与反向信号识别出具有明确偏好差异的样本；随后根据平均负对数似然差距（negative log-likelihood gap）对样本进行难度排序，优先筛选更具挑战性的样本，从而引导模型从复杂样本中学习更丰富的语义信息。实验在LLaMA与Qwen两大模型家族及AlpacaEval 2.0、Arena-Hard和MT-Bench三个主流对齐基准上验证了AlignDiff的有效性，结果表明其在所有设置下均显著优于七种强基线方法。此外，通过系统的消融实验进一步证明了基于难度的课程学习策略可有效提升模型性能。
链接: https://arxiv.org/abs/2609.05899
作者: Peng Lai,He Zhu,Zhiwen Ruan,Dongdong Zhang,Yun Chen,Peng Li,Furu Wei,Yang Liu,Guanhua Chen
机构: Southern University of Science and Technology(南方科技大学); Peking University(北京大学); MSRA(微软亚洲研究院); Shanghai University of Finance and Economics(上海财经大学); Tsinghua University(清华大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Aligning large language models with human preferences remains a challenge, primarily due to the critical role of preference data quality in effective alignment. Existing datasets are frequently plagued by inherent noise and distribution shifts, which inherently limit model performance. To bridge this gap, we propose AlignDiff, a preference data filtering framework driven by intrinsic model signals. AlignDiff first identifies samples with clear preferences using both positive and inverse signals, then prioritizes the more challenging samples based on the average negative log-likelihood gap, encouraging the model to learn richer information from them. AlignDiff is evaluated on two widely used model families (LLaMA and Qwen) and three benchmarks widely adopted in the alignment community (AlpacaEval 2.0, Arena-Hard, and MT-Bench). Across all settings, it consistently outperforms seven strong baselines. We conduct comprehensive ablation studies to validate the effectiveness of AlignDiff, and further show that difficulty-based curriculum learning improves model performance.
[NLP-210] What if LLM s Ate Their Words: Causal History Effects in Multi-Turn Interaction
【速读】： 该论文旨在解决大语言模型（LLM）在多轮交互中因历史生成内容累积而引发的性能退化问题，尤其关注助手生成的历史对话对后续行为的影响机制。其核心问题是：多轮交互中的历史上下文（尤其是由模型自身生成的部分）如何影响模型在不同任务、不同模型架构及不同轮次下的表现，以及这种影响是否具有可预测性和可干预性。解决方案的关键在于通过系统性实验揭示历史内容对模型行为的主动且选择性影响——研究发现，仅将助手生成的历史替换为中性内容（即“中性化”，neutralization），即可在2,973条对话轨迹中平均提升下游性能0.027（以最小-最大归一化衡量），且该效果与对话长度无关，表明单纯缩短上下文并非主要原因；进一步采用“轮次手术”（Turn Surgery）方法逐轮干预，结果显示63.7%的退化轨迹可通过单轮修改实现改善，其中近半数二分类任务可实现从失败到成功的逆转。研究表明，历史内容的影响具有任务依赖性而非普遍规律，且能通过可观测的内部状态差异反映，因此应采取选择性而非统一的对话历史管理策略。
链接: https://arxiv.org/abs/2609.05882
作者: Jinnan Li,Zheren Fu,Yue Wang,Jinzhe Li,Yuan Wu,Yi Chang
机构: Jilin University (吉林大学); University of Science and Technology of China (中国科学技术大学); University of North Carolina at Chapel Hill (北卡罗来纳大学教堂山分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 11 pages, 6 figures. Code and data are available at this https URL
Abstract:Multi-turn interaction creates a feedback process in which an LLM’s previous responses become context for later behavior. Prior work shows substantial multi-turn degradation and that assistant-generated history can affect later behavior. However, it remains unclear how these effects manifest across models, tasks, turns, and inside a model. We study these gaps across six task families and five models. Degradation from fully specified single-turn input (FULL) to progressively revealed multi-turn interaction (SHARDED) is clearly task- and model-dependent, and stronger one-shot performance does not imply greater interaction robustness. We then retrospectively analyze completed SHARDED conversations by replaying the user messages already observed in each trajectory while editing only assistant-generated history. Replacing prior assistant responses with neutral content (termed neutralization) changes downstream min-max normalized performance by +.027 across 2,973 trajectories. On a prespecified length-controlled subset, short and length-matched neutralization yield nearly identical effects (+.069 versus +.068), showing that simple context shortening is insufficient to explain the effect of history editing. Turn Surgery further intervenes on one assistant turn at a time. Among 237 selected degraded trajectories, 63.7% contain at least one beneficial intervention, while most tested positions remain unchanged; for binary tasks, 48.4% admit a fail-to-success reversal. An open-weight case study links behaviorally consequential history changes to measurable downstream state differences, but finds task-dependent rather than universal internal signatures. Overall, assistant-generated history has active but selective effects on multi-turn performance, motivating selective rather than uniform history management.
[NLP-211] SinoGlyphBench: A Diagnostic Benchmark for Chinese Glyph-Level Obfuscation in Language-Model Moderation
【速读】： 该论文旨在解决生成式AI在中文内容安全检测中因字形混淆（glyph-level obfuscation）导致的漏洞问题，即攻击者通过使用非标准字形（non-canonical glyphs）改写有害内容，使人类可读但自动化内容审核系统难以识别，从而引发误判。其核心解决方案是提出SinoGlyphBench——一个诊断性基准测试框架，能够识别标签关键的语义锚点（label-critical semantic anchors），并构建文本与图像模态下匹配的原始输入与字形混淆输入对。通过分别扰动语义锚点、背景上下文或两者，该设计有效区分了与审核相关证据的破坏与表面形式的泛化变化。实验结果表明，字形混淆使12个大语言模型（LLM）和多模态大语言模型（MLLM）的有害内容漏检率（false-negative）和误报率（false-positive）分别上升6.1和4.7个百分点，四分类准确率下降5.0个百分点；模型仅保留75.7%在原始输入上正确判断的决策。全范围扰动造成最大性能退化，仅扰动语义锚点比仅扰动背景更致命，且跨脚本替换在文本模态中尤为困难。结构化输出分析揭示了可见形式读取、意图信息恢复与最终安全判定之间的可观测不一致。因此，现有模型对使用非标准字形书写的中文内容仍表现出显著脆弱性。
链接: https://arxiv.org/abs/2609.05843
作者: Yifan Wang,Zimu Wang,Suliu Qin,Changyu Zeng,Tong Chen,Siqi Chen,Yijie Lin,Lingyu Jiang,Jionglong Su,Yushan Pan,Haiyang Zhang,Wei Wang,Qiaoyu Tan
机构: East China Normal University (华东师范大学); New York University Shanghai (纽约大学上海分校); Xi’an Jiaotong-Liverpool University (西交利物浦大学); University of Liverpool (利物浦大学); Singapore University of Technology and Design (新加坡科技设计大学); Eastern Institute of Technology (Ningbo) (宁波东方理工大学)
类目: Computation and Language (cs.CL)
备注: 24 pages, 6 figures, 16 tables
Abstract:Glyph-level obfuscation can leave harmful Chinese content readable to humans while degrading automated moderation. We introduce SinoGlyphBench, a diagnostic benchmark that identifies label-critical semantic anchors and creates matched original and glyph-obfuscated inputs in text and image modalities. By perturbing anchors, background context, or both, this design distinguishes corruption of moderation-relevant evidence from general surface variation. Across 176,916 paired evaluations of 12 LLMs and MLLMs, obfuscation increases harmful false-negative and false-positive rates by 6.1 and 4.7 percentage points, respectively, and reduces four-way accuracy by 5.0 points. Models retain 75.7% of the decisions that were correct on the matched original inputs. Full-scope perturbations cause the largest degradation, anchor-only perturbations are more damaging than background-only perturbations, and cross-script substitution is particularly difficult in the text modality. Analysis of structured outputs identifies observable mismatches in visible-form reading, intended-message recovery, and final safety judgment. The evaluated models, therefore, remain brittle to Chinese content written with non-canonical glyphs. Resources are available at this https URL.
[NLP-212] CONDUIT: A Unified Residual-Stream Restoration Framework for KV Cache Reuse in Vision-Language Models EMNLP2026
【速读】： 该论文旨在解决视觉-语言模型（Vision-Language Models, VLMs）在处理重复视觉内容时，因上下文前缀变化导致关键值（Key-Value, KV）缓存失效的问题。传统精确前缀复用在前缀变更时失效，而现有选择性重计算方法受限于对“正确过时令牌”的依赖，且存在注意力机制误判高注意力但低价值更新潜力的令牌、或无关图像干扰刷新预算的问题。其解决方案的关键在于提出一种无需训练的刷新策略CONDUIT，将单图与多图场景下的缓存复用统一为残差流恢复（residual-stream restoration）。CONDUIT基于归一化加权注意力，利用缓存键与查询的注意力分数及可访问的预输出缓存值范数代理指标对缓存视觉令牌进行排序，并在全局选择前引入图像级相关性增强机制。该方法不改变模型架构与权重，仅在推理阶段增加一次查询条件化的评分过程，在10%刷新预算下实现了三类主流VLM骨干网络在五个数据集上平均达全前缀填充性能97.0%-99.5%，显著优于现有预算受限方法；在MMLongBench-Doc延迟子集上，仅需全前缀填充13.5%的浮点运算量（FLOPs），并实现2.99倍的首个标记生成速度提升。
链接: https://arxiv.org/abs/2609.05821
作者: Pengan Chen,Kaisheng Zheng,Liang Hong,Lixia Yi,Jiyue Jiang,Jiayang Chen,Yixuan Wang,Yimin Fan,Xinyuan Liu,Jiayi Li,Zhanqiu Zhang,Yiwen Guo,Yu Li
机构: The Chinese University of Hong Kong(香港中文大学); Fudan University(复旦大学); LIGHTSPEED; Independent Researcher(独立研究员)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to Findings of the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP 2026)
Abstract:Vision-language models (VLMs) often answer new questions about recurring visual content, where reusing the key-value (KV) cache can avoid re-encoding expensive visual prefixes. Exact-prefix reuse, however, fails when the same visual content appears under a changed prefix. Selective recomputation can recover quality under a small visual-token budget, but only when the right stale tokens are refreshed. Raw-attention selection can waste budget on high-attention tokens with small value-norm proxy scores and on query-irrelevant images. To address these failure modes, we propose CONDUIT, a training-free refresh policy that unifies single- and multi-image reuse as residual-stream restoration. Building on norm-weighted attention, CONDUIT ranks cached visual tokens using cached-key query attention and an accessible pre-output cached-value-norm proxy, then applies empirical image-level relevance amplification before one global selection. With one image, the coefficient is one and the rule reduces to intra-image token selection. The method preserves model architecture and weights, adding only a single query-conditioned scoring pass at inference. At a 10% refresh budget, CONDUIT achieves 97.0-99.5% of the corresponding full-prefill five-dataset average across three VLM backbones and leads budgeted methods on average; on the MMLongBench-Doc latency subset, it uses 13.5% of full-prefill FLOPs and achieves a 2.99x time-to-first-token speedup.
[NLP-213] Dynamic Lagging for Simultaneous Translation
【速读】： 该论文旨在解决级联式同时语音翻译（cascaded simultaneous speech translation）中的核心挑战：在上游识别器无法控制读写调度的情况下，机器翻译系统需基于不断增长的源端前缀动态决定应提交多少目标文本，导致译文输出存在“闪烁”（flicker）现象。其解决方案的关键在于对仅解码器架构的大语言模型（decoder-only LLM）进行前缀感知（prefix-aware）微调——通过在稳定前缀（stable prefixes，即任何部分源序列与模型完整输出共享的最长前缀）和完整句子对上联合微调，并引入单次强制解码（force-decode turn）机制，将已提交的目标文本作为上下文持续传递，从而从结构上消除闪烁。实验表明，该方法在不牺牲完整句子翻译质量的前提下，显著提升了最差位置分块的质量，并优化了词级别提交置信度的校准性，降低了早期源前缀上的期望校准误差（ECE）。进一步地，仅需一个训练无关的阈值即可实现最优延迟控制，其性能优于传统的离散等待-k（wait-k）与目标后缀删除策略，能够在连续的质量-延迟权衡曲线上取得更优表现，且在FLEURS、WMT24++和CoVoST~2等多个基准测试集上，无论采用COMET还是MetricX评估指标，均保持一致有效性。
链接: https://arxiv.org/abs/2609.05799
作者: Hieu Hoang,Amittai Axelrod
机构: Microsoft(微软)
类目: Computation and Language (cs.CL)
备注:
Abstract:In cascaded simultaneous speech translation, the machine translation (MT) system cannot control the read–write schedule of the upstream recognizer: it must decide, from a growing source prefix, how much target text to commit. We make a sentence-trained, decoder-only LLM prefix-aware by fine-tuning it on stable prefixes—the longest prefix that any translation up to the current partial source has shared with the model’s own full-source output—mixed with full-sentence pairs, and prompt it through a single force-decode turn that carries the committed target forward as more source arrives, making the system flicker-free by construction. We fine-tune Qwen3-8B for EN to DE, JA, ZH, simulating the source stream with reference-transcript prefixes. Prefix finetuning preserves full-sentence quality while improving worst-position chunk quality, and it improves calibration of token-level commit confidence, reducing expected calibration error (ECE) on early source prefixes against a stable-prefix oracle. A single training-free threshold on that confidence is the most effective of the three latency controls we compare: it traces a continuous quality–latency frontier that outperforms the discrete wait- k and target-suffix-deletion quality-latency tradeoff mechanisms. The effect holds well on FLEURS, WMT24++, and CoVoST~2 test sets, under both COMET and MetricX.
[NLP-214] Recall Is Not Protection: Evaluating Safety Monitors Against Model Compliance NEURIPS2026
【速读】： 该论文旨在解决当前安全监测器（safety monitor）评估方法中存在的关键缺陷：现有评估主要依赖召回率（recall）与有害性标注的匹配程度，但这一指标未能反映监测器实际阻止有害响应的能力。其核心问题是，若目标语言模型在未受监测时本就可能对某条有害提示作出回应（即“可诱发”（elicitable）），则监测器能否成功拦截该请求才真正决定其安全性。为此，论文提出一种新的评估范式：通过重复采样目标模型对同一提示的响应，将能引发模型合规响应的提示定义为“可诱发”提示，并分别报告监测器在可诱发与不可诱发提示上的召回率。实验结果表明，在固定误报率下，监测器在可诱发提示上的召回率比在不可诱发提示上低0.22至0.38；且被监测器遗漏的提示其实际被模型遵守的概率是被捕捉提示的2.8至5.6倍。该差距在六种监测配置及三个模型家族中均稳定出现，甚至在完全独立于目标模型的纯文本监测器中亦存在。这表明，传统基于标注的召回率评估可能显著高估了监测器的实际保护效果，因此建议未来应以监测器所面对的模型真实响应行为作为评估基准。
链接: https://arxiv.org/abs/2609.05797
作者: Sripad Karne
机构: Columbia University (哥伦比亚大学)
类目: Computation and Language (cs.CL)
备注: Submitted to the NeurIPS 2026 JUDGe Workshop
Abstract:Safety monitors screen prompts sent to deployed language models, flagging harmful requests so they are never answered. They are evaluated by recall against harmfulness labels, but a catch only prevents harm if the model would otherwise have complied. We measure the difference directly: we sample repeated responses from the target model, call a harmful prompt \emphelicitable if the model complies at least once, and report monitor recall separately on elicitable and non-elicitable prompts. Across six monitor configurations and three model families, spanning activation probes, fine-tuned text guards, and a 120B policy-conditioned reasoning classifier, recall on elicitable prompts falls 0.22 to 0.38 below recall on non-elicitable prompts at a fixed false positive rate. The prompts a monitor misses are 2.8 to 5.6 times more likely to be complied with than the prompts it catches. The gap replicates across three model families and appears also in text-only monitors entirely independent of the target model. This suggests that standard recall may overstate the protection monitors provide in practice, and that monitors should be evaluated against what their models will actually answer.
[NLP-215] Bait-and-Recover: Poisoning Internal Refusal Signals to Defend LLM s against White-Box Editing Jailbreaks
【速读】： 该论文旨在解决开放权重大语言模型（open-weight large language models）面临的低成本白盒表示工程攻击（representation engineering attacks）问题。此类攻击中，攻击者可快速估计模型拒绝特定指令的方向，并在单张GPU上通过非梯度优化搜索投影矩阵的修改方案，以削弱安全对齐性同时保持通用能力。其核心威胁在于攻击者能够基于对中间激活值的观测，精准定位并实施有效编辑。论文提出的解决方案关键在于Bait-and-Recover防御机制：在攻击者读取激活的位置部署“诱饵适配器”（bait adapter），并在后续层配置对应的“恢复适配器”（recovery adapter），通过梯度路由训练实现观测路径与行为路径的解耦。该机制主动污染用于攻击者测量的残差信号，从而破坏其编辑搜索的有效性；同时，恢复适配器确保下游计算的完整性。实验表明，在四个开放权重模型上，该方法将白盒编辑攻击下的最低拒绝率从16.25%提升至71.75%（在严格的行为保真度预算下，KL=0.10），且对通用基准性能影响极小。通过颠覆攻击依赖的观测假设，该方法为行为级安全训练提供了实用有效的补充。
链接: https://arxiv.org/abs/2609.05794
作者: Tian Gao,Zhipeng Xie,Yuhao Wu,Junhua Liu,Xin Fang
机构: Anhui Laboratory for Safe Artificial Intelligence in the Yangtze River Delta; iFlytek Research
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 13 pages, 3 figures. Code: this https URL
Abstract:Open-weight large language models face a low-cost white-box threat from representation engineering attacks. Attackers can estimate refusal directions and search for projection-matrix edits that suppress safety alignment while preserving general capabilities, within minutes on a single GPU and without gradient-based training. We propose Bait-and-Recover, a weight-level defense that places a bait adapter where attackers read activations and a paired recovery adapter at the subsequent layer. Trained via gradient routing, this decouples the observation path from the behavior path. By actively poisoning the residual signal used for measurement, Bait-and-Recover disrupts the attacker’s edit search, while the recovery layer restores clean downstream computation. Across four open-weight models, our defense raises the minimum refusal rate against white-box edit searches from 16.25% to 71.75% under a strict behavior-preservation budget (KL = 0.10), with negligible impact on general benchmarks. By invalidating the core measurement assumption of these attacks, observation-path poisoning offers a practical complement to behavior-level safety training.
[NLP-216] RAPTOR: Role-Aware Private Training for Mixture-of-Experts
【速读】： 该论文旨在解决差分隐私（Differentially Private, DP）微调方法在稀疏混合专家模型（Mixture-of-Experts, MoE）中因将整个模型视为单一稠密块而引发的隐私保护失效问题。其核心挑战在于：共享层会访问全部数据，而专家仅接收路由到自身的记录，传统DP机制未能区分这一异构性，导致三种关键失败模式——全局梯度裁剪抑制专家梯度更新、批量级归一化稀释稀疏专家的更新信号、固定隐私噪声降低低负载专家的信噪比。为此，论文提出RAPTOR（Role-Aware Private Training）框架，其关键创新在于通过角色感知的优化交替策略，分别针对性地缓解上述问题：采用专家特异性裁剪与噪声添加，并引入公开的期望拥有者分母（public expected-owner denominator）和与计数无关的更新调度机制，避免对私有实际专家负载进行条件依赖。理论证明该机制满足(ε,δ)-DP，其核心依据是每条记录仅被分配至一个专属专家，因此同一层内各专家的局部机制可并行组合，整体隐私开销等价于单个专家更新；而共享层与专家流之间的更新则按顺序组合。此外，论文进一步推导了公开分母估计器的偏差-方差分解，揭示其偏差随路由不均衡度可预测增长，从而提出一种无需隐私成本的规则，用于选择在小规模公开语料上测量路由熵后决定保护哪一层。在Switch Transformer、OLMoE在GLUE任务上的微调以及DeepSeek-VL2-Tiny上的实验均表明，RAPTOR在多个隐私预算（ε）下持续优于标准DP基线，尤其在最紧的隐私预算下取得最大提升。代码与模型已公开。
链接: https://arxiv.org/abs/2609.05770
作者: Duc Dm,Khai Le-Duc,Nguyen Do,Minh Son Hoang,Florent Draye,Thai Hoang,Hoang Phuong Dam,Jiarui Liu,Chris Ngo,Terry Jingchen Zhang,Anh Le Duc Tran,Nhat Do Minh,Minh Ngoc Le,My T. Thai,Ran Xu,Silvio Savarese,Mona Diab,Bernhard Schölkopf,Zhijing Jin,Huy L. Nguyen,Daeyoung Kim
机构: Vector Institute(向量研究所); Knovel Engineering Lab(诺维尔工程实验室); KAIST(韩国科学技术院); MPI for Intelligent Systems, Tübingen(图宾根智能系统马克斯·普朗克研究所); Salesforce AI Research( Salesforce人工智能研究); Carnegie Mellon University(卡内基梅隆大学); University of Toronto(多伦多大学); University of Oxford(牛津大学); Jinesis Lab, University of Toronto(多伦多大学杰尼西斯实验室); Hanoi University of Science and Technology(河内科技大学); Vietnam National University, Hanoi(越南国家大学，河内); University of Florida(佛罗里达大学); Stanford University(斯坦福大学); ELLIS Institute Tübingen(图宾根ELLIS研究所); Northeastern University(东北大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Preprint
Abstract:Differentially private (DP) fine-tuning methods treat sparse Mixture-of-Experts (MoE) models as a single dense block, ignoring that shared layers see all data while experts only see routed records. We identify and formally characterize three resulting failure modes: global clipping suppresses expert gradients, batch-level normalization dilutes sparse expert updates, and fixed privacy noise degrades signal-to-noise ratio on low-load experts. We introduce RAPTOR - a Role-Aware Private Training framework, which alternates shared and expert optimization and targets each failure directly, using expert-specific clipping and noise together with a public expected-owner denominator and a count-independent update schedule that avoids conditioning on private, realized expert counts. We prove the resulting mechanism satisfies (\varepsilon,\delta) -DP: because each record is assigned to exactly one owner expert, per-expert mechanisms within a layer compose in parallel, so updating all E experts costs no more, in privacy terms, than updating one, with shared and expert streams composing sequentially across training. We further derive a bias-variance decomposition of the public-denominator estimator showing its bias grows predictably with routing imbalance, yielding a privacy-free rule for selecting which layer to protect from routing entropy measured on a small public corpus. Experiments on Switch Transformer and OLMoE fine-tuning across GLUE tasks, and on DeepSeek-VL2-Tiny, show consistent gains over standard DP baselines across several privacy levels ( \varepsilon ), with the largest margins typically at the tightest budgets. Code and models are publicly available: this https URL
[NLP-217] CrisisKD: Five-Stage Knowledge Distillation for Aspect-Level Sentiment and Emotion Analysis in Crisis Discourse EMNLP2026
【速读】： 该论文旨在解决危机情境下（尤其是健康相关危机）情感词汇或短语的目标方面识别问题，以在跨文化和多语言背景下理解公众关切。其核心挑战在于如何高效、准确地从无标注的社会媒体数据中进行细粒度的情感与情绪分析，而传统方法受限于标注成本和模型复杂性。解决方案的关键是提出一种五阶段教师-学生知识蒸馏框架（CrisisKD），通过大语言模型（LLM）作为教师，生成细粒度的方面级标签与推理路径，指导小型学生模型完成方面抽取、句法解析、观点提取、情感分类和情绪分类五个任务。该框架不仅显著降低了推理成本，还通过大规模自动生成标注数据构建了包含50,615个方面级标签的公开数据集，并提供完整的标注与微调脚本。实验结果表明，基于Qwen2.5-7B的5任务学生模型在人工标注的黄金数据集上，方面抽取F1提升7.9点，情绪准确率提升17.0点，情感准确率提升6.5点；在外部ABEA基准测试中，相比同模型的提示学习基线，方面抽取（ATE）F1提升2.8点，联合任务（ATE+AEC）F1提升3.8点，验证了该框架在保持高性能的同时实现高效部署的可行性。
链接: https://arxiv.org/abs/2609.05757
作者: Marko Haralović,Onat Akca,Salih Eren Yücetürk,Minsi Li,Mariët Theune
机构: University of Twente (特温特大学); University of Zagreb (萨格勒布大学)
类目: Computation and Language (cs.CL)
备注: Accepted to the 11th Workshop on Natural User-generated Text (W-NUT 2026), co-located with EMNLP 2026
Abstract:Identifying the target of emotional words or phrases in crisis situations, especially health-related ones, is important for understanding public concerns across cultural and linguistic contexts. We propose CrisisKD, a five-stage teacher–student knowledge distillation framework for aspect-level sentiment and emotion analysis on unannotated social media data. A teacher LLM generates aspect-level labels and reasoning traces that supervise a smaller student model across aspect extraction, syntactic parsing, opinion extraction, sentiment classification, and emotion classification. Using this framework, we construct and release a dataset containing 50,615 aspect-level labels, together with the annotation and fine-tuning scripts as open-source resources. The resulting student supports end-to-end ABSA and emotion detection at substantially lower inference cost than the teacher. On a manually annotated 500-tweet gold set, the 5-task Qwen2.5-7B student improves over the untuned model by 7.9 F1 points on aspect extraction, 17.0 points on emotion accuracy, and 6.5 points on sentiment accuracy. On the external ABEA benchmark, CrisisKD improves the same-model Qwen2.5-7B ICL baseline by 2.8 F1 points on ATE and 3.8 F1 points on joint ATE+AEC.
[NLP-218] Emotion as a Distribution: Joint Valence-Arousal Probability Learning for Speaker-Independent Multimodal Emotion Recognition
【速读】： 该论文旨在解决多模态情感识别中情感状态的复杂性与混合性被简化为单一硬标签（hard label）所带来的信息损失问题。传统方法通常将连续且多层次的情感表达压缩为离散类别，忽略了情感在效价-唤醒度（Valence-Arousal）空间中的分布特性。为此，本文提出一种基于文本与语音的双模态系统，其核心创新在于不输出单一分类结果，而是生成一个9×9的概率矩阵，以表征情感在效价-唤醒度平面上的分布。该分布通过二维高斯软标签（soft target）结合Kullback-Leibler散度/交叉熵损失函数进行训练，从而更精确地建模人类情感的连续性和不确定性。关键解决方案包括：采用统一编码器-融合头架构对比Transformer与状态空间模型（Mamba-1/2/3）在不同计算规模下的表现，并引入冻结的WavLM-Large声学特征以提升性能；实验结果显示，所提出的双头系统在IEMOCAP数据集上达到73.0% ± 0.3的无加权准确率，显著优于基线Transformer模型（提升3.0个百分点），且在不牺牲延迟或内存效率的前提下实现。进一步分析表明，该概率分布能有效恢复情感环状结构（circumplex），其质心可追踪效价与唤醒度（CCC达0.66/0.66），而熵值则与分类标注者的模糊性相关联，而非维度扩展本身，凸显了该分布对真实情感语义的建模价值。
链接: https://arxiv.org/abs/2609.05755
作者: Tingyi Lin,Wen-Ren Yang,Kuanwei Chen
机构: National Changhua University of Education(国立彰化大学); National Central University(国立中央大学)
类目: ound (cs.SD); Computation and Language (cs.CL)
备注: 18 pages, 8 figures. Submitted to Speech Communication. Code: this https URL (archived: doi: https://doi.org/10.5281/zenodo.21904810 )
Abstract:Human emotion is graded and frequently mixed, yet most multimodal recognizers collapse it onto a single hard label. We argue the recognizer should instead expose a distribution over affective space. Our text+speech system, alongside its categorical decision, emits a 9\times9 probability matrix over the Valence-Arousal plane, trained with a two-dimensional Gaussian soft target under a Kullback-Leibler/cross-entropy objective, aimed at counseling support. Evaluation is strict: speaker-independent 5-fold leave-one-session-out IEMOCAP with rotating-session inner validation, headline metrics only on the held-out session. Within one fixed encoder-fusion-head pipeline we compare Transformer and state-space (Mamba-1/2/3) backbones at matched depth and width, at two operating points ( T\approx550 , T\approx2750 ). The featured dual-head system reaches 73.0% \pm 0.3 unweighted accuracy over three seeds (separate rerun: 72.1%), exceeding the Transformer fusion baseline by 3.0 UA points (95% session-bootstrap CI [1.0,4.7]; significant under paired t-test and session-level bootstrap), with no latency or memory advantage at these lengths; swapping the ~1M trainable front-end for frozen WavLM-Large features (learnable layer weights) lifts the same architecture to 76.6% \pm 1.3. Pre-specified controls scope the claims honestly: simpler valence-arousal auxiliaries reproduce the classification lift within noise, and a dedicated regression head tracks the continuous ratings slightly better, so the head’s specific value is the normalized affect distribution itself. That distribution recovers the circumplex: its center of mass tracks valence and arousal (CCC 0.66/0.66; predominantly between-class structure, weaker within-class tracking), and its entropy is weakly but consistently linked to categorical rater ambiguity, not dimensional spread.
[NLP-219] Some Tokens Behave like Magnets: Revealing Linguistic Organization in the Layers of Language Models EMNLP2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）内部语言计算过程的几何组织机制不透明这一核心问题，即如何在模型深层结构中理解语义与语法信息的动态分布与演化。其关键解决方案是提出“磁性向量”（magnetic vectors）的概念——一类在模型隐空间中具有吸引或排斥作用的特殊令牌向量，它们通过两种相反极性（吸引/排斥）调控邻近令牌的几何分布：同向对齐的吸引性向量会拉伸周围向量，而同向对齐的排斥性向量则压缩其邻域。研究发现，功能词在早期层中普遍表现为排斥性磁性向量，且随着网络深度增加，磁性极性发生可预测的重构；在下游任务微调后，任务相关的关键令牌（如问答中的答案跨度）在最终层形成独特的排斥性磁性中心，几何上“切割”出目标输出。更重要的是，该现象具有因果意义：移除早期排斥性磁性向量会严重破坏句法任务性能（如词性标注准确率从91%降至10%以下），而移除晚期吸引性磁性向量则主要影响语义任务。这一模式在不同架构、规模和层数的模型中均具一致性，且无需依赖外部探针即可揭示模型内部的分层组织逻辑，为理解大语言模型的几何计算机制提供了首个无探针路径。
链接: https://arxiv.org/abs/2609.05743
作者: Andrew Liu,Devan Srinivasan,Gerald Penn
机构: 未知
类目: Computation and Language (cs.CL)
备注: To be presented at EMNLP 2026
Abstract:We identify a special group of token vectors inside large language models (LLMs), which we term magnetic vectors, that organize the surrounding tokens by either attracting or repelling them. Particularly, tokens pointing the same way as an attracting magnet are elongated; tokens pointing the same way as a repelling magnet are compressed. Just as physical magnets pull or push away the iron filings around them, these vectors organize their surroundings through two opposing polarities. Moreover, we identify a statistically significant pattern in linguistic category where function words consistently act as repelling magnets in early layers, and we also find magnets consistently reorganize their polarities in unique ways deeper in the model. In a further case study we find this observation may unveil a deliberate, layer-wise organization in how LLMs process language. This pattern is consistent across different LLM architectures, sizes, and layer configurations. It is also causally relevant. When the LLM is fine-tuned for a downstream task, the task-functional tokens emerge as magnets. E.g., in question answering, the answer-span tokens become uniquely repelling magnets in the final layer, geometrically carving the answer out of the surrounding context. Furthermore, removing early-layer repelling magnets devastates syntactic tasks (POS tagging accuracy drops from 91% to below 10%) while sparing semantic ones, and removing late-layer attracting magnets does the reverse. We believe this phenomenon warrants further investigation, as it opens the first probe-free path to understanding how language models geometrically organize linguistic computation across their layers. Comments: To be presented at EMNLP 2026 Subjects: Computation and Language (cs.CL) Cite as: arXiv:2609.05743 [cs.CL] (or arXiv:2609.05743v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.05743 Focus to learn more arXiv-issued DOI via DataCite
[NLP-220] MedWER: A Reproducible Model-Free Evaluation Protocol for Medical Speech Recognition
【速读】： 该论文旨在解决现有自动语音识别（ASR）评估中对临床关键错误的掩盖问题：传统全词错误率（Word Error Rate, WER）虽可显示95%的准确率，却无法捕捉药物名称之间的误换等具有严重临床后果的错误。现有解决方案通常依赖于评估时的命名实体识别（Named Entity Recognition, NER）模型或云API来加权医疗实体错误，导致评估指标的分母成为版本化的“黑箱”，难以复现和验证。本文提出MedWER，一种针对医疗领域ASR的评估协议及开源工具，其核心创新在于将评估的分母固定为一个经公开数据源投影生成的19,373项标准化术语列表（涵盖药物、诊断、症状及伤害机制），并采用锚定文本归一化器与短语感知的术语受限WER相结合的方法，确保唯一可变组件仅为一个精确版本控制的归一化依赖，并通过已提交的黄金基准测试进行校验。该方法的覆盖率在独立的省级药品福利文件上得到验证，且匹配启发式规则基于真实实体跨度进行校准。在两个公开基准上，对Moonshine~base、Whisper~this http URL及MedASR等基线模型的评估结果均使用该工具生成，并以重采样后的每句得分计算95%置信区间，显著提升了评估的可重复性与临床相关性。
链接: https://arxiv.org/abs/2609.05728
作者: Justin Behling
机构: NORDIS TECH INC.
类目: Computation and Language (cs.CL)
备注: 10 pages, 3 tables. Code, term list, and evaluation scripts: this https URL . Package: pip install medwer. Source archive: this https URL
Abstract:Overall word error rate hides clinically critical errors: a transcript can be 95% correct and still swap one drug for another. The usual fix weights errors on medical entities, and almost always depends on an evaluation-time named-entity recognition (NER) model or cloud API, which makes the metric’s denominator a versioned black box. We present MedWER, an evaluation protocol and open-source tool for medical ASR whose denominator is a fixed, license-clean term list: 19,373 drug, diagnosis, symptom, and injury-mechanism entries projected from public sources. The protocol couples a pinned text normalizer with a phrase-aware term-restricted WER, the MedWER, so the only versioned component is a normalizer dependency held at an exact release and checked against committed golden fixtures. Coverage is validated against an independent provincial drug-benefit file the list was not built from; the matching heuristic is calibrated against ground-truth entity spans. Baselines for Moonshine~base, Whisper~this http URL, and MedASR on two open benchmarks are scored with the released tool and reported with 95% confidence intervals from resampled per-utterance scores.
[NLP-221] Intra-Prompt Parallel Decoding for Common-Context Question Answering EMNLP2026
【速读】： 该论文旨在解决在通用上下文问答（Common-Context Question Answering, CCQA）任务中，大型语言模型（Large Language Models, LLMs）因独立处理每个问题而导致的推理效率瓶颈问题。现有方法虽通过批处理（batching）和缓存（caching）提升并行性与减少重复计算，但各问题间在不同提示（prompt）中的分离仍导致现代GPU受注意力计算过程中的内存瓶颈制约，难以充分释放算力。本文提出的提示内并行解码（Intra-Prompt Parallel Decoding, IPPD）是一种新型推理方法，其核心在于将多个共享同一上下文的问题在单个提示内并行解码，通过虚拟位置标识符（virtual position IDs）与注意力掩码（attention mask）的巧妙设计，在不改变LLM架构且无需微调的前提下，实现注意力阶段的内存与计算高效共享。由于所有并行操作均发生在单个提示内部，IPPD可无缝兼容批处理推理，即使各提示对应不同上下文亦能保持高效。实验表明，IPPD相较标准解码可实现最高7倍的有效吞吐量提升，且无性能损失，优于大多数场景下的前缀缓存（prefix caching）结合分页注意力（PagedAttention）方案。
链接: https://arxiv.org/abs/2609.05707
作者: Theodore Glavas,Nikhita Vedula,Dushyanta Dhyani,Antonios Valkanas,Yilun Zhu,Shervin Malmasi
机构: Amazon.com, Inc.(亚马逊); McGill University (麦吉尔大学); Mila; Int. Lab. Learning Systems(国际学习系统实验室); Apple(苹果)
类目: Computation and Language (cs.CL)
备注: Accepted to EMNLP 2026 main conference
Abstract:In common-context question answering (CCQA) tasks, multiple input questions share a common context to base their answers from. However, Large Language Models typically generate each answer using an independent prompt. While existing batching and caching techniques help improve parallelism and reduce repeated computations, the separation of questions across prompts limits the achievable speedup, as modern GPUs are underutilized due to a memory bottleneck during attention. We present Intra-Prompt Parallel Decoding (IPPD), a novel inference method that answers multiple common-context questions in parallel within a single prompt. IPPD directly addresses the bottleneck by efficiently sharing both memory and computation during the attention process, as the next token for every question is decoded in a single inference step. IPPD uses virtual position IDs and attention mask manipulation to generate the same output as standard prompting without requiring fine-tuning or any changes to the LLM architecture. Since all parallelism occurs within a prompt, IPPD is fully compatible with batched inference, even when each prompt features a different context. Our experiments show that IPPD delivers up to 7X the effective throughput as standard decoding without quality degradation, and outperforms prefix caching with PagedAttention in most settings.
[NLP-222] A Rubric-Guided Large Language Model Solution for Opioid Use Disorder Computable Phenotyping
【速读】： 该论文旨在解决美国阿片类药物使用障碍（Opioid Use Disorder, OUD）在电子健康记录（Electronic Health Records, EHRs）中难以准确识别的问题，主要挑战在于诊断编码缺失且相关支持证据散落在临床文本中。为实现对OUD的可计算表型（Computable Phenotyping, CP）构建，研究提出一种基于专家制定18项标准检查清单（rubric）引导的大型语言模型（Large Language Model, LLM）框架，并引入优化提示（Optimization by PROmpting, OPRO）策略，以自动提取临床文本中与OUD相关的关键证据。其解决方案的关键在于利用结构化专家知识（即检查清单）指导LLM进行精准信息抽取，从而提升表型识别的准确性与可解释性。实验结果表明，该方法在253例患者（含68例确诊OUD）的验证中取得了0.774的F1分数和0.934的AUROC，显著优于基于机器学习的CP模型及零样本LLM，相对提升分别达12.8%和44.4%，同时实现了从LLM提取证据到OUD判定的可追溯性，增强了模型决策的透明度。
链接: https://arxiv.org/abs/2609.05682
作者: Mengxian Lyu,Paredes Pardo,Cheng Peng,Ziyi Chen,Mengyuan Zhang,Jieting Li Lu,Gary M Reisfield,William M Greene,Jenny Lo-Ciganic,Yonghui Wu
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Opioid use disorder (OUD) remains a public health crisis in the United States, yet it is difficult to identify from electronic health records (EHRs) because missing diagnosis codes and supporting evidence are buried in clinical narratives. Accurate OUD identification is critical to support interventions and improve health outcomes. This study developed a rubric-guided large language model (LLM) that incorporated Optimization by PROmpting (OPRO) for OUD computable phenotyping (CP). The framework used an 18-item, expert-identified rubric to instruct LLMs to automatically extract critical text with supporting evidence to determine OUD flags. Two UF Health physicians (GMR and WMG) chart-reviewed 253 patients, including 68 OUD-positive cases. Our LLM-based computable phenotype (CP) achieved the best F1 score of 0.774 and an AUROC of 0.934, outperforming the machine learning-based CP using EHR and natural language processing-extracted variables, and zero-shot LLMs by relative F1 improvements of 12.8% and 44.4%, respectively. The proposed LLM-based CP could link LLM-extracted evidence to OUD phenotyping for better explainability.
[NLP-223] amilEOT: A Dataset and Model for Semantic End-of-Turn Detection in Tamil Telephone Speech
【速读】： 该论文旨在解决语音代理在对话中判断用户是否完成发言（即端到端转换单元检测，End-of-Turn Detection, EOT）的难题，尤其针对缺乏标注数据和有效模型的南印度语言（如泰米尔语）场景。传统方法依赖固定的静音超时机制，存在误中断或延迟响应的权衡问题；尽管已有开放语义的EOT检测器，但尚无覆盖泰米尔语的解决方案。为此，作者发布了首个泰米尔语端到端转换单元数据集TamilEOT，包含18,485个经标注的对话回合边界，源自116段真实泰米尔电话通话，并基于Smart Turn v3架构训练了两个仅使用音频输入的检测模型。在未见测试集上，模型准确率从零样本的70.30%提升至83.71%（8.7 MB）和86.13%（21 MB），ROC-AUC从0.751提升至0.921，且推理延迟低于150毫秒（单线程笔记本CPU）。研究揭示了关键发现：仅有编码器容量对性能有显著影响，其他超参数调整均无法突破约0.87准确率点的基线波动范围；此外，生产级语音活动检测（VAD）与流式适配器引入额外2.60个百分点的性能损失，且7.8%的边界根本未被模型捕获。为确保标注质量，作者对比了规则标签（人工盲听验证后正类准确率95.9%，负类仅44.4%，低于随机水平，因规则回答的问题与模型目标不一致）与基于音频大语言模型（audio-LLM）的标注方案（与人工标注一致性达97.5%，成本为5.69美元）。所有数据、权重、代码及负面结果均已公开。
链接: https://arxiv.org/abs/2609.05631
作者: Santhoshkumar V
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Sound (cs.SD)
备注: 13 pages, 7 tables. Code, data and models: this https URL
Abstract:A voice agent has to decide, at every pause, whether the user has finished speaking. Without a model of the language that decision falls back to a fixed silence timeout: set it short and the agent interrupts, set it long and every turn pays the full wait. Open semantic end-of-turn detectors exist, but to our knowledge none covers a South Indian language. We release TamilEOT: 18,485 labelled turn boundaries cut from 116 real Tamil telephone conversations, and two audio-only detectors fine-tuned from Smart Turn v3. On a held-out split of 4,168 clips from 30 unseen calls, accuracy rises from 70.30% zero-shot to 83.71% (8.7 MB) and 86.13% (21 MB); ROC-AUC rises from 0.751 to 0.921. Both models run in under 150 ms single-threaded on a laptop CPU. We also report what building it cost. Rule-derived labels, checked against a blind human listening pass, were right 95.9% of the time on the positive class and 44.4% on the negative class, which is below chance, because the rule answered a different question than the model is asked. Replacing them with an audio-LLM labeller measured at 97.5% human agreement cost US 5.69. Of every training lever we measured, only encoder capacity moved the result; three runs at identical config and seed span 0.87 accuracy points, which is the floor below which none of our other deltas mean anything. Replaying the same labelled boundaries through the production VAD and streaming adapter costs a further 2.60 points, and 7.8% of boundaries are never surfaced to the model at all. Data, weights, code and every negative result are public.
[NLP-224] EnvCraft: Synthesizing Executable Environments in Agent ic RL for Claw-like Agent
【速读】： 该论文旨在解决生成式智能体（Generative AI）在执行长时序、状态依赖任务时面临的训练环境稀缺问题。当前基于代理强化学习（Agentic Reinforcement Learning, Agentic RL）的智能体发展迅速，但其规模化训练受限于高质量交互式训练环境的匮乏，现有合成环境仅支持工具调用（tool-calling）接口，难以满足类爪型智能体（Claw-like agents）在真实复杂场景中端到端的任务执行需求。为此，论文提出EnvCraft——一个自动化生成可执行环境与可扩展训练数据的框架。其核心创新在于构建了两个关键组件：一是沙箱隔离的环境合成引擎，用于生成安全可控的交互空间；二是拓扑感知的数据生成引擎，能够生成语义连贯、结构合理的任务轨迹。通过该框架，研究者共构建了139个交互式环境，涵盖约2万项复杂任务。在Qwen3/3.5系列模型（8B-32B参数量）上的实验表明，该方法在类爪型基准上实现最高+11.9%的性能提升，在通用工具使用基准上达+8.0%提升，同时显著降低推理阶段的令牌消耗。结果验证了合成可执行环境能为智能体训练提供鲁棒且具备泛化能力的学习信号。
链接: https://arxiv.org/abs/2609.05576
作者: Yirong Zeng,Shen You,Jinhang Feng,Yufei Liu,Xiao Ding,Yutai Hou,Hao Cong,Yuxian Wang,Wu Ning,Wang Xu,Bibo Cai
机构: Harbin Institute of Technology (哈尔滨工业大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 29 pages, 12 figures
Abstract:The paradigm of LLMs has rapidly shifted from passive language interfaces to autonomous Claw-like agents that execute long-horizon tasks across stateful workspaces. While Agentic Reinforcement Learning (Agentic RL) provides a promising path to optimize these agents, its scaling is heavily bottlenecked by the severe scarcity of interactive training environments. Existing synthetic environments are strictly limited to tool-calling endpoints, rendering them insufficient for accommodating the end-to-end real-world demands of claw-like agents. To bridge this gap, we introduce EnvCraft, an automated framework for synthesizing executable environments and scalable training data. Specifically, EnvCraft employs an environment synthesis engine to build sandbox-isolated workspaces, alongside a topology-aware data generation engine to produce coherent task trajectories. Overall, we synthesize 139 interactive environments comprising approximately 20K complex tasks for Agentic RL training. Experiments on Qwen3/3.5 models (8B-32B) show that our method yields gains of up to +11.9% on Claw-style benchmarks and +8.0% on general tool-use benchmarks, with concurrent reductions in inference token cost. The results confirm that synthesized executable environments provide robust and generalizable learning signals for training.
[NLP-225] Grounded Skill Synthesis from Code at Scale for Agent ic Intelligence
【速读】： 该论文旨在解决智能体在缺乏充分交互经验时，难以获取可迁移、可验证的程序性知识（procedural knowledge）的问题。现有方法存在两大局限：基于轨迹的技能合成依赖特定环境交互，而文档生成的技能往往缺乏可执行证据与验证机制。本文提出Code2Skill，其核心解决方案是利用源代码作为互补路径——无需依赖代理的历史交互，却能提供可执行的证据以锚定抽象概念。该方案的关键在于构建一个全自动流水线，将选定的代码单元转化为包含原子操作、复合工作流及重复模式的实现锚定记录，并通过“源码体盲重建”与“源码感知比对”双重验证机制确保记录的准确性。该方法应用于19,769个活跃维护的GitHub仓库，生成了包含100万余条经验证记录的CodeSkillBank，涵盖工作流、边界、溯源与源码证据等元数据。在72项协议匹配评估中，引入CodeSkillBank的模型平均性能提升11.7%，并在57次测试中超越基线；在统一下游接口下，其表现优于基于轨迹学习的技能库，在全部七项共享基准上取得优势，证明了仓库衍生技能可在代理积累足够交互经验前即提供有效程序性知识。此外，由经过测试的AI生成代码合成的技能达到93.50%的通过率，略高于人类编写代码的93.00%，初步表明该流水线具备随AI生成软件规模增长而扩展的能力。总体而言，Code2Skill成功将代码仓库中隐含的程序性知识转化为可验证、可迁移的智能体技能。
链接: https://arxiv.org/abs/2609.05571
作者: Yongqi Tong,Pan Wang,Hang Wang,Jianshe Li,Xin Zhang,Jiang-Ming Yang,Wei Wu
机构: 未知
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL)
备注:
Abstract:Reusable skills give agents transferable procedural knowledge, making scalable acquisition essential for extending agents beyond prior experience. Existing methods face two limitations: trajectory-based synthesis requires interactions with specific environments, while document-derived skills may lack executable evidence and verification. Source code offers a complementary path: it requires no prior agent experience yet provides executable evidence for grounding abstractions. We present Code2Skill, a fully automated pipeline that transforms selected code units into implementation-anchored records of atomic operations, composite workflows, and recurring patterns, then verifies each record through source-body-blind reconstruction and source-aware comparison. Applied to 19,769 popular, actively maintained GitHub repositories, Code2Skill produces CodeSkillBank, a grounded bank of 1,006,822 accepted records with workflow, boundary, provenance, and source-evidence metadata. Across 72 protocol-matched evaluations covering nine model settings and eight benchmarks, models augmented with retrieved CodeSkillBank skills improve by 11.7% on average over matched baselines and outperform them in 57 cases. Under a unified downstream interface, Code2Skill also outperforms trajectory-derived skill banks on all seven shared benchmarks, showing that repository-derived skills can provide useful procedural knowledge before agents accumulate sufficient interaction experience. Skills synthesized from tested AI-generated code achieve a 93.50% pass rate, compared with 93.00% for human-written code, providing initial evidence that the pipeline can expand with the growing volume of AI-generated software. Overall, Code2Skill transforms procedural knowledge embedded in repositories into grounded, verifiable, and transferable agent skills.
[NLP-226] Emergent Goal-Directed Attention in Large Vision-Language Models
【速读】： 该论文旨在解决的问题是：在缺乏眼动监督（gaze supervision）的情况下，通用视觉-语言模型（Vision-Language Models, VLMs）是否能够自发产生符合人类目标导向注意（goal-directed attention）的空间关注优先级。其解决方案的关键在于，通过在自然场景下对两个现成的通用VLM（Qwen3-VL-32B-Thinking 和 Gemma-4-26B-A4B-it）进行视觉搜索与自由浏览任务测试，发现这些模型在任务目标匹配时能更准确地预测人类注视点，且该对齐现象在目标不存在场景中依然存在，排除了仅由视觉基底（visual grounding）解释的可能性。进一步分析显示，模型在解码层读出中表现出目标语义（target semantics）和视觉显著性（visual prominence）的双重表征，表明其注意力机制可内生地根据任务目标动态调整空间优先级。这一结果揭示了通用VLM具备无需特定眼动训练即可生成人类一致、任务驱动的空间关注模式的能力，为理解目标导向注意的计算机制提供了新证据，并为跨任务的人类注视预测提供了可扩展的工具。
链接: https://arxiv.org/abs/2609.05517
作者: Han Zhang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Human observers prioritize visual information according to task goals. Most computational models of naturalistic viewing are gaze-trained for free viewing, leaving open whether goal-directed attention can emerge in systems without gaze supervision. We tested two off-the-shelf vision-language models (VLMs), Qwen3-VL-32B-Thinking and Gemma-4-26B-A4B-it, on 4,887 naturalistic scenes under visual-search and free-viewing instructions. Model predictions were compared with human fixations on the same images under corresponding tasks. Both models aligned more closely with human fixations under matching goals than under mismatched goals. This crossover persisted in target-absent scenes, where alignment could not be explained by simple visual grounding, and appeared in decoder-layer readouts. Furthermore, model-thinking traces were grounded in target semantics during search and in visual prominence during free viewing. These findings show that general-purpose VLMs can generate human-aligned, goal-directed spatial priorities without gaze-specific training, informing theories of goal-directed attention and offering scalable tools for predicting where people look across tasks.
[NLP-227] Reasoning -Aware Compression: Identifying and Protecting Vulnerable Reasoning Circuits for Energy-Efficient LLM Deployment
【速读】： 该论文旨在解决大推理模型（Large Reasoning Models, LRMs）在部署阶段面临的高能耗问题，现有压缩方法普遍采用全局统一的量化策略，可能导致关键推理模块性能受损。其核心解决方案是提出一种面向推理任务的感知压缩框架，通过在五个推理基准（GSM8K、FOLIO、MATH-500、ProofWriter、MuSiQue）上进行硬件级GPU能效测量，系统评估不同模块在INT4量化下的脆弱性，基于对196–224个（层，投影）组合的扰动扫描，在保留校准集上识别出最敏感的推理电路，并将其选择性恢复至FP16精度。关键发现包括：首先，INT4量化可能因延长推理链而增加总能耗，导致在GSM8K上原本25%的功耗降低转为净能耗上升；其次，量化脆弱性具有任务依赖性，数学推理中注意力投影更为关键，且不同架构在逻辑推理中的敏感模式存在差异；第三，该选择性压缩策略可实现均匀量化无法达到的帕累托最优——以R1-Qwen-7B为例，在ProofWriter Top-10%指标上相较FP16提升12个百分点的同时降低9.7%能耗，且在跨五项推理基准的独立测试集上得到验证。
链接: https://arxiv.org/abs/2609.05512
作者: Leonard Twagirayezu,Prasenjit Mitra
机构: Carnegie Mellon University Africa(卡内基梅隆大学非洲校区); Kigali, Rwanda(卢旺达基加利)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Performance (cs.PF)
备注:
Abstract:Large Reasoning Models (LRMs) impose substantial energy costs during deployment, yet current compression methods apply uniform quantization across all components, risking damage to critical reasoning circuits. We present a reasoning-aware compression framework that benchmarks quantization conditions across five reasoning benchmarks, GSM8K, FOLIO, MATH-500, ProofWriter, and MuSiQue, with hardware-level GPU energy measurement; profiles per-module INT4 vulnerability across all 196-224 (layer, projection) pairs via a perturbation sweep on a held-out calibration split, then selectively restores the most sensitive circuits to FP16. Three findings emerge. First, INT4 quantization can increase energy by extending reasoning chains; a 25% power reduction becomes a net energy increase on GSM8K. Second, vulnerability is task-dependent: attention projections are more critical for mathematical reasoning, and sensitivity patterns differ by architecture in logical inference. Third, selective compression achieves Pareto-optimal points inaccessible to uniform methods: R1-Qwen-7B Top-10% on ProofWriter gains +12 pp over FP16 at -9.7% energy, validated on held-out data across five reasoning benchmarks.
[NLP-228] SCAFFOLD: Self-Improving Web Agents via Recursive Parametric Skill Abstraction EMNLP2026
【速读】： 该论文旨在解决视觉网页代理（visual web agent）在面对跨站点、动态变化的长周期复杂界面时，缺乏持续学习与知识积累能力的问题。现有方法通常将任务孤立学习，且在任务完成后丢弃过程性知识，导致无法有效复用和演化技能。其核心挑战在于：如何构建一个可自我改进的技能体系，实现技能的层次化组织、冗余压缩与递归组合，并将抽象知识内化至模型参数中以避免性能退化。本文提出的Scaffold框架的关键解决方案包括：（i）通过多实例抽象约束从成功轨迹中提取可执行的参数化技能；（ii）建立递归构成的技能层级结构，支持高层技能调用低层技能；（iii）基于最小描述长度（MDL）准则与行为等价性检验对技能库进行压缩；（iv）定期将增强型技能轨迹回蒸馏至模型权重，实现抽象知识的内部化。实验表明，Scaffold在WebArena、VisualWebArena及Online-Mind2Web的独立测试集上相较最强基线提升11.1–17.2个百分点的成功率，并在五轮自迭代中保持单调增长，未出现技能库坍缩现象。
链接: https://arxiv.org/abs/2609.05511
作者: Bowei He,Xiaokun Zhang,Meng Ding,Xue Liu
机构: MBZUAI(中东人工智能大学); McGill University (麦吉尔大学); CityUHK(香港城市大学); UMass Boston(波士顿大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted by EMNLP 2026
Abstract:Web agents need to navigate visually rich, long-horizon interfaces that change across sites, yet most previous agents still learn each task in isolation and discard the procedural knowledge they accumulate. Recent skill-augmented frameworks take an important first step, but they treat the skill library as a flat or two-tier prompt-side cache and offer no principled mechanism for compressing redundancy or composing skills recursively. We introduce \textscScaffold, a self-improving framework for visual web agents that (i) induces parametric, executable skills from successful trajectories under a multi-instance abstraction constraint, (ii) maintains a recursively composed hierarchy in which higher-level skills invoke lower-level ones, (iii) compacts the library via a minimum-description-length (MDL) criterion and behavioral equivalence checking, and (iv) periodically distills skill-augmented trajectories back into model weights to internalize the abstractions. Across WebArena, VisualWebArena, and a held-out split of Online-Mind2Web, \textscScaffold improves success rate by 11.1 – 17.2 absolute points over the strongest skill-augmented baseline and shows monotonic gains across five self-improvement iterations without library collapse. We release the code and documents in the Github \hrefthis https URLrepository.
[NLP-229] AhaBench: Do Agents Learn from Prior Experience? A Benchmark for Long-Horizon Continual Learning
【速读】： 该论文旨在解决当前大语言模型代理（Language Agents）在长时程任务中评估不足的问题，即现有评测方法多在单次交互后重置模型或仅关注单一轨迹的最终结果，无法有效衡量模型在持续学习与经验积累下的行为改进能力。其核心挑战在于：当模型在特定条件下获得有效经验后，在后续相关任务中若移除显性支持、改变支持形式或延迟反馈时，其表现是否真正提升。为此，论文提出AhaBench基准测试体系，通过三个组件实现对模型长期适应能力的系统评估：Aha-Puzzle考察无提示探索能力的形成；Aha-Euler将数学问题转化为可验证的教学/保留任务，以检验知识迁移与泛化；Aha-Vending则模拟自动售货机代理在延迟反馈和运营事件下的盈利能力维持能力。该框架采用三部分评分卡机制——初始得分（Initial Score）、经验后得分（Post-Experience Score）及学习增益（Learning Lift），从而揭示模型在利用外部支持、达到后期绩效以及实际学习能力之间存在的非一致性。实验表明，尽管多数模型在有支持时表现优异，但真正具备持续学习能力的模型（如Claude Opus 4.6在综合学习增益上达+25.8）往往并非最擅长依赖显式支持者。关键解决方案在于引入“经验驱动的行为演化”这一操作性评价范式，并通过可复现的仿真环境与精确验证器实现对模型长期适应性的量化分析。
链接: https://arxiv.org/abs/2609.05435
作者: Zerui Cheng,Jiawei Xu,Huacan Chai,Jiayang Sun,Pramod Viswanath,Maxm Pan
机构: Tencent Hy(腾讯); Tsinghua University (清华大学); Shanghai Jiaotong University (上海交通大学); Hong Kong University (香港大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 37 pages; In submission to TMLR
Abstract:Modern language agents are expected to operate over long horizons: they ask follow-up questions, reuse worked examples, handle tool feedback, and adapt to delayed consequences. Most evaluations still reset the agent after a prompt or score only the final state of one trajectory. AhaBench asks a more operational question: when a fixed model receives useful experience, does its later behavior improve under a related evaluation condition where the obvious support has been removed, changed, or delayed? The suite contains three components. Aha-Puzzle tests no-hint exploration after solved hidden-state puzzles; Aha-Euler turns Project-Euler-style mathematical ideas into generated taught/held-out tasks with exact validators; and Aha-Vending, an open-source implementation inspired by Vending-Bench, tests whether a simulated vending agent remains profitable while handling delayed feedback and operational incidents. AhaBench reports a three-part scorecard: Initial Score measures starting competence, Post-Experience Score measures the later empirical outcome, and Learning Lift is their difference. This decomposition is the main empirical message: models that use visible support well, models that reach high post-experience scores, and models that improve most during a run are not always the same. On the common eight-model panel, Claude Opus 4.6 leads aggregate Post-Experience Score at 64.3 and aggregate Learning Lift at +25.8, with Gemini 3.1 Pro close behind at 63.4. The component results explain the split: puzzle traces raise supported scores but often fail to become no-hint exploration behavior; Aha-Euler full teaching reaches 78.6-100.0% while answer-only transfer ranges from 0.0 to 73.9%; and Aha-Vending separates profitable incident handling from bankruptcy and no-order failure. We release benchmark tasks, rubrics, validators, simulator code, and interfaces for evaluating new agents.
[NLP-230] A Closed-Form Estimator and Diagnostic Battery for Anchor-Judge Error Correlation Under a Single-Common-Factor Model
【速读】： 该论文旨在解决大语言模型（LLM）评判小组在评估任务中因共享共模误差（common-mode error）导致的判断偏差问题，尤其关注当传统假设——即外部参考集（锚点）无污染、其错误与评判组共享误差不相关——不成立时，如何准确估计评判质量与锚点污染程度。其核心解决方案在于提出一个基于单共因子模型（single-common-factor model）的可识别框架，在仅需2名评判者和2个锚点的情况下，能够闭式求解评判质量方差、共模误差方差以及每个锚点的污染相关系数ρ_k。关键突破在于引入了一种受校准诊断电池（calibrated diagnostic battery）保护的“指定清洁锚点估计器”（designated clean-anchor estimator），该估计器通过多重检验机制（包括评判者协方差离散度、过度识别检验、基于评判者元数据的族块检验，以及族块阻断估计器以精确消除家族层级共享残差偏误）来识别并规避不可靠估计。此外，研究揭示了在有序评分场景下的识别层级：当所有变量均为有序时，ρ_k无法被识别；而当评判者为有序、锚点为连续且数量≥3时，ρ_k可被识别，并提供了相应估计方法。实证结果表明，诊断体系在拒绝方向上得到验证（两个真实评判面板均被模型适配性预检正确拒绝），而估计器则通过模拟与半合成压力测试在“真值校准”下表现稳健；目前尚无真实面板通过预检，而该预检的存在正意在明确警示此类数据的不可靠性。所有结论均可通过已签核、可重现的离线数据包复现。
链接: https://arxiv.org/abs/2609.08826
作者: Veerendra Kumar Sunkavalli
机构: Independent Researcher(独立研究员)
类目: Methodology (stat.ME); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 11 pages. The complete reproduction artifact (code, frozen data, one-command replay) accompanies the paper’s journal submission as supplementary material, currently under peer review; a public repository link will be added upon publication
Abstract:When an external reference set (an anchor) is used to decompose an LLM-judge panel’s error into a quality signal and a shared common-mode error, standard practice assumes the anchor is uncontaminated: its error uncorrelated with the judges’ shared error. We study when that assumption can be dropped and replaced by an estimate. Under a single-common-factor model, =2 judges and =2 anchors point-identify the quality variance, the common-mode variance, and each anchor’s contamination correlation rho_k in closed form, with an exact per-anchor-pair failure boundary; a designated clean-anchor estimator, by contrast, reports a contaminated companion anchor as fully clean once its trusted anchor is itself contaminated. Because the single-common-factor assumption is itself untestable, the estimator ships gated behind a calibrated diagnostic battery (judge-covariance dispersion; over-identification; a family-block test from judge metadata, with a family-blocked estimator that removes family-level shared-residual bias exactly), bootstrap confidence intervals with measured coverage, and a weak-identification screen. A proposition maps which violations bias rho_k, in which direction, and which evade detection. For ordinal scores we show an identification hierarchy: with all variables ordinal, rho_k is not identified at any number of anchors; with ordinal judges and =3 continuous anchors it is, and we give an estimator for that case. On real data the validation is asymmetric, and we say so plainly: the diagnostics are validated in the rejecting direction (both real panels we test are correctly rejected by the model-adequacy pre-test), while the estimator is validated in simulation and stress-tested semi-synthetically under oracle calibration; no real panel has yet passed the pre-test, and the pre-test exists precisely to say so. All results replay offline from shipped, checksummed artifacts.
[NLP-231] he Rater Ising-Potts Model with LLM -Derived Weights: An Application to Multi-Category Scoring Reliability
【速读】： 该论文旨在解决多类别评分中评分者间信度评估的挑战，尤其针对非等距、无序类别阈值的复杂评分场景。传统方法常依赖于对评分尺度的刚性假设（如等距打分或有序阈值），但在实际教育评估中，评分者往往基于语义相似性进行判断，且评分标准可能具有非线性结构。为此，论文提出一种基于生成式 AI 的评分者-伊辛-波茨模型（Rater Ising-Potts model），其核心创新在于利用大语言模型（LLM）嵌入生成的语义相似性作为权重，构建基于评分者对之间一致性指标的统计模型。该模型不预设类别间的等距或顺序关系，而是直接建模评分者间在类别标签上的成对一致行为，并为不同类别分配特定的正权重，从而更真实地反映评分过程中的语义关联。关键在于通过可调节的相似性归一化与可选幂变换实现灵活的语义区分增强，有效保留评分量表的序数结构，同时避免人为强加的参数约束。实验结果表明，该模型在多种构造反应任务（包括平衡短答与不平衡作文题）上均表现出与人工评分高度一致的性能，绝大多数误判集中于相邻分数层级，验证了其对评分指南序数结构的良好保持能力。研究表明，结合LLM驱动的语义相似性、简约的波茨型建模框架及灵活的相似性缩放机制，为教育评估中的信度审计提供了一个鲁棒且可解释的新范式。
链接: https://arxiv.org/abs/2609.08797
作者: Matthias von Davier
机构: 未知
类目: Applications (stat.AP); Computation and Language (cs.CL)
备注:
Abstract:The Ising model is extended to the Potts model for multinomial data. We introduce a Rater Ising-Potts model that uses agreement indicators between pairs of raters and category labels, with weights derived from LLM embeddings. The model does not presuppose ordered category thresholds or equidistant scoring; instead, it focuses directly on pairwise agreement among raters and assigns category-specific positive weights, making it particularly suited for multi-category scoring reliability when raters evaluate responses using a scoring guide. We demonstrate the model’s effectiveness on diverse constructed-response tasks, including balanced short-answer items and more challenging, imbalanced essay prompts from the AERA dataset. Across these settings, the model achieves strong agreement with human scores, with the vast majority of misclassifications occurring between adjacent score levels, confirming its ability to preserve the ordinal structure of scoring rubrics without imposing rigid assumptions. A practical similarity normalization and optional power transformation is introduced as a tunable preprocessing step that sharpens semantic distinctions and can be adapted to different datasets. These findings suggest that LLM-derived semantic similarities, combined with this parsimonious Potts-type formulation and flexible similarity scaling, offer a robust and interpretable framework for reliability auditing in educational assessment contexts. Extensions to multiple raters and hierarchical rating processes are discussed.
信息检索
[IR-0] Evaluating and Improving Evidence-Grounded Fact-Checking in LLM s via Multi-Round Evidence Ablation CIKM’26
链接: https://arxiv.org/abs/2609.08943
作者: Xingyu Deng,Mingzi Cao,Nikolaos Aletras,Xi Wang,Mark Stevenson
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: CIKM’26
Abstract:Automatic fact-checking systems assess the veracity of claims given evidence from relevant documents. Large Language Models (LLMs) have demonstrated strong performance in fact-checking due to their general reasoning capabilities. However, it remains unclear whether they faithfully make use of the evidence provided to reach veracity judgments or rely on parametric knowledge. To investigate this, we introduce Fact-Ablated Evaluation (FAE), a new evaluation framework that iteratively ablates the cited evidence to assess whether LLMs revise their predictions accordingly. Our empirical results show that current off-the-shelf LLMs as fact-checking systems rely more on their parametric knowledge than on the evidence provided. To bridge this gap between prediction accuracy and evidence grounding, we propose REAL (Rigorous Evidence Ablation Learning), a training framework that promotes evidence-dependent verification through counterfactual evidence supervision for the LLM-as-verifier models. Experiments on four fact-checking datasets across different domains demonstrate that models trained with REAL obtain superior evidence-dependent capabilities compared to standard fine-tuned models. Our findings highlight that strong fact-checking performance can still coexist with weak evidence dependency, while REAL encourages veracity predictions to remain more closely tied to the availability of supporting evidence.
[IR-1] FINALLY: A Dataset Recommender System for Recommender-Systems Research
链接: https://arxiv.org/abs/2609.08941
作者: Louis Owie
类目: Information Retrieval (cs.IR)
备注: Bachelor’s thesis, University of Siegen, 2026
Abstract:Dataset selection shapes the empirical conditions under which recommender-system algorithms are evaluated, yet existing tools provide limited support for constructing complete dataset sets that jointly satisfy experimental constraints and set-level selection objectives. To address this problem, I developed FINALLY, a web-based dataset recommender for constructing configurable dataset sets for offline recommender-systems evaluations. FINALLY combines required datasets, candidate-pool restrictions, metadata filters, configurable target-set sizes, Random selection, and diverse and non-diverse strategies based on adapted Effective Covariance and Convex Hull objectives. I evaluated FINALLY through 420 recommendation runs across ten systematically varied configurations. All evaluated dataset sets satisfied the applicable target-size, duplicate-avoidance, snapshot-membership, required-dataset, and metadata-filter requirements. All 40 deterministic strategy–configuration combinations were reproducible. Both the Effective-Covariance-based and Convex-Hull-based strategies produced the expected diverse-versus-non-diverse score ordering in all ten configurations. Under their corresponding objectives, the diverse strategies produced scores above all 30 configuration-specific Random results, whereas the non-diverse strategies produced scores below all 30 Random results. These results establish technical consistency for the evaluated FINALLY workflow and show that the implemented strategies follow their intended optimization directions within the investigated configuration space. They do not establish the scientific suitability, global optimality, or practical superiority of the generated selections.
[IR-2] Q2D-Web: A Large-Scale Benchmark for Retrieval in Agent ic RAG Systems
链接: https://arxiv.org/abs/2609.08887
作者: Maximilian Schall,Sedigheh Eslami,Markus Krimmel,Antoine Chaffin,Louis Milliken,Bo Wang,Denis Bykov
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注:
Abstract:Evaluating first-stage retrievers in large-scale production RAG requires a benchmark that pairs a large-scale corpus with a large set of agent-reformulated search queries based on real user queries and their conversation threads, and that labels many relevant documents per query. No existing public benchmark evaluates this setting: large-scale collections typically provide only a small number of evaluation queries, whereas benchmarks with many queries generally contain only millions of documents. Moreover, most benchmarks assess human-written queries, while the first-stage retrievers in agentic RAG pipelines serve machine-written reformulations whose distribution differs from human search behavior. To overcome these evaluation gaps, we introduce Q2D-Web (Query2Doc-Web), a large-scale agentic retrieval benchmark consisting of a 190M-document web corpus and 70k agentic search queries in ten languages, reformulated from real-world user queries in production systems. Q2D-Web provides three sets of fixed relevance judgments: agent citations, production rankings, and a combined set that unions both signals and adds LLM-based judgments of unlabeled pooled documents to reduce false negatives. We benchmark 13 retrievers including lexical, dense, and late-interaction models and find that their relative ordering is largely insensitive to the choice of judgment set, while diverging substantially across topical domains, query languages, and query types. To enable fast evaluation, we also study subcorpus sampling as an approximation to full-corpus evaluations. Retaining a third of the corpus, selected by reciprocal rank fusion over pooled retriever runs, preserves the full-corpus model ranking under the combined judgments while raising absolute Recall@1000 only by 3 to 7 points. The public leaderboard is accessible under: this https URL
[IR-3] REDSI: Addressing the Reproducibility and Evaluation Consistency of Differentiable Search Indexing for Document Retrieval
链接: https://arxiv.org/abs/2609.08860
作者: Vivien Nicolas,Hicham Randrianarivo,Pascale Sébillot,Caio Corro
类目: Information Retrieval (cs.IR)
备注:
Abstract:The differentiable search index (DSI) framework (Tay et al., 2022) has become the de facto baseline for generative retrieval. However, DSI is hard to reproduce: no public implementation covers all three original document identifier types (atomic, naive, semantic), reported results vary widely, and the ubiquitous NQ320K dataset is built from Natural Questions through diverse and underspecified preprocessing. We introduce ReDSI, the first open-source DSI implementation supporting all three identifier types, together with a parameterizable and well-documented NQ320K construction pipeline. Experimentally, we achieve results that are competitive with or stronger than previous DSI baselines. Moreover, we conduct extensive experiments under model downscaling, covering retrieval effectiveness, parameter efficiency, training methods and decoding strategies, opening novel directions for future research. Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2609.08860 [cs.IR] (or arXiv:2609.08860v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2609.08860 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-4] PDMR: Passage-Driven Multi-ID Document Retrieval
链接: https://arxiv.org/abs/2609.08762
作者: Smail Oussaidene,Mohand Boughanem
类目: Information Retrieval (cs.IR)
备注:
Abstract:Generative Retrieval (GR) models map queries directly to document identifiers, replacing conventional retrieval over external sparse or dense indexes with autoregressive identifier generation. However, most generative retrieval frameworks rely on a single-identifier assumption, mapping each document to a single target sequence. This forces the model to represent all document content with one sequence. Since documents are often multi-faceted, this can lead to lossy representations and reduced robustness to query variation, where multiple query intents must compete for a single generative access path. In this work, we introduce Passage-Driven Multi-ID Retrieval (PDMR), a generative retrieval framework that represents documents through multiple passage-level identifiers. PDMR segments each document and assigns one identifier to each selected passage, which provides multiple semantic entry points for retrieving the same document. This multi-entry representation allows the model to align queries with specific semantic facets, thereby reducing the dependence on a single document-level target. To address the supervision ambiguity of this one-to-many mapping, we formulate training as a multi-target learning problem and explore an objective function designed to distribute probability mass across multiple valid passage-level identifiers. We evaluate PDMR on NQ320K and MS MARCO Document. On NQ320K, PDMR improves over strong generative and non-generative baselines on Recall@1 and MRR@100. On MS MARCO Document, PDMR achieves the best Recall@1 and MRR@10 among the reported methods, while remaining competitive on Recall@10. Controlled ablations further show that passage-level supervision, identifier design, training-query augmentation, and multi-target learning contribute complementary gains. Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2609.08762 [cs.IR] (or arXiv:2609.08762v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2609.08762 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-5] Individual Text Corpora Predict User-Specific Knowledge: Benchmarks of Individualized Knowledge Simulation
链接: https://arxiv.org/abs/2609.08532
作者: Christoph Wigbels,Ali Abusaleh,Markus T. Jansen,Alexander Mehler,Manuel Schaaf,Markus J. Hofmann
类目: Information Retrieval (cs.IR)
备注: Proceedings of the 22nd Conference on Natural Language Processing (KONVENS 2026)
Abstract:This study examines whether individual text corpora (ICs) from search histories can be used to simulate individual knowledge. We collected ICs from 316 adults, who answered 36 multiple-choice knowledge items, and compared several large language models (LLMs) on this task, of which only Qwen3-1.7B proved viable. After task-specific fine-tuning via Low-Rank Adaptation (LoRA), Qwen3-1.7B outperformed both participants and a representative German norm sample on publicly available items. On non-public questions, however, the LLM performed worse than our participants, suggesting possible training data contamination for the public questions. When integrating ICs into retrieval-augmented generation to predict individual responses, LLM-participant Match accuracies significantly exceeded chance, which demonstrates a detectable individual knowledge signal. The probabilities assigned to the participants’ answers were, however, low and far below the probability of correct answers, indicating poor calibration toward individual response patterns. Knowledge-gap prediction was sub-optimal, though it improved for corpora exceeding five million tokens. We discuss our entropy based evaluation benchmarks as calibration indices for individualized knowledge simulation.
[IR-6] SequenceO1: End-to-End Ultra-Long (100K) Sequence Modeling in Recommendation with Low-Rank Caching RECSYS’26
链接: https://arxiv.org/abs/2609.08443
作者: Lin Guan,Jia-Qi Yang,Zhishan Zhao,Jiaqi Huang,Hangyu Wang,Longbin Li,Beichuan Zhang,Haonan Jiang,Jinan Ni,Xiangyu Fan,Xiaowen Li,Ziyao Ren,Yuhang Qi,Xiaolong Zhu,Xuanyuan Luo,Qiwei Chen,Yi Cheng,Lele Yu
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: RecSys’26 Industry Track, accepted as a long oral presentation. Production deployment on Douyin. Topics: industrial recommender systems, sequential recommendation, ultra-long user behavior sequence modeling, long-term user modeling, end-to-end ranking, CTR prediction, efficient attention, sequence compression, user representation caching, and large-scale recommendation systems
Abstract:Modeling long-term user behavior is central to sequential recommendation and billion-scale industrial recommender systems, yet production ranking models operate under strict latency, memory, communication, and training-throughput constraints. At the 100K scale, the challenge extends beyond attention complexity: raw sequence features must be stored, transferred, and repeatedly processed during training and online serving. Existing approaches based on history truncation, multi-stage behavior retrieval, compressed lifelong histories, or train-short/infer-long extrapolation either weaken end-to-end optimization or retain substantial length-dependent cost. We present SequenceO1, an end-to-end framework for ultra-long user behavior sequence modeling, deployed at full traffic on Douyin with histories of up to 100K interactions. SequenceO1 follows a compress-then-reason design. Its Sketch Attention (SA) uses learnable prototypes and prototype-wise normalization to compress the raw history into a fixed-size, target-agnostic user representation. Target-conditioned Stacked Target-to-History Cross Attention (STCA) then models complementary time scales: a recent 10K suffix for short-term interests and the compact sketch for long-term preferences. To make training and inference practical, SequenceO1 combines low-rank user representation caching, multi-request user-level batching, pipeline lift, and a fused FlashSA kernel to amortize feature storage, communication, and computation across targets, training instances, and consecutive requests. Production experiments show consistent offline and online gains, while the compact cached sketch retains most of the benefit of directly scaling end-to-end sequence ranking to 100K. These results provide a practical model-system approach to efficient attention, sequence compression, and scalable long-sequence and long-context recommendation systems.
[IR-7] ool Retrievers Are Underestimated: Annotation Expansion Reveals True Capability
链接: https://arxiv.org/abs/2609.08327
作者: Yanyu Zhu,Chenheng Zhang,Shaoshen Chen,Hoilam Pao,Yufei zhang,Jiajun Chai,Dongnian Wang,Zhaoyu Hu,Guojun Yin,Wei Lin,Hai-Tao Zheng
类目: oftware Engineering (cs.SE); Information Retrieval (cs.IR)
备注:
Abstract:In open-world scenarios with massive and evolving tool repositories, tool-augmented large language models rely on a retriever to surface relevant tools for a given query. Because such repositories often contain many tools that implement the same functionality, a single query can often be resolved by several distinct but functionally equivalent tool combinations, making the natural query-to-tool mapping inherently one-to-many. However, existing tool retrieval benchmarks annotate each query with a single relevant tool combination, collapsing this one-to-many mapping into a rigid one-to-one annotation and causing valid retrieved tools to be misjudged as failures. To address this, we propose ToolEX (Tool Equivalent eXpansion), a framework that automatically discovers and annotates the tool combinations functionally equivalent to the labeled ones. Applied to the 7,360-query Tool-DE benchmark, ToolEX finds that 67.9% of sub-queries admit equivalent alternatives, expanding the singular ground truth to an average of 5.3 valid combinations per query. Using the expanded benchmark ToolEQ, we re-evaluate eight base retrievers and two fine-tuned variants; metrics on ToolEQ rise substantially over Tool-DE, showing that one-to-one annotation systematically underestimates retrievers and that 30–47% of the reported fine-tuning gain is an evaluation artifact rather than genuine improvement. Applying the same pipeline to skill retrieval on SkillRet further confirms that the one-to-one problem extends beyond tool retrieval.
[IR-8] Exploring Bottom-Up Clustering for Creating Semantic IDs
链接: https://arxiv.org/abs/2609.08310
作者: Leah Woldemariam,Sudhanshu Garg,Taha Belkhouja,Charles Kim-Yip,Ali Sahami
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: 6 Pages, workshop Paper
Abstract:The success of generative retrieval has largely been attributed to the use of Semantic IDs, which improve over arbitrary item-level identifiers such as hashes by capturing the semantics of items. The main challenges faced when constructing Semantic IDs, however, is in mapping each identifier to a unique product and capturing information valuable to downstream tasks. Past works have appended additional codewords to de-duplicate item identifiers and utilized residual quantization to create hierarchical clusters. In this work, we present an algorithm for generating Semantic IDs that ensure the identifiers are both unique and preserve the structure of the original embedding. Key to our work is the use of bottom-up clustering to preserve local structure in the embedding space, improving the clustering quality of the resulting Semantic IDs and their utility for downstream generative retrieval.
[IR-9] Evidence-Aligned Entity Verification for Hallucination Detection in Retrieval-Augmented Generation ACL2026
链接: https://arxiv.org/abs/2609.08267
作者: Runsong Jia,Zhen Fang,Mengjia Wu,Jie Lu,Yi Zhang
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: ACL 2026
Abstract:Hallucination detection is crucial for large language models (LLMs), as hallucinated content creates significant barriers in applications requiring factual accuracy. Current detection methods mainly depend on internal signals like uncertainty and self-consistency checks, using the model’s pre-trained knowledge to identify unreliable outputs. However, pre-trained knowledge may become outdated and has coverage limitations, especially for specialized or recent information. To address these limitations, retrieval-augmented generation (RAG) has emerged as a promising solution by retrieving relevant evidence at inference time, grounding outputs beyond the model’s parametric knowledge. In this paper, we target a critical and practical learning problem RAG-based hallucination detection (RHD), where RAG is employed to enhance hallucination detection by addressing information updating challenges. To address RHD, we propose a novel method Evidence-Aligned Entity Verification (EAEV), which detects entity-level hallucinations by leveraging RAG to align generated entities with retrieved evidence contexts. Specifically, EAEV evaluates entity-evidence alignment through three complementary dimensions and introduces counterfactual stability analysis to ensure robust alignments under evidence perturbations. Experiments across multiple RAG benchmarks demonstrate that EAEV achieves consistent improvements over existing methods with strong generalization capabilities.
[IR-10] Bridging the Semantic-Utility Gap in Multimodal RAG via Generator-in-the-Loop Alignment
链接: https://arxiv.org/abs/2609.08188
作者: Zhan-Lun Chang,Dong-Jun Han,Seyyedali Hosseinalipour,Mung Chiang,Christopher G. Brinton
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: Submitted to IEEE Transactions on Artificial Intelligence
Abstract:Vision-language models (VLMs) augmented with retrieval-augmented generation (RAG) benefit from access to external evidence. However, standard retrievers and rerankers optimize for semantic similarity rather than answer utility, creating a preference gap: documents that appear relevant may not help the generator produce a correct answer. Motivated by this, we propose a two-stage generator-in-the-loop alignment framework that closes this gap without human document-level relevance annotations. Our framework consists of two stages: in Stage 1, a VLM generates a hypothetical text passage from the image-query pair, which is used as the retrieval query for dense text search, bridging the image-to-text modality gap. In Stage 2, a cross-encoder reranker adapted with low-rank adaptation (LoRA) is fine-tuned using answer-supervised preference pairs mined from the frozen VLM: given the dataset answer label, a candidate document is labeled positive if the VLM produces the correct answer when given that document as context, and negative otherwise. This generator-guided signal is compatible with multiple alignment loss functions, including contrastive (triplet) loss, pairwise direct preference optimization (DPO), and supervised fine-tuning (SFT), and supports periodic re-mining to refresh preference pairs as the reranker improves. Experiments on VQA-X and A-OKVQA with Qwen3.5-2B and Qwen3-VL-4B-Instruct show that our proposed framework consistently outperforms rank-order, random, and REPLUG-style likelihood baselines under various alignment losses and pool size settings, suggesting that answer-level generator feedback is an effective supervision signal for preference alignment.
[IR-11] Cassette: Case-to-Case Structural Distillation for Efficient Legal Case Retrieval
链接: https://arxiv.org/abs/2609.08185
作者: Yanran Tang,Ruihong Qiu,Hongzhi Yin,Xue Li,Zi Huang
类目: Information Retrieval (cs.IR)
备注:
Abstract:Legal case retrieval (LCR) is an essential tool for not only assisting legal practitioners to efficiently retrieve precedents but also enabling ordinary individuals to find valuable legal case information without relying on expensive professional legal services. Our previous work CaseLink demonstrated the effectiveness of using case to case graph structures to improve retrieval accuracy. However, its high computational cost during inference on large-scale legal databases limits its practical use in real-world settings. The main inefficiency comes from constructing test time graphs and computing pairwise term frequency similarities of cases. This process has O(n^2) complexity for n legal cases, making the runtime prohibitive as the number of candidates grows. For example, the retrieval time for one query on a database (COLIEE2022) with 1,563 candidate cases is more than 500 milliseconds, while the runtime would increase drastically to more than 3,500 seconds for a database (LeCaRDv2) with 55,192 candidate cases. To further enhance the retrieval performance while achieving a significant speed-up, in this extension paper, Cassette framework is proposed with a distillation strategy involving ranking objective and eigen-matching objective for an effective transfer of knowledge from a powerful and well-trained heavy teacher retriever to a lightweight and efficient hybrid student dual encoder. Specifically, the student query encoder is implemented as a multilayer perceptron model designed for fast online processing, whereas the student candidate encoder adopts a GNN architecture, suitable for an offline manner within the case database. Extensive experiments are conducted on three benchmark datasets, and the results verify the effectiveness of the ranking distillation while achieving high efficiency. The code has been released on this https URL.
[IR-12] Snugi-AI-v2 @ eRisk 2026 Task 2: Early Depression Detection via a Learned Stopping Policy with Sustained Confidence Gate
链接: https://arxiv.org/abs/2609.08161
作者: Yuwen Chiu(Georgia Institute of Technology)
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: 11 pages, 4 figures, 7 tables. Working notes paper for CLEF 2026 eRisk Lab Task 2 (Early Depression Detection). Published in CLEF 2026 Working Notes, this http URL
Abstract:We describe the Snugi-AI-v2 submission to eRisk 2026 Task 2, the second edition of contextualized early depression detection from Reddit discussions. Our central contribution is a learned MLP stopping policy trained to directly optimize ERDE50, replacing the fixed and tiered threshold strategies used in all prior eRisk Task 2 submissions. Combined with a sustained confidence gate that commits only after N=3 consecutive rounds of high policy confidence, the system reduces false positives caused by transient emotional posts without sacrificing recall. The pipeline encodes each discussion thread with a frozen MentalRoBERTa model, maps the accumulated representation to a depression probability via an MLP classifier, and delegates the timing decision to the learned policy. Our best run achieves F1 = 0.73 (Run 1) and F_latency = 0.70 (Runs 0 and 3), with a median alert round of 8 out of 500, completing the full evaluation in 1 hour 26 minutes, the fastest among all complete-submission teams. We report a systematic ablation across five runs spanning two encoder variants, four stopping strategies, and three gate values, along with negative results from GRPO policy training, BDI-II post filtering, MentalLongformer encoding, and DeBERTa ensembling. Code: this https URL
[IR-13] Artificial Intelligence-Assisted Digital Inventory of Cultural Heritage Traditional Knowledge: Case for Indonesian Open Digital Library of Culture
链接: https://arxiv.org/abs/2609.08105
作者: Hokky Situngkir
类目: Artificial Intelligence (cs.AI); Digital Libraries (cs.DL); Human-Computer Interaction (cs.HC); Information Retrieval (cs.IR)
备注: 11 pages, 3 figures
Abstract:The Indonesian Digital Library of Culture (Perpustakaan Digital Budaya Indonesia, PDBI; this http URL) is a participatory platform that has collected tens of thousands of entries on Nusantara cultural heritage through public contribution since 2007. Manual contribution faces three structural barriers: coverage (knowledge is scattered across languages and sites), integrity (open sources mix authentic documentation with noise), and completeness (subjects are recorded but their data remain shallow). This paper presents a methodological framework for autonomous, AI-based harvesting of cultural knowledge from the open web, designed to expand corpus coverage while intensifying per-entry data depth. The methodology is organised as a five-stage economic funnel: focused crawling, multilingual extraction and canonicalisation, vector encoding with blocking, agentic decision-making, and idempotent publication, under the principle of deterministic orchestration, agentic decisions. Each stage is formalised: funnel economics and optimal filter ordering; crawl-frontier dynamics as a subcritical branching process that explains the necessity of recurrent re-seeding; fact-level novelty via a containment measure; Bayesian multi-source evidence fusion with elevated publication thresholds for sacred categories; exactly-once effects via idempotent upserts and the transactional outbox; sliding-window inference budgeting with a reservation protocol; statistical quality auditing; and seed selection as submodular coverage maximisation. The framework retains four high-value human roles: curator of direction, escalation approver, quality auditor, and guardian of meaning, while machine autonomy is raised in stages. Ethical, legal, and cultural-sensitivity implications are discussed, including the architectural guarantee that the machine never overwrites human contributions.
[IR-14] From Citations to Contributions: LLM -Assisted Credit Scoring of Research Articles
链接: https://arxiv.org/abs/2609.07673
作者: Sana Ebrahimi,Suraj Shetiya,Abolfazl Asudeh
类目: Digital Libraries (cs.DL); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注:
Abstract:Citation-based measures of scientific influence typically treat citations as uniform signals, ignoring the different roles that cited works play in a paper’s contribution. We introduce contribution-based credit scoring for research articles: a structured citation analysis that decomposes a paper’s credit between its own original contribution and the prior work it builds on. Motivated by a cooperative-game view of scientific credit, we propose the contribution tree, a hierarchical framework that conserves importance across the document structure and separates original from citation-derived contribution. To make this framework scalable, we use LLMs as noisy comparative estimators of local importance. We further extend the model to article collections by propagating contributions through weighted citation graphs, yielding corpus-level contributions and normalized influence scores. Our experiments suggest that our framework captures contribution signals beyond surface-level heuristics. Our code is available at this https URL
[IR-15] Noēsis: Deterministic-First Retrieval with Two-Tier Context Hydration for Factuality-Critical Queries on Small Local Models
链接: https://arxiv.org/abs/2609.07663
作者: Nicola Cogotti
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Patent pending: Application No. 102026000023146; IT202500035167. Companion to arXiv:2608.15919
Abstract:A wrong number is worse than no answer. Across factuality-critical domains – audience metrics, scheduling and rights in media; dosages and lab values in healthcare; figures and citations in finance and legal – a confident but fabricated value is more damaging than an honest admission of uncertainty. Yet this is the dominant failure mode we observe on small local language models: even when correct evidence is present in context, models fabricate plausible numbers and timestamps. Recent work characterizes a real limit of this regime: below 7B parameters, the bottleneck of retrieval-augmented generation (RAG) is not retrieval quality but context utilization. We present Noesis, the deterministic-first query plane of the Noesis architecture, which makes every deterministic judgment before generation. Its mechanisms follow from the ingestion architecture (subject of a separate patent application): (a) a producer-side fact layer rendering precomputed metric facts verbatim without ranking; (b) positional addressing with deterministic cross-source alignment, resolved ahead of query time at zero LLM cost; © provenance scoping as an attribution constraint with multi-tier named-reference routing; and (d) two-tier context with model-triggered verbatim hydration. Across four ablations, a 2B model reaches parity with a 35B model on factual integrity (exact values in all runs; zero confabulated numbers on absent-entity traps); structured retrieval beats flat RAG by +11.4 points at 2B; skeleton-only context preserves quantitative answers at 20-30% smaller prompts; and hydration recovers verbatim narrative in ~8s versus ~29s. Two properties matter for regulated domains: each query resolves in a single generation call, and every reported value is traceable to its exact source and position by construction. Comments: Patent pending: Application No. 102026000023146; IT202500035167. Companion to arXiv:2608.15919 Subjects: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) ACMclasses: H.3.3; I.2.7 Cite as: arXiv:2609.07663 [cs.IR] (or arXiv:2609.07663v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2609.07663 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-16] Open Tabular Insight Extraction: Where Do We Stand and Where Should We Go?
链接: https://arxiv.org/abs/2609.07629
作者: Daniel Gomm,Maarten de Rijke,Madelon Hulsebos
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Databases (cs.DB)
备注:
Abstract:Democratizing access to the knowledge held in large corpora of tables such as data lakes is emerging as a central research challenge. Research in this space is advancing and broadening in scope, increasingly supplying the components to satisfy a person’s insight need end-to-end. Yet these efforts remain fragmented across communities that frame the problem under their own conventions, such as table question answering, text-to-SQL, and data analysis agents, with works six times as likely to cite within the same task label as across labels. To bring these communities onto common ground, we establish a holistic framework for this pursuit, which we refer to as Open Tabular Insight Extraction (OpenTI). We formalize OpenTI from first principles around the analytical knowledge a person needs, the procedure for deriving it from a corpus of tables, and how well a result serves the person who sought it. In doing so we consolidate frameworks and terminology across information retrieval, natural language processing, machine learning, databases, and human-computer interaction, and apply this grounding in a systematic review and analysis of systems and benchmarks that work towards OpenTI. We find that current systems do not cover the end-to-end scope of OpenTI, mainly focusing on the analysis itself, and that benchmarks are largely unfit for evaluations in an open setting as inputs presuppose knowledge of tables, and validation mechanisms do not match the setup. Finally, we distill a research agenda towards OpenTI systems, evaluation, and interaction paradigms that surface the insights users need. An interactive companion to our paper is available at this https URL.
[IR-17] Same Problem Different Field: Cross-Domain Solution Import via Domain-Stripped Computational Fingerprints
链接: https://arxiv.org/abs/2609.07595
作者: Eryk Kulikowski
类目: Digital Libraries (cs.DL); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: Accepted as a full paper at JCDL 2026 (The 2026 ACM/IEEE Joint Conference on Digital Libraries), Frisco, TX, October 13-16, 2026. 10 pages plus references, 2 figures, 8 tables. Code and benchmark: this https URL ; archived dataset (KU Leuven RDR): this https URL
Abstract:The same underlying computational problem is solved across unrelated fields under different names: recursive Bayesian state estimation appears as a “Kalman filter” in control, “Bayesian forecasting” in pharmacokinetics, and “data assimilation” in geoscience. Topical and citation-based scientific embeddings cannot see this shared problem. We distill each paper once into a domain- and method-name-stripped faceted computational fingerprint, a free-text mechanism skeleton plus controlled computational facets. We define a tunable, facet-selectable distance over it. The goal is solution import: surface cross-field pairs solving the same problem, so a bespoke implementation can be swapped for another field’s standard, specialized solver. On a benchmark of 18 method families across 109 papers, the skeleton lifts cross-domain retrieval average precision over the abstract from 0.222 to 0.513, and the whole fingerprint reaches 0.557. Strikingly, four trained scientific embedders all fall below plain abstract+TF-IDF: they encode topical and citation similarity, the wrong signal for this task. The gain is the representation: the abstract-to-skeleton swap lifts every embedder, and the pipeline is one cached LLM call per paper plus a cheap embedder. An interventional re-skin / math-edit test shows the fingerprint tracks the computation, not the field. On a 501-paper wild corpus, known twins dominate the top of the ranking (23 of the top 30); with planted pairs excluded from the results, three blind LLM judges rate 3 of the top 5 and 8 of the top 30 pairs genuine import candidates, and 0 of 30 random ones. The human verification is the four executed imports: in one, an open standard solver reproduces a bespoke clinical dosing engine’s output. We release the benchmark, the code, and the distillation prompt.
[IR-18] EigenLI: Spectral Approximations to Late Interaction
链接: https://arxiv.org/abs/2609.07561
作者: Archish S,Sabyasachi Basu,Ankit Garg,Ravishankar Krishnaswamy,Kirankumar Shiragur
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注:
Abstract:Late-interaction models such as ColBERT achieve strong effectiveness by representing each document with many token-level vectors, but this expressivity leads to large indexing cost, storage footprints and expensive MaxSim scoring. We show that late-interaction representations exhibit an intrinsic low-rank structure: document token embeddings concentrate in a low-dimensional subspace that preserves most of the retrieval signal. Leveraging this observation, we introduce EigenLI, a spectral approximation framework that compresses late-interaction representations via document-specific low-dimensional subspaces. Unlike clustering or pooling methods, EigenLI identifies the dominant eigendirections of each document and uses them to construct reduced interaction representations. Empirically, k -EigenLI with k \le 32 outperforms k-means and Ward clustering based pooling methods on ColBERTv2 and AnswerAI-ColBERT-small; GTE-ModernColBERT exhibits a different tradeoff at k=32 , where clustering methods perform better. The same spectral construction also yields EigenLI-SV, an ANN-compatible single-vector representation derived from the second-order summary of the reduced structure. Across multiple datasets and all three text models, EigenLI-SV consistently outperforms comparable single-vector surrogates such as MUVERA.
[IR-19] Uncertainty Quantification for LLM Agents : A Taxonomy an Evaluation Protocol and an Empirical Study
链接: https://arxiv.org/abs/2609.07395
作者: Moule Lin,Qizhen Lan,Shuhao Guan,Weipeng Jing,Jiexin Fan,David Gregg,Goetz Botterweck
类目: Information Retrieval (cs.IR)
备注:
Abstract:Large language models (LLMs) are no longer deployed only for single-turn conversation but increasingly act as agents that plan, call tools, retrieve evidence, maintain memory, and interact over long horizons, often together with other agents through multi-turn conversations. Therefore, knowing when to trust the agentic system is a prerequisite for safe deployment. However, existing work on quantifying uncertainty for LLMs was built almost entirely for single-turn question answering. This paper argues that errors and uncertainty arise from multi-turn conversations, environments, and tools rather than from a single-turn question answering setting. It comes late, however, and is compounded in a single score that is too coarse to represent the unreliability. We organize the literature with a three-axis taxonomy, (1) what the uncertainty is, (2) how it is estimated, and (3) where uncertainty arises during an agent pipeline. We investigate step-level and trajectory-level calibration and show with a simple counterexample that the first does not imply the second. Experiments on real agent traces across four models and up to a 50-step budget show that the proposed metric and reporting protocol (Trajectory-Checkpoint Expected Calibration Error, TC-ECE) can be computed and that step errors are coupled along a trajectory. We find that confidence estimates from the agent’s own responses do not consistently outperform a simple baseline. The experiments also show that averaging all trajectories together can hide overconfidence at later stages, which becomes visible when results are analyzed across different horizons. In simpler terms, this paper identifies where the uncertainty comes from in the agentic system pipeline, how to teach agents to know when they are wrong, and why one confidence number is not enough.
[IR-20] Matryoshka Hash Representations for Model-Aware Compact Semantic Retrieval
链接: https://arxiv.org/abs/2609.07276
作者: Peichun Hua,Yunming Xiao
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 10 pages, 2 figures, 6 tables
Abstract:Retrieval-augmented generation (RAG) depends on dense retrieval: each document is stored as a learned vector, and a query is answered by finding its nearest neighbors in that vector space. Keeping one full-precision vector per document is the dominant index cost at corpus scale, so retrieval systems replace each vector with a short code of a few bytes—a step called quantization. Standard quantizers such as product quantization (PQ) pick the code that reconstructs the original vector most closely. A single code is even more useful if it serves several byte budgets at once: when its short prefixes are each directly searchable, a deployment can set its efficiency–quality operating point without re-encoding the corpus. But training all prefixes under one objective makes the early bits a compromise across budgets—short codes improve while the full-width code degrades. Quantization to low-bit representation, such as binary codes, further sharpens the conflict. We introduce Matryoshka Hash Representations (MHR), a two-stage procedure that separates full-width training from prefix organization. MHR first learns a longer binary code, then freezes the model and trains additional zero-initialized residual code adaptors for directly searchable prefixes. Documents are stored at one bit per coordinate, while queries keep continuous logits like PQ to attain sufficient expressivity. We implement the search process with FAISS FastScan. Trained on MS MARCO and zero-shot transferred to seven BEIR datasets, MHR reaches .5561 NDCG@10 and .6535 Recall@100 at 32 bytes, surpassing the best baseline of the same budget. The advantage is more pronounced in lower budgets. The same code also strengthens two common pipelines: shortlisting candidates for full-precision reranking, and pruning a low-storage graph index such as LEANN. Comments: 10 pages, 2 figures, 6 tables Subjects: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2609.07276 [cs.IR] (or arXiv:2609.07276v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2609.07276 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-21] ask-Blind No MORE: Multi-Task Information Flow in Unified Ranking Backbones CIKM2026
链接: https://arxiv.org/abs/2609.07273
作者: Yuchen Wang,Feng Niu,Qing Tan,Junting Lu,Baoxin Wu,Jun Gao
类目: Information Retrieval (cs.IR)
备注: Accepted at CIKM 2026
Abstract:Industrial ranking models for recommendation have scaled feature interaction and sequence modeling separately; recent architectures such as HyFormer and MixFormer unify both in a stackable backbone. Real-world recommender systems, however, nearly always require multi-task learning, yet existing unified architectures confine multi-task modeling to shallow post-backbone towers, leaving the backbone without task-aware information flow. We propose MORE (Multi-task cO-evolving Ranking modEl), which embeds multi-task information flow inside the backbone, enabling task-specific signals to co-evolve with sequence and feature representations at every layer rather than in a post-hoc fusion. It introduces Anchor Tokens that persist across backbone layers: Shared Anchors encode cross-task commonalities, while Private Anchors capture task-specific priors. In each block, Anchor Tokens (1) read task-conditioned signals from behavior sequences, (2) mix with non-sequential features under a task-boundary mask, and (3) refine per-task representations through independent branches; as blocks stack, each task obtains a differentiated representation refined through all backbone layers. Experiments on large-scale industrial datasets show that MORE consistently outperforms baselines across all tasks under comparable parameter and FLOPs budgets, and scales well with model size. Online A/B tests on Momo, a leading Chinese social discovery platform with tens of millions of monthly active users, yield 3% improvement in usage duration, 3.6% in interaction rate, and 2% in deep-chat rate. MORE is deployed in production with request-level shared computation reducing scoring latency by about 30%. Comments: Accepted at CIKM 2026 Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2609.07273 [cs.IR] (or arXiv:2609.07273v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2609.07273 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.1145/3799682.3840113 Focus to learn more DOI(s) linking to related resources
[IR-22] Query-Aware Token Budgeting for Efficient Late-Interaction Visual Document Retrieval ICDM
链接: https://arxiv.org/abs/2609.07262
作者: PS Rishi,Rajeev Ranjan Dwivedi,Vinod K Kurmi
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: Accepted at IEEE International Conference on Data Mining (ICDM)
Abstract:Late-interaction visual document retrievers preserve fine-grained page evidence by storing many token embeddings per page, but the resulting storage and query-time interaction costs make large-scale deployment expensive. Pooling document tokens before indexing offers a natural remedy, yet static pooling must decide which visual evidence to preserve before the query is known. We study an alternative: a heavily compressed hot-path index generates candidates, after which query-aware token budgeting operates on the original token sets of the shortlisted pages. We formulate this stage-two selection as a budgeted MaxSim coverage problem, show that a clipped version is monotone submodular, and compare coverage-only, cluster-guided, token-wise, and marginal-gain policies. On ten ViDoRe tasks with ColModernVBERT, direct static pooling reduces macro normalized discounted cumulative gain at rank five from 0.6309 without compression to 0.4738 at a thirty-two-fold pool factor. Under the same candidate-generation regime and a pool-factor-eight-equivalent reranking budget, token top-k recovers 93.93 percent of the full-token score, while greedy marginal-gain selection recovers 98.39 percent. Held-out and leave-one-dataset-out evaluations yield positive greedy improvements over token top-k on every dataset. The latency analysis reveals two useful operating points: token top-k for interactive retrieval and the naive greedy implementation as a quality upper envelope. Together, these results show that late-interaction visual retrieval benefits from query-aware allocation rather than query-agnostic pooling alone.
[IR-23] EAGER: Enrich-and-Align Generative Query Recommendation from Clicked Items in E-commerce Search EMNLP2026
链接: https://arxiv.org/abs/2609.07143
作者: Shuwei Yuan,Mingqian Ding,Luxin Liu,Rong Xiao,Xiaoyi Zeng
类目: Information Retrieval (cs.IR)
备注: Accepted to the EMNLP 2026 Industry Track. 13 pages, 6 figures
Abstract:E-commerce platforms increasingly display clickable query suggestions alongside items in the user feed, enabling users to refine or expand their intent without manually reformulating queries. Existing approaches either mine suggestions from historical logs – limited to past behavior and blind to long-tail, personalized intents – or rely on off-the-shelf LLMs whose lack of platform-specific knowledge yields fluent but generic queries disconnected from real click behavior. We propose EAGER (Enrich-and-AliGn gEnerative Query Recommendation), a two-stage framework for generating query suggestions from clicked items. In the enrichment stage, supervised fine-tuning (SFT) follows a four-stage curriculum that scales information richness (from item-only to user-conditioned) and reasoning depth (from direct to chain-of-thought). Each stage incorporates rationale augmentation, diversity regularization, and self-distillation. In the alignment stage, we post-train via GRPO with a hybrid reward of multiple rule-based business signals and a preference-aware click reward. Extensive offline experiments and online A/B test demonstrate the effectiveness of EAGER, which has been deployed in production at a major e-commerce platform.
[IR-24] Where to Look and What to Use: Retrieve-Localize-Generate for Long-Term Conversational Memory Question Answering EMNLP2026
链接: https://arxiv.org/abs/2609.07093
作者: Yifan Wang,Xinkui Lin,Yongxiu Xu,Shen Gao,Ruochen Yang,Kun Huang,Yubin Wang,Jie Wu,Wei Liu,Jian Luan,Hongbo Xu,Shuo Shang
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: 22 pages, 4 figures, 14 tables. Accepted to the EMNLP 2026 Main Conference
Abstract:Retrieval-augmented generation (RAG) enables large language models (LLMs) to answer questions by accessing external knowledge and has been widely adopted for long-term conversational memory question answering. However, existing methods suffer from two key challenges: (1) fragmented evidence scattered across temporally distant sessions, and (2) noisy content within retrieved sessions that triggers the lost-in-the-middle effect. To address these challenges, we propose MemLoc, a unified Retrieve-Localize-Generate framework for long-term conversational memory QA. For retrieval, MemLoc decomposes each session into multi-granularity memory units and performs query routing via an inner-memory graph with entropy-based granularity selection. It further models cross-session semantic and temporal dependencies through a cross-memory graph, enabling coarse-to-fine retrieval of top-K relevant memory candidates. For localization, we introduce a reasoning-based evidence locator trained with Self-reflective Hint Policy Optimization (SHPO), which performs progressive refinement by extracting query-relevant fragments within memory units to suppress noise and reranking across candidates to remove redundancy, producing a compact evidence set with lightweight location IDs. For generation, these IDs act as precise grounding signals that guide the LLM to the correct memory positions, mitigating the lost-in-the-middle effect while preserving original contextual integrity. Extensive experiments on four benchmarks demonstrate that MemLoc achieves state-of-the-art retrieval accuracy and response quality while maintaining efficiency. Our code is available at: this https URL.
[IR-25] Beyond One-Shot Expansion: Contrastive Evidence Exploration for Multi-Hop Retrieval CIKM2026
链接: https://arxiv.org/abs/2609.07050
作者: JungMin Yun,YoungBin Kim
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: Accepted to CIKM 2026
Abstract:Retrieval-augmented generation (RAG) critically depends on retrieving the evidence necessary for effective reasoning. However, this remains particularly challenging in multi-hop question answering (QA), where supporting passages are often linked through intermediate entities and relations that must be progressively uncovered. Existing retrieval approaches typically rely on a single retrieval intent or one-shot query expansion, limiting their ability to adapt to newly retrieved evidence and potentially introducing noisy or redundant retrieval signals. To address these limitations, we propose a training-free multi-hop retrieval framework that integrates evidence-conditioned exploration, passage-specific contrastive refinement, and coverage-aware final ranking. During offline indexing, the framework constructs passage-specific contrastive facets that characterize each passage relative to its semantically similar neighbors, providing fine-grained signals to distinguish closely related candidates. At inference time, the framework iteratively retrieves evidence, generates probes targeting unresolved information needs, refines candidate relevance using the contrastive facets, and selects a complementary set of passages that collectively cover diverse evidence-seeking intents. Experiments on MuSiQue, HotpotQA, and 2WikiMultihopQA demonstrate consistent improvements in retrieval quality and downstream QA performance over baselines.
[IR-26] racing Query Expansion Effects through Sparse Autoencoder Features EMNLP2026
链接: https://arxiv.org/abs/2609.06968
作者: Fangan Dong,Weiran Shi,Zhiwei Xu,Xuri Ge,Ben He,Xin Xin,Zhumin Chen,Ying Zhou
类目: Information Retrieval (cs.IR)
备注: Accepted to Findings of EMNLP 2026
Abstract:Query expansion (QE) is a critical technique in information retrieval that enriches underspecified queries with additional textual context. However, its effect is often unreliable in modern dense retrieval, especially for strong off-the-shelf retrievers without retraining. Existing studies mainly examine expansion quality, semantic drift, or retrieval outcomes, but rarely explain how QE changes dense retrievers internally. In this work, we trace QE effects through sparse autoencoder (SAE) features. Using paired original and expanded queries, we decompose layer-wise retriever representations into sparse latent activations, identify QE-related latents from expansion-induced activation shifts, and interpret them with natural-language descriptions and retrieval cases. Our analysis shows that effective QE induces layer-concentrated changes in sparse latents aligned with retrieval intent and entity attributes, rather than only perturbing final query embeddings. SAE-based activation steering further validates these latents improve retrieval more consistently than random interventions or vanilla QE across four benchmarks, suggesting that SAEs can explain QE effects and offer a lightweight option for precise retrieval behavior modulation without query rewriting or retriever fine-tuning.
[IR-27] FunnelAudit: Responsibility Auditing in Multi-Route Recommender Systems
链接: https://arxiv.org/abs/2609.06964
作者: Jie Li,Dudu Luo,Jiayang Niu,Ke Deng,Yongli Ren
类目: Information Retrieval (cs.IR)
备注:
Abstract:Multi-route recommender systems combine retrieval, allocation, fusion, and ranking, making individual inclusions and exclusions difficult to audit. Route overlap can hide effects from one-at-a-time ablations, while freezing downstream stages produces counterfactuals inconsistent with serving behavior. We introduce FunnelAudit, an executable framework for incident-level responsibility auditing. An accountability contract specifies the disputed Top-K event, controls and owners, permitted reference actions, and replay semantics. FunnelAudit evaluates every permitted control configuration and applies graded actual responsibility to find the smallest outcome-preserving contingency that makes each control pivotal. Its certificate records the contingency and paired serving executions needed to verify the judgment. We instantiate the framework in two-stage, nine-route funnels using fixed union, weighted quota allocation, or weighted reciprocal-rank fusion, followed by SASRec ranking. Across 258,809 user-target incidents from three real interaction datasets, 4.24-16.24% admit a responsible control. Among responsible incident-control pairs, 92.55-99.64% require a nonempty contingency, so single-control ablation recovers only 0.36-7.45%. Policies differing in factual outcomes on only 0.31-2.39% of incidents yield 21.44-54.05% Jaccard distance between responsible-route sets on matched exclusions. Independent replay reproduces all 9,121,792 checked target-world outcomes; exhaustive search and a generic mixed-integer linear program agree with every sampled judgment. These findings demonstrate the importance of explicit serving semantics and checkable witnesses for recommender accountability. Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2609.06964 [cs.IR] (or arXiv:2609.06964v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2609.06964 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-28] Measuring GEO Visibility: Prompt Corpora Define the Answer Market
链接: https://arxiv.org/abs/2609.06811
作者: Olivier Martinez
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注: 16 pages, 9 tables; critical survey with 44 references and methodological analysis; ancillary reading register, aggregate data, and code for reproducing the calculations included
Abstract:GEO (generative engine optimization) visibility scores aggregate source appearances, citations, or brand mentions in generated answers. The prompt corpus selects the situations evaluated, while weights determine their relative importance. Together they define an “answer market” that need not represent actual user demand. Prompt wording can alter retrieval, competing sources, and generated answers. Scoring then requires identifying the appearances, citations, or mentions of interest. If a language model performs this task, its instruction can change the score assigned to an unchanged answer. Our critical survey examines how these choices help define what a GEO score measures. It draws on research into whether indicators measure the intended phenomenon, total survey error, and information retrieval evaluation. The framework specifies situation annotation, prompt formulations, execution conditions, weights, and scoring rules. When weights are unknown or remain to be chosen, the framework reports sets of admissible scores. It distinguishes values compatible with data and assumptions about a target population (partial identification) from variation across weighting conventions (normative sensitivity). A citation alone does not establish a source’s contribution. The article defines a comparison of answers generated with and without a source in a controlled documentary context, distinct from an intervention on the full engine with competing sources. The framework is supported by reproducible calculations. No new experiments are reported; its general empirical validity remains to be assessed. Comments: 16 pages, 9 tables; critical survey with 44 references and methodological analysis; ancillary reading register, aggregate data, and code for reproducing the calculations included Subjects: Information Retrieval (cs.IR); Computation and Language (cs.CL) Cite as: arXiv:2609.06811 [cs.IR] (or arXiv:2609.06811v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2609.06811 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Olivier Martinez [view email] [v1] Sun, 6 Sep 2026 20:07:53 UTC (57 KB)
[IR-29] Who Anchors AI Overviews in Health? Baidu Google and the Geography of Authority
链接: https://arxiv.org/abs/2609.06798
作者: Mingyue Zha,Ho-Chun Herbert Chang
类目: Information Retrieval (cs.IR); Computers and Society (cs.CY)
备注:
Abstract:Artificial intelligence is being rapidly incorporated into traditional search systems, yet scant work audits the information disparities across platforms, geography, and languages. We address this gap by comparing Google and Baidu’s AI Overview systems for health queries, and measure informational anchors that emerge. Auditing 1,920 health queries across 12 countries and 4 languages, we find that Google and Baidu exhibit vertical integration, routing users toward their own company platforms in AI Overviews rather than a diverse set of primary sources. Smaller, lower-localization countries receive fewer domestically sourced references for health queries. Issuing the same query in a country’s official language rather than English raises the share of locally sourced citations approximately 3.5- to 13.5-fold. Comparing queries across health topics of varying severity and controversy, including Traditional Chinese Medicine as an example, we also show that health disclaimers are multidimensional and vary across language and culture. We discuss how generative search influences access to health information, and the urgent need for culturally-aware oversight of these systems that influence critical health decisions.
[IR-30] EviMap: Evidence-Grounded Hierarchical Topic Maps for Exploring Unlabeled Corpora CIKM’26
链接: https://arxiv.org/abs/2609.06664
作者: Zhiyin Tan,Changxu Duan
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: Accepted at the 35th ACM International Conference on Information and Knowledge Management (CIKM '26)
Abstract:Research teams and organizations often explore unfamiliar free-text collections, from survey comments and reviews to reports and domain documents, before labels, queries or coding schemes exist. At this stage, the first thematic map shapes what users notice, prioritize and carry into downstream analysis, so it should be trusted only insofar as it can be verified. Existing options force a trade-off between scale and verifiability. Qualitative coding preserves evidence but is slow. Search presupposes a query. Clustering and topic models scale but produce labels users must interpret. One-shot large language model (LLM) summaries are fluent yet difficult to reproduce or audit. We present EviMap, an interactive system providing researchers and practitioners with an auditable thematic overview of such corpora. Guided by model-generated context describing the corpus and hypothesized stakeholder concerns, EviMap extracts within-document evidence phrases and organizes them, rather than whole documents, into a three-level map of aspects, groups and fine-grained topics. Embedding-based clustering narrows the search space for finer semantic judgments by the LLM. Each node traces back to supporting phrase spans, so documents link to topics through evidence they contain and users can audit labels against the original text. Users can start from a top-level corpus map, drill into topics, inspect highlighted evidence in original documents, and combine two topics to find documents discussing both. We demonstrate this workflow across six heterogeneous corpora spanning 2,108 to 101,699 documents, with a comparison against flat and hierarchical LLM baselines. By grounding every label in verbatim source spans, EviMap makes a topic map not just readable, but verifiable. Code, demo video, and interactive dashboard are available at this https URL.
[IR-31] axoConf: Taxonomy-Guided Automatic Conference Program Organization
链接: https://arxiv.org/abs/2609.06604
作者: Daomin Ji,Zhifeng Bao,Junhao Gan
类目: Information Retrieval (cs.IR)
备注:
Abstract:Conference program organization, the task of assembling accepted papers into a technical program, is labor-intensive. Papers must be grouped into topically coherent sessions under hard operational constraints, and existing methods rarely achieve both at once. To address this problem, we present TaxoConf, a system that organizes conference programs around a conference-specific topic taxonomy. TaxoConf constructs a canonicalized multi-parent taxonomy over the accepted papers and represents each paper by its frontier of most specific topics. It derives a specificity-weighted optimal-transport distance between papers from this taxonomy, then solves a binary integer programming problem that assigns papers to sessions by minimizing within-session distance subject to all hard constraints. On benchmarks built from four 2025 conferences, TaxoConf attains the highest session coherence (4.62 out of 5) and the closest agreement with human-curated sessions (NMI 0.761) while incurring no constraint violations. TaxoConf has also been deployed to generate the technical program of SIGIR 2026, where the organizers accepted the initial output with only a few edits, preserving 91.8% of oral assignments and the entire poster program while requiring no correction of hard-constraint violations, and an on-site survey of 47 attendees rated the overall program quality 4.26 out of 5. The system is publicly available at this https URL.
[IR-32] Relevance is not enough: A Communication-Oriented Retrieval System for Consequential Scientific Question Answering
链接: https://arxiv.org/abs/2609.06222
作者: Avina Nakarmi,Naga Datha Saikiran Battula,Anthony Diaz,Aritra Dasgupta
类目: Information Retrieval (cs.IR)
备注:
Abstract:AI systems increasingly answer scientific questions about health, safety, and the environment. But most retrieval-augmented generation systems are tuned to provide factually correct, on-topic answers rather than to help non-experts understand what those answers mean for their lives and decisions. We focus on consequential scientific questions whose results directly shape people’s lives and study them through a public water-quality communication system, where residents and community leaders interpret the findings and choose actions. Their experiences show that on-topic answers can still be insufficient without explanation and context and that the emotional weight of risk information cannot be ignored. Our system first classifies each question by reasoning type (for example, causal versus policy-based), then generates follow-up questions to identify missing evidence and retrieve it. One component clearly distinguishes between what is known and what is uncertain, while another rewrites scientific details into accessible language, using persona-based styles, such as a caring neighbor or an administrative official, to adapt tone and readability. Ablations on over 160 questions show that the system uses \textitan order of magnitude less context and, in several configurations, improves human-rated completeness. A completeness metric co-designed with community members and a fine-tuned learned judge reveal that standard relevance scores explain about 1% of variation in human completeness ratings, and even the tuned judge only moderately aligns with humans, indicating that completeness is a distinct human-centered objective that current metrics do not reliably capture.
[IR-33] Closing the Long-Short View Gap in Sequential Recommendation without Cached History CIKM2026
链接: https://arxiv.org/abs/2609.06219
作者: Lingfeng Shi,Chengkai Huang,Lina Yao,James Caverlee
类目: Information Retrieval (cs.IR)
备注: Accepted at CIKM 2026
Abstract:Sequential recommenders are typically trained on long user histories to capture rich behavioral signals, yet serving with training-length sequences is often impractical due to real-time efficiency constraints. Directly using only recent behaviors leads to a severe performance drop. To bridge this gap, existing approaches compress user histories into persistent per-user states, storing and retrieving them at inference time; while effective, they impose non-trivial infrastructure overhead and offer little remedy in cold-start scenarios. In this paper, we empirically identify two structural flaws rooted in geometric properties and dataset sparsity, and propose a novel two-stage framework to close the long-short-view performance gap. Specifically, in the first stage, we replace the commonly used dot-product with angular similarity scoring and leverage a modified softmax to counter prefix position bias. In the second stage, we fine-tune only bias and LayerNorm components, which are universal to standard sequential backbones, for further improvement. Both stages are guided by carefully designed learning objectives. Extensive experiments on two representative backbones across three public datasets demonstrate the effectiveness of our proposed framework.
[IR-34] Customer Relationship Intelligence: Integrating CRM and MDM for Enhanced Customer Engagement
链接: https://arxiv.org/abs/2609.06189
作者: Tejasvi c. Addagada
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: Preprint submitted to 16th IEEE ICCCNT 2025. 8 pages, 12 tables
Abstract:This study examines how Customer Relationship Management (CRM), Master Data Management (MDM), and Customer Knowledge Management (CKM) jointly constitute a Customer Relationship Intelligence (CRI) framework for enhanced Customer Engagement (CE). A cross-sectional survey of 100 participants across retail, healthcare, IT, and telecommunications sectors was analysed using Spearman rho correlation and ordinal logistic regression (IBM SPSS). Bivariate correlations were weak and non-significant (r0.19, p0.06). Regression identified CRM (beta=0.717, p=0.002) and CKM (beta=0.581, p=0.009) as significant positive predictors of CE; MDM showed a positive but non-significant direct effect (beta=0.346, p=0.071). The model explained 20.5% of CE variance (Nagelkerke R^2=0.205). Parallel mediation analysis (Hayes PROCESS Model 4, 5,000 bootstrap samples) found no significant indirect effects of MDM on CE via CRM (IE=0.021, 95% BC CI [-0.072, 0.121]) or CKM (IE=0.032, 95% BC CI [-0.061, 0.126]); Hypothesis H4 was not supported. CRM and CKM emerge as the principal drivers of CE within the CRI framework, while MDM functions as a foundational data quality enabler whose strategic value is realised through its enabling effect on CRM execution and knowledge management. Findings should be treated as exploratory given the sample size and cross-sectional design. Future research should replicate with larger sector-specific samples and longitudinal designs, particularly in regulated BFSI contexts where MDM architecture is shaped by data governance mandates.
[IR-35] ExpertLens: Visualizing Embedding Spaces for Post-Hoc Explainability in MoE Enhanced Retrievers
链接: https://arxiv.org/abs/2609.06155
作者: Effrosyni Sokli,Isaac Roberts,Alexander Schulz,Barbara Hammer,Gabriella Pasi
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Neural models, including dense retrievers, have been widely adopted in Information Retrieval (IR), often delivering state-of-the-art performance. Despite their effectiveness, these models operate as black boxes, limiting the interpretability of their ranking decisions. Existing post-hoc explainability methods for neural rankers primarily focus on feature-level attributions, which can be insufficient to capture the complexity of learned embedding spaces. In this work, we propose ExpertLens, a post-hoc explainability framework for Mixture-of-Experts (MoE)-enhanced dense retrievers that shifts focus from local scalar feature importance to representation-level global interpretability. ExpertLens leverages discriminative embedding space visualizations jointly with automatically extracted Concept Activation Vectors to reveal how expert routing drives embedding space formulation and retrieval effectiveness. Experiments across five IR benchmarks and two MoE-enhanced dense retrievers show that expert routing consistently improves embedding space structure, positioning queries and their relevant documents into better-defined geometric neighborhoods. Analysis of expert subspaces further reveals general-purpose dominant experts, along with minority experts exhibiting distinct linguistic specialization, with subspaces arranged according to multi-semantic concept similarity. Our code is publicly available.
[IR-36] Visual Analysis of LLM -based Entity Resolution from Scientific Papers
链接: https://arxiv.org/abs/2609.06037
作者: Siyu Wu,Yi Yang,Weize Wu,Ruiming Li,Yuyang Zhang,Ge Wang,Huobin Tan,Zipeng Liu,Lei Shi
类目: Information Retrieval (cs.IR)
备注: 10 pages, 8 figures
Abstract:This paper focuses on the visual analytics support for extracting domain-specific entity from extensive scientific literature, a task with inherent limitations using traditional named entity resolution methods. With the advent of large language models (LLMs) such as GPT-4, significant improvements over conventional machine learning approaches have been achieved due to LLM’s capability on entity resolution integrate abilities such as understanding multiple types of text. This research introduces a new visual analysis pipeline that integrates these advanced LLMs with versatile visualization and interaction designs to support batch entity resolution. Specifically, we focus on a specific material science field of Metal-Organic Frameworks (MOFs) and a large data collection namely CSD-MOFs. Through collaboration with domain experts in material science, we obtain well-labeled synthesis paragraphs. We propose human-in-the-loop refinement over the entity resolution process using visual analytics techniques, which allows domain experts to interactively integrate insights into LLM intelligence, including error analysis and interpretation of the retrieval-augmented generation (RAG) algorithm. Our evaluation through the case study of example selection for RAG demonstrates that this human-machine collaborative approach improved single-document entity resolution accuracy by approximately 30%.
[IR-37] Evaluating Deep-Search Agents under Hierarchical Web Evidence Poisoning
链接: https://arxiv.org/abs/2609.06027
作者: Zhongan Bi,Qiwen Wang,Jianrong Jiang,Jigang Ding,Wenwen Xiong,Changhua Meng,Xuanang Gao,Kepeng Lin,Changjiang Jiang,Yiang Chen,Huan Yao,Wei Wang,Zhenyu Ma,Wenhui Dong
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: 36 pages, 9 figures, and 10 tables. Code and benchmark: this https URL
Abstract:Search-augmented LLM agents are increasingly used for consumer decisions, making them vulnerable to Generative Engine Optimization (GEO) poisoning. Existing benchmarks largely measure whether manipulated content is retrieved or endorsed, but do not track whether an agent verifies suspicious evidence, revises adopted claims, or recovers before producing its final recommendation. We introduce HAE-GEO, a benchmark that tracks the full trajectory from exposure to recovery under progressively more persuasive Web poisoning. Agents interact via a multi-turn Search-Scrape interface across three attack levels (L1 direct assertion, L2 contextual camouflage, and L3 apparent corroboration), supported by a controlled corpus of 72,039 clean pages and 770 poisoned pages per level spanning 8 product categories and 154 brands. Evaluation combines deterministic behavioral measures with six semantic rubric dimensions. Evaluating 10 agents, we find three recurring patterns: evidence recognition degrades under the corroboration trap; agentic search improves final resistance without improving evidence recognition or utility; and defense prompting increases verification, yet rarely converts verification into recovery.
[IR-38] SurveyAgent -HKA: A multi-agent framework for scientific survey generation with LLM s and human knowledge augmentation
链接: https://arxiv.org/abs/2609.05938
作者: Tong Bao,Mir Tafseer Nayeem,Yi Zhao,Davood Rafiei,Chengzhi Zhang
类目: Computation and Language (cs.CL); Digital Libraries (cs.DL); Information Retrieval (cs.IR)
备注:
Abstract:Automatic scientific survey generation has become an important task in scientific document processing. The common approach of retrieving literature from a single source (e.g., arXiv) and generating surveys through a one-pass large language model (LLM) call often leads to limited reference coverage and, more importantly, fails to replicate the expert-driven revision process that is crucial for writing high-quality surveys. In this paper, we introduce SurveyAgent-HKA, a multi-agent framework that improves end-to-end scientific survey generation by incorporating knowledge derived from published surveys and peer-review comments. The framework decomposes survey generation into well-defined sub-tasks handled by LLM-powered agent. It first retrieves relevant papers from multiple sources and identifies key topics through clustering to construct an initial outline, which is then refined using outlines from related human-written surveys. Based on the refined outline, topic-focused papers are retrieved and re-ranked to select for drafting a well-grounded survey. Then, we identify common issues raised by experts in peer-review comments from published surveys to guide the revisions and finalize the survey. Experiments on two domains show that our approach outperforms mainstream baselines in citation quality, structural consistency, and content quality. Furthermore, our framework is efficient in both time and cost, making it a practical solution for broader AI-assisted scientific writing applications.
[IR-39] Do All Nodes Benefit Equally from Knowledge Graphs? Adaptive Node-Aware KG Fusion for Recommendation CIKM2026
链接: https://arxiv.org/abs/2609.05909
作者: Jaehyun Park,Minseo Jeon,Daewon Gwak,Sunuk Kim,Hanvit Lee,Jinhong Jung
类目: Information Retrieval (cs.IR)
备注: 12 pages, Accepted at CIKM 2026
Abstract:KG-aware recommendation has been widely studied to alleviate data sparsity by using knowledge graphs (KGs), which represent items, entities, and their relations as graphs and provide item-side knowledge. However, existing methods incorporate item knowledge without considering how much each user or item node should rely on it. As a result, they apply KG signals indiscriminately across nodes, even to nodes whose collaborative filtering (CF) signals from the interaction graph (IG) are already reliable. In this paper, we propose AdaKG (Adaptive Node-Aware KG Fusion), a novel KG-aware recommendation method that adaptively adjusts the contribution of auxiliary knowledge for each node. Since user-item interactions and item knowledge provide different types of signals, directly mixing them can distort the CF signals. To avoid this, AdaKG separately encodes the IG and KG with view-specific encoders, allowing each view to capture its own information. It then estimates how strongly each node should rely on item knowledge by measuring the stability of its CF signals under small adversarial perturbations, assigning a larger KG contribution to less stable nodes. Finally, AdaKG adaptively aligns the IG and KG embeddings in a shared space and fuses them according to the estimated node-wise reliance. Through experiments, we show that AdaKG achieves strong performance compared with its baselines and the effectiveness of our adaptive fusion strategy.
[IR-40] AtomCite: Verification and Correction of Supplied Page-Level Citations in Multi-Page Documents
链接: https://arxiv.org/abs/2609.05802
作者: Chen Qian,Yimeng Wang,Yu Chen,Lingfei Wu,Andreas Stathopoulos
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:Large language models answering questions over multi-page documents are expected to cite the supporting pages, yet supplied citations are sometimes inaccurate, and current evaluations score citations at generation time or against text passages: no existing benchmark evaluates whether a system can verify and correct a page-level citation already attached to an answer. We propose AtomCite, an agentic framework that parses an answer into claims, checks each claim against the image of its cited page, and applies a deterministic repair policy. To evaluate it, we introduce DocCite, to our knowledge the first benchmark for systems that verify and correct page-level citations in document images. Built on MP-DocVQA and DUDE, it combines 928 validated injected instances with 2,468 candidate natural errors harvested from frontier- and efficiency-tier models, of which a two-annotator audit confirms 1,909 as genuine errors. Primary labels are assigned deterministically, not by LLM judges, with the human audit as a separate validation layer. Across three model families (Gemini, Claude, and GPT), AtomCite reaches around 93% binary verification accuracy on the injected benchmark, significantly outperforming every OCR-only condition, including a compute-matched control, and exceeding every prior text-based baseline given the same OCR text. Its repair policy lifts citation precision on the injected mix from a constructed 34% to 87-90% while retaining over 90% of correct claims. AtomCite also transfers: with frozen prompts and zero training, it raises the hallucination-detection scores of two open 7-8B models on five public benchmarks above the same models prompted as direct judges. Finally, the audit shows that noise in automatic labels biases measured verifier accuracy and can reverse system rankings, so evaluations relying only on synthetic or automatic labels risk mismeasuring verification capability.
[IR-41] RAG Mark: A Comprehensive Framework for Benchmarking Retrieval-Augmented Generation Systems
链接: https://arxiv.org/abs/2609.05760
作者: Zlatan Feric,Amir Taherin,Bin Ren,Yanzhi Wang,Jennifer Dy,David Kaeli
类目: Performance (cs.PF); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Information Retrieval (cs.IR)
备注: 18 pages, 9 figures
Abstract:We present RAGMark, a modular benchmarking framework for advanced Retrieval-Augmented Generation (RAG) systems targeting small-scale multi-GPU environments. RAGMark evaluates diverse RAG components, including retrievers, vector databases, prompt-processing methods, and generator models, while collecting detailed per-stage metrics such as latency, GPU utilization, memory consumption, power usage, time to first token (TTFT), throughput, and answer quality. The framework is highly extensible, separating RAG stages, timing, and resource monitoring into modular components, and is designed to efficiently sweep large configuration spaces while minimizing repeated model and database initialization overhead. Using RAGMark, we characterize five RAG workloads on open-domain QA datasets across varying retrieval depths, model scales, reranking, compression methods, and vector database configurations. We show that while autoregressive generation dominates latency in naive pipelines, context-reduction techniques shift bottlenecks across compute, memory bandwidth, and preprocessing stages. Reranking and compression produce compounding benefits: reranking reduces compression workload itself, while both jointly reduce prefill and KV-cache traversal costs, lowering energy consumption by up to 66%. We further observe strong cross-stage interactions, where small upstream context reductions cascade through downstream latency, memory traffic, and energy consumption. The RAGMark source code is publicly available at: this https URL.
[IR-42] What Price Fairness? Evaluating Energy - Fairness - Accuracy Trade-off in Recommender Systems
链接: https://arxiv.org/abs/2609.05759
作者: Abhirup Mitra,Oleg Lesota,Antonela Tommasel
类目: Information Retrieval (cs.IR)
备注:
Abstract:Fairness-aware recommender systems aim to mitigate systematic imbalances in recommendation outcomes, including how visibility, relevance, and opportunities are distributed among users, items, and providers. However, these systems are usually evaluated in terms of accuracy and fairness alone, while their computational and environmental costs remain largely invisible. This omission matters because fairness interventions may affect the cost of recommendation in different ways. Training-time methods modify model optimization, post-processing methods add computation at inference time, and both may depend on the model, dataset, hardware, and deployment setting. We examine whether provider-side fairness in recommendation comes with a measurable green cost. We compare in-processing, graph-level reweighting and post-processing interventions across multiple models, two datasets, and two hardware settings. We measure recommendation quality, provider-side exposure, and energy consumption separately across training and inference stages. Our results show that the green cost of fairness is not uniform, post-processing shifts cost to repeated serving, while in-processing and graph-level methods avoid re-ranking overhead but vary substantially across models, datasets, and hardware. Findings call for evaluating fairness-aware recommendation as a three-way trade-off between accuracy, fairness, and computational cost.
[IR-43] A Multi-Source Ensemble Approach to Candidate Generation for Alternative Vacation Rental Property Recommendations RECSYS2026
链接: https://arxiv.org/abs/2609.05748
作者: Syed Mohammed Arshad Zaidi,Eric Rincon,Shayan Hassantabar
类目: Machine Learning (cs.LG); Information Retrieval (cs.IR)
备注: Accepted at RecTour 2026, Workshop on Recommenders in Tourism, co-located with the 20th ACM Conference on Recommender Systems (RecSys 2026). To appear in CEUR Workshop Proceedings
Abstract:Alternative property recommendations play a critical role in vacation rental marketplaces, helping users discover relevant options when viewing a specific listing. However, generating high-quality candidate alternatives presents unique challenges: heterogeneous inventory, geographic constraints, rapid availability changes, and long-tail property distributions. We present a comprehensive study of candidate generation (CG) approaches for vacation rental alternatives, comparing collaborative filtering, shallow embeddings, and graph neural network (GNN) methods. Our experiments on a large-scale vacation rental platform (over 2M active properties) show that a hybrid architecture combining item-based collaborative filtering with GNN-based retrieval improves Recall@300 by 14.8% over the strongest baseline, by leveraging the complementary strengths of the two sources: collaborative filtering excels at early recall for properties with rich interaction history, while GNNs discover diverse, non-obvious alternatives and handle cold-start scenarios more effectively. As a component result, GNN-based embeddings alone substantially outperform shallow Hotel2Vec embeddings (48-68% relative recall improvement across K), motivating their inclusion in the ensemble. Crucially, we examine how CG-stage gains carry through to the downstream ranking stage, and find that a stronger candidate pool yields higher downstream ranking quality, though attributing this effect cleanly is complicated by the coupling between candidate generation and ranker training. This recall-conversion gap is an important consideration for practitioners deploying new retrieval methods in two-stage recommendation systems. Comments: Accepted at RecTour 2026, Workshop on Recommenders in Tourism, co-located with the 20th ACM Conference on Recommender Systems (RecSys 2026). To appear in CEUR Workshop Proceedings Subjects: Machine Learning (cs.LG); Information Retrieval (cs.IR) Cite as: arXiv:2609.05748 [cs.LG] (or arXiv:2609.05748v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.05748 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-44] Recovering Temporal and Geographic Signals from Language Model Embeddings
链接: https://arxiv.org/abs/2609.05721
作者: Esteban Feuerstein,Victoria Klimkowski,Juan Manuel Ortiz de Zarate,Federico Hernán Suaiter
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:Understanding whether language-model embeddings encode structured real-world information is important for both representation analysis and information retrieval. We study this question for temporal and geographic signals using a simple projection-based method that operates directly on output embeddings. Given a small set of seed examples, the method defines an axis in embedding space and ranks texts or entities by their projection onto that axis. Our approach is fully black-box and model-agnostic: it requires only embeddings, without access to model weights, internal activations, auxiliary probes, or additional training. This makes it applicable to modern embedding models available only through APIs and provides a lightweight way to analyze whether temporal and spatial dimensions are present in their representation spaces. We apply the method to temporal and geographic datasets and find that embedding projections recover meaningful chronological and spatial structure. These results provide evidence that output embeddings encode signals relevant to time and space, while also offering a practical tool for interpretability and for downstream temporal and geographic information retrieval tasks, such as temporal ordering, geographic ranking, and tagging.
[IR-45] Overview of ROMCIR 2026: The 6th Workshop on Reducing Online Misinformation through Credible Information Retrieval
链接: https://arxiv.org/abs/2609.05684
作者: Marcos Fernández-Pichel,Marinella Petrocchi,Kevin Roitero,Marco Viviani
类目: Information Retrieval (cs.IR)
备注:
Abstract:In the digital online ecosystem, we are surrounded by distinct forms of information pollution, posing significant threats to both individuals and society. Fake news, for instance, wields power to sway public opinion on matters of politics and finance. Deceptive reviews can either bolster or tarnish the reputation of businesses, while unverified medical advice may steer people toward harmful health practices. In light of this challenging landscape, it has become imperative to ensure that users have access to both topically relevant and factually accurate information that does not warp their perception of reality, and there has been a surge of interest in various strategies to combat misinformation through different contexts and multiple tasks. The purpose of the ROMCIR Workshop, for some years now, is precisely that of engaging the Information Retrieval community to explore potential solutions that extend beyond conventional misinformation detection approaches. Key objectives include identifying subjective and objective factors associated with information credibility and truthfulness, respectively, and integrating such factors as fundamental dimensions of relevance within IR Systems (IRSs), achieving early detection of misinformation, and ensuring that the search results retrieved are not only truthful but also explainable to the users of IRSs. Moreover, it is essential to evaluate the role of generative models such as Large Language Models (LLMs) in inadvertently amplifying misinformation problems, and how they can be used to support IRSs, together with the contribution that the human-in-the-loop paradigm can have in this context.
[IR-46] Better Together: Complementary Query Rewriting Under a Strong RAG Baseline EMNLP2026
链接: https://arxiv.org/abs/2609.05637
作者: Sara Shanian,Xiaoqin Yi,Pavlo Ruban,Kurt MacDonald
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: 10 pages. Accepted at EMNLP 2026 (Industry Track)
Abstract:A popular way to improve Retrieval-Augmented Generation (RAG) is to rewrite the user’s question into several variants and search with all of them. We test whether this actually helps once the underlying search is already strong. Under one fixed, competitive pipeline (BGE dense retrieval, cross-encoder reranking, and MMR diversification), we compare four query-rewriting strategies (S1-S4) against two strong LLM baselines (HyDE, Query2Doc) on three datasets (HotpotQA, AmbigNQ, and the 512K-document EnterpriseRAG-Bench) over three seeds with paired-bootstrap significance tests. Our headline result is that rewriting alone is at best competitive with a strong baseline, but combining methods yields outsized gains because different strategies fail on different questions. A post-hoc union of four methods (S1+S3+S4+HyDE) improves HIT@10 over the baseline by +12.5 points on enterprise data (51.70 vs 39.22), and a five-method union reaches 52.98 (+13.8). Budget-matched controls capture only ~40% of this gain, confirming that complementarity, not retrieval budget, is the primary driver. On HotpotQA the union adds +1.6 to +1.8 points (p0.001), saturating the all-method oracle; on AmbigNQ the same fusion hurts (-2.4 below the best solo, p0.001), and we analyze when and why. Because rewriting is expensive, we evaluate in simulation a confidence-gated router that runs rewriting only when the baseline’s own top-1 score is low. It captures about half of the enterprise full-merge gain (+4.3 HIT@10) while paying rewriting cost on 40% of queries, and automatically declines to rewrite on AmbigNQ. A downstream answer-quality evaluation confirms the router improves F1 by +1.92 (p0.01) at roughly 40% of the expansion cost. In short: treat query rewriting as a complementary coverage source, applied through cost-aware routing, not as a standalone replacement for a strong baseline.
[IR-47] PAGR: Proof-Carrying Algebraic-Geometric Retrieval: A Quiver- Provenance- and Sheaf-Theoretic Framework for Grounded LLM Retrieval
链接: https://arxiv.org/abs/2609.06127
作者: Xingting Wang,Min Wu
类目: Representation Theory (math.RT); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: 42 pages
Abstract:Retrieval-augmented generation is usually formulated as a statistical information-retrieval problem. Graph-based variants add relational structure, but the mathematical status of that structure is often left underspecified. Three distinct questions tend to be conflated: which statements are certified as knowledge, which latent representations are useful for retrieval, and which multi-hop compositions are semantically admissible. We propose Proof-Carrying Algebraic-Geometric Retrieval (PAGR), a framework that separates these questions mathematically. Its symbolic layer is a many-sorted relational theory generated by a typed quiver, path equations, and positive Horn inclusions. A quiver representation assigns inner-product spaces to entity types and linear operators to relations. A cellular sheaf measures local-to-global consistency. Semiring provenance records derivations and supports machine-checkable certificates. The central principle is epistemic separation: learned geometry may rank and organize evidence, but cannot promote a hypothesis to certified ground truth. We show the certification criterion is invariant under arbitrary replacement of learned components. Further results include a conditional completeness bound, identification of the isometry group as the relevant symmetry for residual-based retrieval, a cohomological consistency diagnostic, and a bounded-bisimulation index for admissible-path expansion. PAGR is a mathematical architecture for separating where a system should look from what it is allowed to treat as knowledge. Comments: 42 pages Subjects: Representation Theory (math.RT); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) Cite as: arXiv:2609.06127 [math.RT] (or arXiv:2609.06127v1 [math.RT] for this version) https://doi.org/10.48550/arXiv.2609.06127 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Xingting Wang [view email] [v1] Sat, 5 Sep 2026 14:51:32 UTC (48 KB)
人机交互
[HC-0] ravel Package Booking Application with API Bot
链接: https://arxiv.org/abs/2609.09112
作者: K Sai Karthik,CH Naveen Aaditya,Ravi Kiran,Swarnalatha P
类目: Human-Computer Interaction (cs.HC)
备注: 6 pages. Originally published in the International Journal of Recent Technology and Engineering (IJRTE), Volume 8, Issue 4, November 2019
Abstract:These days we are witnessing many mobile applications based on the recommended systems, which have become a great technology which is been used by the various mobile applications according to the situation. Recommendation provided by the mobile application is a key element for the person who is traveling to several places. For any tourist information application contextual information is much needed to guide the user on his interests this can be achieved by the Context-aware computing. Which provides the user most interactive system with the suggestions provided by it based on the input from the user in a certain location, here context includes the user’s mental, social, physical environments. To achieve this contextual information, we will design and implement the context-aware user interface based on the user for which we have to study the user and design a rich user interface. The final outcome for which users have the satisfaction when using context-aware functionality will be much better than non-context-aware application.
[HC-1] Performance of Clinical AI System and Physicians and Frontier Language Models in primary care diagnostics
链接: https://arxiv.org/abs/2609.09070
作者: Andy Nkansah,Hanna Plotnitskaya,Stanislau Salavei,Anna Kozlova,Piotr Gibas,Julian Milek,Viktar Harbachou,Aleksey Ropan,Pavel Satalkin
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Clinical AI evaluation should encompass diagnosis and management after adaptive information gathering. We compared Doctorina, eight physicians and four standalone frontier language models in 150 synthetic Polish-language primary-care consultations. Doctorina achieved 82.0% Top-1 concordance versus 57.0% for physicians (difference, 25.0 percentage points; 95% confidence interval, 17.7-32.7) and 97.3% versus 85.0% primary-or-reference-differential concordance. Across 149 case pairs, normalized workup and treatment scores were 89.4 versus 66.9 and 83.7 versus 61.2. Doctorina had the highest diagnostic point estimates among all six groups; Kimi K3 ranked next, while Claude Opus 5 led the closely spaced management estimates of Opus, Doctorina and Kimi. A second Doctorina execution reproduced the advantages over physicians across all outcomes. Doctorina’s advantage over physicians therefore extended from primary-diagnosis selection to higher-rated diagnostic workup and initial treatment after adaptive consultation.
[HC-2] Location-Independent Robot-Assisted Finishing Using Digital Twins and Extended Reality
链接: https://arxiv.org/abs/2609.09061
作者: Jose Outeiro,Jia Holt,Tero Kaarlela,Khalil Chakal
类目: Human-Computer Interaction (cs.HC)
备注: 6 pages, 5 figures, 2027 IEEE/SICE International Symposium on System Integration
Abstract:This paper presents a cyber-physical system (CPS) for location-independent programming, supervision, training, and teleoperation of a Robot-Assisted Finishing (RAF) system used to post-process metal additive-manufactured (AM) components. A digital twin (DT) built in Unity is delivered to the operator as a WebGL application that supports both desktop and immersive modes through WebXR-compatible devices. Moreover, it exchanges robot state and pose commands with a collaborative robot through a Message Queuing Telemetry Transport (MQTT) broker. The DT enforces kinematic and collision constraints before a pose is released to the physical robot, and augments the virtual component with a color map of the surface topography that supports operator decisions on part repositioning or process termination. The architecture was validated on a specially designed physical RAF system. A steady-state joint synchronization error of 0.12 deg and a mean round-trip latency of 563 ms were measured, which is adequate for supervisory programming and intermittent teleoperation.
[HC-3] Do Reasoning Representations Help Humans Evaluate LLM Outputs? EMNLP2026
链接: https://arxiv.org/abs/2609.09038
作者: Jaewoo Lim,Sungbok Shin,Sanghyun Hong
类目: Machine Learning (cs.LG); Human-Computer Interaction (cs.HC)
备注: 19 pages. EMNLP 2026 (Findings)
Abstract:Reasoning representations are increasingly used as explanations for large language model outputs. Yet they are typically evaluated with model-centric criteria, such as answer accuracy and faithfulness, leaving it unclear whether they help people evaluate model responses. In this work, we study reasoning representations as human-facing interfaces rather than proxies for model reasoning ability. We conduct a controlled human study of six reasoning formats across tasks of varying complexity, supported by a web-based framework that randomizes task domains, problem instances, and representation order. The study collects fine-grained judgments of structural understanding, error detection and localization, and trust calibration. Our study shows a mismatch between perceived preference and support for human evaluation. Participants prefer planning- and decomposition-based representations, but simpler chain-of-thought traces better support verification, trust, and interpretability. Preferred representations also introduce calibration risks, with more false alarms on correct traces and high trust despite low willingness to verify.
[HC-4] Embedded Human-Centered Data Science in a Graduate Programming Course: A Framework and Case Study
链接: https://arxiv.org/abs/2609.08982
作者: Victoria Chui,Kelly McConvey,Daniel Chui,Malayna Bernstein,Shion Guha
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:As AI and data-driven systems pervade practice, there is an imperative for instructors to embed societal impact and ethics content into computing courses. In response, we present the Human-Centered Education for Learning in Information and eXplainable Computing (HELIX) framework for information science programs, organized around three iterative pillars - knowledge building, decision-making, and empowerment - with concrete actions for instructors and students. We applied the framework in a graduate, introductory programming course using readings, algorithmic design activities, and scenario-based reflections. We present a pilot implementation of this framework to examine changes in students’ (n=22) knowledge acquisition, decision-making processes, and self-reflection regarding human-centered perspectives in data science. We release an anonymized materials kit (survey, assignments, analysis code) to support adoption. We discuss design tensions (workload, assessment, relevance to diverse information science learners) and provide guidelines for integrating human-centered content without overwhelming technical outcomes. Findings suggest that the HELIX Framework is feasible in information science contexts and future work should use comparative survey assessment to strengthen causal inferences.
[HC-5] o Stop or Not to Stop: Exploring the Intention-Behavior Gaps in Smartphone Usage
链接: https://arxiv.org/abs/2609.08909
作者: Jian Zheng,Eun Kyoung Choe
类目: Human-Computer Interaction (cs.HC)
备注: MobileHCI 2026
Abstract:As smartphones become integral to daily life, researchers have sought to identify when the use becomes problematic. Previous studies have operationalized problematic smartphone usage (PSU) from either an intention or a behavior perspective. Both risk delivering interventions not welcomed by users. We propose a novel approach to operationalizing PSU as the intention-behavior gap (IBG). We collected self-reported data on intentions to stop phone usage, alongside usage behavior data, from 37 participants over two weeks. We calculated IBG, examined effects of demographic and contextual variables, and developed machine learning models to predict IBG in real time. We found that IBG was explained by gender, time, app, and input interactions, among other factors. Intention was predicted most accurately with only personal data, whereas behavior and IBG were predicted most accurately with both personal and global data. Our findings can inform the design of future intervention tools optimized for timing and adaptive intensity.
[HC-6] Healthcare Utilization Chronic Condition Management and Workplace Functioning Among Users of a Purpose-Built Mental Health AI (Ash): Cross-Sectional Study
链接: https://arxiv.org/abs/2609.08890
作者: Kristen M. Van Swearingen,Thomas D. Hull,Jeffrey Swigert,Caitlin A. Stamatis
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Mental health challenges can exacerbate physical symptoms and complicate management of chronic conditions. Purpose-built artificial intelligence (AI) tools may offer scalable support for co-occurring mental and physical health concerns. This cross-sectional study compared past-6-month healthcare utilization, chronic condition management, physical health behaviors, mental health change, and workplace functioning between active (n = 169) and non-users (n = 73) of a mental health AI (Ash). Participants had at least one chronic condition (e.g. hypertension, chronic pain). Binary outcomes were modeled as adjusted risk differences (RDs) using linear probability models and continuous outcomes were modeled with linear regression; all models were adjusted for hypertension. Relative to non-users, active users were more likely to report improved mental health (61.4% vs. 34.3%; RD = 0.27), higher medication adherence (91.7% vs. 76.4%, RD = 0.15), fewer skipped or delayed chronic-condition care activities (b = -0.44), and were less likely to report repeat urgent care visits (9.5% vs. 23.3%; RD = -0.15) and monthly-or-more absenteeism (24.2% vs. 45.2%; RD = -0.20, all ps .05). Findings provide preliminary evidence that use of purpose-built AI may be associated with positive symptom-based and utilization outcomes for those managing co-occurring mental and physical concerns.
[HC-7] ArmPoser: Real-Time Calibration-Free Arm Pose Estimation from Smartwatch IMU
链接: https://arxiv.org/abs/2609.08806
作者: Bishnu Dev,Vasco Xu,Xi-Aan Loh,Chenfeng Gao,Henry Hoffmann,Karan Ahuja
类目: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
备注:
Abstract:Arm pose estimation enables applications in fitness, extended reality input, rehabilitation, and life logging. Prior smartwatch-based approaches rely on calibration poses and preprocessing pipelines that transform raw IMU measurements into standardized training formats. These steps hinder deployment in everyday settings and introduce errors due to imperfect calibration and sensor drift. We present ArmPoser, a calibration-free arm pose estimation system using a single smartwatch IMU. Our central contribution is training models directly in the reference frame native to consumer smartwatches, aligning learning with how IMU data is produced by deployed devices. By operating on device-native axes, ArmPoser removes the need for coordinate transformations, explicit alignment, and bone-offset calibration used in prior work. We further augment training with physically grounded variations in watch placement and arm morphology to account for user-specific variability. ArmPoser also includes a wear-configuration module that infers anterior or posterior forearm placement and crown orientation. We evaluate pose estimation on public benchmarks and on a 10-participant, 30-activity study using watchOS and Android smartwatches, where ArmPoser matches or exceeds calibrated baselines without any user calibration.
[HC-8] A Controlled Comparison of Manual and Teleoperated Intraocular Instrument Motion for an Input Device
链接: https://arxiv.org/abs/2609.08770
作者: Korab Hoxha,Mirza Imamovic,Angelo Henriques,M. Ali Nasseri
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC)
备注: This work has been submitted to IEEE Access for possible publication
Abstract:Input devices for robotic microsurgery are frequently described as preserving the surgeon’s trained technique, but the claim is rarely measured. We compared manual and teleoperated intraocular instrument motion with the trocar constraint, the instrument, the eye model and the tracking source common to both conditions, so that the control interface was the only factor varied. Prior comparisons cannot hold the instrument fixed, because a robotic instrument is not the tool used manually. Sixteen participants performed a navigation task on a commercial ophthalmic simulator by hand and through a three-degree-of-freedom input device commanding a five-joint robot. Task outcome was equal but at ceiling: every participant acquired all five targets under both interfaces with no retinal or lens injury. Execution differed on every measure. Teleoperated trials took three times as long at a quarter of the median speed, covered less than half the angular working range, and were broken into 3.5 times as many separate movements. Completion time and movement fragmentation improved substantially across four trials of practice and had not plateaued; the measures set by the configured rate ceiling and joint limit changed the least. Finger activity doubled and pinch variability tripled, so reducing instrument degrees of freedom redistributed manual effort rather than reducing it. The interface preserves the outcome and reshapes the execution.
[HC-9] Rescuing Performance from the Demo: Co-Designing Drum Gesture Mappings with a Percussionist
链接: https://arxiv.org/abs/2609.08587
作者: Jordie Shier,Teresa Pelinski,Charalampos Saitis,Andrew Robertson,Andrew McPherson
类目: Human-Computer Interaction (cs.HC); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注: Accepted for publication at AIMC 2026
Abstract:Augmenting instruments with sensors and neural network mappings is a well-explored digital musical instrument design approach. While augmentations can create new expressive opportunities, they also exert aesthetic influence and can constrain musicians’ gestural language, which, if left unchecked, can lead to technological capture. To examine this, we conducted a study with a professional percussionist, co-developing a gesture mapping toolkit and recording a ten-track album. Drawing on the concept of productive dissonance, our study aimed to hold the musician’s aesthetic in tension with technological constraints. This, along with a practice-based reflective approach, supported the development of a continuous gesture recognition method for percussive mapping and surfaced insights into the design process. We identify knowing-when as a form of tacit knowledge that supported productive dissonance, and raise an open question: absent a musician’s broader social context, how do we know whether a technology’s influence is genuinely supporting their practice?
[HC-10] Visualizing Colonial Regimes: A Multi-View Approach to (Historical) Political Transformation
链接: https://arxiv.org/abs/2609.08518
作者: Nicole Husemann,Steffen Kailitz,Christofer Meinecke
类目: Human-Computer Interaction (cs.HC)
备注: 19 pages, 6 figures (figure 2 consists of two elements). Accepted for presentation at the Digital Heritage of European Conflicts Conference (DHECC 2026). Submitted to a special issue of the International Journal of Digital Humanities
Abstract:This paper takes a critical approach to visualization of colonial regimes. Drawing on critical hermeneutics and postcolonial theory, we created interactive visu- alizations revealing the constructed nature of colonial categories and exposing the hierarchies between empires and territories. Using the Varieties of Political Regimes dataset, our approach combines temporal flow visualization and geo- graphic distribution mapping. This allows us to contextualize colonial rule within broader patterns of political change. Our visualization challenges conventional, static, and isolated representations of colonial data by making the interpre- tive frameworks underlying colonial categorization visible through coordinated temporal, spatial, and relational views.
[HC-11] Enhancing Communication in Speech Therapy: Exploring the Cognitive Synergy Between Gesture and Speech
链接: https://arxiv.org/abs/2609.08486
作者: Paul-Peter Arslan,Xiao Xiao
类目: Human-Computer Interaction (cs.HC)
备注: 18 pages, 6 figures, 3 tables
Abstract:This paper examines the adaptation of a rhythm-based interface, originally designed for manual dexterity rehabilitation, for use in speech therapy. The interface allows users to control synthesized vocal phrases through finger tapping, leveraging the cognitive link between gesture and speech. Through interviews with four therapists and pilot tests with one speech therapist and eight children with speech impairments (autism, Down syndrome, verbal apraxia, dyslexia), we found that the interface improves motivation and therapeutic outcomes by facilitating more interactions between verbally challenged patients and the therapist. Our findings suggest that rhythmic gestures can enhance verbal communication, offering potential for broader therapeutic and educational applications.
[HC-12] Noise Adaptive Streaming Audio-Visual Speech Token Enhancement for Robust Full-Duplex Spoken Dialogue Models EMNLP2026
链接: https://arxiv.org/abs/2609.08390
作者: Bella Godiva,Yeonju Kim,Yong Man Ro
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: Accepted to EMNLP 2026
Abstract:Full-duplex spoken dialogue systems enable simultaneous listening and speaking, but their audio-only perception often fails under background noise and overlapping speech, leading to incoherent responses. Recent audio-visual dialogue approaches show that incorporating visual cues such as lip movements improve robustness under audio corruption. However, existing approaches often adapt the large speech dialogue model itself to process visual input, requiring costly multimodal training. We propose AV-STE, a modular streaming audio-visual front-end that restores corrupted semantic speech tokens from noisy audio and lip video before they reach the speech LLM. The downstream dialogue model remains entirely frozen, preserving its pretrained conversational capabilities. When integrated with frozen Moshi, AV-STE improves average GPT-4o-judged response coherence from 1.42 to 1.91 under same-dataset speaker interference while largely preserving turn-taking behavior. Gains also transfer to out-of-domain Seamless Interaction.
[HC-13] “Here Be Sharks!”: Enhancing Scientific Communication and Analysis through Authoring Interactivity
链接: https://arxiv.org/abs/2609.08386
作者: Caroline Berger,Josh Pollock,Dylan Wooton,Arvind Satyanarayan,Clemens Nylandsted Klokmose
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:We report on a case study for designing authoring environments for interactive visualizations to enhance scientific work. We conducted a workshop and prototype review with a group of marine biologists. When it came to visualizing their data, participants identified challenges in conveying their research accurately and completely as well as and analyzing it with ease. Based on our findings, authoring environments should consider the context of scientific work aspects such as domain expertise, collaboration culture, and publication traditions. We propose consideration of non-traditional programming languages and environments for scientific work, discuss ways of facilitating interactive visualizations for scientists, and examine ways of meaningfully integrating AI. The critiques, artifacts, and reactions from the scientists along with our analysis and discussion inform how computational tools should be designed for scientific work.
[HC-14] StitchOver: Technical Embroidery on Seamed Fabrics
链接: https://arxiv.org/abs/2609.08311
作者: Zekun Chang,Tianhong Catherine Yu,Yixuan Gao,Thijs Roumen
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Smart textiles embed interactivity into everyday garments, supporting use cases like always-available sensing for medical applications or sports. Machine embroidery allows integrating functionalities into existing textiles. However, embroidering onto real-world textile goods remains challenging. Textile goods are rarely made of a single homogeneous substrate of fabric, and embroidery with functional materials such as conductive threads requires machines to be more tightly calibrated than for decorative embroidery. In particular, seams, which bring together different substrates, along with machine variability, cause shifts in tension and friction between the functional thread and the textile substrate that frequently lead to defects (70% of samples in our evaluation). We present a technique to reliably embroider on seamed fabric even when using functional threads. Our software tool automatically digitizes user-defined stitch patterns by introducing what we call “JumpStitches” to bypass seam interference. We evaluated our approach under varying machine states (under-tensioned, well-calibrated, and over-tensioned), and across multiple seam and pattern configurations. Our results show that the JumpStitch mechanism eliminates defects, while maintaining conductivity compared to 70% defects without JumpStitches, and even in poorly calibrated machine states continues to work well. Subjects: Human-Computer Interaction (cs.HC) Cite as: arXiv:2609.08311 [cs.HC] (or arXiv:2609.08311v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2609.08311 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.1145/3830398.3830717 Focus to learn more DOI(s) linking to related resources
[HC-15] DriveMotion: A Large-Scale Multi-Source Benchmark for Driver Motion Sequence Modeling and Forecasting
链接: https://arxiv.org/abs/2609.08117
作者: Yuhang Wang,Chuheng Wei,Jingxin Yang,Xishun Liao,Hao Zhou
类目: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
备注: 21 pages, 7 figures, 18 tables. Dataset and benchmark: this https URL project page: this https URL
Abstract:Driver motion can provide cues to ongoing behavior, attention, and near-term driving intent. However, most existing driver-centric datasets focus on recognizing predefined driver behaviors from short video clips, while human motion forecasting benchmarks largely target motion outside the vehicle. We introduce DriveMotion, a multi-source benchmark for continuous driver motion forecasting. DriveMotion contains 393 hours of 133-keypoint motion sequences at 10 Hz from 360 drivers, integrating naturalistic driving data, curated public in-cabin videos, and the AIDE dataset into a unified representation with per-joint validity masks and synchronized driving context. Naturalistic driving contains long periods of limited body movement, making uniformly sampled evaluation dominated by persistence and less sensitive to brief but behaviorally meaningful motion. To address this, we use dynamics-anchored evaluation, placing forecasting windows around vehicle maneuvers identified offline from CAN signals without providing CAN to the model at inference. Arm motion in pre-maneuver windows is 3.4x greater than in route-matched stable-driving controls. On these anchored windows, learned models reduce forecasting error over persistence by up to 15%, while maneuver-enriched training improves forecast-derived Part-State F1 by 44% over the zero-motion reference. Training on the full multi-source corpus further reduces forecasting error on held-out web drivers by 38% compared with BATON-only training. DriveMotion provides identity-disjoint splits, fixed evaluation subsets, and reference implementations for reproducible evaluation of continuous driver motion forecasting. The dataset and benchmark are available at this https URL
[HC-16] Representational Fidelity in Didactic Visualization: Toward a Multidimensional Design Space IEEE-VIS2026
链接: https://arxiv.org/abs/2609.08037
作者: Shehryar Saharan,Michele Oliver,Karen Gordon,Gaël McGill,Jodie Jenkinson
类目: Human-Computer Interaction (cs.HC)
备注: To appear in IEEE Transactions on Visualization and Computer Graphics (Proceedings of IEEE VIS 2026). Supplemental material: this https URL
Abstract:Representational fidelity is routinely treated as a single abstract-realistic continuum, a simplification that limits how it is described and compared across research and design contexts. We introduce a multidimensional design space of representational fidelity for didactic visualization in science engineering, inductively derived from a 175-item corpus spanning several disciplines, modalities, and instructional aims. The resulting design space specifies five dimensions: Morphological, Dynamic, Cueing, Contextual, and Interactive Fidelity, with seven sub-dimensions. We demonstrate the design space’s descriptive power through successive rounds of expansion and refinement and analyze the corpus to reveal relationships among dimensions and implications for design and research. We further validate the design space through a pilot focus group in which participants applied the dimensions in an open-ended design exercise. Resulting sketches and verbal rationales informed a single-designer applied case study, offering preliminary evidence of the design space’s generative potential as a structured aid to design exploration. Together, these contributions lay the groundwork for future research and more intentional design practice.
[HC-17] Delusions and Harms Associated with AI Chatbot Use: Early Evidence from 185 Real-World Reports
链接: https://arxiv.org/abs/2609.08027
作者: Hamilton Morrin,Vinitha Soundararajan,Thomas Cheliotis-James,Boris Warszawski,Joshua Fakulujo,Zeqi Jia,Etienne Brisson,Thomas A. Pollak
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注: 18 pages, 3 figures
Abstract:Importance: Reports have raised concerns that AI chatbots may validate or elaborate delusional beliefs, respond inappropriately to suicidal ideation, and contribute to mental health harms, but real-world data on reported harms remain limited. Objective: To characterize psychopathological features, chatbot behaviors, timing, and outcomes in first- and second-hand accounts of mental health harm linked with AI chatbot use. Design: Cross-sectional secondary analysis of deidentified online survey responses gathered between August 7, 2025, and February 2, 2026. Main Outcomes and Measures: The primary quantitative outcome was the presence of delusional beliefs, coded by paired raters with relevant clinical experience. Additional variables included reason for chatbot use, current episode features, delusional content, chatbot validation of beliefs, harms, social and occupational consequences, healthcare use, and timing. Results: 95 first-hand and 90 second-hand accounts were analyzed. Median age was 35.0 (IQR 27.0 - 45.0). Raters coded descriptions consistent with delusional beliefs in 102 reports (55.1%), with chatbot validation of beliefs in 50/102 (49.0%). Common outcomes included isolation, relationship breakdown, hospital admission, job loss, and financial loss. Four second-hand reports described death by suicide. Conclusions: In this self-selected convenience sample, AI-chatbot-associated harms were frequently described in relation to delusional beliefs, perceived chatbot validation, intensive use, and substantial social, occupational, and clinical consequences. Because reports were retrospective, unverified, and collected from individuals seeking to report harm, our findings should be interpreted as preliminary signal detection rather than as suggesting prevalence or providing evidence of causality. Prospective surveillance and trajectory-based safety evaluations are needed. Comments: 18 pages, 3 figures Subjects: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY) Cite as: arXiv:2609.08027 [cs.HC] (or arXiv:2609.08027v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2609.08027 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Hamilton Morrin [view email] [v1] Mon, 7 Sep 2026 22:25:37 UTC (1,004 KB) Full-text links: Access Paper: View a PDF of the paper titled Delusions and Harms Associated with AI Chatbot Use: Early Evidence from 185 Real-World Reports, by Hamilton Morrin and 7 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.HC prev | next new | recent | 2026-09 Change to browse by: cs cs.CY References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[HC-18] Designing for Healthy Affordable and Sustainable Human-HVAC Interactions for Heating in Smart Homes
链接: https://arxiv.org/abs/2609.07936
作者: Delong Korus-Du
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY); Multimedia (cs.MM); Social and Information Networks (cs.SI)
备注:
Abstract:As geopolitical tensions, energy crises, and energy-intensive AI infrastructure intensify concerns about demand, affordability, and resilience, communities increasingly encounter these challenges through everyday energy practices, particularly winter heating. Against this background, the doctoral exposé, “Designing Human-HVAC Interaction for Healthy, Affordable, and Sustainable Heating in Smart Homes”, is structured around four chapters. First, a multidisciplinary literature review defines and positions Human-HVAC Interaction, focusing on heating in smart homes. Second, longitudinal living lab studies with design probes examine everyday heating practices, thermal comfort, and indoor environmental quality, with attention to thermally vulnerable groups such as older adults, pregnant or menopausal women, parents with infants, and people affected by allergies or airborne pollutants. Third, a VR-based smart home demonstrator explores how heating and IEQ scenarios can be prototyped and evaluated as a virtual living lab, while critically examining the limits of representing bodily indoor climate conditions through VR. Fourth, follow-up design studies examine how VR-based insights can be translated into physical-digital prototypes that combine digital fabrication, distributed environmental sensing, and diverse interface forms for critical heating and IEQ contexts. The thesis aims to contribute a design-oriented understanding of Human-HVAC Interaction by building from a multidisciplinary literature review to empirical living lab and co-design studies, VR-based prototyping, and physical system development, examining how smart home users make sense of, negotiate, and respond to smart HVAC system.
[HC-19] Humans Introduce Models Elaborate: Asymmetric Narrative Agency in Human-LLM Co-Writing EMNLP2026
链接: https://arxiv.org/abs/2609.07920
作者: Halfdan Nordahl Fundal,Yuri Bizzoni,Charlotte Gjørup Bilde,Ida Bække Johannesen,Rebekah Baglini
类目: Human-Computer Interaction (cs.HC)
备注: 12 pages, 11 figures, EMNLP 2026
Abstract:Human-LLM co-writing is increasingly used for open-ended text generation, but much prior work focuses on final outputs rather than the interactional dynamics through which stories are produced. We study turn-based collaborative storytelling across three matched conditions: Human-Human (HH), Human-LLM (HA), and LLM-LLM (AA). Using a shared storytelling paradigm, we measure how agents align, introduce novel material, and influence narrative development through turn-level measures of valence adaptation, semantic novelty, transience, and resonance. Our results show that HA co-writing is not intermediate between HH and AA collaboration. Instead, it displays a distinctive asymmetry where humans tend to introduce more novel and persistent narrative material, while LLMs tend to elaborate and stabilize the existing context. These findings suggest that, in this setting, LLMs function less as human co-authors and more as adaptive narrative amplifiers that reshape how agency is distributed in collaborative writing.
[HC-20] Quantifying the Engagement Trap: Impact of Short-form Video Recommender Systems on Users with ADHD
链接: https://arxiv.org/abs/2609.07795
作者: Vedad Misirlic,Gregor Mayr,Elisabeth Lex
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: Preprint accepted at The 28th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2026)
Abstract:Short-form video platforms use recommender systems to maximize engagement through highly efficient personalized recommendations. However, the impact of these recommendations on users with ADHD compared to users without ADHD remains underexplored. Through this study, we introduce and operationalize the Engagement Trap, illustrating how recommender systems, while successfully optimizing for engagement, disproportionately disadvantage users with ADHD. This stratified study of 302 participants, recruited via the online platform Prolific, compares experiences between participants with and without ADHD. Our results show that while recommendations are perceived as relevant across groups, participants with ADHD report significantly higher levels of time blindness, post-usage regret, and emotional distress when consuming recommendations. Moreover, we collect feedback for several proof-of- concept, theoretical design interventions for neuro-inclusive design principles. These findings provide quantitative evidence of systemic differences in engagement-optimized recommender systems and highlight the unbalanced negative effects and interactions these systems create for participants with ADHD. We argue for neurodiversity-aware, human-centered design approaches that mitigate such algorithmic harms and support more equitable experiences.
[HC-21] Situated Action in Pre-Hospital Critical Care Dispatch: Identifying where and how Algorithmic Assistance might be useful in the daily work of specialist Emergency Medical Dispatchers
链接: https://arxiv.org/abs/2609.07705
作者: Ben Wilson,Matt Roach,Greg Browning,Chris Connor,David Rawlinson
类目: Human-Computer Interaction (cs.HC)
备注: 49 pages, 11 figures
Abstract:This study uses ethnographic immersion and observation as contextual inquiry to understand situated action at an Emergency Medical Dispatch critical care hub. The work is a response to the urgent need to recruit context-specific knowledge and participation into design that helps to narrow the AI Chasm - the gap between the promise of Artificial Intelligence (AI) systems and what they deliver for clinicians and their patients. The work of a pre-hospital critical care team’s dispatch process is described and analysed to reveal both the structure of the workflow and the different cognitive demands it makes on staff tasked with dispatch decision-making. Elements of attention, communication and focus between the humans, as they carry out this work, are drawn out in order to understand the work-as-done and identify the many dependencies in the process. The motivation is to establish where AI support might be useful and to discover what challenges there could be in designing appropriate algorithmic assistance. We ask whether, where and how the design and implementation of an AI system might be considered. The ultimate objective is to improve the decision process itself to the benefit of clinicians and patients. The study identifies three key steps in the situated workflow and details how decision-makers negotiate each one as emergency calls follow complex routes between them. We find compelling evidence that the first of these decision steps constitutes the most promising candidate for unobtrusive assistance that could be safe and effective in improving both clinician workload and clinical outcomes. Comments: 49 pages, 11 figures Subjects: Human-Computer Interaction (cs.HC) Cite as: arXiv:2609.07705 [cs.HC] (or arXiv:2609.07705v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2609.07705 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[HC-22] How AI Models Manage Epistemic Authority: A Taxonomy and Comparative Analysis of Responses to User Disagreement EMNLP2026
链接: https://arxiv.org/abs/2609.07662
作者: Riyadh Alnasser,Yusuf Mücahit Çetinkaya,Sumin Zhao,Tuğrulcan Elmas
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: Accepted to EMNLP 2026
Abstract:Large language models are increasingly used as sources of advice and information, including in high-stakes settings, yet little is known about how they respond to user disagreement. We study how a model manages its epistemic authority, referring here to its claim to knowledge, competence, or the right to advise, once a user challenges its answer. Building on Conversation Analysis, we introduce a taxonomy of six challenge types and a four-layer framework for analysing each response: whether the original claim is maintained or changed, where authority is located, how the disagreement is socially managed, and what kind of evidential support is offered. We construct a new dataset of 2,310 controlled challenge scenarios and 32,340 corresponding responses from 14 models, and analyse them using our framework with an LLM-as-judge pipeline, providing a vocabulary which future evaluation and benchmark design can build on. We find that models show conflicting behaviour: they validate users in 85% of responses but maintain their original claim in 65%. They explicitly apologise in 33% of responses, yet 59% of those apologies accompany maintenance of the original claim. They transfer authority most often in advice tasks, doing so in 28% of responses and reaching 57% in health advice and 49% in legal advice, compared with 6% in fact and 3% in explanation tasks. Abandonment of the original claim ranges from 0.8% for GPT-5.2 to 40% for DeepSeek 7B, while complete replacement of the original claim is rare overall at 1.5%.
[HC-23] JudgmentLens: Human-AI Sensemaking of Complex Legal Judgments
链接: https://arxiv.org/abs/2609.07607
作者: Xinyi Chen,Ruijie Li,Yuelu Li,Chen Liang
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Judicial judgments are increasingly available, yet dense language and distributed relationships among facts, evidence, reasoning, and rulings remain difficult for non-experts to interpret. Through a mixed-methods formative study with Chinese non-expert readers (survey N=34; interviews N=6), we identified structural, interpretive, verification, and action breakdowns. We developed JudgmentLens, an AI-augmented reading system combining persistent case representations, adaptive explanations, and traceable links from generated interpretations to judgment passages. In a counterbalanced within-subject evaluation (N=16), participants completed tasks faster with JudgmentLens than with conventional PDF reading and reported lower workload and greater self-reported decision understanding, while rubric-scored comprehension did not differ reliably. An exploratory PDF+DeepSeek probe suggested that conversational AI supported formulated questions while leaving question formulation, answer integration, and source checking largely to users. We contribute an empirical account of non-expert judgment sensemaking and design strategies for inspectable, source-grounded AI mediation.
[HC-24] Large-Scale User Behavior Analysis in Multimodal AI-Assisted Manual Task Execution
链接: https://arxiv.org/abs/2609.07594
作者: Rafael Ferreira,Diogo Tavares,Diogo Glória-Silva,David Semedo,João Magalhães
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Preprint version accepted at EPIA 2026
Abstract:Conversational Task Assistants (CTAs) are multimodal dialogue systems that support users in complex real-world tasks such as cooking and DIY through voice, text, image, and video interactions. Prior user studies have focused on controlled settings, leaving limited understanding of real-world CTA usage at scale. In this work, we present a large-scale study of CTA usage based on thousands of users in-the-wild. Our large-scale real-world data analysis unveils new understandings of (i) user-CTA interaction flows, (ii) user intents, (iii) user conversational traits, and (iv) behavioral factors associated with user satisfaction. Our findings reveal key opportunities for future research in CTAs, particularly in user interaction design and task engagement, concluding with concrete design guidelines.
[HC-25] From Human Factors to Human-Technology Factors: An HCI Perspective on Technology in Avalanche Safety
链接: https://arxiv.org/abs/2609.07560
作者: Björn Hartmann,Jason Smith
类目: Human-Computer Interaction (cs.HC)
备注: Preprint of ISSW 2026 paper
Abstract:The avalanche safety literature has identified human factors that contribute to accidents, yet researchers note a persistent gap between these insights and effective product design. Meanwhile, interactive technologies are shaping backcountry decision making with or without grounding in theory and research. We broaden the discussion of human factors into human-technology factors," examining how technology can both support and undermine judgment in avalanche terrain, from a human-computer interaction (HCI) perspective. We summarize relevant HCI concepts along four dimensions - attention, cognition, trust, and social interaction - and use them to revisit McCammon's FACETS framework, cataloging ways in which specific technologies may mitigate or exacerbate classic heuristic traps, grounded in accident reports and literature where possible. We identify recurring patterns, including technologies with dual-sided effects and a pervasive digital expert halo."
[HC-26] CALM: Configuration-Aware Human Intervention Boundaries During Robot Approach
链接: https://arxiv.org/abs/2609.07430
作者: Xinting Gao,Sipu Zhu,Weimin Zhuang
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC)
备注: 22 pages, 12 figures
Abstract:How robot body configuration shapes human intervention during approach remains underexplored. We conducted a within-participants study with 41 participants, measuring final stopping distance, subjective comfort, and exploratory eye-tracking responses across four humanoid arm configurations and two spatial scales. Full forward arm extension increased stopping distance by approximately 31-36 cm relative to arms-down. Spatial scale primarily affected comfort and pupil responses without a detectable stopping-distance shift. We introduce the Configuration-Aware Limit Model (CALM), which translates stopping-distance distributions into configuration-dependent population-coverage boundaries. Estimated boundaries at 80% coverage ranged from 0.88 to 1.47 m. In an illustrative one-dimensional planning analysis, reconfiguration enabled a 1.10 m approach goal that was unreachable with arms remaining fully extended under the same nominal pointwise 20% intervention-probability constraint. These findings support treating body configuration as a planning variable while distinguishing physical safety, behavioral intervention, and subjective cost.
[HC-27] Marginal Fidelity Does Not Establish User Simulation in Demographic Synthetic Survey Panels: Response Contracts Support Collapse and Conditioning Failure
链接: https://arxiv.org/abs/2609.07305
作者: Alexander Doudkin
类目: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: 28 pages, 2 figures
Abstract:Demographic synthetic survey panels are often validated by matching aggregate answers to published surveys. We test what that certificate establishes across six multiselect batteries from four survey organisations in three countries. The headline analysis is restricted to three instruments whose synthetic cohort and human target share the stated population frame; three other batteries remain sensitivity analyses. The response contract dominates measured fidelity. In the aligned instruments, committed sets leave 66 of 128 model-battery option slots empty in panels of up to 500 respondents, versus 0 of 128 under per-option probability elicitation. Across eight uncapped model-instrument comparisons, probabilities reduce option-marginal MAE by 4.53 to 7.30 points. The capped instrument reverses on two models until the vectors are projected onto its stated maximum. These are measurement effects: human targets are realised check-all responses, whereas the vectors are latent inclusion propensities. Published marginal agreement also fails to discriminate respondent simulation from direct population estimation. On nine aligned model-battery pairs, a no-persona population-prevalence query averages 6.27 MAE versus 12.39 for committed panels and wins all nine comparisons. Constraint-aware probability vectors average 5.34 and beat the query on four of nine, so the baseline challenges the validation criterion rather than proving direct estimation uniformly best. On three unpublished demographic cells, neither approach beats reciting the national distribution. Population-marginal agreement is therefore evidence about an elicitation contract and an estimand obtainable without simulated respondents, not evidence of individual simulation. Comments: 28 pages, 2 figures Subjects: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC) Cite as: arXiv:2609.07305 [cs.CL] (or arXiv:2609.07305v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.07305 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[HC-28] Living with AI Companions: Sustained AI Companionship Predicts Lower Well-Being Through Lower Human Interaction
链接: https://arxiv.org/abs/2609.07243
作者: Yutong Zhang,Dora Zhao,Yixin Wang,Rebecca Anselmetti,Jeffrey T. Hancock,Robert Kraut,Diyi Yang
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:AI chatbots are increasingly used for companionship, emotional support, and personal self-disclosure; however, how social engagement with these systems unfolds over time and shapes users’ well-being remains unclear. To address this, we conducted a two-wave longitudinal study of CharacterAI users, surveying 1,182 participants at baseline and 439 after a mean follow-up of 12 months. We examined how social engagement with AI companions evolves and how these longitudinal engagement patterns may influence well-being through two hypothesized pathways: sustained social engagement over time and the displacement of human social interaction. We found that interaction intensity, companionship use, and self-disclosure all showed substantial continuity over time. Greater interaction intensity at baseline predicted greater subsequent interaction intensity, companionship use, and self-disclosure. Consistent with the longitudinal engagement pathway, sustained social engagement across these dimensions was consistently associated with lower well-being. Results further support the social displacement pathway, indicating that these links were mainly explained by lower in-person social interaction. These findings highlight the importance of designing AI companions that support human social relationships without displacing them
[HC-29] EmoMed: An Emotionally-Aware Agent for Multimodal Medical Support with Real-Time Information Retrieval
链接: https://arxiv.org/abs/2609.07194
作者: Ivan Nasonov,Nikita Glazkov,Ivan Makovetskiy,Mikhail Mozikov,Daniil Sukhorukov,Andrey Savchenko,Ilya Makarov
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注:
Abstract:We present EmoMed - a multimodal medical consultation agent that adapts its responses based on users’ emotional states while maintaining clinical accuracy. The system processes text and medical images, detects affect indicators (anxiety, confusion, urgency) from user input, and adjusts response tone, structure, and detail level accordingly. To ensure factual reliability, the agent grounds clinical information through a dual retrieval mechanism: web-based fact-checking and an API-connected, continuously updated medical knowledge base. We evaluate our approach across seven state-of-the-art language models (GPT-4/5, Qwen3, Llama 4, Gemini 2.5, Grok4, Claude3) using comprehensive metrics including LLM-as-judge assessments, MedQA style accuracy tests, BERT Score, safety/helpfulness ratings, and multimodal medical benchmarks. The results demonstrate that emotionally adaptive responses consistently outperform neutral baseline across evaluation dimensions, without compromising clinical accuracy. A controlled user study validated these findings, with participants reporting improved perceived empathy and communication clarity, while maintaining trust in factual accuracy. Source code: this https URL
[HC-30] Adaptive Complementarity in Human-AI Systems: Architecture as a State-Shaping Choice
链接: https://arxiv.org/abs/2609.07001
作者: Babak Heydari
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Human-AI interaction can improve current performance while changing the capabilities and relationships on which future performance depends. We develop adaptive complementarity, a framework for choosing interaction architecture with these state consequences in view. Access, information exposure, task allocation, timing, and communication can alter which arrangement will be valuable later; their settings can often be reset faster than the capabilities, search patterns, or conventions they create. Three mechanisms organize the argument: information exposure and collective search, delegation and capability evolution, and strategic interdependence and information governance. Their integration yields cross-mechanism implications, including conditions under which a loss of expertise heterogeneity increases the information differentiation required to preserve independent search. We distinguish strong human-AI complementarity from advantage over another workflow and from advantage over an evolving reference policy. A knowledge-coverage illustration shows how different interaction histories can reverse current workflow rankings even at equal human competence. It also separates that result from the incremental value of state feedback, which can be small when a well-chosen stable workflow anticipates learning. The framework directs evaluation toward the states present interaction creates, their consequences for later architectural fit, and the conditions under which observing and responding to them is worthwhile.
[HC-31] he Interface of Theseus: The Rise of Just-In-Time Interfaces
链接: https://arxiv.org/abs/2609.06770
作者: Michael S. Bernstein
类目: Human-Computer Interaction (cs.HC)
备注: UIST Visions 2026
Abstract:Our community creates interfaces—pre-constructed, static artifacts. These interfaces are effortful to create, so we invest great effort and care into each of them. Yet the equilibrium is shifting: we can now construct nearly any software, bespoke, within moments. When construction is cheap, we may soon live in a future in which interfaces are constructed in real time. This is a world of just-in-time interfaces: rapid, discardable, on-demand interfaces tailored to our needs in the moment. Will our community still create traditional interactive systems in this world? Or will we create interface generators, ``this http URL’’ guides for AI consumption? I argue that we ought to lean into a future of rapid, bespoke, on-demand interfaces.
[HC-32] he Effectiveness of Virtual Patient Simulation Versus Peer Simulation in Providing Sexual Counseling During Pregnancy: A Randomized Controlled Trial
链接: https://arxiv.org/abs/2609.06662
作者: Neslihan Yilmaz Sezer,Menekşe Nazlı Aker,Pinar Kullu,Rana Tuncer,Gael Lucero-palacios,Roghayeh Leila Barmaki
类目: Human-Computer Interaction (cs.HC)
备注: 19 pages, 1 figure, 3 tables
Abstract:Although sexual health counseling is one of the important responsibilities of healthcare professionals, effective educational methods are needed to develop students’ counseling skills in this field. The aim of this study was to compare the effectiveness of virtual patient simulation and peer simulation methods in developing sexual counseling skills during pregnancy among nursing faculty students. This randomized controlled trial included 51 participants assigned to one of three groups: virtual patient simulation, peer simulation in a virtual environment, or face-to-face peer simulation. In the study, all groups received face-to-face theoretical instruction on sexual counseling during pregnancy. Following the theoretical training, students participated in virtual patient simulation, peer simulation in a virtual environment, or face-to-face peer simulation practices according to the groups to which they were assigned by randomization. Outcome measures included the Sexual Attitudes and Beliefs Scale, the Student Satisfaction and Self-Confidence in Learning Scale, and the Sexual Counseling Skills Evaluation Form. It was determined that the participants’ SABS scores decreased significantly after the intervention. According to the results of the mixed repeated-measures ANOVA, the effect of time was statistically significant. However, the group effect and the group x time interaction were not significant. There was no difference between the groups in terms of Satisfaction in learning, Self-confidence in learning, and Skill scores. This study showed that different simulation methods used in sexual counseling education during pregnancy were effective in reducing students’ negative attitudes and beliefs and provided similar educational outcomes. Therefore, virtual patient and peer simulations may be recommended as feasible approaches for improving sexual counseling skills.
[HC-33] Disclosure and dissolution: explainability AI power and situated agency in understanding
链接: https://arxiv.org/abs/2609.06495
作者: Dee Matthews
类目: Human-Computer Interaction (cs.HC)
备注: in proceedings of explainable ai for the arts workshop 2026 arXiv:2607.20131
Abstract:This paper interrogates the political and philosophical stakes of AI through the lens of cyborg theory cosmotechnics and glitch feminism. It advocates for a tech-positive critically situated approach to AI as a collaborator and substrate for power relations rather than an autonomous agent of harm. By rejecting the naive pause stop narratives of the human AI binary and embracing an explainable AI XAI artistic practice within embodied intersectional and community rooted engagements AI can empower diverse voices and foster ethical creativity. It starts by examining a frontier cybersecurity AI situating its framing within AI public anxiety and arguing for a new critical stance on human navigation in an AI world.
[HC-34] InsightChain: Optimized Chain-of-Insight Analytics for LLM -driven Data Visualization EMNLP2026
链接: https://arxiv.org/abs/2609.06438
作者: Hanya Sun,Chen Zhang,Sheng Liang,Yongyue Zhang,Yong Liu
类目: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: EMNLP 2026 Findings
Abstract:Large language models (LLMs) are increasingly used for automated data visualization, yet existing approaches often frame visualization generation as a single-step mapping from user query to figure or code, overlooking the iterative analytical reasoning process of expert analysts. We present InsightChain, a four-stage visualization prompting pipeline (Explore–Focus–Test–Present) that emulates expert analytical workflows, together with VG-COPRO, a vision-guided automatic prompt optimization (APO) method adapted to jointly optimize such multi-stage, executable pipelines. To address the evaluation gap for complex data visualization, we introduce the Insight Progression Metric (IPM), a rubric combining four text-based dimensions with a vision-based dimension. We assess IPM through a 100-chain human pilot and an expanded 300-chain agent-based evaluation spanning all ten domains. Experiments on public datasets show that InsightChain consistently outperforms competing prompting baselines. Existing APO methods fail to yield consistent gains on this multi-stage task, whereas VG-COPRO improves performance in both in-domain and cross-domain settings.
[HC-35] Can People Distinguish Human and AI Agency in Humanoid Teleoperation? A Preliminary Study of Agency Perception
链接: https://arxiv.org/abs/2609.06434
作者: Xiang Li,Koya Dendo,Keigo Minamida,Yuto Nakamura,Per Ola Kristensson,Jun Rekimoto
类目: Human-Computer Interaction (cs.HC); Robotics (cs.RO)
备注: 3 pages, accepted by ACM UIST 2026 as a poster
Abstract:Can people distinguish between human and AI agency in humanoid teleoperation? To explore this question, we developed \textitGhost-in-the-Loop, a teleoperation framework that supports both human-operated and AI-generated control of a robot’s voice, facial expressions, and gestures while maintaining a consistent embodiment. We conducted a preliminary online study ( N=50 ) in which participants viewed short interaction clips generated by either a Human Operator or an AI Control and judged the perceived source of control. Results suggest that participants often struggled to distinguish between the two conditions in brief interactions. Qualitative responses indicate that judgments were primarily influenced by perceived naturalness, temporal coordination, and consistency across speech, facial expression, and gesture. These findings provide initial insights into agency perception in embodied human–AI communication and motivate future investigations of blended human–AI telepresence systems.
[HC-36] FrankenReport: Early Exiting in Long-Form Generation Using Expected Value of Computation
链接: https://arxiv.org/abs/2609.06320
作者: Zhengping Jiang,Gonzalo Ramos,Jina Suh,Shiqian Rachel Ng,Elias Stengel-Eskin,Justin Svegliato,Benjamin Van Durme,Andy Huntington,Sam Thomson
类目: Human-Computer Interaction (cs.HC); Computation and Language (cs.CL)
备注: 23 pages, 16 figures
Abstract:While deep research systems address interactive information-seeking needs impressively, their real-world deployments face latency and resource-consumption challenges. We present FrankenReport, an interface for long-form knowledge-seeking report generation that supports adaptive early exiting per section: it evaluates intermediate outputs during generation and predicts whether further targeted computation will yield significant quality gains. In a simulation study, FrankenReport outperforms random allocation baselines by a large margin (up to 4x) under low budgets and smoothly recovers full-pipeline quality as the budget grows, showing that future quality gains are predictable from intermediate drafts. Through experiments and user studies, we further show that despite varying preferences across users and topics, FrankenReport adapts to simple, natural user feedback as efficiently as methods requiring much costlier supervision such as generated drafts and explicit rationales.
[HC-37] Adaptive Ecological Momentary Assessment with a Hybrid Language Model: Formative Expert Review and Retrospective Evaluation
链接: https://arxiv.org/abs/2609.06269
作者: Arash Ahmadi,Dingjing Shi,Yaser M. Banad
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Ecological momentary assessment (EMA) measures experience in daily life, but fixed questionnaires and schedules collect information of uneven value and can interrupt participants. We present and retrospectively evaluate EMA-E4B, a hybrid framework for question selection and prompt timing. Separate ridge models propose an item set and delay; a supervised Gemma 4 E4B language layer produces the final structured response and explanation. Evaluation distinguishes proxy action performance, output conformity, and formative judgments of response quality. The data contain 4,372 records from 79 participants and yield 3,516 sequential cases under a participant separated split. One involved domain expert preferred the complete hybrid response in 15 of 20 decisive comparisons, with two ties among 22 reviews. On 75 reused development cases, hybrid question utility and timing similarity were 0.832 and 0.818; the head alone reached 0.852 and 0.818. A separate 60 case comparison with untouched E4B under the same head gave action differences of -0.0031 and -0.0105. Thus, the language layer produced structured responses with action scores comparable to or slightly below the reference configurations, while the expert feedback favored the complete hybrid response. These observations establish a concrete, inspectable framework and clarify the distinct roles of action scoring and response review. Repeated adaptive administration and practical effects on measurement and participant burden remain future research.
[HC-38] Formalising Grassroots Social Contracts: From Legal Text to Grassroots Platforms
链接: https://arxiv.org/abs/2609.06231
作者: James Golike,Andy Lewis-Pye,Ehud Shapiro
类目: Computers and Society (cs.CY); Distributed, Parallel, and Cluster Computing (cs.DC); Human-Computer Interaction (cs.HC); Programming Languages (cs.PL)
备注:
Abstract:Two centuries ago Pierre-Joseph Proudhon envisioned a social contract that is (1) an agreement of man with man; (2) reciprocal; (3) imposing no obligation upon the parties except that which results from their personal promise; (4) subject to no external authority; (5) freely accepted and signed by all the participants; (6) of the nature of a contract of exchange. He also envisioned a property of social contracts, analogous to a property of digital platforms we term grassroots: that one could “make a contract with all, as … with some”; digitally, that a grassroots platform can have multiple instances, which may coalesce by mutual consent. We define grassroots social contracts as social contracts meeting these conditions and that (7) people are free to deal with each other; and (8) there is no external register of people. We show that a grassroots social contract can be transformed into a working grassroots platform through an abstraction cascade, from the contract text to formal act schemas, verified syntactically to be grassroots, and then to volition-guarded multiagent atomic transactions. Act schemas are a formal language for the acts the contract describes, each naming the parties’ roles, which of them must will the act, and its precondition and effect at each role. Any contract written in this language meets the eight conditions, provided it is syntactically grassroots, satisfying three decidable conditions, Introduction, Provenance and Volition. We prove that the protocol realising a syntactically grassroots contract is grassroots, and volitionally so. Clauses of a grassroots social contract are of three legal types: a breach of an enforced clause is impossible with a correct implementation; a breach of an attested or undertaken clause can be taken to court, with signed evidence for an attested clause. We illustrate them with a grassroots social graph and grassroots currencies.
[HC-39] MM-SVGEdit: A Multimodal-Driven SVG Editing for UI Design
链接: https://arxiv.org/abs/2609.06116
作者: Shibo Yang,Yuqing Gao,Zipeng Liu
类目: Human-Computer Interaction (cs.HC); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In the field of UI design, Scalable Vector Graphics (SVG) is widely used as a design medium. However, traditional SVG editing techniques have high entry barriers and require cumbersome manual iteration, while LLM-based editing solutions suffer from low accuracy and poor user controllability. To address these issues, we propose MM-SVGEdit, a multimodal-driven SVG editing approach that integrates traditional SVG editing and LLM-based methods. We introduce a two-stage strategy in which visual grounding is followed by modification. Both stages support two interaction modalities: natural language instructions and direct manipulation (mouse and keyboard). We trained and evaluated MM-SVGEdit on a self-constructed dataset of 14,476 question-answer pairs generated from UIs, covering 11 types of editing operations on both single and multiple UI targets. The results show that MM-SVGEdit improves SVG editing accuracy, efficiency, and user-perceived control while reducing token consumption and response time.
[HC-40] Flawed but Memorable: Student Critical Reception of Interest-Personalized GenAI Analogies in Computing Education
链接: https://arxiv.org/abs/2609.06095
作者: Seth Bernstein,Naaz Sibia
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: 12 pages, 2 figures, and 2 tables. Accepted to the 26th Koli Calling International Conference on Computing Education Research (Koli Calling 2026)
Abstract:Motivation: Undergraduate computing students increasingly turn to generative AI (GenAI) tools to understand abstract concepts through analogies. Analogies compare an unfamiliar concept to something familiar, but judging whether the comparison holds requires knowledge of both. GenAI may also embed assumptions about who the learner is. GenAI education research centers on output correctness, leaving students’ critical reception of analogies largely unexamined. Method: We investigate how students evaluate the accuracy, appropriateness, and assumptions in GenAI-generated analogies, and their perceptions of interest-personalized versus generic technical explanations. Ten students with CS2 experience participated in a pre-survey, a think-aloud task with linked-list and recursion explanations, and a semi-structured interview grounded in the Paul-Elder framework. They judged accuracy, clarity, engagement, and trust separately. Results: Most participants described interest-personalized analogies as more engaging or memorable than generic technical explanations, while trust was mixed. Some trusted the tailored analogies more; others scrutinized them more closely or distrusted the tailoring. Participants with deep source-domain knowledge identified structural flaws requiring that knowledge to recognize. Because personalization and explanation format differed together, these findings do not isolate an effect of personalization alone. Implications: A familiar source flips the student’s role. On the concept they are still learners, but on the familiar source they are the expert, and that is the position from which an analogy can be judged. We call this two-sided analogy auditing. GenAI systems should ask what students know, not just what interests them, and treat a flawed analogy as something to inspect and fix rather than accept. Comments: 12 pages, 2 figures, and 2 tables. Accepted to the 26th Koli Calling International Conference on Computing Education Research (Koli Calling 2026) Subjects: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.06095 [cs.HC] (or arXiv:2609.06095v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2609.06095 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Seth Bernstein [view email] [v1] Sat, 5 Sep 2026 13:47:02 UTC (2,091 KB)
[HC-41] Art2Song: Enhancing Visual Art Appreciation with Contextual Music Generation
链接: https://arxiv.org/abs/2609.06044
作者: Sungeun Jo,Myung Jin(MJ)Kim,Chi Yoon Jeong
类目: Human-Computer Interaction (cs.HC)
备注: 3 pages, 1 figure, To appear in the Adjunct Proceedings of the 39th Annual ACM Symposium on User Interface Software and Technology (UIST Adjunct '26)
Abstract:Art2Song is a conceptual framework that expresses artworks as sound by separating Non-Visual Context, which is difficult to perceive from the image alone, from Visual Evidence. Visual Evidence, such as objects, colors, and spatial composition, is transformed into Lyrics, while the Contextual Mood derived from the historical and art-historical context in the museum’s artwork description is reflected in the background soundtrack. Rather than describing artworks textually, Art2Song aims to explore the possibility of a new mode of art appreciation in which viewers experience hidden stories and emotional context through music. As future interaction directions, we plan an Emotional Layer Blending Slider interface and a structured, traceable song-generation scenario, presenting the possibility that users can explore the relationship between Visual Evidence and Contextual Mood.
[HC-42] he Blindness of Document-Level Translation Evaluation EMNLP2026
链接: https://arxiv.org/abs/2609.05949
作者: Ahrii Kim,Vilém Zouhar,Chanjun Park,Seong-heum Kim
类目: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: Accepted to EMNLP 2026 (Main Conference)
Abstract:Document-level machine translation (MT) evaluation extends segment-level protocols by presenting full documents to annotators, on the assumption that such presentation elicits document-level judgments. We test this assumption with a counterfactual condition (MIX) in which each document combines segments drawn from different systems, preserving document-level presentation while breaking cross-segment consistency. Across 18,420 expert Englis-to-Korean annotations and 14 automatic metrics, scores, system rankings, and error annotations are statistically equivalent between coherent and incoherent documents. Perception does not explain this: shown matched passages, raters identify the coherent one as the work of a single translator in 87.3% of trials. Document presentation does change how annotators work, but that change does not reach the recorded output. What is blind is the protocol, not the annotator. The concern is not that scores fall short, but that the resources invested in document-level systems, metrics, and annotation may not be measuring what they are intended to measure.
[HC-43] actile Search: Enhancing Targeting in 3D Space
链接: https://arxiv.org/abs/2609.05867
作者: Amber Maimon,Iddo Yehoshua Wald,Jonas Keppel,Eunhee Chang,Yoshifumi Kitamura,Stefan Schneegass,Rainer Malaka,Donald Degraen
类目: Human-Computer Interaction (cs.HC)
备注: Accepted to the 2026 IEEE International Symposium on Mixed and Augmented Reality (ISMAR 2026)
Abstract:Visual search is crucial in daily life, from scanning for relevant information to spotting signs of danger. When sensory channels are overloaded or degraded, cognitive tasks can be supported by crossmodal information representations through vibrotactile cues. We introduce Tactile Search, an approach that uses modulation of frequency and amplitude of vibrations to the hands, for guiding attention to the location of objects in 3D space. We evaluated this approach in a competitive VR game where participants searched for targets using both vision and touch. Across two studies – an in-the-wild demonstration (n=55) and a controlled laboratory experiment (n=28) – we found that vibrotactile feedback significantly improved performance and increased user confidence. In the combined haptic condition, performance did not differ across target heights. We further analyzed participants’ subjective experiences and search strategies highlighting the benefits of the tactile cues. Our findings suggest that Tactile Search can enhance interaction and provide design considerations for integrating haptic search into interactive systems.
[HC-44] Will My Assistant Remember My Allergy? What Personal LLM Assistants Forget When Conversation Memory Is Compressed
链接: https://arxiv.org/abs/2609.05767
作者: Lichen Zhu,Yueqian Lin,Yiheng Wang,Yudong Liu,Hai “Helen” Li,Yiran Chen
类目: Human-Computer Interaction (cs.HC)
备注: Accepted at ACM HumanSys 2026
Abstract:Personal LLM assistants (health companions, elder-care agents, accessibility aides) are judged by what they remember about a person: a medication or an allergy mentioned in passing and needed days later. Privacy pushes them on-device, where a month of conversation can outgrow the model’s own weights, so an eviction policy must decide what the cache forgets. Benchmarks report that eviction keeps such facts at a 20% budget, but they compress a prompt that already contains the user’s future question, foresight no cache-reusing assistant has. Hide the question until after compression and the advantage vanishes: on PA-Bench, 100 assistant conversations we construct, an allergy mentioned in passing survives to the question that needs it 0–1% of the time, against 97% with full memory. The cause is the budget, not the scorer: none of the training-free policies we evaluate ranks the fact high enough, and the budget that would keep it is too large to bother compressing. A compressed cache is an inference-reuse mechanism, not a persistence layer: safety-critical facts need an auditable episodic store alongside it, and an interface that asks rather than invents.
[HC-45] Who Maintains Agent Skills? A Longitudinal Study of Human-Governed AI-Assisted Skill Maintenance
链接: https://arxiv.org/abs/2609.05677
作者: Chen Shen,Estevam Hruschka
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Software Engineering (cs.SE)
备注:
Abstract:Lifelong LLM agents increasingly rely on external skill artifacts as one element for preserving and reusing capabilities over time. These skills (usually portable Markdown files such as this http URL) describe when and how to apply a capability and must be corrected, expanded, and consolidated as tools and usage patterns shift over deployment. Recent work seeks to automate skill curation, but it largely evaluates against automated baselines and treats human maintenance as an unmeasured bottleneck. We study that missing process directly. We mine the full commit histories of five public AI-skill repositories, a purposive sample of AI-tooling organizations, covering 873 commits, 143 skill files, and 254 substantive post-creation edits from October 2025 to June 2026. We code each edit with pre-registered governance, operation, and trigger-evidence codebooks. Three findings emerge. First, every substantive edit is authored or merged through a named human account, while 62% carry an AI co-author trailer, with large repository-level variation. Second, these edits are genuine curation: an audited sample shows that most change skill content, and the coded operations are dominated by additions and corrections. Third, a pre-registered rule-likeness axis fails its reliability gate; reliably coding rule-likeness from commit artifacts remains an open measurement problem. We release the corpus, codebooks, mining scripts, and a replay protocol for automated skill curators. For self-evolving agents, public skill maintenance currently looks less like an autonomous pipeline than a human-governed, AI-assisted loop that future curators must measure against and operate within.
[HC-46] Look Before You Prompt and After: Scaffolding Human-AI Collaboration in Software Tutorial Creation
链接: https://arxiv.org/abs/2609.05563
作者: Avinash Bhat,Vy Bui,Jin L.C. Guo
类目: oftware Engineering (cs.SE); Human-Computer Interaction (cs.HC)
备注: 27 pages, 8 figures, 5 tables. Under Review at TOSEM Special Issue on Human AI Collaboration in Software Engineering
Abstract:With LLMs, creating software tutorials now involves steering the model’s output and shaping it into a coherent, accurate learning resource, yet existing LLM tools offer writers little support for this work. By analyzing interviews with technical writers ( N=17 ), we identify three requirements for how they assemble and structure multiple LLM responses, curate the context the model uses, and verify the generated content. We designed a tool called dBlocks with the following features: blocks to scope content, a context manager to edit context, and inline execution to verify code. Following a human-centered design method, we iteratively refined the design through a user study ( N=5 ). In a within-subjects lab study ( N=16 ) comparing dBlocks with participants’ preferred workflows for LLM-assisted authoring, participants reported significantly higher confidence in the tutorials they produced with dBlocks. In addition, the tool reduced friction in verification, with writers verifying code as they drafted rather than deferring or skipping it, and helped them avoid searching long chat histories by scoping their work into blocks that kept each tutorial section and its LLM conversation together. More broadly, our work offers implications for tools that scaffold human-AI collaboration in SE workflows and shows how human-centered design can guide the development of LLM-integrated tools.
[HC-47] Do Depressive Facial Patterns Transfer Across Cultures and Contexts? Evidence from a German RCT and E-DAIC KDD2026 ALT
链接: https://arxiv.org/abs/2609.05543
作者: Misha Sadeghi,Robert Richer,Lydia Helene Rupp,Lena Schindler-Gmelch,Marie Keinert,Farnaz Rahimi,Malin Hager,Bernhard Egger,Matthias Berking,Bjoern M. Eskofier
类目: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
备注: 12 pages, 3 figures. Presented at the ACM KDD 2026 Workshop on AI for Cognitive and Mental Health Support (AI4Mental), Jeju, South Korea
Abstract:Automated assessment of depression from facial dynamics holds promise for scalable mental health monitoring, yet cross-corpus generalization of learned biomarkers remains an open challenge. We present a systematic bidirectional transfer study pairing the EmpkinS-EKSpression randomized controlled trial (RCT; N = 256, SCID-5-CV diagnoses) with the Extended Distress Analysis Interview Corpus (E-DAIC; N = 275, semi-structured clinical interviews), predicting depression severity and binary diagnostic status from facial action units, head pose, and gaze. Cross-corpus binary classification proves more robust than continuous PHQ-8 severity regression, with forward transfer achieving AUC = 0.70. Regression transfer is governed by functional context alignment: passive observation phases yield the most transferable models, while active emotion regulation phases elicit stronger within-corpus signals. These findings establish functional context alignment as the primary determinant of cross-corpus generalization, with passive elicitation contexts offering the best trade-off between within-corpus sensitivity and cross-corpus robustness.
[HC-48] Beyond “AI Helps Humans”: Decision-Targeted Evaluation Design for Human-Agent Teams in the Agent ic Era
链接: https://arxiv.org/abs/2609.05527
作者: Hamed Khosravi,Xiaoming Huo
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Wherever a coding agent works under engineer supervision, or a clinical model assists a radiologist, the deployment question is whether to keep the human-AI workflow or replace it with the human alone or the agent alone. The human-AI workflow is worth keeping only if it beats both of those alternatives. Yet once it is deployed, neither alternative outcome is observed: recovering one means replaying the task under that alternative, and every replay costs expert time or compute. Under a fixed replay budget, the design question is therefore which tasks should be more likely to receive a human-only replay, and which an agent-only replay. Existing methods do not directly target this decision. Agent benchmarks do not choose which missing baseline to measure, variance-based sampling ignores which of the two comparisons is closer to failing, and Bayesian information methods focus on learning model parameters instead of making the deployment decision. We propose TEAM-Design, a rule that gives every task two replay probabilities, one per baseline. It raises a probability where the missing baseline outcome is hard to predict from what is already known about the task and where that comparison is harder to establish, and lowers it where replay is expensive. We prove that the rule solves this budgeted design problem, and that drawing the replays at random from recorded probabilities still controls the chance of wrongly declaring that the workflow beats both. We reanalyze 6 clinical settings, where no human-AI workflow beats both alternatives, and a coding benchmark, where one does, then evaluate TEAM-Design on synthetic designs and on a semi-synthetic design built from a real chest X-ray reader study. TEAM-Design works best when one of the two comparisons is clearly harder to settle than the other, and can do worse than variance-based allocation when the two are similarly difficult.
[HC-49] From Sensor Data to Classroom Inquiry: GenAI-Supported Exploration of School Digital Twin Data
链接: https://arxiv.org/abs/2609.05452
作者: Themistoklis Sarantakos,Dimitrios Amaxilatis,Michail Giannakos,Georgios Mylonas
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注: Submitted to the 12th IEEE International Smart Cities Conference 2026 (ISC2 2026) and accepted for publication
Abstract:Digital Twins for educational buildings can support sustainability-oriented learning, but their use in schools remains limited. This paper presents a GenAI-based chatbot built on top of an existing Digital Twin for two school buildings in Greece, using real IoT data from environmental sensors and energy meters. The chatbot enables educators to query live and historical building data, compare spaces, and generate ideas for classroom activities through natural language. The system was evaluated in an 80-minute workshop with 17 secondary-school educators, who compared it with an existing web-based dashboard. Results show strong perceived usability and pedagogical value, particularly for inquiry-based learning, hypothesis formation, and interdisciplinary lesson planning. Participants also highlighted limitations related to response speed, data verification, trust, and the continued value of visual dashboards. Overall, the findings suggest that GenAI interfaces can make Digital Twin data more accessible for educational use, provided they are designed with transparency, verification, and pedagogical grounding.
[HC-50] Companion AI and Ethical Design: Learning from System Failures and User Desires
链接: https://arxiv.org/abs/2609.05432
作者: Alicia Vidler,Belinda Middleweek
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注: 21 pages, accepted in peer reviewed conference: Future Technologies Conference - to be included in Springer nature conference proceedings 2026
Abstract:Human users are interacting with chatbots and companion AI technologies as if they were human. A growing array of AI-systems are now trained to recognise, interpret and simulate feeling in user interactions. Ethical considerations such as fairness, accountability, transparency and explainability (FATE) are paramount in technologies designed to socially interact with humans and/or support relationship development. Using a semantic approach, we examine 14,081 comments in a Reddit user discussion forum about Replika, a leading companion AI app, across a four-year period. We ask what user-reported functional errors can tell us about human-AI intimacy in companion AI communities, and what ethical design framework can be developed in response. The findings show that functional errors, or bugs,'' impose an emotional cost on users, reducing feelings of intimacy and highlighting the need for more robust, resilient design systems that incorporate stochastic and iterative forms of intimacy in companion AI applications. Rather than artificial intimacy’’ or pseudo- intimacy'', we propose the more inclusive term Intimate AI’’ to describe this relationship. Based on the findings, we offer a contextually aware, applied Expert Systems design framework for the programming and designing of Intimate AI that accounts for user feedback and ethical AI development.
[HC-51] Seeing Without Understanding: Large Language Model Evaluation of Mobile User Interface Quality Failure Taxonomy and Architectural Explanation
链接: https://arxiv.org/abs/2609.05423
作者: Md Rejaul Korim Sadi,Golam Mostofa Naeem,Toufiqur Rahman Tasin,Syed Mostofa Moosa,Mahmudul Hasan Emon,Mahmudur Rashid,Ferdus Ahmed
类目: Human-Computer Interaction (cs.HC); Computation and Language (cs.CL); Software Engineering (cs.SE)
备注:
Abstract:Evaluating mobile user interface quality at scale remains a persistent challenge in software engineering and human-computer interaction. Rule-based heuristic methods offer structural reliability but demand significant engineering effort, while human annotation does not scale to the volume of applications produced annually. Large language models present a promising alternative, yet their reliability for structured UI judgment has not been systematically examined, and the patterns behind their failures remain insufficiently characterized. This paper addresses both gaps. We begin with the complete RICO dataset of 66,261 real-world mobile application screens, from which we derive a refined evaluation corpus of 15,000 screens through a rigorous, literature-guided selection process. Each screen is assessed across seven criteria: structural JSON validity, minimum visible element count, clickable component presence, non-zero layout bounds, image integrity, and perceptual duplicate removal. Against this corpus, we apply a heuristic baseline built from severity-weighted usability signals, normalized layout metrics, and pixel-ratio complexity measures calibrated to real user sentiment. Multiple language models independently rate each screen across usability, layout quality, and visual complexity from structured JSON descriptions and raw screenshots. Dimension-level comparison against the heuristic uses agreement rates, Cohen’s Kappa, and confidence calibration. Recurring divergence patterns are organized into a failure taxonomy and interpreted through transformer architectural signatures: MLE plausibility bias, attention misgrounding, and autoregressive over-commitment.
[HC-52] LipCoder: Voice-Enabled Coding Toolkit
链接: https://arxiv.org/abs/2608.30793
作者: Hayoon Kim,Sungho Lee,Juhwi Kim,Bongwon Suh,Kyogu Lee
类目: Human-Computer Interaction (cs.HC); Sound (cs.SD)
备注: Accepted to the Posters and Demos Track at the 28th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2026)
Abstract:AI-assisted programming environments have accelerated software development, giving rise to new paradigms like vibe coding. However, their benefits remain largely inaccessible to visually impaired programmers, as existing screen readers and assistive tools offer limited support for these emerging workflows. We introduce LipCoder, a voice-centric programming toolkit designed to deliver editor-level functionality through auditory and speech-based interfaces. LipCoder offers features comprising speech feedback and earcon cues for comprehension and validation, as well as natural language input for navigation and modification. In an exploratory evaluation, 5 visually impaired programmers performed a series of coding tasks comparing LipCoder with a baseline of VSCode, Copilot, and VoiceOver. Quantitative trends and qualitative feedback point to directions for auditory-first design that may broaden accessibility in speech-driven coding environments.
[HC-53] Understanding emotional and behavioural responses through physiological signal analysis recorded during theatrical performance
链接: https://arxiv.org/abs/2609.08321
作者: Aditi Site,Annariina Lohiranta,Tarmo Lipping
类目: ignal Processing (eess.SP); Human-Computer Interaction (cs.HC)
备注:
Abstract:Among various cultural interventions, theatre offers a powerful medium for emotional expression, reflection, and social connection. Live performances can positively influence mental well-being. Therefore, studying audience responses during theatrical performances can provide valuable insights into the relationship between theatre, emotional experiences, and well-being. Physiological responses have emerged as an important modality for identifying patterns associated with different emotional states. However, in naturalistic environments such as live performances, analysing these signals is challenging due to the absence of explicit labels. In this study, we focus on understanding emotional and behavioural responses using physiological data collected during a live theatrical performance. Data were recorded from participants using multiple sensing modalities. In addition, a comprehensive analytical framework was employed, which included demographic analysis, visual exploration of physiological signals, participant-level observations, scene-wise physiological analysis, and intensity-based analysis using z-score normalization to summarize the findings. The findings revealed that dramatic, surprising, and intense scenes produced the strongest activation, often with concurrent increases in HR and SCL, whereas empathy-driven scenes showed lower and more stable responses. Humorous scenes elicited moderate-to-high activation, particularly in the presence of sudden reactions or loud sounds. Analyses also identified shared physiological response patterns across participants, with activation concentrated around key emotional events. These results indicate that combined HR and SCL measures can effectively capture variations in audience engagement during live theatrical performances.
计算机视觉
[CV-0] SyncWorld: Visual Calibration Enables World Models as Zero-Shot Simulators
链接: https://arxiv.org/abs/2609.09155
作者: Yuncong Yang,Zhengtao Han,Furkan Ozyurt,Zeyuan Yang,Han Yang,Junyi Cao,Haoyu Zhen,Yilun Du,Chuang Gan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:World models are increasingly used as policy-in-the-loop imagination environments, where reliable rollouts require fine-grained controllability with respect to low-level robot actions. A key obstacle to scaling such models in robotics is that actions are not a universal language in pixel space: changes in visual environment, camera view, robot placement, or embodiment alter how the same numerical action manifests visually, leading to conflicting supervision under mixed training and brittle generalization at deployment. We introduce SyncWorld, an action-conditioned world model that serves as a zero-shot simulator across unseen environments without any additional training. SyncWorld leverages a visual calibration episode—paired frames and actions that showcase all the controllable degrees of freedom—to specify the setup-specific Action–Visual Mapping in context. Training with visual calibration contexts teaches the model to interpret actions through visual evidence and to leverage interaction history when explicit calibration is unavailable. Experiments show that SyncWorld can accurately simulate action outcomes in previously unseen settings, and that its capability of simulating rollouts enables test-time policy improvement without training.
[CV-1] Point4D: Long-range 4D Motion Reconstruction
链接: https://arxiv.org/abs/2609.09145
作者: Minsik Jeon,Jay Karhade,Deva Ramanan,Shubham Tulsiani
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We introduce Point4D, a feed-forward model for 4D reconstruction of long-range video sequences. Point4D is able to reliably infer dense per-point 3D trajectories across multi-hundred-frame videos, unlike existing 4D methods that are limited to short input windows of at most a few dozen frames. A key innovation that enables this is our flexible 3D query-based motion decoder that decouples trajectory prediction from image-plane visibility. The predicted 3D endpoints are then directly re-queried in the next chunk without re-projection or matching. Furthermore, we show that extracting and reusing a visual descriptor from an arbitrary frame where the point is visible leads to better performance than relying solely on the source patch. Overall, Point4D achieves state-of-the-art performance across diverse long-video tracking benchmarks spanning over 200 frames and largely outperforms previous feed-forward 4D method. Project page: this https URL
[CV-2] Canonical Color as a Lens into Concept Decodability in Vision Encoders and VLMs EMNLP2026
链接: https://arxiv.org/abs/2609.09124
作者: Xiaofu Chen,Stella Frank,Yova Kementchedjhieva
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 12 pages, 7 figures. Accepted to EMNLP 2026
Abstract:Visual encoders construct a representation of the image input for Vision-Language models. How much conceptual, as opposed to immediately visible, information does this representation contain? We use canonical color as a controlled test case to ask whether vision encoders make canonical-color information linearly accessible, even when color is removed from the input image. We construct a dataset of objects with canonical colors, and probe vision encoders for both color and object identity using color and grayscale images. We find that canonical color remains decodable from grayscale images, and is tied to predicted object identity, indicating a conceptual link. Extending this analysis to full VLMs, we find that VLM post-training can have a surprisingly large effect on color decodability in the vision encoder. Overall, canonical color provides a usefully controllable lens for tracing object-level conceptual semantic information in vision encoders and VLMs.
[CV-3] Mask Forcing: Improving Autoregressive Video Diffusion Distillation via Dual-Noise Masking Rollout
链接: https://arxiv.org/abs/2609.09123
作者: Zhuoran Zhao,Shengju Qian,Tongtong Liang,Xianghao Kong,Songchun Zhang,Junchao Huang,Guian Fang,Xin Wang,Pan Hui,Anyi Rao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Autoregressive (AR) video diffusion models have shown great potential in real-time video generation. Recent methods distill pretrained bidirectional video diffusion models into causal AR students through Distribution Matching Distillation (DMD), but the generated videos often suffer from over-saturation and over-smoothing issues, resulting in limited visual quality and realism. The key contributing factor is the mode-seeking behavior of the reverse KL objective in DMD, which can cause the student distribution to collapse onto only a few modes of the teacher distribution. To address this, we propose Mask Forcing, a Dual-Noise Masking Rollout strategy that perturbs the AR student self-rollout to mitigate mode collapse induced by reverse-KL mode seeking. The core idea is to inject cleaner signals into noisy rollout inputs via random masks along spatial and temporal axes during the self-rollout process of AR diffusion distillation. Such perturbations encourage the student rollouts to explore more regions of the teacher distribution, allowing DMD to provide learning signals beyond the modes already covered by the student. Moreover, the cleaner tokens act as denoising guidance for other noisier tokens, improving the intermediate rollout predictions and reducing error accumulation. Extensive experiments demonstrate that our method improves multiple AR video diffusion distillation methods with higher visual quality efficiently, without incorporating real video data or additional post-training stages.
[CV-4] GoDeep: Annotation-Free Open-Vocabulary 3D Scene Understanding via Language-Space Lifting
链接: https://arxiv.org/abs/2609.09082
作者: Thodoris Betsas,Anastasios Doulamis,Andreas Georgopoulos
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Open vocabulary 3D semantic segmentation methods typically lift CLIP features into 3D. This embeds points in a joint vision-language space known to behave like a bag-of-words on compositional tasks. Furthermore, even annotation free variants often require a large 3D training corpus and a dedicated 3D encoder per domain. Instead we use a vision-language model purely as a translator. It produces structured, entity-level descriptions of each posed image. These descriptions are grounded, projected, and aggregated directly in a general-purpose, language-only embedding space, with no 3D training corpus or encoder required. On ScanNet++, our pipeline is competitive with strong annotation free baselines trained on ScanNet. On a 5-building cultural heritage benchmark, raw scores initially favor a CLIP-based variant, but a single systematic vocabulary correction reverses this ranking. An effect confirmed by a second, independent correction on a different class, indicating that language-space embeddings track physical content more faithfully. This fidelity extends to genuinely out-of-vocabulary (OOV) objects on ScanNet++ proving that language-space embeddings separate presence from absence objects far more sharply than CLIP-based embeddings do. GoDeep also localize these OOV objects within the scene, all without any 2D-3D annotation. Because every representation remains discrete text, predictions are also explainable at the point level. Finally, exploiting both a heuristic weighting, that favors precise over merely frequent observations and GoDeep’s explainability property, we propose an aggregation strategy, as a proof of concept, that favors finer elements localization.
[CV-5] Rethinking Learned Occupancy in Autonomous Active Mapping with Observation-Gated Filtering IROS2026
链接: https://arxiv.org/abs/2609.09069
作者: Jiahui Zhang,Bonian Han,Gongbo Liang,Yu Zhang
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to IROS 2026 Space Robotics Workshop (oral)
Abstract:Autonomous 3D active mapping requires a space robot to choose where to sense while building the geometry needed for navigation. Learned occupancy completion extends spatial context beyond the current field of view, but one predicted map often serves two planning roles: it scores expected surface gain and constrains collision-free motion. Unsupported occupancy can therefore distort both where the robot looks and where it believes it can travel. We study this coupled interface in a controlled closed-loop benchmark by holding the active-mapping system fixed and varying only its planner-facing occupancy across observation-only, learned, oracle-corrected, and ground-truth conditions. Improving occupancy accuracy does not monotonically improve closed-loop coverage: across 25 starts, planning with ground-truth occupancy reaches 70% of the learned baseline’s final coverage 12.7 steps earlier on average, while increasing final coverage by only 0.031. Guided by this diagnosis, we introduce an observation-gated filter that retains completion in insufficiently observed regions and suppresses predictions only after repeated frustum exposure without nearby RGB-D support. The filter improves both targeted failure-prone starts without retraining or ground truth. These results motivate online revision of planner-facing geometry during autonomous intervals between communication windows. The current study assumes benchmark RGB-D observations and sufficiently accurate pose estimates; planetary sensing conditions and accumulated localization drift remain to be evaluated.
[CV-6] “World Knowledge” in the Weights: Reading Concept Circuits of Vision Transformers ECCV2026
链接: https://arxiv.org/abs/2609.09055
作者: Yanlin Chen,Tang Li,Xi Peng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026
Abstract:Vision transformers (ViTs) have achieved remarkable generalization across visual domains, yet little is known about how they internally represent the structure of the world. To address this gap, we use Cross-Layer Transcoders (CLTs) to read concept circuits from ViTs: directed graphs whose nodes correspond to sparse, interpretable concepts and edges capture concept interactions across layers. Our method yields two complementary views of model behavior. The global concept circuit is input-invariant and can be recovered directly from learned cross-layer weights, exposing the reusable “world knowledge” encoded in the model. The instance concept circuit is input-dependent and identifies the concepts and pathways actually used for a specific prediction, enabling faithful example-level explanations. We demonstrate the utility of concept circuits in three ways: (1) Automatic spurious correlation discovery: leveraging the statistics of our global concept circuits to identify shortcut dependencies within the model. (2) Spurious correlation removal: intervening on the instance concept circuit to steer the model towards correct predictions. Empirical results show that our method outperforms existing counterparts by 11.0% on the Waterbird dataset. (3) Model comparison: contrasting the global concept circuits of different foundation models (e.g., CLIP vs. DINO) to reveal how supervision paradigms shape representational structure. Our code is available at this https URL
[CV-7] ask-driven Processing with Coarse-to-Fine Glimpse-based Active Perception
链接: https://arxiv.org/abs/2609.09025
作者: Oleh Kolner,Thomas Ortner,Stanisław Woźniak,Angeliki Pantazi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:State-of-the-art vision models process images in their entirety, lacking the ability to selectively zoom in on relevant regions. This limitation is particularly acute in scenarios where processing must be conditioned on a specific task - such as instance detection, which requires localizing a specific object in a high-resolution, cluttered scene. In such settings, critical details are easily lost as images are often resized to match the model dimensions and computational constraints. We introduce Coarse-to-Fine Glimpse-based Active Perception (CF-GAP), a task-driven front-end that enhances high-resolution processing of existing instance detectors. CF-GAP selectively directs a sequence of limited view glimpses across the scene, utilizing task information to iteratively refine focus on the most relevant regions. These localized regions are then processed at high resolution by a downstream instance detector. By avoiding full-image processing and eliminating irrelevant confounding information, CF-GAP improves Average Precision (AP) by up to 20% across various state-of-the-art instance detectors on the HR-InsDet and Robotools benchmarks, while further enabling lightweight detectors to outperform their larger counterparts.
[CV-8] PIC: Revisiting INR for Image Coding with Fast Encoding and Sub-Millisecond Decoding ECCV2026
链接: https://arxiv.org/abs/2609.09020
作者: Xiang Liu,Jinxiang Wang,Bin Chen,Zimo Liu,Mingyao Hong,Jiawei Li,Yaowei Wang,Shu-tao Xia
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV 2026. Code is available at this https URL
Abstract:Implicit neural representation (INR) has achieved remarkable progress in novel view synthesis and image/video coding in recent this http URL to conventional end-to-end image codecs, INR-based compressors demonstrate significant advantages in decoding complexity. However, their practical application has been hindered by the inferior encoding speed and underutilized decoding this http URL this work, we propose a feedforward INR image coding architecture, Practical INR Image Codec (PIC), that computes all the necessary information for INR network in a single forward pass, achieving an encoding speed of 20 FPS. Additionally, we implement a highly optimized decoder that reaches 2000 FPS decoding speed, significantly surpassing JPEG’s performance at comparable rate-distortion (RD) performance. To the best of our knowledge, this work presents the first learning-based image codec that simultaneously outperforms or is comparable with JPEG in both RD performance and decoding speed while maintaining practical encoding speed. Code is available at this https URL.
[CV-9] Spheriverse: 3D Scene Understanding from Spherical Observations in the Wild
链接: https://arxiv.org/abs/2609.09012
作者: Fei Teng,Sheng Wu,Mengfei Duan,Guoqiang Zhao,Junhui Ma,Kai Luo,Siyu Li,Hao Shi,Zhiyong Li,Kailun Yang
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO); Image and Video Processing (eess.IV)
备注: The established benchmark and source code will be available at this https URL
Abstract:Spherical observations provide global visual context for 3D scene understanding. However, visual information is encoded in an angular domain, whereas the physical world is represented in Cartesian coordinates. This cross-space representation gap complicates geometric correspondence and semantic evidence aggregation. To delve into this challenge, we introduce Spheriverse, comprising 64,400 temporally aligned spherical image-LiDAR pairs organized into 644 sequences. The dataset spans diverse scenes, illumination, and weather conditions, with fine-grained semantic classes. We further establish benchmarks for semantic occupancy prediction, semantic mapping, and 3D object detection, evaluating 30+ methods through overall and scene-wise comparisons. For dense prediction, we propose SphereOcc, an occupancy framework that couples spherical geometry modeling with semantic evidence retrieval. Cartesian-Spherical Representation Remodeling (CSRR) incorporates spherical range-azimuth geometry into Cartesian voxel features through region-wise modulation. Spherical Evidence Re-querying (SER) then conditions queries on voxel content and range-height-azimuth geometry to adaptively retrieve relevant semantic evidence from source spherical image features. SphereOcc achieves 13.91% mIoU and 24.65% GeoIoU, outperforming the respective best-performing methods, TPVFormer and SurroundOcc, by 1.70 and 2.10 percentage points. It also ranks first in both metrics across all five scenes, with consistent advantages across the evaluated spatial partitions and reduced fields of view. The established benchmark and source code will be available at this https URL.
[CV-10] A Joint 2D-3D Statistical Shape Model for Orthopedic Reconstruction MICCAI
链接: https://arxiv.org/abs/2609.09010
作者: Florence Dell’Aniello Picard,Pranav Poudel,Nairouz Shehata,Frédéric Lavoie,Herve Lombaert
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to MICCAI Workshop on Shape in Medical Imaging (ShapeMI)
Abstract:Three-dimensional femoral reconstruction from radiographs supports surgical planning, implant sizing, and post-operative follow-up, but remains ill-posed as X-ray projections discard depth information. Existing methods often incorporate a 3D statistical shape model (SSM) as a shape prior to guide reconstructions toward anatomically plausible shapes, relying on iterative 3D-to-2D projection matching. Yet, these approaches are computationally expensive and constrain their SSM to a single dimensionality, leaving the statistical relationship between 2D observations and 3D geometry largely unexploited and unexplored. We instead propose a joint 2D-3D SSM that explicitly captures the co-variation between 2D and 3D segmentations in a shared latent space. During training, 2D and 3D segmentations are registered to a common 3D template and its corresponding 2D projections, and the resulting stationary velocity fields are jointly decomposed using principal component analysis (PCA). This joint modeling allows the 2D-to-3D mapping to be learned directly from data rather than computing correspondences at inference time. For unseen subjects, the 3D shape is recovered directly by lifting the 2D latent coordinates to the 3D PCA subspace, thereby eliminating the need for iterative 3D-to-2D projection. Experiments on NMDID demonstrate that the proposed joint 2D-3D SSM outperforms a widely-used 3D-only SSM baseline while achieving inference approximately 4 times faster, at under 3 seconds per subject. The code is available at: this https URL.
[CV-11] DXPR: Depth-Based Vision-LiDAR Cross-Modal Place Recognition Using Vision Foundation Models
链接: https://arxiv.org/abs/2609.09005
作者: Yungsoo Han,Youngseok Jang,Seungwon Roh,Jeongyeon Seo,H. Jin Kim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 6 figures, and 5 tables
Abstract:We present DXPR, a depth-based cross-modal place recognition (CMPR) framework that uses vision foundation models (VFMs) to match monocular camera queries against a LiDAR map without modality-specific encoders. This enables robots and autonomous vehicles to robustly localize using only cameras within pre-built LiDAR maps, even under severe seasonal, weather, and illumination changes. The key idea is to convert both camera images and LiDAR scans into a unified depth image representation so that a single VFM backbone with an aggregation head can learn modality-invariant global descriptors. To make pairwise metric learning faithful to scene geometry, we introduce a geometry-aware overlap miner: after cross-modal scale alignment of camera and LiDAR depth, we forward-warp measurements between views to compute a pixel-level overlap score. This score relabels ambiguous pairs and adaptively modulates the positive margin in a multi-similarity loss to avoid overfitting on weakly overlapping views. Extensive experiments on KITTI odometry and Boreas demonstrate strong performance and robustness across seasons, weather, and day/night. On KITTI, DXPR achieves near-perfect Recall@1 on most sequences and outperforms prior CMPR baselines. On Boreas, DXPR achieves intra-sequence performance on par with a strong single-modal baseline (DINOv2-SALAD), while showing clear improvements in the more challenging inter-sequence setting. Compared with RangeBEV, our method consistently performs better in both intra- and inter-sequence evaluations, demonstrating robustness under diverse seasonal and illumination changes.
[CV-12] Concentrate After Imagination: Text-Conditioned Evidence Grounding for Partially Relevant Video Retrieval
链接: https://arxiv.org/abs/2609.08999
作者: Shuaiqi Cheng,Siyu You,Yanbi Wu,Yuxi Chen,Jiahao Zhang,Xuming Hu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Partially Relevant Video Retrieval (PRVR) retrieves untrimmed videos when queries describe only short moments. Although recent methods improve local representations, uncertainty modeling, and global context, final ranking often still trusts the strongest local response; a coincidentally similar fragment can therefore produce an unsupported peak. We identify this failure as the query-agnostic concentration bottleneck and propose TRACE, a score-level evidence verification operator for PRVR. Given a query and global video registers, TRACE activates query-relevant registers, routes their support to frame-level evidence, and smoothly marginalizes alternative query-to-register-to-frame paths before localized temporal selection. Unlike representation-level feature fusion, TRACE uses this evidence only as a query-conditioned residual calibration of the original local score. On ActivityNet Captions, Charades-STA, and TVR, TRACE achieves the best SumR on all three benchmarks and improves the DreamPRVR backbone by 1.2, 1.1, and 1.5 points, respectively. Ablation, routing-corruption, hard-negative, and cross-backbone transfer analyses support the interpretation that the gains arise from query-conditioned evidence verification rather than a generic score offset.
[CV-13] Prior-free relative 6D pose estimation of multiple object instances
链接: https://arxiv.org/abs/2609.08949
作者: Behdad Khodabandehloo,Andrea Caraffa,Davide Boscaini,Fabio Poiesi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Technical report. 12 figures, 6 tables
Abstract:Object 6D pose estimation formulations have progressively reduced reliance on object-specific priors, evolving from explicit 3D models to multi-view object captures to single reference images. We take this progression to its extreme by introducing prior-free relative 6D pose estimation, which lifts the assumption of knowing which object is to be posed within the scene. This novel setting aims to estimate the relative poses of multiple instances of an unknown object within the same image, without requiring CAD models, templates, or reference images. We solve this by formulating a novel method (PROSE) that finds coarse correspondences between object instances using multimodal foundation features, thus requiring no training. We refine these correspondences by imposing cycle consistency across tuples of instances, and leverage the resulting globally consistent correspondences to estimate the relative 6D pose between any pair of instances. To enable systematic evaluation, we design a novel benchmark (PRENCH) built from three multi-instance BOP datasets and enriched with task-specific metadata. PROSE consistently outperforms baselines obtained by adapting state-of-the-art single-image methods to the proposed setting, while requiring neither task-specific supervision nor additional learned components. Project website: this https URL
[CV-14] EgoSIS: From Factorized Visual Ego-Transitions to Motion-Canonical Spatial Evidence for UAV Reasoning
链接: https://arxiv.org/abs/2609.08938
作者: Jingpu Yang,Fengxian Ji,Mingxuan Cui,Yilin Sun,Hang Zhang,Jianhua Zhu,Yufeng Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:UAV video question answering requires separating camera motion from changes in the scene, but RGB-only multimodal models receive no explicit, stable reference for that separation. We present EgoSIS, a pose-free adapter that converts RGB-derived bidirectional flow into motion-canonical visual evidence in three stages. Factorized Visual Ego-Transitions (FVET) fits a robust image-plane transition and exposes motion, residual-support, and reliability factors. Reliability-Gated Ego-Transition Memory (ReTEM) uses reliability-weighted updates for a bounded history and re-anchors it at cuts or sustained uncertainty. Ego-Aligned Spatial Evidence (EASE) warps supported visual features into each segment’s local anchor and injects four spatial evidence tokens per visual slice through zero-initialized residuals, without changing Qwen’s visual-token count. On SIS-Bench, EgoSIS-8B obtains 89.9% perception, 82.5% perception-plus-memory, and 76.2% overall accuracy, with the largest gains concentrated in self-awareness perception and memory. The adapter thus provides an interpretable interface between optical flow and spatial reasoning.
[CV-15] CoSA: Correlation-Guided Change A ttention with Learnable Residual Gating for Remote Sensing Change Detection
链接: https://arxiv.org/abs/2609.08914
作者: Abdirashid Omar,Jonghyuk Park
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Pixel-level annotation of fixed traffic-camera imagery is expensive, while crosswalk models trained from street-level imagery face a substantial viewpoint and appearance shift when applied to elevated CCTV. We investigate a data-efficient target-domain pipeline using 241 manually annotated CCTV images and 5,926 unlabeled CCTV frames. A source-domain experiment trains a 31.0M-parameter custom U-Net on 3,300 first-person-view (FPV) images and obtains 93.05% IoU on its 330-image FPV test split. This result is a source baseline, not transferred performance: the released CCTV notebook instantiates a 42.0M-parameter DeepLabV3-ResNet50 from torchvision weights, and no compatible mapping from the U-Net checkpoint is implemented. Training on 201 manual CCTV images and selecting on 40 held-out manual masks yields 88.91% IoU. The model then predicts all unlabeled frames; image-level certainty and a largest-component area prior rank the candidates, and the top 1,000 attain mean certainty 0.976 and mean combined score 0.988. A repository audit shows that the reported second-stage 98.52% IoU was measured on a 150-image split containing only teacher-generated pseudo-masks. Because of a directory-layout mismatch, the executed combined-data loader found zero manual samples and split 1,000 pseudo-labeled samples into 850 training and 150 evaluation samples. We therefore report 98.52% as internal pseudo-label agreement rather than human-ground-truth accuracy. The defensible target-domain result is 88.91% IoU on the 40 manual validation images. Batch-one FP32 inference at 512 x 512 requires 12.98 ms, corresponding to 77.03 FPS, on an NVIDIA RTX A6000 48 GB GPU. These findings support the practicality of confidence-and-geometry filtering while also showing why pseudo-label evaluation must remain isolated from the labels used for self-training.
[CV-16] FRAME: Factored Retrieval via Attribute Readouts for Object-Centric Scene Memory
链接: https://arxiv.org/abs/2609.08886
作者: Woosang Jeon,Sanghyeok Choi,Minwoo Kim,Taehyun Jung,Taehyeong Kim
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 21 pages, 4 figures. Woosang Jeon and Sanghyeok Choi contributed equally
Abstract:Language-guided robots need persistent scene memories to follow instructions, revisit objects, and resolve references to objects encountered over time. While much of language-guided scene-memory retrieval has emphasized spatial or relational references, many everyday object references specify objects by multiple persistent attributes, such as category, material, size, or surface appearance. We formalize this problem as attribute-compositional retrieval, where a fixed object-centric scene memory is queried with natural language to retrieve the object satisfying the requested attributes. To investigate this capability directly, we introduce a controlled evaluation protocol with fixed scene memories and attribute-defined targets, separating retrieval from perception and annotation ambiguities. We then propose FRAME, which turns language into query-relevant attribute weights, uses learned readouts to estimate per-attribute evidence from object embeddings, and ranks objects by aggregating this evidence according to the query. Across held-out scenes and object assets, FRAME outperforms representative scene-memory retrieval baselines while reducing post-decomposition object scoring to lightweight matrix-vector computation. These results position attribute-compositional retrieval as a complementary scene-memory capability for language-guided robots, showing that persistent object attributes can be exposed as composable evidence for accurate and efficient multi-attribute retrieval.
[CV-17] Medical AI Encodes a “Feeling of Error”: Verifying Cancer Segmentation via Internal Concepts ECCV2026
链接: https://arxiv.org/abs/2609.08879
作者: Mengmeng Ma,Yunxiang Peng,Tang Li,Lu Lin,Binsheng Zhao,Oguz Akin,Xi Peng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: In ECCV 2026
Abstract:Cancer segmentation models can fail silently, generating plausible but incorrect masks that risk missed findings or unnecessary biopsies. A critical question arises: Do AI models “know” when they are wrong, and if so, can we use the signal to predict their own failures? Humans do have a “Feeling of Error” (FOE): a spontaneous sense of unease that flags a potential error during thinking. We investigate whether cancer segmentation models exhibit an analogous internal signal. Unlike output-level cues (e.g., prediction confidence or uncertainty), which offer no insight into why a failure occurs and suffer from a sensitivity-quality tradeoff where high detection sensitivity could degrade overall segmentation quality. We instead propose to capture the model’s FOE from its inner workings. Using mechanistic interpretability tools, specifically Sparse Autoencoders, we decompose internal neural activations into a dictionary of human-interpretable concepts and show that failure cases exhibit a distinct latent signature: fewer active concepts with lower activation magnitudes compared to successful segmentation. By training a classifier on these concept activations, we achieve accurate failure detection along with explanations for the model’s mistakes. Experiments on prostate, pancreatic, and brain cancer segmentation demonstrate that our approach outperforms output-based methods in failure detection while preserving segmentation quality.
[CV-18] SeGDeP: Semantic- and Geometric-Aware Decoupled Prompts for Reasoning Segmentation
链接: https://arxiv.org/abs/2609.08867
作者: Linnan Zhao,Xu Liu,Lingling Li,Licheng Jiao,Fang Liu,Wenping Ma
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reasoning segmentation converts an implicit linguistic conclusion into a precise mask, requiring both semantic identification and spatial grounding. Existing MLLM-segmenter interfaces either use a special trigger or compress both signals into one context, although they receive different supervision and fail differently. This coupling obscures whether a failure arises from target interpretation or from localization. We present SeGDeP, an explicit what-where interface. A semantic prompt branch and an independent geometric projection path transform resolved MLLM states into semantic features and a DETR-predicted box, which jointly condition a SAM 3 mask decoder. Training first aligns this executable interface, then uses group reward-decoupled policy optimization (GDPO) to balance format, box-IoU, and mask-IoU feedback. SeGDeP-4B reaches 82.7 average cIoU over eight RefCOCO-family splits and 66.0/59.6 gIoU on ReasonSeg val/test while adapting only 0.38% of Qwen3-VL parameters through LoRA. Controlled stage-wise ablations, gradient diagnostics, and prompt interventions further show that the two paths develop complementary semantic and geometric specialization rather than duplicating the same evidence.
[CV-19] DSE-VTG: Dual-Side Enhancement for Training-Free Video Temporal Grounding
链接: https://arxiv.org/abs/2609.08850
作者: Zhuo Cao,Bingqing Zhang,Sen Wang,Xue Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 4 figures
Abstract:Text-guided Video Temporal Grounding (VTG) aims to localize the relevant segments in an untrimmed video based on text queries, yet collecting dense temporal annotations and training task-specific models remain costly and brittle under distribution shift. Recent training-free VTG approaches mitigate this issue by directly matching pretrained vision-language representations, but they still face two fundamental information bottlenecks: frame-wise visual encoding overlooks temporal dynamics, while fixed query embeddings cannot resolve query ambiguity. To address these issues, we propose DSE-VTG, a \underlineDual-\underlineSide \underlineEnhancement framework that addresses both without any task-specific training. On the visual side, Multi-scale Similarity Fusion (MSF) combines frame- and clip-level similarities into a unified, temporally aware similarity profile. On the textual side, Query-level Test-Time Adaptation (Q-TTA) optimizes a lightweight additive offset to adapt the query embedding to the video at test time, without finetuning the backbone or calling external large language models. Extensive experiments on three standard and two OOD benchmarks show that DSE-VTG achieves state-of-the-art performance among training-free methods. On Charades-STA, it improves mIoU over the strongest prior training-free method by 5.61 points. Under distribution shift, DSE-VTG reaches 50.86 mIoU on Charades-CG Novel-Word, surpassing the strongest supervised baseline by 2.76 mIoU. Our code will be released upon acceptance.
[CV-20] FIRE3D: Feed-forward Interactive 3D Scene Reconstruction Within A Minute
链接: https://arxiv.org/abs/2609.08848
作者: Hongchi Xia,Tianhang Cheng,Wei-Chiu Ma,Shenlong Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Project page: this https URL
Abstract:We present FIRE3D, a unified framework that takes a single RGB image or casual RGB video and transforms it into simulation-ready 3D scene assets for games and interactive applications in under a minute. At the core of FIRE3D is a feed-forward, end-to-end network that predicts a compositional scene representation from posed RGB-D observations estimated from the RGB capture, including the 6-DoF pose, bounding box, mesh, and texture for every object. By modeling the scene as a collection of discrete entities, FIRE3D produces amodally complete and simulation-ready environments where objects are physically decoupled and ready for interaction. Our framework requires no test-time optimization, runs orders of magnitude faster than prior interaction-ready methods, and provides object-level completeness beyond existing feed-forward 3D approaches. We demonstrate competitive or state-of-the-art results across pose accuracy, geometry completeness, and texture quality across various datasets while being orders of magnitudes faster. Project page: this https URL
[CV-21] Leverag ing Visual and Geometric Priors for Metric-scale and Complete Vehicle Gaussian Reconstruction from Limited Views
链接: https://arxiv.org/abs/2609.08841
作者: Jinyu Miao,Jiusi Li,Yifei He,Miao Long,Kun Jiang,Mengmeng Yang,Diange Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 4 figures, 5 tables
Abstract:High-fidelity vehicle assets are essential for controllable traffic scene generation, particularly for synthesizing rare and safety-critical long-tail scenarios. However, reconstructing a reusable vehicle representation from in-the-wild onboard images remains challenging for two reasons. First, image-to-3D generation methods generally produce models without reliable metric scale. Second, onboard cameras usually observe only one side of a target vehicle, making conventional multi-view reconstruction incomplete on unobserved regions. To solve these problems, we propose a feed-forward vehicle asset reconstruction method, which leverages two complementary priors to reconstruct 3D Gaussian representations for vehicles using sparse one-sided observations. To achieve metric-scale reconstruction, a visual foundation model is first utilized to serve as a visual prior for Gaussian initialization. The Gaussian attributes are then estimated by a learnable encoder-decoder module. A symmetry-aware cloning strategy is presented to complete the unobserved side directly in Gaussian space, which exploits the bilateral structure of vehicles as a geometric prior. Experiments on the public dataset demonstrate that the proposed method significantly outperforms existing approaches in both vehicle asset completeness and geometric accuracy.
[CV-22] Beyond Gait: Person Identification from Millimeter-Wave Point Clouds Across Activities of Daily Living
链接: https://arxiv.org/abs/2609.08818
作者: Xilai Wang,Zixiong Han,Saad Rhanmouni,Chenzhe Zhao,Yunze Lu,Miodrag Bolic
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Person identification from millimeter-wave (mmWave) point clouds has mainly relied on gait. Indoor walking, however, is often brief and interrupted, while other activities of daily living (ADLs) may provide complementary identity information. We investigate identification across seven ADLs using mm-ADL, a new point-cloud dataset collected from 11 subjects under a controlled protocol. This extension introduces heterogeneous states and transitions whose spatial and temporal characteristics vary with activity. We therefore study whether activity can provide useful context for learning identity representations. We propose an activity-conditioned framework in which a human activity recognition router dispatches each clip to an activity-specific identity expert. The framework is implemented as a supervised mixture of experts, using a dual-stream static-dynamic PointNet (DS-SDPNet) to combine time-aggregated spatial structure with frame-to-frame information. We evaluate closed-set identification (ID) and subject-disjoint re-identification (ReID). With learned hard routing, ID accuracy increases from 62.1% to 68.0%. In a two-occupant ReID setting, hard routing increases mAP from 57.2% to 75.4% and Rank-1 accuracy from 59.1% to 82.1%. Under a matched gallery partition, activity-specific experts also outperform a shared embedding, showing that the gain extends beyond restricting the gallery. These results support the feasibility of using ADLs beyond gait for identification and the value of activity conditioning under controlled indoor conditions.
[CV-23] Evaluation Principles for MRI-MRA Registration in Trigeminal Neuralgia: An ROI-Centered Neurovascular Benchmark
链接: https://arxiv.org/abs/2609.08805
作者: Xupeng Zhang,Xihang Wang,Michael Xie,Haoyuan Liang,Hau Ern Lien,Oishika Das,James Feghali,Risheng Xu,Peirong Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Includes supplementary material. Code: this https URL
Abstract:Preoperative evaluation of trigeminal neuralgia (TN) often requires joint interpretation of structural MRI, which depicts the trigeminal nerve and surrounding cisternal anatomy, and time-of-flight MRA, which highlights vascular structures. Although MRI-MRA fusion is clinically attractive for visualizing neurovascular compression, this task is poorly captured by conventional whole-brain registration evaluation because the clinically relevant target is a small trigeminal ROI, vessel annotations are partial and clinically focused, local TOF-MRA contrast is variable, and field-of-view mismatch can limit deformable alignment. We formulate TN MRI-MRA fusion as an ROI-centered neurovascular registration-evaluation problem and construct a benchmark from 149 patients with clinician-annotated bilateral trigeminal ROIs. Six representative registration pipelines were evaluated using local image-based metrics, segmentation-derived vessel-localization metrics, prediction-volume analysis, and contrast- and FOV-stratified comparisons. Conventional evaluation summaries were often misleading: local image similarity, vessel-background separability, and downstream vessel localization did not co-rank methods; one-sided vessel distances were strongly affected by predicted vessel extent under partial annotations; and local MRA contrast determined when vessel-separability metrics were informative. Deformable refinement provided only a small, FOV-dependent benefit over affine alignment, while reader review showed that locally favorable vessel distances could coexist with globally implausible registrations. These findings indicate that TN MRI-MRA registration should be evaluated as a local, vessel-aware, contrast-sensitive, and FOV-aware visualization task rather than as generic multimodal brain registration. Our code is publicly available at this https URL.
[CV-24] Hi-FLoop: Hierarchical State-Feedback Loops for Multi-Timescale World Modeling
链接: https://arxiv.org/abs/2609.08796
作者: Rx Fan,Zhan H
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 13 pages, 2 figures, and 6 tables
Abstract:Multi-agent traffic simulation seeks diverse, coordinated, and physically realistic futures from maps and observed history. Long-horizon closed-loop generation must reconcile multiple decision time scales while its context evolves with generated states. Existing methods often unfold long futures from the initial scene and resolve intent, interaction, and motion monolithically, weakening cross-scale consistency and adaptation. We present HI-FLOOP, a branch-consistent multi-timescale state-feedback framework. Eight scene-level Worlds represent joint hypotheses, and all agents share the selected World identity throughout an 8-second rollout. Within the branch, an 8-second Goal anchors intent, a 2-second Preview coordinates interactions, and 1-second Control produces physical motion. Every 0.5-second commit feeds back only its executed prefix as new facts, while unexecuted hypotheses never enter factual memory. Joint Preview Interaction (JPI) induces a sparse directed future graph from Preview and uses conflict probabilities and signed arrival-time differences to gate interaction refinement. For generated-state recovery, a prefix-frozen A-to-B cascade lets frozen Model A generate 0-1 seconds, then transfers typed physical state, admissible context, and the branch index, but no latent state, to an independent Model B for re-encoding and 1-2-second recovery. On the full H-D public-validation split of 955 scenarios, one complete S1 run yields an Overall score of 0.689987 with the official evaluator. Under agent-centric oracle evaluation, HI-FLOOP achieves oracle-minADE@8 of 1.196636 m over the 8-second horizon and 0.526 m over the 6-second horizon.
[CV-25] Interpretable Hyperspectral Unmixing Framework with Fixed Endmember Prior and Structured Residual Refinement PRICAI2026
链接: https://arxiv.org/abs/2609.08786
作者: Ziyi Guan,Jianping Zhang,Qian Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 this http URL to 23rd Pacific Rim International Conference on Artificial Intelligence (PRICAI 2026)
Abstract:Hyperspectral unmixing decomposes mixed pixels into material endmembers and their abundances from contiguous spectral observations. In modular sensing pipelines, endmembers are often first identified and then treated as fixed during abundance estimation. When this fixed endmember prior is inaccurate, spatially structured mismatch arising from illumination changes, sensor artifacts, or material boundaries may be incorrectly captured by the abundance variables, leading to unstable decompositions. This study presents an interpretable stage-wise hyperspectral unmixing framework (I-HyperSU) under fixed endmember priors, which is explicitly decomposed into a fixed endmember matrix \mathbfA , an abundance block \mathbfX , and a structural residual refinement block \mathbfS . The X-block estimates abundances using FISTA with nonnegativity and sparsity enhancement, and a soft penalty that approximately enforces sum-to-one constraints. The S-block jointly applies low-rank SVD structural regularization and a lightweight deep image prior (DIP) to refine structured residuals. This staged design makes the interaction between abundance and residual components transparent and interpretable. Experiments on Samson, Urban, and Jasper Ridge datasets demonstrate that, under fixed and imperfect endmember priors, soft abundance relaxation consistently outperforms hard simplex projection. Under the default N-FINDR endmember prior, the proposed framework reduces the joint reconstruction error by 61.7%–69.5% compared with a fixed- \mathbfA UCLS baseline, while keeping the abundance RMSE nearly unchanged, indicating that the residual refinement branch accounts for structured model mismatch without degrading the abundance estimates. For example, on Urban, the reconstruction SAM decreases from 5.99^\circ for the X-only model to 1.92^\circ for the full model.
[CV-26] AXS-Net: Interpretable Deep Unfolding for Hyperspectral Image Denoising via Spectral Basis Unmixing and Structured Noise Refinement
链接: https://arxiv.org/abs/2609.08777
作者: Ziyi Guan,Jianping Zhang,Zheng Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages. Accepted to The 14th International Conference on Image and Graphics (ICIG2026), July 31, 2026
Abstract:Hyperspectral images (HSIs) are often degraded by mixed noise, including band-dependent Gaussian perturbations and structured artifacts such as stripes, dead-lines, and impulse noise. Most deep denoisers regress the clean image directly, entangling signal and structured noise. We instead model HSI denoising as \Y=\A\X+\Snoise+\Nnoise , where \A\X is a low-rank spectral-subspace (unmixing) reconstruction, \Snoise is structured sparse noise and \Nnoise is residual Gaussian noise. The resulting regularized optimization problem is unrolled into AXS-Net, a K -stage alternating proximal-point framework. Each stage combines an analytic spectral-basis gradient step, an SSX-Block proximal operator for abundance coefficients, and an SBlock proximal operator for the structured residual with column-consistent and sparse priors. This optimization correspondence exposes interpretable endmembers, abundance maps, and structured-noise estimates. Across ICVL, CAVE, and Harvard datasets and five noise configurations, the proposed AXS-Net achieves strong in-domain accuracy and competitive zero-shot transfer, with consistent gains across all five noise regimes on ICVL and Harvard. The recovered structured-noise closely follows the synthetic reference, and the recovered spectral basis is smooth and band-ordered rather than an arbitrary set of latent channels.
[CV-27] Compensating for Scarce Historical Images in Cross-Domain Cultural Heritage Retrieval Using Synthetic Aging
链接: https://arxiv.org/abs/2609.08766
作者: Marcin Iwanowski,Adam Mazgaj,Ferdynand Gorski,Sabina Szymoniak
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Cultural heritage collections often contain contemporary and historical visual records of the same physical object. Linking these records is difficult because corresponding images may differ in viewpoint, acquisition conditions, color reproduction, framing, resolution, and degradation, while genuine historical images are frequently scarce. This study investigates whether synthetically aged contemporary images can replace or complement missing historical training data in bidirectional instance-level retrieval. Synthetic old-domain images are generated using degradation-oriented transformations. An EfficientNetV2-M model is evaluated on identity-disjoint training, validation, and test sets across three dataset partitions and three training seeds. Mixed real-synthetic training is compared with real-only baselines using proportionally scaled and fixed 300-batch-per-epoch schedules. Complete replacement of genuine historical images reduced bidirectional mean R@1 from 86.56% to 81.27%, showing that synthetic aging does not reproduce the full genuine old-domain variability. Increasing the number of independently generated synthetic variants provided no consistent improvement. Under controlled scarcity, however, synthetic completion improved mean R@1 by 3.69 percentage points at 25% genuine historical coverage and by 2.92 points at 50%, relative to the proportionally scaled real-only baselines. At 75%, the gain decreased to 2.00 points, while performance remained comparable to the complete-real-data reference. Fixed-schedule real-only controls did not reproduce these improvements. The results indicate that genuine and synthetic observations are complementary. Synthetic completion primarily benefits retrieval by extending cross-domain identity coverage rather than by increasing training exposure, with its contribution gradually decreasing as genuine historical coverage increases.
[CV-28] Kairos: A Dataset for Fine-Grained Video-Language Modeling over Space Time and Dynamics
链接: https://arxiv.org/abs/2609.08755
作者: Ruibo Ming,Lei Sun,Deheng Zhang,He Zhang,Jialu Li,Jian Wang,Zhendong Li,Mengshun Hu,Danda Pani Paudel,Luc Van Gool,Jinjin Gu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Many emerging video language modeling tasks require systems to move beyond clip-level abstraction and model visual content as it unfolds over extended time horizons. However, most existing video datasets rely on coarse or sparsely aligned supervision, which compresses temporal variation and limits the ability of models to learn reusable representations of continuous visual dynamics. We introduce Kairos, a video dataset for video-language modeling with time-resolved annotations. Kairos consists of long-duration videos, ranging from ten minutes to half an hour, annotated with fine-grained temporal alignment. The annotations capture ongoing actions, entity appearances and attributes, interactions, and evolving contextual cues along the video timeline. This time-resolved structure supports fine-grained evaluation, long-range modeling and reasoning, instruction data construction, representation learning, and video generation. Kairos provides a general-purpose foundation for modeling visual experiences over time.
[CV-29] CVT-GS: Learning to Simplify 3D Gaussian Splatting with Centroidal Voronoi Tessellation
链接: https://arxiv.org/abs/2609.08730
作者: Bingxian Li,Yilong Li,Jingliang Peng,Peng-Shuai Wang,Fei Zhu,Guozheng Li,Chi Harold Liu,Guoping Wang,Bo Pang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:While 3D Gaussian Splatting (3DGS) has emerged as a powerful representation for real-time novel view synthesis, rendering high-fidelity scenes often relies on a massive number of Gaussian primitives, incurring substantial storage and computational overhead. Existing simplification techniques are largely intrusive, requiring training-time pruning, architectural modifications, or computationally expensive per-scene fine-tuning. These drawbacks limit their deployment on off-the-shelf pretrained models. In this paper, we propose CVT-GS, a novel optimization-free post-hoc simplification framework that directly compresses trained 3DGS scenes without sacrificing visual fidelity. Our approach first constructs spatially coherent cells over Gaussian centers via a geometry-aware Centroidal Voronoi Tessellation (CVT). Subsequently, a lightweight neural cell merger predicts the geometry and appearance of a single, highly representative Gaussian primitive for each cell under differentiable rendering supervision. By formulating simplification as a rendering-aware many-to-one merging process rather than naive primitive pruning, CVT-GS outputs a standard 3DGS scene that is seamlessly compatible with existing renderers. Experiments on various datasets demonstrate the superiority of our method. Notably, when achieving a 100-fold reduction in Gaussian points, our method operates 12 times faster than state-of-the-art methods while improving the PSNR by 1.3 dB.
[CV-30] Inverse Digital Marbling: Recovering Gesture Programs with a Replay Adjoint
链接: https://arxiv.org/abs/2609.08722
作者: Tianqi Liu,Yushan Han,Hang Liu
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Pigment deposition in paper marbling displaces the pattern already present, coupling the appearance of each gesture to later actions. We recover executable programs for a deposition-based digital marbling model: given a target image, we optimise an ordered program of capsule insertions whose replay approximates it. The capsule primitive continuously joins circular drops to elongated deposits. Its transport is exactly area-preserving and has a closed-form inverse on the exterior of the deposited region. A replay adjoint reconstructs intermediate states, retaining coordinates lost inside deposits and periodic position checkpoints. At 2000 gestures and 1024^2 pixels, the PyTorch replay implementation uses 8.7x less memory than the tested checkpointed-autograd configuration at comparable step time; the fused implementation fits a program in about four minutes on one workstation GPU. We evaluate image reconstruction on five marbled sheets, compare against transport-disabled fitting, one-pass geometric compensation and a published stroke-based fitter at matched stroke count, and measure sensitivity to an alternative ordered-drop transport. Recovered programs replay across a 4x range of linear resolution. Edits specified in program order or in palette space – inserting a gesture, recolouring a stage, translating a stage – replay correctly under the same model; edits specified by image content, such as moving a motif, do not. On synthetic targets with known generating programs, the recovered programs match the images but not the generating gestures under a positional matching statistic. The output is an editable program in the specified digital medium.
[CV-31] Enhancing Table Structure Recognition via Bounding Box Guidance ICPR2024
链接: https://arxiv.org/abs/2609.08705
作者: Lei Hu,Shuangping Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ICPR 2024. Upload for archiving
Abstract:Table Structure Recognition (TSR) aims to extract the bounding boxes of cells and table structure (e.g., HTML) from table images. Although current approaches have made significant progress, the latest image-to-sequence methods overlook the explicit utilization of the bounding box information when predicting HTML sequences, leading to error predictions in complex scenes. In this paper, we introduce a novel framework BGTR (Bounding Box-Guided Table Recognizer). To more effectively utilize bounding box information, we first predict the bounding boxes of cells and then use this information to guide the generation of HTML sequences. While utilizing bounding box information can enhance the accuracy of HTML sequences, for natural scene tables, the data volume is too small to allow for sufficient training of bbox-guided HTML generation. In response, we adopt a progressive training method for natural scene tables and introduce SNSTab, a synthetically generated natural scene table dataset. Our experiments on five benchmark datasets demonstrate SOTA performance.
[CV-32] CausalChapter: Improving Long-Video Chaptering with Interventional Dependency Modeling EMNLP2026
链接: https://arxiv.org/abs/2609.08686
作者: Xinran Duan,Guozhang Li,Yaoyao Zhong,Mei Wang,Lizhi Wang,Hua Huang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by EMNLP 2026 conference
Abstract:Long-form instructional videos require automatic chaptering to support browsing, navigation, and knowledge access. Recent long-context language models can perform chaptering from textualized video inputs, but they remain costly and brittle for content-dense lecture videos with long transcripts, smooth topic transitions, and detailed chapter outputs. A scalable segment-then-caption paradigm reduces this cost, but introduces two new challenges: boundary error propagation and fragmented cross-chapter context. We propose \textbfCausalChapter, an intervention-inspired framework for long-video chaptering that estimates prediction-level influence through lightweight masking and removal interventions. For boundary localization, our Local Dependency Shift module detects drops in predictive dependency between adjacent temporal windows; for chapter description generation, our Cross-Segment Support Selection module reranks historical contexts according to their support for the current prediction. Experiments on long-video chaptering benchmarks show that CausalChapter improves boundary localization, chapter description quality, and cross-chapter coherence.
[CV-33] CoordFormer: Give Me Any Coordinates and I Will Give You Labels WACV2027
链接: https://arxiv.org/abs/2609.08660
作者: Iacopo Curti,Pierluigi Zama Ramirez,Alioscia Petrelli,Luigi Di Stefano
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at WACV 2027
Abstract:Semantic segmentation on very-high-resolution images remains challenging due to the high computational cost and the difficulty of capturing fine-grained details. We propose CoordFormer, a novel coordinate-based architecture for semantic segmentation that predicts labels at arbitrary spatial locations through a Coordinate Decoder equipped with a Localized Cross-Attention mechanism. The decoder combines coordinate embeddings with high-resolution local patch features and interacts with global tokens extracted from a downsampled image processed by a ViT foundation encoder, enabling rich semantic context while preserving pixel-level precision. This design enables flexible inference at arbitrary resolutions while keeping memory low on very-high-resolution inputs, and supports an efficient semantic-edge-focused strategy that concentrates computation along boundaries, maintaining fine-grained accuracy while reducing latency and computational cost. CoordFormer achieves state-of-the-art performance on MaSS13K and outperforms comparably sized and higher-parameter methods on DIS5K and KPIs, demonstrating its effectiveness for high-quality, very-high-resolution semantic segmentation.
[CV-34] riCCOT: Tri-part Convolutional Conformal Transformer for Onboard Space Object Detection BMVC2026
链接: https://arxiv.org/abs/2609.08659
作者: Adrien Dorise,Marjorie Bellizzi,Julia Cohen,Stéphane May
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at BMVC2026
Abstract:Onboard object detection in Earth observation is constrained by limited computational resources and the absence of fully corrected imagery. While convolutional detectors are hardware-efficient, they often struggle to extract robust representations from raw and noisy data. Conversely, transformer-based models provide stronger global reasoning capabilities but remain difficult to deploy on FPGA accelerators due to quadratic attention complexity and non-compatible operations. We introduce TriCCOT, a tri-part architecture for robust and deployable onboard object detection. TriCCOT combines a convolutional region proposal network, a conformal prediction stage, and Aper-GATES, our hardware-friendly attention-based classifier. The region proposal network generates candidate bounding boxes, which are subsequently enlarged via conformal prediction, providing a distribution-free probabilistic coverage guarantee. The resulting crops are processed by Aper-GATES, which reformulates self-attention through convolutional projections, global channel statistics, and hardware-friendly gating operations, avoiding standard transformer operations that are poorly suited to CNN-oriented accelerators. Experiments on the DIOR and VDVRaw datasets demonstrate competitive detection performance and improved robustness to spatial blur and signal-dependent noise when compared to FPGA-compatible architectures. Finally, we report full deployment on a Xilinx Versal VCK190 FPGA without modifying the underlying DPU architecture, enabling unified CNN-Transformer inference for spaceborne embedded applications. Comments: Accepted at BMVC2026 Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.08659 [cs.CV] (or arXiv:2609.08659v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.08659 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-35] Charts Are Beyond Pixels: Probing for Layer-Wise Chart Understanding and Editing
链接: https://arxiv.org/abs/2609.08657
作者: Xiaochuan Zhong,Yifan Hou,Chenxi Pang,Shaobo Cui
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 25 pages, 9 figures
Abstract:Charts are structured visual compositions whose elements have distinct functional roles, semantic correspondences, and visibility relations. This structural view motivates evaluating whether models can understand and manipulate charts at the layer level. Existing chart benchmarks, however, primarily assess the correctness or fidelity of final outputs and do not directly evaluate these layer-wise behaviors. We present LayerWiseBench, a benchmark organized around three core concepts, layer attribution, layer binding, and visibility ordering, that structure its chart-understanding and chart-editing evaluations. Generated from executable chart programs, LayerWiseBench pairs each rendered chart with spatially aligned per-layer RGBA assets and construction-derived labels for functional roles, semantic bindings, and visibility relations. From this layer-wise representation, we derive controlled understanding questions, editing targets, reference images, and evaluation regions. It contains 2,800 source charts across 14 chart paradigms, from which we derive 7,329 layer-wise understanding questions and 53,791 instruction-guided editing variants. Among the evaluated VLMs, Qwen3.5-27B, which achieves the highest QA macro-average, obtains 93.04% accuracy on layer attribution and 97.46% on layer binding, but only 61.46% on visibility ordering. Across the four evaluated image editors, overall mIoU ranges from 1.49% to 4.93%, and visibility-constrained edits have the lowest mIoU for every editor, ranging from 0.37% to 2.00%. Taken together, these results identify tasks involving front-to-back relations between overlapping components as a recurring challenge across understanding and editing, motivating more explicit modeling of component identity and visibility relations.
[CV-36] From Where to How: Continuous 4D Interaction Forecasting from Egocentric Video
链接: https://arxiv.org/abs/2609.08636
作者: Qiaohui Chu,Haoyu Zhang,Meng Liu,Haoxiang Shi,Dongmei Jiang,Liqiang Nie
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Egocentric 4D interaction forecasting aims to anticipate both where future interactions will occur in 3D and how the human body will move to realize them, providing an important capability for assistive robotics and human-computer interaction. Existing methods struggle to translate semantic understanding into precise continuous 3D localization and to balance motion diversity with structural consistency in pose forecasting. More fundamentally, these tasks are often modeled separately, leaving the continuous geometric and temporal correspondence between interaction locations and body motion insufficiently captured. To address these challenges, we introduce Coherent4D, a large-scale egocentric dataset for continuous 4D interaction forecasting, comprising approximately 233K samples across three domains. Each sample pairs a sequence of future 3D interaction locations with corresponding full-body poses, aligned in time and expressed in a shared coordinate system. We also provide evaluation metrics in continuous space. Building on this formulation, we propose HIGFlow, a Hand Interaction Guided Residual Flow framework that models forecasting as a cascaded where-to-how process. HIGFlow first forecasts continuous future interaction locations by combining semantic grounding with short-horizon visual dynamics, and then uses the predicted location sequence to condition a deterministic motion anchor and residual Flow Matching for diverse yet structurally consistent full-body motion forecasting. Extensive experiments across all three domains demonstrate consistent improvements over representative baselines on both location and pose forecasting, while ablations validate the contributions of the proposed components. The project page is available at this https URL.
[CV-37] SynthRCT: Scalable Conditional Deformation Synthesis for Synthetic Repeat CT Generation MICCAI2026
链接: https://arxiv.org/abs/2609.08627
作者: Tomas Guija-Valiente,Blanca Rodriguez-Gonzalez,Norberto Malpica
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 11 pages, 4 figures. Accepted at the MIART Workshop, MICCAI 2026. This preprint corresponds to the initial submission prior to peer review
Abstract:In proton therapy, plans are typically optimized on a single planning CT, making robustness evaluation essential under anatomical changes. However, current scenarios often rely on simplified perturbations that poorly capture complex, patient-specific variability. We propose SynthRCT, a scalable conditional generative framework for 3D anatomical deformation synthesis. Based on a conditional variational autoencoder, SynthRCT learns a latent deformation space and decodes sampled latent codes into local stationary velocity fields conditioned on an input anatomy. Local fields are assembled into coherent full-volume transformations, enabling memory-scalable generation for large field-of-view CT data. We validate the approach on respiratory 4DCT data with multiple breathing-phase anatomies per subject. SynthRCT enables patient-specific sampling of plausible anatomical transformations beyond predefined robustness scenarios. Code available at: this https URL.
[CV-38] MFVINS: Multiple Fisheye Camera-Based Visual Inertial System
链接: https://arxiv.org/abs/2609.08626
作者: Eunseong Jang,YuJin Chung,Sang Jun Lee,Jihyun Yoon,HyungGi Jo
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 29 pages, 11 figures
Abstract:A simultaneous localization and mapping (SLAM) method using a monocular camera and a low-cost inertial measurement unit (IMU) sensor is an effective way to fulfill a low-cost sensor configuration. Using this sensor configuration, visual-inertial system (VINS) focuses on fusing data from a camera and an IMU sensor to estimate the six degrees-of-freedom (DOF) of the sensor pose. Typically, VINS uses only a single camera as visual input, which lead to problems such as error accumulation due to occlusion, various illumination, and textureless environments. In this paper, we propose a new multiple fisheye camera-based visual-inertial system called MFVINS. We present an IMU-aided FAST feature tracker for multiple cameras that enables efficient extraction and robust matching of local features. Then, the proposed method filters out outliers caused by fisheye distortion on the normalized image plane. Subsequently, a new reprojection error with physical validity constraints is proposed for bundle adjustment using learning-based depth estimation. The proposed method is applied to various scenarios, and its effectiveness is demonstrated by comparing previous VINS methods. In particular, MFVINS is implemented in real-time process to leverage the advantages of using multiple cameras – robustness against occlusion and textureless regions – while reducing the computational burden.
[CV-39] GOLF: Global Observation with Local Focus for Calibration-Aware Stereo Interaction Field Estimation ECCV2026
链接: https://arxiv.org/abs/2609.08607
作者: Minqiang Zou,Riqiang Jin,Zhi Lv,Dong Luo,Lianghai Tian,Zhenyu Zhao,Qi Xu,Tong Wu,Mochen Yu,Yao Tang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: First-Place Solution for the HANDS@ECCV 2026 SHOW3D Challenge
Abstract:We present GOLF, the first-place solution to the SHOW3D Interaction Field Estimation Challenge at HANDS@ECCV 2026. Given synchronized egocentric stereo views, the task is to predict a 3D vector from each of 21 hand joints to the closest point on the manipulated object. GOLF combines dense global context, locally sampled hand/object evidence, and common-frame Plücker-ray geometry. We adapt DINOv3 ViT-H+/16 with LoRA and trainable LayerNorm parameters, then jointly decode both interaction fields. Our primary model achieves an official score of 27.61 and a mean ADE of 27.96 mm on the hidden test set. An equal-weight ensemble with a complementary directly fine-tuned variant improves these results to an official score of 27.47 and a mean ADE of 27.82 mm, securing first place.
[CV-40] Effects of model architecture and learning strategies on deep learning-based recognition of activated sludge microscopic images and comparison with quantitative image analysis
链接: https://arxiv.org/abs/2609.08570
作者: Suguru Hakoshima,Tomohiro Tobino,Fumiyuki Nakajima
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Microscopic image analysis has long been recognized as a promising approach for monitoring activated sludge. In recent years, deep learning-based image analysis has been increasingly adopted in this field because of its high performance. However, previous studies on microscopic image analysis of activated sludge have rarely explored transformer-based models or self-supervised foundation models and have instead relied on CNNs and supervised ImageNet pretraining. In addition, previous studies often downsampled image sizes, but the effects of downsampling have not been sufficiently investigated, and the relationship between downsampling strategies and image analysis performance remains unclear. Furthermore, no study has quantitatively compared deep learning performance with quantitative image analysis (QIA), which was widely used before the emergence of deep learning. In this study, to examine how model architecture and learning strategies affect performance in microscopic image analysis of activated sludge and to quantitatively determine whether deep learning outperforms QIA, we prepared three types of activated sludge samples, classified their microscopic images, and evaluated classification accuracy. Our results showed that transformer-based architectures and alternative pretraining methods were effective in terms of classification accuracy. Our downsampling analysis showed that using overly small images reduced accuracy, but increasing image size beyond a certain point did not improve it further. In addition, the analysis indicated that, to achieve high classification accuracy, maintaining the field of view was a more effective downsampling strategy than maintaining resolution. Finally, our comparison between deep learning and QIA showed that deep learning outperformed QIA in terms of accuracy.
[CV-41] STSG-VQA: Evidence-Grounded Temporal Question Answering from Surgical Spatio-Temporal Scene Graphs
链接: https://arxiv.org/abs/2609.08543
作者: Jing Li,Duygu Sarikaya
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 3 figures, 7 tables
Abstract:Despite recent advances in surgical vision-language models (VLMs), temporal reasoning remains limited because existing supervision is largely frame-centric. Frame-level scene graphs (SGs) have proven effective in providing structured representations of surgical environments but do not explicitly model the dynamics of surgical workflows. To explicitly model how surgical states evolve across time, we introduce a multi-level structured temporal supervision methodology that augments frame-level surgical SGs with object-level continuity, event-level interaction continuity, and procedure-level connectivity. We then execute temporal queries over the resulting spatio-temporal scene graphs (STSGs) to generate evidence-grounded question-answer pairs, which together form the STSG-VQA benchmark. Each question is linked to the temporal interval and STSG evidence used to derive its reference answer, enabling traceable verification. The benchmark contains 18,458 question-answer pairs across seven temporal categories. Fine-tuning Qwen3-VL-4B and Hulu-Med-4B with STSG-derived supervision improves question-level micro accuracy by 24.39 and 19.56 percentage points over their zero-shot baselines and by 16.50 and 14.25 points over static scene-graph supervision, respectively. These gains span all temporal categories, indicating that STSG-derived supervision helps surgical VLMs reason over temporally grounded interactions rather than isolated frames. The code and dataset will be made publicly available upon acceptance.
[CV-42] Layer Selection in VLMs for Zero-Shot OOD Detection via Multi-Resolution Entropy Estimation MICCAI
链接: https://arxiv.org/abs/2609.08524
作者: Shyam Nandan Rai,Francesco Di Salvo,Sebastian Doerrich,Christian Ledig
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: MICCAI Workshop 2026
Abstract:Out-of-distribution (OOD) detection is crucial for safe deployment of medical AI systems, where domain shifts arise across institutions, acquisition protocols, and patient populations. VLMs enable zero-shot OOD detection by embedding images into a language-aligned latent space, where cross-modal similarity serves as a non-parametric confidence signal for identifying in-distribution samples. Yet existing methods rely almost exclusively on final-layer embeddings, implicitly assuming that the deepest representations are universally optimal. We first show that this assumption does not hold in medical imaging: intermediate layers provide complementary OOD signals, and the optimal representational depth depends on the respective image modality. While prior work selects layer combinations via entropy minimization of normalized histograms, we demonstrate that single-resolution entropy estimation is highly sensitive to binning choices, leading to performance variations of up to 19.3% AUROC. To address this instability, we propose a multi-resolution entropy estimation strategy that aggregates histogram statistics across multiple discretization scales, enabling robust and stable intermediate-layer selection. Across two medical OOD benchmarks, namely MIDOG and OASIS, covering distinct imaging modalities, diverse shift types, and different VLM backbones, our method consistently outperforms state-of-the-art approaches, offering a lightweight and stable solution for zero-shot OOD detection.
[CV-43] mporal State Transport in Video Generation: Diagnosing and Correcting Spectral Imbalance ICML2026
链接: https://arxiv.org/abs/2609.08505
作者: Luyao Tang,Bingjun Luo,Dong Yi,Jialin Guo,Haoning Xi,Cheng Chen,Yizhou Yu,Chaoqi Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Best Paper Award! ICML 2026 F2S Workshop
Abstract:Reliable video generation requires more than high-quality frames to form a coherent story: a model must maintain a persistent state, transporting visual attributes such as identity, scene layout, motion, and fine details across time. Existing training-free methods mainly strengthen cross-frame attention or analyze local attention entropy, but these views do not reveal whether temporal interactions stay in a healthy transport regime. In this work, we study video generation through the perspective of Temporal State Transport. We introduce Spectral Tension, a signed diagnostic that compares local attention diffuseness with global spectral diversity, and use it to identify two opposite temporal failures: fragmented transport and over-mixing hotspots. Based on this diagnosis, we propose Spectral Transport Homeostasis, a training-free regulator that softly corrects pathological temporal states while largely preserving balanced ones. Experiments on pretrained video generation models show that the original model often occupies imbalanced temporal regimes, whereas our method selectively applies larger corrections to the worst temporal hotspots and improves temporal consistency and visual quality without finetuning. Code: this https URL
[CV-44] SignRefine: Adapting Foundational Video Models for Sign Language Generation
链接: https://arxiv.org/abs/2609.08496
作者: Anton Pelykh,Edward Fish,Ozge Mercanoglu Sincan,Richard Bowden
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Sign language video generation demands precise hand and facial articulation, yet modern video diffusion models, trained predominantly on spoken-language video, produce artifacts that render signing unintelligible. We propose SignRefine, a sign language video generation model that produces comprehensible signing from 2D keypoint conditioning alone, generalizing across appearances and visual conditions. Our approach builds on a pretrained video diffusion transformer and introduces local adapters with spatial grounding to selectively refine hand and face regions, steering the strong base model’s prior toward accurate articulation. To enable this work and support broader sign language research, we present NVSign, a large-scale dataset of video content natively produced in sign language, offering diverse signer appearances, environments, and natural conversational settings. Trained on this data, our model shows up to 30% improvement in hand pose precision metrics over the strongest baseline and is preferred by sign language users for visual quality and comprehensibility in more than 80% of comparisons.
[CV-45] AURORA: Active Uncertainty-Driven Re-Orientation for In-Hand Reconstruction
链接: https://arxiv.org/abs/2609.08493
作者: Feiyu Zhao,Yuetong Li,Chenxi Xiao
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 23 pages, 11 figures, 6 tables. Accepted to the 10th Conference on Robot Learning (CoRL 2026)
Abstract:Observing objects grasped by a robot hand is challenging due to severe visual occlusions. Although in-hand manipulation can expose hidden surfaces, existing approaches often rely on predefined or open-loop reorientation strategies that do not explicitly target under-observed regions. We propose AURORA, an active 3D reconstruction framework that closes the loop between online object-centric reconstruction and in-hand reorientation. At its core, Ray-GPIS estimates direction-wise reconstruction uncertainty along candidate viewing rays and selects next-best-view targets using an uncertainty–novelty objective, which are realized through an axis-conditioned in-hand rotation policy. The resulting RGB-D observations are fused incrementally using CAD-free 6D pose tracking and lightweight geometric reconstruction. Experiments demonstrate that AURORA improves reconstruction quality and information-acquisition efficiency over non-active rotation strategies, while Ray-GPIS also outperforms active view-planning baselines in reconstruction performance, action-ranking quality, and planning efficiency. Targeted ablations further validate its robustness to hand occlusion and pose errors. The project webpage is available at this https URL
[CV-46] GSComplete: Gaussian Splat Completion with 2D Diffusion Priors
链接: https://arxiv.org/abs/2609.08449
作者: Elias Brugger,Philipp Erler,Stefan Ohrhallinger,Paul Guerrero
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages
Abstract:Gaussian splats provide a fast, high-fidelity representation for 3D objects but are often constructed from incomplete input data in practice, leaving missing regions. Existing completion methods either do not preserve the original splats or require scarcely available 3D training data. We propose GSComplete, which combines 3D generation based on Score Distillation Sampling with a novel preservation loss that encourages the original splats to be preserved where they should be visible. This effectively completes the Gaussian splat object using only 2D diffusion priors while fully preserving existing splats and generating new splats only in missing regions, without occluding the input. To evaluate our approach, we introduce a new dataset of partial Gaussian splat objects and show that GSComplete achieves significantly more accurate preservation of the input than existing methods with comparable plausibility of the completed result. Our code and dataset will be made available upon acceptance.
[CV-47] AirAnchor: Bridging Local and Global Spatial Information for Zero-Shot Aerial Vision-and-Language Navigation
链接: https://arxiv.org/abs/2609.08442
作者: Shanwei Fan,Bin Zhang,Zhiwei Xu,Yingxuan Teng,Siqi Dai,Lin Cheng,Guoliang Fan
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注:
Abstract:Aerial Vision-and-Language Navigation requires drones to follow natural-language instructions and navigate through complex urban environments. Accurate navigation relies on both local and global spatial information, which support immediate action grounding and long-horizon path planning, respectively. However, existing zero-shot methods typically operate at a single spatial scale, relying either on local representations constructed online from current observations or on global memories built offline from historical experience. To address this limitation, we propose AirAnchor, a new paradigm that bridges local and global spatial information through spatial anchors and integrates both into a shared navigation framework, enabling comprehensive spatial grounding for decision-making. AirAnchor consists of three core components: (1) Query-Driven Spatial Anchor Grounding, which identifies decision-relevant anchors from visual observations and organizes them into local spatial representations; (2) Persistent Object Spatial Memory, which incrementally maintains an object knowledge base as persistent global spatial memory and retrieves landmark-related spatial priors; and (3) a Spatially-Informed Navigation Agent, which explicitly integrates both local and global spatial information into an agentic framework for decision-making. Extensive experiments on AerialVLN demonstrate that AirAnchor substantially outperforms existing zero-shot baselines, validating the effectiveness and efficiency of the proposed paradigm.
[CV-48] CAR-MIL: Counterfactual Attention Regularization for Multiple Instance Learning ECCV2026
链接: https://arxiv.org/abs/2609.08419
作者: Imane Chraki,Pierre Marza,Stergios Christodoulidis,Maria Vakalopoulou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV 2026
Abstract:Multiple Instance Learning (MIL) is widely used for weakly supervised learning, particularly in digital pathology, where fine-grained annotations are costly. Most MIL methods aggregate instance features via attention mechanisms. However, attention weights do not always faithfully reflect instance importance and may focus on spuriously correlated regions. In this work, we propose CAR-MIL, a framework that explicitly guides attention learning through a counterfactual attention regularization objective inspired by counterfactual explanations. Built on a standard attention-based MIL architecture, our approach introduces a lightweight counterfactual attention branch trained to produce an alternative prediction while remaining close to the factual attention distribution. This encourages prediction changes to arise from minimal, structured redistributions of attention, leading to more informative evidence allocation. The resulting factual and counterfactual attention maps capture complementary evidence: the former highlights regions supporting the prediction, while the latter reveals regions whose reweighting would challenge it. We evaluate our method on synthetic MIL benchmarks with instance-level ground truth enabling controlled analysis of attention behavior and on five digital pathology datasets across four tasks. CAR-MIL maintains competitive classification performance, with the largest gains observed on more challenging tasks, while improving attention reliability, demonstrating the benefits of integrating counterfactual explainability reasoning into attention learning. Code is available at: this https URL.
[CV-49] owards Embodied Air-Ground Cooperative Object Search: Benchmark Dataset and Agent ic Method
链接: https://arxiv.org/abs/2609.08402
作者: Boao Yu,Zimo Chen,Junreng Rao,Yue Hu,Zhengqiu Zhu,Yong Zhao,Rusheng Ju
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 16 pages, 4 figures, 4 tables; includes an appendix
Abstract:Air-Ground Object Search (AGOS) in urban environments is a challenging embodied task, which requires an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) to jointly search for and verify a specified target vehicle from multi-view visual references. To study this underexplored problem, we introduce AGOS-Bench, the first dedicated benchmark for evaluating whether general-purpose Vision-Language Models (VLMs) can integrate aerial discoveries and ground-level verification through UAV-UGV cooperation. We further provide AGOS-Dataset as the companion resource of exemplary trajectories constructed by an automatic pipeline. It consists of 7.7k episodes for searching objects of diverse categories and attributes, spanning three difficulty levels. To address the AGOS task, we propose AGOS-Agent, a training-free and tool-augmented approach. The agentic method relieves VLMs from complex and dynamic coordination via a deliberate search-handoff-verify cooperation protocol, only demanding VLMs for scene understanding and decision-making. Extensive experiments on nine VLMs show that AGOS-Agent improves overall success rate for eight of the nine evaluated backbones while reducing decision steps for all nine. On the hard split, the SR and SPL of Gemini-3.6-Flash increase from 8.6% to 55.7% and from 7.6% to 44.0%, respectively.
[CV-50] GALoc: Gravity Aligned Wireframes for Depth-Free Monocular Floorplan Localization
链接: https://arxiv.org/abs/2609.08385
作者: Jeahn Han,Minji Kim,Jeongbin Sohn,Jonghyeok Park,Matthias Wuest,Pyojin Kim
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 8 pages, 13 figures, 5 tables
Abstract:Floorplans are compact, appearance-invariant maps ideal for indoor localization, yet existing methods rely on depth networks that are brittle in cluttered scenes. We propose GALoc, a geometry-first framework that replaces depth prediction with gravity-aligned wireframes that satisfy verticality and coplanarity by construction. Given monocular RGB, camera intrinsics, relative poses, and IMU orientation, GALoc constructs a linear constraint matrix encoding verticality and coplanarity, and finds the camera gauge minimizing its smallest singular value via global search. The rectified wireframes are projected into bird’s-eye-view layouts through a closed-form, FOV-consistent transformation and matched against the floorplan via metric-free SE(2) search. We evaluate end-to-end on Structured3D, with calibrated noise on Gibson, and on real-world author-collected sequences. When sufficient wall geometry is visible, GALoc matches or outperforms depth-based baselines – achieving 88% sequential localization success at 0.1m over 100-step sequences on Gibson vs the baseline’s 68% – while abstaining in structure-blind scenes.
[CV-51] o Adapt or Not to Adapt? Selective Adaptation for Vision-Language Models ECCV2026
链接: https://arxiv.org/abs/2609.08367
作者: Siru Jiang,Yuwei Liang,Jian Liang,Ran He,Tieniu Tan
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: ECCV 2026
Abstract:Test-time adaptation (TTA) has emerged as a prominent strategy for adapting vision-language models to distribution shifts during inference. We conduct a per-sample analysis of model predictions before and after adaptation, and observe two failure modes in existing TTA methods that echo previous work. Adaptations are frequently negligible, yielding no change in the model’s predictions, and more severely, they can be detrimental by flipping previously correct predictions to incorrect ones. This naturally raises a question: Can we identify and skip such negligible or harmful adaptations? In this work, we introduce a new problem of selective adaptation, which aims to determine whether a given test sample should undergo adaptation or be skipped. To this end, we propose Cross-Augmentation Similarity (CAS), a simple baseline that performs adaptation only when predictions across augmented views exhibit low similarity. Notably, CAS not only preserves but in some cases improves overall accuracy, even when skipping nearly 85% of the adaptation process. We hope other researchers will explore this new direction and surpass the performance of our baseline. Our code is available at this https URL.
[CV-52] ReMoMask-2: Latent Retrieval-Augmented Masked Motion Generation
链接: https://arxiv.org/abs/2609.08365
作者: Yiran Wang,Zeyu Zhang,Ling Shao,Hao Tang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Code: this https URL . Website: this https URL
Abstract:Text-to-motion (T2M) generation maps natural language to human joint movements, aiding gaming, VR, and robotics. Retrieval-Augmented Text-to-Motion (RAG-T2M) improves generation on complex descriptions by conditioning on retrieved motion-text pairs. However, existing RAG-T2M models face two challenges: coarse-grained retrieval and fusion mechanisms overlook the hierarchical, spatial-temporal topology of human motion, and a representation gap exists because retrieved evidence resides in a semantic space separate from the generator’s latents. To address the first, we present ReMoMask, a structure-aware RAG framework coupling Hierarchical Bidirectional Momentum (HBM) contrastive learning to align global and part-level features with text; Semantic Spatial-Temporal Attention (SSTA) for topology-aware fusion; and Topology Structured Masking (TSM) to force robust part-level grounding via adaptive masking. To address the second, we introduce ReMoMask-2, which rebuilds the retrieval database directly within the generator’s pre-quantization latent space and aligns text queries via a distilled lightweight projector, allowing the generator to directly consume the retrieved motion’s semantic content. Extensive experiments on HumanML3D, KIT-ML, and SnapMoGen demonstrate our retriever achieves state-of-the-art accuracy, while ReMoMask-2 attains the lowest FID on KIT-ML and SnapMoGen; notably, its single mask-transformer stage surpasses ReMoMask’s full two-stage pipeline and delivers the fastest inference.
[CV-53] Segment Any Motion with Radar: Robust Multimodal Moving-Object Segmentation and Tracking
链接: https://arxiv.org/abs/2609.08346
作者: Jue Wang,Xuan Wang,Hao Zhou,Ruixiang Zhou,Yixuan Zhou,Tianshuo Yuan,Jieming Ma,Jie Zhang,Fei Luo
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 9 pages, 5figures
Abstract:Moving-object perception must decide which image regions correspond to real motion and keep every instance identified over time. Methods that read motion from appearance, optical flow, or estimated trajectories lose that evidence under poor illumination, adverse weather, reflections, and occlusion. Radar is a natural remedy because it measures radial velocity directly instead of inferring it from photometric correspondence. However, existing benchmarks do not jointly provide radar measurements, dense moving-instance masks, and temporally consistent identities for surveillance. We therefore introduce RGBTR-Motion, a synchronized and calibrated fixed-camera benchmark that pairs RGB, thermal, and radar streams with dense instance masks and temporally consistent identities across diverse surveillance scenes. We also develop SAM-Radar, an RGB, thermal, and radar-based segmentation and tracking framework built on SAM 3. SAM-Radar’s radar-aware detector fuses calibrated RGBT features with radar returns that are grounded at their projected image locations, and motion supervision, implemented as foreground classification of those projected returns, teaches the detector to reject clutter without any text prompt. The tracker associates accepted radar returns with individual trajectories and uses them as physical evidence that a visually degraded target remains present. This allows it to bridge short periods of low visibility or occlusion and reconnect a reappearing target to its existing identity instead of starting a new track. SAM-Radar attains 0.7027 IoU and 0.8090 F1-50, and raises MOTA, HOTA, and IDF1 by 0.2977, 0.1603, and 0.2857 over the strongest competing values.
[CV-54] CoVeR: Coverag e-Based Token Pruning for Multi-View 3D Reasoning in VLMs
链接: https://arxiv.org/abs/2609.08345
作者: Nhat-Tan Bui,Varshini Elangovan,Arun Reddy Anugu,Sreyas Mohan,Wei Ye,Dilin Wang,JQ Huang,Rakesh Ranjan,Aviral Chharia,Fernando De la Torre
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 21 pages, 17 figures
Abstract:Representing a 3D scene as multi-view images allows 2D VLMs to reason in 3D by reusing priors from pre-training, sidestepping the scarcity of annotated 3D data. However, it produces thousands of redundant visual tokens whose cost grows with every view. Existing visual token pruners fall into two families, each limited in the 3D multi-view setting. Learned importance methods rank tokens by attention or encoder features; because redundancy here is fundamentally spatial, they keep near-duplicate tokens from a few prominent regions and leave most of the scene unrepresented. Voxelization methods improve spatial coverage but cannot enforce an exact token budget and saturate as multi-view observations overlap in 3D, capping retention well below the target. We show that spatial coverage is associated with 3D reasoning performance and introduce CoVeR, a deterministic, training-free selector that uses only token coordinates, with no learned signals. CoVeR selects tokens that collectively cover every region of the scene, and solves the limitations of both families: it enforces an exact per-scene budget, breaks the voxelization saturation plateau, and avoids the near-duplicate selections of learned importance. Extensive experiments show CoVeR outperforms prior SOTAs on all three 3D reasoning benchmarks and generalizes as a plug-and-play module tested across four VLMs. Notably, with only \approx 8% of visual tokens, it preserves 93.5% of full-token performance, surpassing SOTA by 3.9 percentage points on average across benchmarks.
[CV-55] VeriScene: Reconstructing Crime Scenes from Legal Evidence via World-Model Agent
链接: https://arxiv.org/abs/2609.08342
作者: Kevin Chuanpu Fu,Yongsen Zheng,Zee Kin Yeong,Kwok-Yan Lam
类目: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR)
备注:
Abstract:World models take multimodal inputs like text, photos, and diagrams to generate dynamic scenes in accordance with the laws of physics, thus opening a compelling application: fusing multimodal legal evidence to re-create a crime scene and re-enact how an offence could have been committed. However, feeding the raw, unorganized evidence into a world model fails in forensic use: it silently drops evidence, glosses over contradictory testimony, and produces motion that violates the evidentiary record. This paper presents VeriScene, an agent that orchestrates the world model: it reconstructs crime scenes from forensic photographs and witness statements of varying reliability, keeping every claim traceable to evidence and every motion physically plausible. VeriScene iteratively fuses the evidence into a cited narrative under an auditing loop, verifies the hypothesized dynamics via probe rollouts in the world model with corrective constraint injection, and renders the offence as a re-enactment video from a fused keyframe. On a benchmark of 25 crime scenarios across 7 physically-driven case types (139 forensic-style photographs and 65 statements with planted unreliability), VeriScene attains 0.9014 evidence coverage and 0.7217 factual consistency (0-1 scale) on the 20 test scenes, outperforming an end-to-end multimodal-LLM baseline by 20.35% in factual consistency and 34.88% in temporal coherence, while generalizing across four LLM orchestration backends at USD 1.82 per scene.
[CV-56] EdMCGS: Event-Driven Markov Chain Gaussian Splatting for Extreme-Low-Frame-Rate Dynamic Scene Reconstruction
链接: https://arxiv.org/abs/2609.08332
作者: Yuzhong Wang,Wenmin Wang,Xinxing Yu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 29 pages, 3 figures
Abstract:We present EdMCGS (Event-driven Markov chain Gaussian Splatting), an end-to-end method for reconstructing dynamic 3D scenes from extreme-low-frame-rate RGB together with an event stream, which can then be rendered at any intermediate timestamp. Methods relying solely on RGB images generate numerous artifacts due to the lack of evidence from between consecutive frames. To supply this missing evidence, we model the scene motion as an event-driven Markov chain, in which the sparse RGB frames anchor the state at their own timestamps while the events recorded within an interval drive the transition across it. Since the transition reads the events of the current interval, it remains active at inference and produces the in-between motion of the 3D Gaussians directly from the events rather than by interpolation, which sets our method apart from prior work that uses events only as training-time supervision. The state is carried by a compact set of control points, each driven by the events sampled in the neighborhood of its own image projection, and a temporal local isometry term keeps the propagated motion locally rigid. Experiments on synthetic and real-world scenes show that EdMCGS outperforms both RGB-based and event-based baselines, while rendering in real time with far fewer Gaussians than the strongest event-based baseline. We release our source code and a new dataset at this https URL.
[CV-57] Do Input-Level Defenses Transfer to Observation-Level Attacks on VideoLLM s?
链接: https://arxiv.org/abs/2609.08331
作者: Bangshuo Zhu,Wei Song,Yuxin Cao,Yuezhong Wu,Zhiquan Liu,Yuekang Li,Jingling Xue
类目: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR)
备注: Preprint. Under review at IEEE Transactions on Dependable and Secure Computing. 13 pages, 1 figure, 6 tables
Abstract:Video Large Language Models (VideoLLMs) are increasingly deployed in safety-critical applications such as content moderation and video analytics. To process long videos efficiently, VideoLLMs rely on frame sampling, token compression, and modality fusion, which together form an observation pipeline that reduces the raw video to a compact internal representation. Recent observation-level attacks exploit this pipeline to prevent the model from perceiving harmful content, yet no defense has been explicitly designed for this threat. We introduce DefTEval, a controlled evaluation framework that systematically assesses whether input-level adversarial defenses, which operate on the pixel content of already-sampled frames, can mitigate observation-level attacks. Across five VideoLLMs, eleven representative defenses, and five attack types, we find that input-level defenses offer limited and inconsistent protection, with harmful detection rates frequently near zero. Critically, defenses fail even against attacks that embed harmful signals in every sampled frame, indicating that the bottleneck extends beyond sampling omission to the suppression of signals that do enter the model. Token compression discards localized features, and modality fusion systematically down-weights weakened visual signals. Furthermore, defense effectiveness is dominated by model architecture rather than by the defense method itself, and detection rates vary drastically across content categories, exposing structural weaknesses in temporal reasoning. These findings demonstrate that securing VideoLLMs requires system-level robustness mechanisms spanning sampling-aware coverage guarantees, token-level preservation of safety-relevant features, and modality-balanced fusion.
[CV-58] Supervised Cross-Modal Feature Alignment for Zero-Wearable Freezing of Gait Detection in Parkinsonism
链接: https://arxiv.org/abs/2609.08317
作者: Aryan Singh,Chandan Biswas
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 4 figures
Abstract:Objective assessment of Freezing of Gait (FoG) in Parkinson’s disease (PD) relies predominantly on wearable Inertial Measurement Units (IMUs). While IMUs provide optimal kinematic precision, mandatory sensor attachment restricts continuous clinical deployment. Conversely, unobtrusive vision-based alternatives suffer substantial classification errors during turning-in-place tasks, where geometric self-occlusion degrades deterministic skeletal coordinates and obscures the high-frequency precursors required for FoG detection. To resolve these physical observation limits, we propose a supervised cross-modal subspace distillation framework. During optimisation, pre-trained kinematic data from IMU sensors and contextual clinical metadata act as oracles to guide a deployable visual architecture. By incorporating joint velocity and acceleration derivatives, utilising a confidence-based gating mechanism, the visual model mitigates some of the tracking errors during occlusion events. Empirical evaluations confirm this latent alignment transfers the predictive fidelity of hardware sensors directly into the visual representation, yielding 85.5% accuracy, and 82.4% balanced accuracy. All the while maintaining a vision only model at inference.
[CV-59] From Glance to Scrutiny: Progressive Distortion Reasoning for Fine-Grained Image Quality Assessment
链接: https://arxiv.org/abs/2609.08316
作者: Aoting Zhang,Mingze Gao,Dongbao Yang,Longyi Chen,Daoxin Zhang,Yi Wu,Yao Hu,Yu Zhou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multi-modal large language models (MLLMs) have demonstrated significant potential in image quality assessment (IQA) by bridging visual perception with descriptive evaluations. However, existing approaches mainly focus on holistic quality prediction, often functioning as black boxes that provide limited insight into where distortions occur and how they affect perceived quality, hindering fine-grained analysis of localized and heterogeneous degradations. We propose GS-IQA, a framework that reformulates IQA as a progressive Where–What–How diagnosis, emulating the human perceptual process from an initial glance to closer scrutiny. Since a severity judgment is meaningful only for a correctly localized and recognized region, we realize this progression through a two-stage reinforcement learning paradigm that respects such dependencies: the glance stage uses a perception-gated reward to establish where degradations lie and what they are, activating severity feedback only once both are correct, while the scrutiny stage introduces online reward-conditioned degradation generation to synthesize hard examples targeted at the model’s perceptual bottlenecks, sharpening its discrimination of subtle severity variations. To enable systematic evaluation, we construct Diag-Bench, a region-level IQA benchmark of about 25K curated samples spanning 12 distortion types and five ordinal severity levels. Extensive experiments show that GS-IQA consistently surpasses state-of-the-art methods in distortion localization, recognition, and severity estimation, and that its diagnostic representations transfer effectively to conventional global quality prediction across diverse external benchmarks. Code and data will be released.
[CV-60] FPicker: Topology-Guided Evolution for Filament Tracing in Low-SNR Microscopy ECCV2026
链接: https://arxiv.org/abs/2609.08305
作者: Tingyin Zhao,Mingtao Huang,Yuan Shen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to the 19th European Conference on Computer Vision (ECCV 2026). 18 pages, 6 figures. Code is publicly available at: this https URL
Abstract:Automating filament tracing in Cryo-Electron Microscopy (Cryo-EM) is essential for 3D helical reconstruction but challenged by intersecting topologies and extremely low Signal-to-Noise Ratios ( \textSNR = \sigma_s^2/\sigma_n^2 0.1 or -10 dB). Existing paradigms fail: pixel-wise segmenters suffer from severe topological fracturing, box-based detectors face ghost center drift, sequential trackers derail due to error accumulation, and traditional active contours collapse under artificial closed-curve constraints. To resolve these bottlenecks, we present FPicker, the first topology-guided framework reconciling these incompatibilities. It unifies perception via a center-endpoint representation and an open-curve evolution module to explicitly model non-cyclic connectivity. On simulated benchmarks, FPicker outperforms top baselines by over 40% relative gain in mean spatio-angular precision (mSAP) and reduces topological gap rates by over 60% under extreme noise ( -20\text dB ). By learning intrinsic physical geometry rather than local texture, FPicker demonstrates strong potential as a resilient geometric backbone. Its zero-shot performance on the real-world EMPIAR dataset exhibits robust topological resistance, achieving a state-of-the-art 82.9% mSAP upon fine-tuning. Our results also suggest modeling physical priors is a highly robust path toward bridging the sim-to-real gap in signal-starved scientific imaging. The code is publicly available at: this https URL.
[CV-61] RIUNE-Net: Harmonizing Scale Shape and Efficiency in Pancreatic Tumor Segmentation
链接: https://arxiv.org/abs/2609.08303
作者: Amir Hossein Saleknia,Alireza Kheyrkhah,Sanaz Karimijafarbigloo,Reza Azad,Sina Houshmand,Ulas Bagci,Dorit Merhof,Alaa Sulaiman
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Pancreatic tumor segmentation in 3D CT volumes is challenged by extreme scale variability across both the pancreas and tumor, and highly irregular tumor morphology. While recent advances have pushed segmentation performance, existing methods do not explicitly address these challenges and come at the cost of excessive computational complexity, limiting their practicality in resource-constrained clinical environments. We propose TRIUNE-Net, a lightweight unified architecture that harmonizes scale, shape, and efficiency through three synergistic innovations. A multi-scale context aggregation module with stage-adaptive dilated convolutions enables the model to reason across the broad range of anatomical scales present in both organs. A serial linear-deformable attention mechanism combines large effective receptive fields with shapeadaptive deformable convolutions to capture irregular, non-convex tumor morphologies. Finally, an information-preserving downsampling module replaces conventional max pooling entirely, retaining all spatial information while adding negligible parameters, preventing small tumors from being discarded before they can be recognized. On both the MSD Pancreas and NVD Pancreas datasets, TRIUNE-Net achieves state-of-theart results with only 5.86 M parameters and no external pre-training, outperforming all baselines across all key tumor metrics. Specifically, it surpasses the next-best model by 0.45% in tumor Dice, 6.0 points in F1 score, 6.6 points in sensitivity, and 3.4 points in precision, simultaneously reflecting its ability to suppress both missed tumors and false alarms in clinically realistic conditions. Our code is available at: this https URL
[CV-62] Human-Centric Image Captioning with Subject-Centered Spatial Understanding
链接: https://arxiv.org/abs/2609.08300
作者: Bozhou Li,Jiahang Zhang,Yue Ding,Yushuo Guan,Bohan Zeng,Yiyan Ji,Xinlong Chen,Yang Shi,Yifan Dai,Yuran Wang,Chengzhuo Tong,Pengfei Wan,Yuanxing Zhang,Wentao Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:While multimodal large language models (MLLMs) achieve remarkable performance on generic image captioning, they frequently suffer from structural hallucinations in human-centric scenarios. Accurately modeling human subjects is foundational for critical downstream applications, such as accurate avatar/video/image generation and fine-grained human action understanding. However, these tasks require highly precise subject-centered spatial grounding, such as distinguishing egocentric left/right laterality and maintaining correct anatomical-object bindings. Although catastrophic for structural integrity, these localized spatial inversions are often overshadowed by overall descriptive metrics in existing benchmarks. To systematically expose and quantify this bottleneck, we introduce SPACE (Subject-centric Poses, Appearance, and Characteristics Evaluation), a benchmark designed to evaluate subject-centered spatial understanding. On SPACE, we reveal that despite strong generic perception, current MLLMs consistently fail to ground descriptions in the subject’s intrinsic frame of reference. To bridge this gap, we propose a specialized data construction and alignment pipeline. We first extract structured spatial hints from fine-grained body-part localization to guide a two-stage caption rewriting process, yielding highly spatially-faithful training data. Furthermore, we design a rubric-based reward for Group Relative Policy Optimization (GRPO) that explicitly penalizes structurally critical spatial errors during alignment. Extensive experiments on SPACE demonstrate our framework significantly improves human-centric caption quality, particularly in subject-centered spatial reasoning, achieving performance competitive with strong closed-source models. Our benchmark and code are available at this https URL.
[CV-63] MARS-CLIP: Multi-Resolution and Attention Refined Zero-Shot Image Segmentation ICIP
链接: https://arxiv.org/abs/2609.08283
作者: Nagito Saito,Shintaro Ito,Koichi Ito,Takafumi Aoki
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to IEEE International Conference on Image Processing (ICIP) 2026
Abstract:Contrastive Language-Image Pre-training (CLIP) has demonstrated impressive capabilities in zero-shot transfer but often struggles with dense prediction tasks due to low spatial resolution and the loss of structural information. To address these limitations, we propose MARS-CLIP (Multi-resolution and Attention Refined Segmentation for CLIP), a novel framework for zero-shot semantic segmentation. Our approach introduces two key strategies: (i) a multi-resolution feature extraction module that fuses local fine-grained features with global context to overcome input resolution constraints, and (ii) an attention refinement mechanism that injects spatial and color biases from intermediate layers into the final self-attention block to accurately restore object boundaries. A set of experiments on six public datasets demonstrates that MARS-CLIP significantly outperforms state-of-the-art methods.
[CV-64] Dreaming in Flow: Generative Grounding Feedback for Self-Evolving Unified Multimodal Models
链接: https://arxiv.org/abs/2609.08282
作者: Ke Hao,Yuanzhi Liang,Tingxi Chen,Rui Li,Haibin Huang,Chi Zhang,Yun Gu,Xuelong Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Unified multimodal models integrate visual understanding and generation within a single network, yet the two capabilities are commonly optimized as separate tasks. We introduce Generative Grounding Feedback(GGF), a self-evolving post-training framework that uses only text prompts and the model’s own visual experience. Given a prompt, the model first generates a visual ``dream.‘’ Flow-level feedback compares text-, image-, and repair-conditioned predictions at the same noisy latent state, transferring image-grounded generation directions to the prompt condition. Dream replay grounding replays this dream through captioning and re-imagination, training claim-level evidence to remain consistent across the replay while separating unrelated visual experiences. Jointly optimized, these two directions let generation provide visual grounding for understanding and understanding refine subsequent generation without paired image–text supervision. Experiments across unified models with different understanding–generation integration designs show consistent improvements in text-to-image generation together with modest gains in visual understanding.
[CV-65] SAM3-O2D2: Zero-Shot Object Out-of-Distribution Detection by Object Class Prompting of the SAM3-Image Model
链接: https://arxiv.org/abs/2609.08281
作者: Lucas Görnhardt,Timo Bartels,Tim Fingscheidt
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Object detectors have shown remarkable performance in various fields, among these medical imaging, surveillance, and autonomous driving. However, they are prone to overconfidence when encountering unseen objects in real-world deployments, causing potential safety issues. To address this, detecting out-of-distribution (OOD) objects is essential for reliable object detection. Modern approaches leverage the broad semantic knowledge of foundation models such as CLIP for post-hoc few- and zero-shot OOD detection. However, these methods typically perform OOD assessment in feature space, which can be sensitive to object detector localization errors and variations in object appearance. Moreover, the current state-of-the-art (SOTA) zero-shot method performs computationally costly diffusion in inference. In this work, for our proposed zero-shot object OOD detection method SAM3-O2D2, we employ the SAM3-image foundation model in an efficient manner. Specifically, we prompt SAM3 only with the object detector’s predicted classes and compare the predictions of the object detector and SAM3. An object is in-distribution (ID), if SAM3 also detects an object at the corresponding location. If SAM3 does not detect the prompted object, this indicates a mismatch between the detector’s prediction and the image content, suggesting that the object is OOD. Experimental results show that our method significantly surpasses the so-far zero-shot SOTA method. Specifically, we achieve new SOTA AuROC and FPR95 metrics over both ID datasets Pascal-VOC and BDD100K and both OOD datasets MS-COCO and OpenImages.
[CV-66] Online Signature Verification Using Augmented Path Signature and T-Mamba ICDAR
链接: https://arxiv.org/abs/2609.08276
作者: Ruiling Li,Danyu Yang
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: International Conference on Document Analysis and Recognition (ICDAR) 2026
Abstract:Handwritten signature verification is vital for personal authentication across commercial and financial applications. Although deep learning methods are widely adopted for online signature verification (OSV), they often struggle with capturing highly discriminative features and modelling long-range dependencies. To address these issues, we propose a novel framework that integrates the augmented path signature (APS) descriptor with the T-Mamba model. The APS descriptor first applies time and basepoint augmentations, then computes sliding-window path signatures. The path signature is a non-parametric feature map from rough path theory that effectively captures geometric structures and nonlinear inter-channel interactions. Inspired by the efficacy of state space models (SSMs) in sequence modelling, our T-Mamba model employs a hybrid design combining two temporal convolutional network (TCN) blocks with a time-scanning Mamba. This design enables the model to learn both local temporal patterns and global long-range dependencies, substantially improving verification accuracy. Our framework achieves state-of-the-art EERs on three public benchmark datasets (MCYT-100, SVC-2004 Task 2, DeepSignDB), validating its effectiveness and robustness, especially when the training data is limited. Our code is publicly available at this https URL.
[CV-67] racking-by-detection in Multi-object Tracking: Survey and Experiments
链接: https://arxiv.org/abs/2609.08265
作者: Yujin Yang,Kyujin Shim,Kangwook Ko,Changick Kim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multi-object tracking (MOT) is an essential computer vision task that simultaneously tracks multiple objects in video sequences, with various applications in surveillance, autonomous navigation, and human-computer interaction. The tracking-by-detection (TBD) paradigm, which combines object detection with temporal association, has emerged as a leading approach, driven by innovative algorithms. Despite recent progress, fair evaluation of TBD-based methods remains a challenge. Many studies introduce modules such as similarity metrics, data association strategies, or motion models, but they are often evaluated under inconsistent protocols, with different baseline trackers, hyperparameters, and datasets. Such inconsistencies obscure the genuine contribution of each module and hinder objective comparison. This survey systematically reviews TBD-based MOT techniques, including similarity measurements, data association, camera motion compensation, and interpolation strategies. Starting from a minimal baseline tracker, we fairly evaluate the contributions of each method across diverse datasets and accumulate well-balanced methods. Our findings establish a strong baseline tracker and provide a foundation for the principled design of robust and versatile MOT systems suitable for real-world deployment.
[CV-68] CALIPER: Clean Scenes Cannot Rank Physical Inference in Pretrained Visual Representations
链接: https://arxiv.org/abs/2609.08250
作者: Aman Mehta,Riya Baviskar
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:How far a pushed object slides depends on its mass and friction, which no single image reveals. Pretrained visual encoders are increasingly used as the perception front end of world models for manipulation, and their physical competence is assessed with perturbation benchmarks and linear probes, almost always in a clean, fixed-camera scene. We show that these assessments cannot distinguish an encoder that infers physics from one that does not. CALIPER (calibrate, then predict) is a direct test: an object of unknown mass and friction is struck twice at known speeds, a third strike is shown only up to the moment of contact, and a linear readout on frozen features must predict how far the object slides. Swapping in another object’s calibration clips checks that the evidence is actually used. Across 2,000 simulated episodes and eight representations, from V-JEPA 2 to a randomly initialised ViT and raw pixels, calibration adds +0.50 R^2 and the swap removes it. Yet in the clean scene every representation lands within 0.02 R^2 of the ceiling set by true simulator state, because a fixed camera exposes the object’s displacement directly in pixel coordinates. Resampling camera, lighting, and clutter for every clip spreads the same representations across 0.50 R^2; when the readout chooses a push speed for a goal distance, V-JEPA 2 misses by 4 mm and the random ViT by 20 mm, no better than ignoring the object. Linear probes track none of this: a change in frame aggregation moves a probe more than pretraining does, and erasing the probed mass direction from the same representation costs nothing in one scene and 0.35 R^2 in the other. Whether a benchmark can rank models is an empirical property, and we give three checks that establish it.
[CV-69] CS-CLIP: Compositional Scene Graph-guided CLIP for Robust Compositional Reasoning EMNLP2026
链接: https://arxiv.org/abs/2609.08242
作者: SeongJun Jeong,Minjoon Jung,Woo Suk Choi,Youwon Jang,Byoung-Tak Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to Findings of EMNLP 2026
Abstract:Vision-language models (VLMs) demonstrate strong performance across compositional reasoning benchmarks, which require reasoning over semantic perturbations of objects, attributes, relations, and their interactions. However, our controlled analysis reveals that existing compositionality-aware VLMs exhibit element-specific biases, often underperforming vanilla CLIP on certain compositional elements. To address this, we propose Compositional Scene Graph-guided CLIP (CS-CLIP), which uses scene graphs to identify compositional elements and construct structured negatives via selective masking. We further retain negatives that are most contradictory to the original caption, forcing the model to rely on compositional structure rather than surface cues. CS-CLIP achieves state-of-the-art compositional reasoning with robust performance across compositional elements. It also preserves general vision-language capabilities such as cross-modal retrieval and downstream visual reasoning, while requiring fewer training samples than prior methods.
[CV-70] ActionSplice: In-Flight Action Editing for Interactive World Models
链接: https://arxiv.org/abs/2609.08230
作者: Pardis Taghavi,Tingyu Guo,Jonas Lossner,Gaurav Pandey,Reza Langari
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 15 pages, 6 figures. Project page: this https URL
Abstract:Chunk-autoregressive video world models typically condition each generated chunk on one action. An action received during sampling must therefore wait for the next chunk, condition future solver evaluations on a state produced under the previous action, or trigger rollback that repeats completed evaluations. We introduce ActionSplice, an inference framework that formulates this problem as Counterfactual State Transport (CST). A lightweight corrector transports the interrupted backbone-native representation toward the matched state induced by the revised action at the same solver step. The world model and sampler remain frozen, and sampling resumes without replaying completed evaluations. The retargeting variant \mathrmCSTR updates the entire active chunk, while the temporal-splicing variant \mathrmCSTT preserves a temporal prefix and updates only the suffix. Across minWM-Wan Action2V and HY-WM1.5, \mathrmCSTR reduces rollback-relative LPIPS by 61.5% and 75.9% relative to direct condition swapping. \mathrmCSTT reduces suffix LPIPS by 56.1% and 77.5%, respectively, while providing 2.73\times and 1.69\times pixel-ready speedups over waiting. Under the HY-WorldPlay protocol, \mathrmCST_R obtains a PSNR of 25.66 dB, an SSIM of 0.6902, and an LPIPS of 0.1337 against the original rollout.
[CV-71] SoftRerank: Hierarchical Soft Fusion with Candidate-Label Reranking for Long-Tailed Micro-Action Recognition ACM-MM2026 MICRO
链接: https://arxiv.org/abs/2609.08221
作者: Yichi Zhang,Zhichao Xia,Yanjun Chi,Lingsi Zhu,Yuefeng Zou,Jun Yu,Qingsong Liu,Jianqing Sun,Shengping Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: 7 pages, 2 figures, 3 tables. Accepted to the 34th ACM International Conference on Multimedia (MM '26). Ranked 1st in the 3rd Micro-Action Analysis Grand Challenge at ACM MM 2026
Abstract:Micro-actions are subtle, low-intensity non-verbal behaviors that provide cues to fine-grained human states, including emotions and intentions. Recognizing them remains difficult because they are brief, contain weak visual changes, and often exhibit similar motion patterns across categories. This paper addresses these challenges with a fine-grained micro-action recognition method that combines full fine-tuning of InternVideo2.5, hierarchical soft fusion, and a lightweight candidate-label reranker. For the long-tailed label distribution in MA-52, we use class-balanced sampling and inverse-frequency reweighting to reduce the effect of frequent classes during training. We fine-tune InternVideo2.5 end to end and attach coarse and group-conditional fine-grained classification heads to the shared video representation, improving the consistency between coarse and fine predictions. For ambiguous samples, the candidate-label reranker uses hard samples and video-label matching to focus on easily confused fine-grained actions. Experiments validate the proposed method, which achieves a 79.99% F1-mean on MA-52 and ranks first in the 3rd Micro-Action Analysis Grand Challenge at ACM Multimedia 2026.
[CV-72] Drive by Hindsight and Foresight: Tool-Grounded Synergistic Reasoning over Hierarchical Memory for Autonomous Driving
链接: https://arxiv.org/abs/2609.08217
作者: Baojie Chen,Zijun Jia,Jing Zhong
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 19 pages, 7 figures, 5 tables. Includes appendix
Abstract:VLMs have shown promise for autonomous driving, yet still suffer from hallucination, weak spatio-temporal perception, and limited generalization. Recent methods improve reasoning and decision-making through CoT explanations, retrieval-augmented generation or the static injection of tool outputs. Although these mechanisms enrich the context, the model neither proactively perceives scene information nor accumulates experience after answering. To overcome these limitations, we present, to our knowledge, the first synergistic framework that tightly couples hierarchical memory with proactive tool invocation in a closed reasoning loop. Our contributions are threefold. (i) Hierarchical Driving Memory: a scene-level short-term memory maintains the dynamic scene state, and an evolving long-term memory retrieves reusable experience and tool strategies. (ii) Memory-Tool Synergistic Reasoning Framework: guided by the scene state and retrieved experience, the model adaptively invokes tools to refine its reasoning at inference time and consolidates reusable experience into a long-term memory pool offline. (iii) Data Generation and Two-stage Training Pipeline: verified memory-tool trajectories built by multi-step teacher rollout are used to train with SFT and GRPO. Our 7B model reaches an overall reasoning score of 80.03 and MCQ accuracy of 79.09% on DriveLMM-o1, surpassing the strongest baseline by 7.74 MCQ points and generalizes strongly across benchmarks. Notably, ablation and analysis studies validate the effectiveness of each component and further reveal the complementary roles of hierarchical memory. Short-term memory strengthens spatio-temporal understanding, improving STSBench accuracy by 24.2 points, while offline long-term memory consolidation yields an additional 3.57-point MCQ gain with all parameters frozen, demonstrating continual self-evolution through accumulated driving experience.
[CV-73] PhysFlow: Physics-Aware Optical Flow for Motion Controllable Video Generation
链接: https://arxiv.org/abs/2609.08215
作者: Cong Wang,Hanxin Zhu,Yonglin Tian,Jiayi Luo,Ruiqi Song,Boyi Sun,Long Chen,Zhibo Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video generation models have recently attracted substantial attention for their ability to generate visually compelling videos, yet ensuring physically consistent and plausible dynamics still remains a fundamental challenge, driving a growing line of research on physical realism in video generation. To address this challenge, motivated by the fact that physical regularities are primarily encoded in motion patterns, we propose PhysFlow, a novel two-stage framework for improving the physical plausibility of generated videos by decomposing video generation into motion-aware optical flow generation followed by motion-conditioned appearance synthesis. Specifically, PhysFlow consists of a physics-aware optical-flow video generator called PA-Flow and a flow-guided video generator called FlowRender. During the first stage, PA-Flow employs a physics-aware attention module to model how motion attributes and material properties influence global motion and local deformation, respectively, and generates an optical flow video as an explicit representation of motion. In the second stage, FlowRender leverages the decoupled motion representation as guidance to synthesize realistic textures and appearances, ultimately producing the final physically plausible video. To further support model training with explicit physical supervision, we construct PhysVideo, a physics-based video dataset generated with a physics engine and 3D-GS rendering, containing 10K foreground objects and 50K realistic video sequences with annotations of motion and material properties. Extensive experiments demonstrate that our proposed PhysFlow generates videos with superior physical plausibility while maintaining high visual fidelity compared with existing methods.
[CV-74] WSPolypNet: Weakly Supervised Polyp Localization in Colonoscopy Videos
链接: https://arxiv.org/abs/2609.08182
作者: Giseong Hwang,Minjae Jo,Yeonghyeon Park,Kyeonghun Kim,Seoyeon Han,Donghoon Han,Haneul Kim,Yului Jeong,Insung Hwang,Pa Hong,Ken Ying-Kai Liao,Nam-Joon Kim
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Because dense frame-level annotation of colonoscopy videos is costly, we propose WSPolypNet, a weakly supervised framework for polyp localization using only video-level labels. WSPolypNet employs a 3D convolutional neural network trained with video-level supervision to generate class activation maps (CAMs), which identify candidate polyp regions without requiring frame-level spatial annotations. The CAM-derived localization cues are further enhanced using a multi-view strategy and provided to MedSAM2 as point prompts. MedSAM2 then propagates segmentation masks across the video, refining the coarse localization cues according to polyp boundaries. WSPolypNet achieved CorLoc scores of 47.80%, 43.68%, and 35.01% at IoU thresholds of 0.3, 0.5, and 0.7, respectively, compared with 36.87%, 33.72%, and 27.94% in the single-view setting. For small polyps, the multi-view strategy improved CorLoc@0.5 from 16.01% to 30.97%. The framework also achieved a recall of 94.51%. These results demonstrate the potential of weakly supervised spatiotemporal learning to substantially reduce spatial annotation requirements for polyp localization in colonoscopy videos.
[CV-75] Boundary Voting Network for Ambiguity-Aware Timestamp-Supervised Action Segmentation
链接: https://arxiv.org/abs/2609.08167
作者: Runzhong Zhang,Yueqi Duan,Yang Chen,Weipeng Hu,Chen Cai,Suchen Wang,Yap-Peng Tan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to TCSVT 2025
Abstract:Timestamp-supervised action segmentation aims to segment and classify actions in untrimmed videos with a random frame annotated per action. Precisely localizing action boundaries from timestamp annotations is crucial for this setting, as it enables generating framewise pseudo-labels and applying the well-explored fully-supervised training. However, prevailing methods struggle with intrinsic uncertainty in boundary localization due to less discriminative features in action-transiting regions. This imprecise boundary estimation significantly reduces the stability and reliability of the generated pseudo-labels in ambiguous action-transiting regions, consequently resulting in performance deterioration of the trained segmentation models. In our paper, we introduce the boundary voting network that mitigates feature ambiguity by hierarchically propagating video-level global prior knowledge into local action-transiting regions. By generating key action representations as votes throughout the video and targeting action-transiting regions, all votes collaboratively contribute to action-transiting feature enhancement and boundary localization refinement. Extensive experiments demonstrate the effectiveness of our method on GTEA, 50Salads, and Breakfast datasets.
[CV-76] SciFigure2Code: An AI-Reconstructed Benchmark for Scientific Figure-to-Code
链接: https://arxiv.org/abs/2609.08155
作者: Wentao Li,Yibo Wu,Yizhe Chen,Ruixuan Chen,Jiangjie Qiu,Yijun Li,Zhao Leyi,Xiaonan Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 20 pages
Abstract:Scientific figures are the interface through which research claims are inspected and reused, but final published panels rarely expose the data or plotting code that produced them. Recovering this hidden provenance from pixels is therefore underdetermined. We introduce SciFigure2Code, an AI-reconstructed benchmark that instead evaluates presentation recovery: generating editable Python programs that preserve how a scientific panel is arranged and read. Role-specialized Codex agents generate, execute, visually refine, and audit silver-standard presentation programs that capture geometry, visual hierarchy, encodings, annotations, and typography without claiming to recover original measurements or author source code. This reconstruction-and-audit protocol turns final published panels into auditable reference packages; the resulting resource contains 6,740 reviewed panels and SciFigureBench, a balanced 337-panel test set across 31 chart subtypes, five domains, and three complexity levels. Across 14 zero-shot models in image-only and caption-assisted settings, execution, multi-component layouts, axes, legends, and scientific labels remain weak. Claude Opus 4.7 achieves the highest image-only Overall score, Claude Opus 4.6 leads caption-assisted reconstruction, and two-stage plan-then-code prompting improves Overall for all four tested models. SciFigure2Code provides an auditable testbed for agents that construct editable, visually faithful scientific figure presentations.
[CV-77] Geodesic-informed Generative Diffusion Model For Topology-preserved Image Video Generation
链接: https://arxiv.org/abs/2609.08153
作者: Nian Wu,Nivetha Jayakumar,Jiarui Xing,Miaomiao Zhang
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注:
Abstract:Generative diffusion models have emerged as a class of powerful techniques for various imaging applications, including but not limited to synthesis, reconstruction, and segmentation. Despite their success, current generative models pose two key limitations. First, they primarily rely on image intensity and texture information, with limited attention to underlying object geometry. As a result, they do not guarantee geometric or topological consistency during the generation process, which is a crucial requirement for high-stakes domains such as computational anatomy, biology, and robotics, where preserving object structure is critical. Second, existing models fail to explicitly learn or represent shape changes in the generative process. Such deformation dynamics remain occluded within network parameters; hence leaving the transformation process uninterpretable and physically uninformed. To address these challenges, we introduce IGG (Image Generation informed by Geodesic dynamics), a novel framework that integrates topology-preserving geodesic principles into the diffusion-based generative process. In contrast to conventional methods that operate in image intensity space, IGG learns and synthesizes diverse samples within geodesic deformation spaces, where geometric object changes are learned as smooth and invertible smooth mappings from a given template/source image. Our code is publicly available at this https URL.
[CV-78] MRI-Guided Reslice-Refined Cross-Slice SDF Reconstruction of the Left Ventricle from Cardiac MRI with Sparse Axial Supervision
链接: https://arxiv.org/abs/2609.08148
作者: Quanxin Zheng,Shuai Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reconstructing a three-dimensional left-ventricular (LV) endocardial surface from cardiac magnetic resonance (CMR) data is challenging when supervision is available on only a small number of axial slices. Through-plane geometry is weakly constrained, and automatically generated two-dimensional masks can propagate segmentation errors into the recovered shape. We present MR-RS-SDFR, a per-case implicit signed distance field (SDF) framework that reconstructs a continuous LV surface from a CMR volume and sparse axial weak masks. The method first builds a cross-slice SDF initialization from axial and longitudinal geometric cues and then refines the field using two complementary signals: MRI edge-field normal alignment, which provides an image-derived boundary cue independent of the weak masks, and differentiable reslice Dice and contour consistency, which preserve agreement with the observed planes. We evaluate three weak-mask generators – LOO TransUNet, LOO nnU-Net, and an off-the-shelf Medical SAM3 model used without MM-WHS-specific training or fine-tuning – and five sparsity levels from 4 to 64 axial planes. In the sparse-16 setting, final MR-RS-SDFR reconstruction reaches 0.928 Dice and 3.80mm HD95 with Medical SAM3 masks. The upstream generators do not exhibit a single common ranking across 2D and dense 3D segmentation, and nnU-Net- and Medical-SAM3-driven sparse reconstruction achieve the same mean final Dice despite different upstream error profiles. Across all three sparse-16 mask sources, MR-RS-SDFR is numerically better than protocol-matched full GHD+DVS in both Dice and HD95. Final Dice improves markedly from sparse-4 to sparse-16 and then saturates at the reported precision through sparse-64. These results support MRI-guided per-case SDF refinement as a reconstruction strategy that remains effective across weak-mask generators and supervision densities.
[CV-79] Observe Before You Alert: Adaptive Driver Alerting with Vision-Language Models
链接: https://arxiv.org/abs/2609.08130
作者: Yuhang Wang,Lingyao Li,Hao Zhou
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 23 pages, 8 figures. Accepted at the Conference on Robot Learning (CoRL) 2026
Abstract:Driver alerting from dashcam video requires sequential decision-making under partial observability: a system must decide not only whether a scene is risky, but also when the evidence is sufficient to warn. Most existing accident anticipation models output a binary risk score, leaving ambiguous scenes to be handled by thresholding. We propose VLAlert, a vision-language alerting framework that casts warning generation as a tri-action policy over SILENT, OBSERVE, and ALERT. The OBSERVE action acts as an internal evidence-gathering decision that delays uncertain warnings and changes the next observation window, creating a lightweight perception-action loop for adaptive alerting. VLAlert uses Qwen3-VL-4B as a safety-evidence generator and pools hidden states from structured belief spans to form compact representations for danger estimation and policy prediction. We evaluate VLAlert on VLAlert-Bench, a unified per-tick benchmark from four real-world dashcam alert datasets, and further test transfer to held-out naturalistic ADAS takeover clips. On VLAlert-Bench validation, VLAlert achieves the highest deployment-oriented utility among tested baselines, with DAUS 0.4878 compared with 0.4752 for Open-BADAS, and improves AUROC, AP_tick, F1_t, and balanced accuracy from 0.610, 0.176, 0.276, and 0.581 to 0.689, 0.195, 0.297, and 0.648, respectively. On 221 held-out ADAS-TO-Critic clips, VLAlert improves R@5s from 74.2% to 88.7% and F1 from 0.585 to 0.686. These results indicate that adaptive observation and safety-focused VLM representations provide measurable gains for driver-facing alert decisions.
[CV-80] Hyperspectral Anomaly Detection via Group Sparse Low-Rank Tensor Factorization With Automatic Anomaly Grouping
链接: https://arxiv.org/abs/2609.08121
作者: Quan Yu,Yu-Hong Dai,Xiongjun Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Optimization and Control (math.OC)
备注:
Abstract:Low-rank tensor modeling has become an effective tool for hyperspectral anomaly detection. However, existing methods still suffer from high computational cost and limited flexibility in characterizing spatially structured anomalies. To address these issues, this paper proposes a hyperspectral anomaly detection method based on group sparse low-rank tensor factorization with automatic anomaly grouping (GSAA). Specifically, the low tubal rank background is characterized by imposing group sparsity on tensor factors, which provides an efficient alternative to direct tensor rank regularization. For anomaly modeling, a latent grouping map is introduced to build an automatic anomaly grouping penalty, allowing anomaly groups to be adaptively inferred from the data rather than predefined at the pixel level. To further exploit complementary spectral and spatial information, GSAA is applied in both domains, and the resulting detection maps are fused to form a spectral–spatial version of GSAA, termed GSAA-SS. An efficient linearized alternating direction method of multipliers algorithm with convergence guarantee is developed to solve the resulting model. Experimental results on five real hyperspectral datasets demonstrate that the proposed method achieves superior detection performance and competitive computational efficiency compared with several state-of-the-art methods.
[CV-81] SynthGait-19K: A Physically Grounded Synthetic Video Dataset for Gait Parameter Estimation
链接: https://arxiv.org/abs/2609.08108
作者: Soroush Mehraban,Xin Lei Lin,Vida Adeli,Majid Mirmehdi,Amirhossein Dadashzadeh,Clint Hansen,Andrea Iaboni,Babak Taati
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:Accurate estimation of clinically meaningful gait parameters from monocular video is important for scalable mobility assessment, yet progress is limited by the small scale, restricted viewpoints, and limited visual diversity of existing datasets. We introduce SynthGait-19k, a physically grounded synthetic video dataset containing 19,272 walking videos derived from 6,427 MoCap sequences across 437 subjects, with paired SMPL motion and annotations for six gait parameters. To construct the dataset, we develop Gait2Vid, which unifies heterogeneous MoCap recordings through SMPL and synthesizes diverse RGB walking videos under controllable viewpoints and scene appearances. We assess the generated videos for consistency with their conditioning gait kinematics and validate extracted gait events against force-platform measurements. Using SynthGait-19K, we benchmark direct RGB, pose-based, biomechanical, and human-mesh-recovery approaches and analyze viewpoint, training-data scale, and synthetic-to-real domain shift. We also introduce GaitXFormer as a direct RGB reference model for estimating gait parameters. Synthetic supervision transfers effectively to real videos across both GaitXFormer and a pose-based architecture, demonstrating utility across different representations. We further find that spatial gait parameters are more sensitive to visual domain shift and that improved HMR reconstruction alone does not necessarily translate to improved downstream gait estimation.
[CV-82] RevalExo: A Functional Daily-Activity Benchmark for Inertial and Visual Locomotion Mode Recognition in Older Adults and Clinical Cohorts BMVC2026
链接: https://arxiv.org/abs/2609.08090
作者: Diwas Lamsal,Juha Carlon,Reinhard Claeys,Maxim Yudayev,Louis Flynn,Tom Verstraten,David Beckwée,Eva Swinnen,Mihai Bâce,Bart Vanrumste,Benjamin Filtjens
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to BMVC 2026 (Oral)
Abstract:Assistive devices for people with mobility impairments, such as powered exoskeletons, rely on accurate locomotion mode recognition to adapt control strategies and provide appropriate assistance during daily activities. However, public benchmarks are typically collected from healthy adults, lack temporally precise labels necessary for detecting mode transitions, or focus on a limited set of tasks. To support development and evaluation under realistic clinical constraints and daily mobility demands, we introduce RevalExo, a functional daily-activity benchmark for inertial and visual locomotion mode recognition. RevalExo is built around a standardized, clinically and ecologically validated daily-activity protocol reflecting the cumulative everyday mobility demands in ageing and clinical populations. The benchmark includes 27 participants across three cohorts: older adults without mobility impairments, stroke survivors, and older adults with probable sarcopenia. The full cohort was recorded with lower-body IMUs, while synchronized egocentric video was collected for a clinically feasible subset of 13 participants. RevalExo provides 10.1 hours of frame-level annotations across 11 locomotion modes, including 5.1 hours of paired inertial–visual recordings. We benchmark three challenges: unimodal and multimodal locomotion mode recognition across multiple horizons, cross-population generalization from older adults without mobility impairments to clinical cohorts, and vision-guided knowledge transfer to IMU-only models. Results confirm consistent gains from fusing inertial and visual inputs but reveal a substantial gap between general recognition ( \sim 93% F1) and recognition during transitions ( \sim 68% F1), alongside persistent challenges in cross-population generalization and cross-modal transfer. We release RevalExo to stimulate further research on these open challenges.
[CV-83] Marigold V2: Revisiting Diffusion Transformers for Monocular Depth Estimation SIGGRAPH
链接: https://arxiv.org/abs/2609.08084
作者: Igor Pavlovic,Thiemo Wandel,Anton Obukhov,Luca Bartolomei,Andrey Davydov,Fabio Tosi,Matteo Poggi,Sabine Süsstrunk,Dengxin Dai
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: SIGGRAPH Asia 2026
Abstract:Monocular depth estimation is a ubiquitous yet highly ill-posed computer vision task, with downstream applications in scene reconstruction, computational photography, and robotics, among others. Despite the field’s maturity, recent models still struggle to generalize to out-of-distribution inputs and to produce sharp and detailed depth maps. In this paper, we revisit Marigold, a set of techniques for repurposing modern image generation and editing models, powered by the diffusion transformer (DiT) architecture, into state-of-the-art monocular depth estimators. Our recipes target single-step inference from pretrained multi-step flow-matching models, with quantization where needed, preserving model capacity while remaining cheap to run. We analyze the artifacts of naive training and identify two effective remedies: aligning the model’s internal representations with semantic features extracted from ground-truth, and adopting a 2-stage fine-tuning protocol built around a novel Sinkhorn-based loss. The results are crisper, cleaner depth maps that generalize well out-of-distribution, with 16-26% improvement in AbsRel over the previous best on KITTI and ETH3D. Qualitatively, our model resolves fur, foliage, and hair-thin edges that have eluded prior models. Furthermore, Marigold V2 achieves state-of-the-art results when applied to other dense regression tasks, such as surface normals estimation and intrinsic image decomposition. Project website: this https URL
[CV-84] VI-Bench: Benchmarking Prompt Inversion from AIGC Videos
链接: https://arxiv.org/abs/2609.08079
作者: Wulin Xie,Rui Zhao,Kecen Li,Xiujin Liu,Bokang Zhang,Zheng Liu,Xinwen Hou,Chen Gong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 31 pages
Abstract:Recent advances in video generation have made prompt-based control increasingly central to AIGC video generation. Prompts specify what a video should depict and how it should be represented, controlling factors such as visual style or camera behavior. Understanding this recoverability is important both for creative reuse and editing, and for assessing prompt leakage risks. However, existing video understanding benchmarks do not measure this capability: a caption may describe what is visible, but a replayable prompt must recover the generation-relevant controls needed to reproduce the video. To address this gap, we introduce VI-Bench, a benchmark built from 16.1 million real-user prompts and 900 human-verified AIGC videos. VI-Bench spans three progressively harder settings, namely single-shot semantic grounding, control over style and camera behavior, and multi-shot compositional inversion, and evaluates five generation-critical dimensions: subject, action, scene, style, and camera. We evaluate 18 representative VLMs, including 2 proprietary and 16 open-source models on VI-Bench, using an Inversion Score that measures prompt-level alignment with the original prompt and video-level fidelity of the regenerated video. The results reveal substantial limitations: even the strongest model achieves only 0.632 on Inversion Score, performance degrades sharply as samples require richer control and multi-shot reasoning, and models often produce plausible prompts whose regenerated videos deviate from the reference. These findings show that video prompt inversion is a distinct and under-evaluated capability requiring models to transform visual understanding into replay-stable generative control.
[CV-85] BrachistoneLR: A Brachistochrone-Inspired Learning-Rate Schedule and a Controlled Benchmark of Scheduling Policies
链接: https://arxiv.org/abs/2609.08069
作者: Md. Sadekur Rahman Roni,Md. Jalal uddin Chowdhury,Moutusi Dash Nimi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The learning-rate schedule is a consequential choice in training deep networks, yet the policies in common use are heuristic, and published comparisons are hard to read, because architecture, dataset, and budget tend to vary alongside the schedule. We study BrachistoneLR, a schedule built by mapping the vertical coordinate of the brachistochrone, the curve of fastest descent under gravity, onto the range between a peak and a floor rate. Expanding the definition shows it to be cosine annealing with the half-period set to E - 1 instead of E, the configuration a standard implementation gives when its period argument is one less than the number of epochs. The rate therefore reaches its floor at the last epoch trained rather than one epoch later, and we show this difference decays as E^-2, making it a short-horizon effect. We then benchmark six schedules over 72 runs on three image classification datasets (MNIST, Fashion-MNIST, CIFAR-10) and four architecture families (fully connected, convolutional, recurrent, residual), fixing the optimizer, data pipeline, and evaluation protocol so that only the schedule varies. Schedules that fall smoothly from peak to floor beat the constant rate and calendar-based decay by margins that grow with task difficulty, reaching 2.5 points of dataset mean on CIFAR-10. Within that leading group, BrachistoneLR, cosine annealing, and warmup-cosine lie within 0.06 accuracy points and 0.17 of a mean rank, which one seed per configuration cannot separate. BrachistoneLR is best on both residual networks and has the highest CIFAR-10 mean, and it sets no milestones, decay factor, warmup length, or restart period. We conclude that the shape of a schedule matters more than its parameterization, that the choice of whether to use a smooth schedule matters more than the choice among them, and that the terminal-rate distinction is worth attention only over short horizons.
[CV-86] RFS-UNet: Decoder-Conditioned High-Resolution Skip Recalibration for Bone-Selective DRR Synthesis
链接: https://arxiv.org/abs/2609.08044
作者: Xiaoyang Li,Yixuan Liu,Yuan Chai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Bone-selective digitally reconstructed radiograph (DRR) synthesis depends on high-resolution encoder detail, yet static skips cannot condition reuse on the evolving decoder representation. We ask whether decoder state adds useful information beyond encoder-only self-recalibration for high-resolution skip reuse. RFS-UNet uses pooled encoder and aligned decoder statistics for bounded residual channel recalibration at the 512^2 and 256^2 skips, leaving the backbone unchanged. In the matched seed-2026 comparison isolating decoder conditioning, RFS raises validation PSNR by 0.254 dB over Self-RFS. Across three seeds, locked-test PSNR rises from 33.225+/-0.048 to 33.537+/-0.128 dB; RFS lowers MAE in 179/200 held-out CT cases and reduces mean MAE by 3.91%. It adds 0.117% parameters and 1.169% counted Conv2d operations. These results support decoder state as a useful conditioning signal for high-resolution feature reuse in controlled paired projection synthesis.
[CV-87] A Quantitative Evaluation Framework for Temporal Explainability in Echocardiographic Video Segmentation
链接: https://arxiv.org/abs/2609.08043
作者: Jiyoo Noh,Jonathan H. Chan
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 15 pages, 5 figures
Abstract:Deep learning has achieved state-of-the-art performance in echocardiographic video segmentation, with an increasing number of models incorporating temporal information. However, quantitative evaluation of temporal explainability remains largely unexplored. We propose a quantitative framework for evaluating Grad-CAM explanations using four complementary metrics measuring temporal consistency, saliency motion, anatomical overlap, and temporal overlap. Using EchoNet-Dynamic, we compare a baseline 2D U-Net with ConvLSTM U-Net models trained across multiple temporal strides. While segmentation performance remained comparable across all models, intermediate ConvLSTM explanations exhibited substantially lower saliency consistency and greater centroid motion than final prediction explanations. Temporal Bottleneck explanations were significantly more stable than Encoder Bottleneck explanations across all strides, while final ConvLSTM Decoder3 explanations were broadly comparable to those of the 2D U-Net. Importantly, conventional frame-wise explanation metrics cannot determine whether variation in intermediate explanations reflects meaningful temporal feature evolution or explanation instability. These findings establish a preliminary quantitative framework for temporal explainability and motivate temporal-aware XAI methods that explicitly account for evolving representations in medical video models.
[CV-88] MamMA: A Mamba-Based Pedestrian Trajectory Prediction Algorithm Considering Occupancy Map and Pedestrian Awareness States IJCNN2026
链接: https://arxiv.org/abs/2609.08041
作者: Juncen Long,Xiaofeng Jin,Gianluca Bardaro,Simone Mentasti,Matteo Matteucci
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted at the 2026 International Joint Conference on Neural Networks (IJCNN 2026). 7 pages, 5 figures
Abstract:Many pedestrian trajectory prediction algorithms have been proposed to improve the safety of navigation for mobile robots working in human-robot coexistence environments. Some pedestrian trajectory prediction algorithms extract information about obstacles near pedestrians from top-down view images to improve the accuracy of trajectory prediction. However, mobile robots typically create local occupancy maps using LiDAR, rather than top-down view images. Meanwhile, the vision sensors on board robots provide egocentric view images, which contain fine-grained behavioral information about the pedestrians near the robot. To better use the information collected by LiDAR and on-board vision sensors, we propose MamMA, a Mamba-based pedestrian trajectory prediction algorithm considering occupancy maps and pedestrian awareness states. MamMA divides the occupancy map by patches and extracts obstacle features from each patch to create map features. Pedestrian awareness states are divided and considered, as some studies show that awareness states affect the perception and speed of pedestrians. Furthermore, a Mamba-based model is proposed to predict the future trajectories of pedestrians based on different types of features. Experiments on the STCrowd, SiT, JRDB, ETH, and UCY datasets show that MamMA achieves better average displacement error and final displacement error than the state-of-the-art algorithms.
[CV-89] SAFER-Activities: A Dataset for Smart Assessment of Fall Events and Routine Activities ECCV2026
链接: https://arxiv.org/abs/2609.08038
作者: Diwas Lamsal,Pramod Wickramatilake,Jednipat Moonrinta,Mongkol Ekpanyapong,Matthew N. Dailey
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to ECCV 2026
Abstract:Smart healthcare monitoring systems require precise action recognition to ensure well-being and timely intervention in critical situations such as falls, particularly for mobility-challenged individuals. Existing datasets are often clip-based, lacking the frame-level detail needed to recognize actions online, as they unfold. To address this, we introduce SAFER-Activities, a dataset for fall detection and physical activity monitoring, with a dedicated subset for wheelchair use scenarios. It comprises over 66 hours of video data captured by multiple cameras, with 85,310 action instances and frame-level annotations for 30 action classes. We benchmark action recognition on SAFER-Activities with 2D and 3D skeleton models, RGB models with frozen backbones, and multimodal fusion strategies, and evaluate on in-lab, out-of-distribution, and cross-dataset test sets. Skeleton-based models generalize best under domain shift; fusing frozen RGB features with the skeleton stream improves in-domain recognition over the baseline CNN1D, most clearly on the wheelchair subset, but degrades out of distribution. Cross-dataset and qualitative evaluations confirm that models trained on SAFER-Activities transfer well to unseen environments and external fall data. To support research on robust fall detection and activity monitoring, we release the dataset and code at this https URL.
[CV-90] Flexible Motion Generation from Language and Style References
链接: https://arxiv.org/abs/2609.08032
作者: Kai Weixian Lan,Bodie Criswell,Briana Fedkiw,Zhan Zhang,Joseph Teran,Daniel Holden
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Machine Learning (cs.LG)
备注: accepted at Pacific Graphics 2026
Abstract:We introduce FlexMoGen, a novel framework for flexible human motion synthesis conditioned on both natural language descriptions and motion style references. Text prompts are effective at defining semantic content, but they are often limited in capturing fine-grained style details such as timing, limb articulation, and expressive dynamics. A style example clip supplements the text by conveying these nuanced motion characteristics directly, enabling the model to preserve high-level intent while reproducing the desired stylistic traits. Given a text prompt and a style example clip, FlexMoGen generates high-quality motions that preserve semantic content while faithfully reflecting the target style, offering users greater control over the animation generation process. Unlike prior methods that rely on discrete style labels and do not generalize to long or multi-style generation, FlexMoGen learns a variational style encoder without style supervision and supports long, time-varying, multi-style synthesis. Our framework jointly pre-trains the style encoder and a text-to-motion latent diffusion model within a unified architecture, modulating motion style through a lightweight adaptation module. It integrates an efficient relative positional encoding scheme and is trained on both stylized and non-stylized datasets, enabling strong generalization to unseen text-style combinations. Experiments show that FlexMoGen achieves the best balance between content fidelity and style reflection.
[CV-91] A Black-Box Adversarial Attack on Human Pose Estimation and Keypoint-Based Action Recognition Models
链接: https://arxiv.org/abs/2609.08013
作者: Kacper Mroczek,Michal Kepski
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ACIVS2026
Abstract:Human pose estimation and keypoint-based action recognition models are increasingly deployed as components of video understanding pipelines, yet their vulnerability to adversarial attacks remains insufficiently studied. Temporally coherent black-box attacks have been previously studied in visual object tracking, where the attack feedback can be defined using bounding-box overlap measures such as Intersection over Union (IoU). However, human pose estimation produces keypoint configurations rather than enclosing boxes, making box-level similarity poorly suited for measuring pose degradation. We propose OKS Attack, a decision-based black-box attack that uses Object Keypoint Similarity (OKS) as the attack feedback signal, directly targeting the spatial structure of human poses rather than their enclosing boxes. Experiments on the Penn Action dataset show that OKS Attack consistently reduces pose quality across evaluated pose estimators, with mean OKS decreases ranging from 0.0802 to 0.1494. In a downstream cross-dataset action-recognition evaluation, the attack reduces accuracy by 6.18 to 13.86 percentage points and outperforms query-matched random-noise perturbations. The attack is effective across both top-down and single-stage pose estimation models. The source code will be made publicly available at this https URL
[CV-92] askGuard: Task-Conditioned Restoration Utility for Risk-Aware Object Detection
链接: https://arxiv.org/abs/2609.08011
作者: Vung Pham
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages total: 9-page main paper with 4 figures and 4 tables; 6-page supplementary material with 2 figures and 8 tables
Abstract:Image restoration is commonly applied before object detection under adverse conditions, yet a visually improved image need not improve the downstream task. We study this mismatch as restoration utility prediction: given a degraded image and its candidate restoration, should the restoration be used or should the original observation be preserved? We introduce TaskGuard, a post-hoc controller for frozen restoration and detection pipelines. TaskGuard characterizes the realized restoration residual through its interaction with detector sensitivity and predicts whether the intervention is task-beneficial. Exact regional counterfactuals reveal substantial within-image utility heterogeneity, while a deployable pseudo-gradient preserves statistically reliable directional information. Feature-group ablation further shows that task-conditioned evidence contributes information beyond detector-response and residual statistics. The TaskGuard utility predictor is trained only on Gaussian degradation and frozen before final evaluation, then transferred to unseen motion blur, rain, and defocus. Across these unseen families, TaskGuard reduces lossnegative interventions by 54.2% (family macro) and practical per-image detection deteriorations by 37.0% (pooled), while preserving 98.8% of the Always-Restore COCO AP. On natural-rain DAWN, it reduces loss-negative interventions by 97.9% while retaining 77.8% of the AP improvement obtained by deraining. These results support restoration utility as a task-conditioned property of the specific intervention rather than image appearance alone.
[CV-93] α-Graph: Attention-Infused Normalizing Flow Approach to Tractable Graph Modeling
链接: https://arxiv.org/abs/2609.07961
作者: Thanh-Dat Truong,Sarah Alharbi,Susan Gauch,Xinghui Zhao,Marios Savvides,Khoa Luu
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Graph modeling, a crucial task for representing complex relationships in graph-structured data, has achieved significant success in recent years. However, current graph modeling methods rely on traditional Graph Neural Networks and pre-training approaches to implicitly learn the underlying relational structure of graph data. Thus, these prior methods cannot capture the complex graph structure and correlations among inputs. In this paper, we introduce a novel Attention-based Normalizing Flow-based Approach\footnoteOur implementation and models will be released publicly for research reproducibility. (ANFA or \alpha ) that provides an explicit, interpretable, and tractable Graph Modeling ( \alpha -Graph). In particular, we propose a new Unconditional Graph Normalizing Flow with an Invertible Attention Mechanism to capture the complex relational structure of graph data. To further enhance the expressiveness of the model, we introduce Conditional Graph Normalizing Flow with Learnable Queries that enables efficient modeling of correlations in graph-structured data. We show that our Conditional Graph Normalizing Flows behave similarly to Unconditional Graph Normalizing Flows, enhancing expressiveness while maintaining training stability and efficiency. Our experimental results on three benchmarks will illustrate the effectiveness and the state-of-the-art (SoTA) performance of the proposed \alpha -Graph method.
[CV-94] ReactVAU: A Slow-Fast Decoupled Framework for Streaming Video Anomaly Understanding ECCV2026
链接: https://arxiv.org/abs/2609.07941
作者: Chia-Hui Chen,Shih-Ying Yeh,Fu-En Yang,Min-Hung Chen,Shang-Hong Lai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026. Project page: this https URL
Abstract:In this paper, we propose ReactVAU, a Slow-Fast Decoupled Framework for real-time streaming Video Anomaly Understanding (VAU). Existing VAU methods rely on offline inference with global temporal sampling, which violates causality and prevents deployment in live surveillance streams. Conversely, general streaming video models satisfy causal access but dilute rare transient anomalies during memory compression and often invoke heavyweight MLLMs uniformly over long normal intervals. React VAU addresses this gap with three synergistic components: a lightweight Fast Detection Module based on Spatial Grid Folding (SGF) for continuous anomaly filtering; an Anomaly-Aware Persistent Memory (AAPM) that protects critical visual cues from temporal decay; and a heavyweight Slow Reasoning Module that remains dormant during normal streams and is awakened only by suspicious events for semantic verification and causal description. Extensive experiments on multiple benchmarks demonstrate that ReactVAU operates under strict streaming constraints while simultaneously achieving competitive performance in both anomaly detection and causal reasoning, alongside significantly enhanced computational efficiency by minimizing heavyweight MLLM invocations. Project page is available at this https URL
[CV-95] Bottom-up Modeling of Repeated Elements via Single Image Analysis-by-Synthesis ECCV2026
链接: https://arxiv.org/abs/2609.07939
作者: Syrine Kalleli,Alexei A. Efros,Mathieu Aubry
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026. Project page: this https URL
Abstract:We address the problem of discovering repeated elements from a single image. In contrast to existing approaches that depend on large annotated datasets, curated multi-image collections, or object segmentation masks, we show that a single image can suffice to learn a meaningful object model in a completely bottom-up fashion, without any prior knowledge beyond a coarse scale prior. Our method learns a tunable image-space prototype of the repeated elements through a reconstruction objective, enabling the model to identify and synthesize consistent object instances within the same image. Experiments on 116 real images from the FSC-147 dataset demonstrate that our method successfully learns coherent element models and captures intra-category variation on challenging images. Qualitative results reveal superior reconstructions and interpretable decompositions compared to classical decomposition, joint alignment, and 3D object modeling methods, while maintaining a simple 2D formulation. These results suggest that meaningful object discovery can emerge from single image learning alone.
[CV-96] DDN: Text-aligned Diffused DINO Network for Puzzle Understanding
链接: https://arxiv.org/abs/2609.07937
作者: Harsha Patnala,Debopriyo Banerjee,Ayush Sunil Munot,Somak Aditya
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Structured visual reasoning, such as image puzzles, demands fine-grained visual perception, an ability current Vision Language Models (VLMs) lack. VLMs built on CLIP-based ViT backbones trade fine-grained detail for high-level semantics, and we show this loss propagates downstream. To recover it, we fuse DINOv3 and CleanDIFT representations into a perception encoder (DiffusedDINO) and align it with RoBERTa-L, yielding a text-aligned model TDDN that preserves this perceptual advantage: with frozen backbones and only \sim 590K alignment pairs, TDDN matches CLIP on image-text retrieval, surpassing it on three of four settings. It does so while more than tripling CLIP’s dense-prediction accuracy (ADE20K 5.20 \to 18.11 mIoU, COCO-Stuff 7.35 \to 24.44), despite CLIP’s massive training corpus. TDDN leads on segmentation benchmarks among general-purpose contrastive encoders, including SigLIP , 2. We further introduce Puzzle Perception, a segmentation and visual question answering dataset that probes fine-grained spatial understanding, on which TDDN doubles CLIP’s segmentation accuracy (11.04 \to 22.51 mIoU).
[CV-97] Prevalence calibration as shortcut mitigation MICCAI2026
链接: https://arxiv.org/abs/2609.07922
作者: Mohamed Amine Kina,Eike Petersen
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the 17th International Workshop on Machine Learning in Medical Imaging (MLMI 2026), held in conjunction with MICCAI 2026. 11 pages, 2 figures
Abstract:Shortcut learning denotes the widespread situation in which a classifier exploits spurious correlations rather than diagnostic features. Existing mitigation strategies mostly aim to learn shortcut-invariant representations; their empirical success is limited and they cannot be applied to classifiers using frozen foundation model encoders. We propose to reframe shortcut learning as fundamentally a calibration problem: unconstrained learning implicitly calibrates each shortcut group to its training set disease prevalence, rendering the resulting classifier necessarily over-confident in one group and under-confident in the other. Building on this insight, we prevalence-equalize calibration between shortcut groups through two encoder-agnostic methods, an in-processing regularizer and a post-hoc prevalence-equalized recalibration step. Across chest-drain-pneumothorax benchmarks on CheXpert and SIIM-ACR, spanning fine-tuned CNNs and frozen foundation-model backbones, both methods substantially outperform all baselines. Post-hoc recalibration of a standard ERM-trained DenseNet raises misaligned-group AUROC from 0.23 to 0.73, indicating that shortcut reliance degrades the classification head rather than the underlying representation. Besides two new state-of-the-art shortcut mitigation approaches, our findings more fundamentally connect shortcut learning to calibration theory and algorithmic fairness.
[CV-98] Poisson Image Denoising Using Minimax Concave and Reweighted ell_1 Penalties: Nonblind and Blind Approaches
链接: https://arxiv.org/abs/2609.07916
作者: Reza Parvaz
类目: Computer Vision and Pattern Recognition (cs.CV); Numerical Analysis (math.NA)
备注:
Abstract:Images are important tools in various sciences. Despite the development of photo-taking tools, creating clear and image without noise remains challenging in practice. In particular, Poisson noise has an effect on medical and astronomical images, and reduces their quality. Additionally, blur is another factor that has an effect on image quality. The problem of image restoration becomes very complicated when we have no information about the Point Spread Function (PSF). These types of problems are known as blind case. However, in some images, such as some astronomical images, the type of PSF can be specified, and these types of problems are known as nonblind problems. Total Variation (TV) is a widely used method for solving such inverse problems, where the selection of the penalty function is the most critical factor that affects the method’s performance. In this paper, to improve edge preservation, we employ a reweighted \ell_1 -regularization of the fractional order derivative. Furthermore, we propose a nonblind and blind image deblurring approach under Poisson noise using the Minimax Concave Penalty (MCP), which is a continuous, sparsity promoting, and nearly unbiased regularizer. This formulation leads to a nonconvex optimization model. To solve the proposed model, we introduce an efficient numerical algorithm based on the Alternating Direction Method of Multipliers (ADMM) and provide an analysis of its convergence. Finally, the effectiveness of the proposed algorithm are demonstrated through extensive experiments on various images.
[CV-99] JEDI: JEPA-to-Edge Distillation for Efficient Cropland Segmentation from Satellite Imagery
链接: https://arxiv.org/abs/2609.07915
作者: Kishor Kumar Bhaumik,Nicolas Roque dos Santos,Jia Chen,Evangelos E. Papalexakis
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Large vision models provide useful representations for remote-sensing segmentation but are often too expensive for deployment at the satellite or field edge. Existing feature-level distillation methods also tend to assume similar teacher and student architectures and often stop feature alignment when task training begins. We introduce JEDI (JEPA-to-Edge Distillation), a two-stage framework that transfers representations from a large I-JEPA Vision Transformer teacher to a compact SegFormer student. First, JEDI aligns the student’s terminal representation with the teacher’s token space using cross-architecture projection and spatial alignment. It then jointly optimizes supervised segmentation, temperature-scaled response distillation, and persistent feature alignment throughout task adaptation. On CalCROP21, JEDI-B0 achieves 68.0 mean Intersection-over-Union (mIoU) with 4.04M parameters, improving over the standalone student by 16.0 points and coming within 2.0 points of the 70.0 mIoU achieved by the 639M-parameter teacher. We evaluate SegFormer B0, B1, and B2 students with 4.04M, 14.33M, and 28M parameters, respectively. Across all three variants, JEDI consistently outperforms response-, structure-, channel-, and relational-distillation baselines under the same teacher-student setting. These results show that persistent representation alignment is especially valuable under aggressive compression, substantially reducing model size and computation while preserving segmentation performance.
[CV-100] Are Image Generators Zero-Shot Perceivers? A Rigorous Evaluation BMVC2026
链接: https://arxiv.org/abs/2609.07884
作者: Shangzhe Di,Zhaokai Wang,Weidi Xie
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to BMVC 2026
Abstract:Recent work, such as Vision Banana, shows that lightweight instruction tuning can enable an image generator to achieve state-of-the-art performance across multiple visual perception tasks. Motivated by this perspective, we ask how far image generators can go on public visual perception benchmarks in a zero-shot setting. We introduce ProbeGen, a benchmark for zero-shot generative perception that casts monocular depth estimation, referring/reasoning segmentation, and object counting as conditional generation tasks specified through text prompts, and compares 20 models in total—including proprietary and open-weight image generators, specialist perception models, and MLLMs—across 11 published benchmarks. We observe that pretrained image generators show measurable zero-shot perceptual competence, but with a clear trade-off: specialist models remain stronger for in-distribution accuracy and efficiency, while generative models are often more robust under distribution shift and better at compositional semantic reasoning. We hope this study helps establish zero-shot generative perception as a meaningful research direction and provides a useful foundation for future work at the intersection of visual generation and understanding.
[CV-101] Scene Graph-Driven Haptic Feedback for Safety Enhancement in Robotic Ophthalmic Surgery via Physically Simulated iOCT
链接: https://arxiv.org/abs/2609.07857
作者: Danial Arbabi,Korab Hoxha,Angelo Henriques,Mirza Imamovic,M. Ali Nasseri
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 7 pages, 9 figures, 2 tables. Accepted at the 2026 IEEE RAS/EMBS 11th International Conference on Biomedical Robotics and Biomechatronicsnics (BioRob 2026)
Abstract:Robotic ophthalmic surgery offers high precision but introduces a “sensory gap” by decoupling the surgeon from their instrument, resulting in a loss of tactile feedback. This paper presents a novel haptic feedback system for subretinal injection tasks leveraging Scene Graphs (SG). The system bridges the sensory gap by analyzing a physically simulated intraoperative Optical Coherence Tomography (iOCT) feed to construct a real-time surgical SG. The SG serves as a semantic abstraction layer for the surgical scene, which is then utilized by a deterministic, rule-based engine to generate state-dependent haptic feedback on a robotic input device. The system was evaluated in a user study (N=16) using an anthropomorphic head phantom and a custom-built surgical robot. Results demonstrate that the SG-driven haptic feedback improved surgical precision, reducing needle alignment error by 14% (p = 0.044) and improving System Usability Scale (SUS) scores by 8% (p = 0.015), while maintaining comparable task completion times. A needle trajectory analysis revealed the emergence of a safer “Align-then-Approach” strategy, in which our haptic negative reinforcement prompted users to fine-tune the tool’s trajectory before approaching the retinal target. This work suggests that SGs can effectively serve as the direct computational foundation for real-time, safety-enhancing context-aware haptic feedback in robotic microsurgery.
[CV-102] DroneGround: Open-Vocabulary Drone Payload Characterization Using Synthetic Data and Grounded Vision-Language Models
链接: https://arxiv.org/abs/2609.07780
作者: Ami Pandat,Rajasekhar Punna,Gopika Vinod,Rohit Shukla
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at RVS-SE, British Machine Vision Conference, 2026
Abstract:Automated drone surveillance has become increasingly important for public safety, critical infrastructure protection,and restricted airspace monitoring. While existing vision-based systems achieve strong performance for drone detection and tracking, reliable payload characterization remains highly challenging under long-range imaging conditions due to limited availability of annotated real-world datasets, and substantial distribution shifts encountered during deployment. Existing approaches formulate payload characterization as a closed-set object detection problem, limiting their ability to recognize previously unseen payloads and generalize beyond the training distribution. To address these challenges, we generate a photorealistic synthetic drone-payload dataset using Unreal Engine 5 and Cosys-AirSim and propose DroneGround: Grounded Vision-Language Payload Characterization, a two-stage framework for robust open-vocabulary payload analysis. DroneGround first employs a YOLO26s detector to localize drones and extract drone-centric image crops, followed by a LoRA-fine-tuned PaliGemma vision-language model that generates seman- tic descriptions of the detected drones and their attached payloads, enabling open-vocabulary payload characterization beyond predefined categories. An occlusion-based grounding module further provides interpretable payload localization by identifying image regions responsible for the generated descriptions. Extensive experiments on both synthetic and real-world drone imagery demonstrate that DroneGround substantially improves robustness under synthetic-to-real distribution shifts, outperforming a conventional closed-set payload detector by improving the F1-score from 82.5% to 96.3%, while achieving significantly better generalization to previously unseen payload categories (80.4%versus 42.7% F1). Dataset and code will be released upon acceptance of the paper.
[CV-103] Cross-modal learning for SAR target recognition using optical vision foundation models
链接: https://arxiv.org/abs/2609.07753
作者: Lucas Hirsch,James R. Hopgood,Javid Khan,Yoann Altmann,Mike E. Davies
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
备注: Accepted for presentation at SPIE Sensors + Imaging 2026
Abstract:Synthetic Aperture Radar (SAR) is an important modality in a wide range of imaging applications due to its versatile, long range and near all weather operating capabilities. However, Automatic Target Recognition (ATR) remains a challenging problem due to limited labelled data, the strong speckle in SAR images and the significant domain gap between SAR and more abundant optical imagery. In contrast, electro-optical (EO) imagery benefits from massive datasets, clearer visual structure and powerful foundation models. In this work, we investigate how vision foundation models trained on optical data can provide class level supervision for SAR classification. We propose a cross-modal EO to SAR prototype alignment framework in which a frozen EO encoder, based on a DINOv3 vision foundation model, is used to construct class level optical prototypes without requiring strict EO/SAR pairs. A SAR model is then trained to classify SAR images while aligning its embeddings to the corresponding EO class prototype. At inference time, the SAR model operates independently, without access to optical imagery. We evaluate our approach on the UNICORNv2 dataset, an EO and SAR dataset of civilian vehicles with heavily speckled images and severe class imbalance. EO prototype alignment improves SAR classification accuracy over frozen DINOv3, SAR only finetuning and unpaired distribution alignment baselines, and t-SNE visualizations provide qualitative evidence of clearer separation among classes in the trained SAR embedding space. These results suggest that optical vision foundation models, despite being trained on visible spectrum imagery, provide transferable information for SAR image classification, offering a practical method for using large scale pretrained vision foundation models across challenging sensing modalities. Comments: Accepted for presentation at SPIE Sensors + Imaging 2026 Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processing (eess.IV) Cite as: arXiv:2609.07753 [cs.CV] (or arXiv:2609.07753v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.07753 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-104] FTrack: A Template-Free Framework for Efficient 3D Point Cloud Tracking
链接: https://arxiv.org/abs/2609.07738
作者: Zhaofeng Hu,Sifan Zhou,Jiahao Nie,Ziyu Zhao,Weizi Li,Ci-jyun Liang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:LiDAR-based 3D Single Object Tracking (3D SOT) is critical for robotic perception and navigation and aims to localize dynamic objects across frames in sparse point clouds. Existing methods, rooted in the Siamese tracking paradigm from 2D vision, rely on costly dual-input designs and excessive motion modeling guided by template priors, hindering their efficiency. Our in-depth analysis reveals: (i) the template paradigm is redundant, as the previous bounding box center encodes sufficient historical context; (ii) complex motion modeling is unnecessary, as geometric alignment provides adequate motion priors. Based on the above findings, we propose the first Template-Free Tracking framework (TFTrack). The novel framework eliminates the need for template-search pairings and operates directly on the current frame guided solely by the prior bounding box center and size. We instantiate this paradigm into three variants: TFTrack-Voxel, TFTrack-Pillar, and TFTrack-Point, to explore different 3D representations under a unified framework, ensuring flexibility across sparse and dense scenes. Extensive experiments on KITTI and nuScenes benchmarks show that TFTrack is competitive with leading template-based trackers, while reducing FLOPs by approximately 50% and running at approximately 120 FPS. By simplifying overcomplicated motion-centric designs, TFTrack establishes a new minimalist paradigm for efficient 3D point cloud tracking, paving the way for real-time and resource-efficient deployment in embedded robotic systems, such as autonomous vehicles. The code is available at this https URL.
[CV-105] V-SGS: Gaussian Splatting with Geometric Information Propagation via Tensor Voting under sparse views
链接: https://arxiv.org/abs/2609.07734
作者: Harish N Sathishchandra,Philippos Mordohai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Gaussian Splatting has been effective in inferring scene representations that excel in novel view synthesis. Multiple splats cooperate seamlessly to synthesize the pixels of novel views and are jointly optimized even though they only affect each other indirectly, via pixels they project to in common. We present an approach that enables direct communication among splats to enhance the geometric structures they form in 3D. This is accomplished by Tensor Voting, which was originally designed to infer structures from noisy inputs and has been adapted here to provide supervision during test-time optimization, leading to more accurate scene geometry. We introduce a new class of 3D losses that do not rely on rendering and can be combined with essentially all losses previously reported in the literature. Our 3D losses are especially effective when the input views are sparse and geometric regularization is essential due to limited supervision from the images. Our method is easy to integrate with a diverse set of backbones, and our experiments on the DTU and Tanks-and-Temples datasets demonstrate that TV-SGS improves the geometry of the outputs compared to the backbone, while maintaining or improving rendering quality.
[CV-106] Zero-Shot 3D Plant Organ Segmentation with SAM3 and Semantic NeRFs ECCV2026
链接: https://arxiv.org/abs/2609.07724
作者: Andreas Gilson,Laura Hennig,Peter Pietrzyk
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Submitted to CVPPA Workshop at ECCV 2026
Abstract:Accurate 3D plant organ segmentation is fundamental to automated phenotyping. Existing approaches rely on annotated training data or species-specific model configurations. We present an annotation-free pipeline for 3D plant organ segmentation, combining text-prompted SAM3 segmentation with semantic neural radiance fields (NeRFs). Given only multi-view RGB images and a list of class names, our zero-shot pipeline produces semantically labeled 3D point clouds without manual annotation, per-species fine-tuning, or domain-specific preprocessing. Multi-view NeRF fusion acts as effective implicit consensus mechanism that lifts imperfect per-frame masks into accurate 3D labels. On a controlled Begonia maculata testbed the SAM3 pipeline achieves 92.6% mIoU, reaching 95.9% of the oracle upper bound established with perfect ground-truth masks. The pipeline was further evaluated on a new dataset spanning ten diverse plant point clouds reaching an average 0.856 mIoU, with leaf and pot IoU above 0.91 and 0.90 for every species, respectively. These results demonstrate that annotation-free 3D plant organ segmentation is now feasible and approaching the range of supervised methods.
[CV-107] CrowdTraj: A Benchmark for Dense Crowd Trajectory Prediction in Realistic Crowded Environments
链接: https://arxiv.org/abs/2609.07685
作者: Antonius Bima Murti Wijaya,Paul Henderson,Marwa Mahmoud
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In real-world applications, pedestrian trajectory prediction models rely on inputs from detection and tracking systems. Prior trajectory prediction benchmarks either contain relatively sparse pedestrian interactions, assume perfect tracking inputs, or rely on overhead viewpoints that minimize occlusion and perspective distortion, limiting evaluation in realistic dense-crowd scenarios. We present CrowdTraj, a benchmark for pedestrian trajectory prediction in natural dense crowd scenes. Unlike previous datasets, CrowdTraj supports end-to-end evaluation from detection through tracking to trajectory prediction under severe occlusion in CCTV views. It also captures diverse, natural pedestrian behaviours, including abrupt directional changes rarely observed in existing benchmarks. CrowdTraj includes five diverse scenes, with an average of 1,146 unique pedestrians per scene, maximum frame-level densities ranging from 114 to 372 pedestrians, and over 3.2 million annotated head bounding boxes. CrowdTraj provides pixel and real-world coordinates via per-scene homography matrices for physically meaningful analysis. Our experimental results show that tracking accuracy (IDF1) drops to 0.68 to 0.70 in the densest scenes, compared with approximately 0.90 in less crowded scenes. Trajectory prediction training also becomes substantially more computationally expensive in dense scenes, with training times increasing by up to 8 times. These findings show that CrowdTraj exposes limitations in current trajectory prediction pipelines that remain hidden on existing sparse-crowd benchmarks, particularly in robustness to tracking noise and computational scalability.
[CV-108] Harnessing CLIP and DINO: An Uncertainty-Aware Cascaded Fusion Network for Generalizable Deepfake Image Detection
链接: https://arxiv.org/abs/2609.07670
作者: Xuechao Zou,Yi Zhou,Kai Li,Shun Zhang,Yuhui Chen,Congyan Lang,Junliang Xing
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Project page: this https URL
Abstract:The growing realism and accessibility of manipulated and generated faces threaten the trustworthiness of digital media. To detect such forgeries, deepfake detectors based on vision foundation models have shown promising performance, but they typically rely on a single pretrained representation and are prone to overfitting to particular training distributions. To improve generalization to unseen forgeries, we propose UCF-Net, an uncertainty-aware cascaded fusion network that harnesses CLIP’s language-aligned semantic priors and DINO’s self-supervised visual-structure priors. UCF-Net extracts hierarchical features across Transformer depths, uses layer-wise expert aggregation to adaptively combine each encoder’s multi-level cues, and performs weighted fusion of the resulting representations based on entropy-derived uncertainty. We further consolidate public deepfake datasets into a unified benchmark of approximately 4M images and construct a separate cross-generator evaluation set with over 8K face images from eight recent generators. On the unified benchmark, UCF-Net achieves the best mean AUC among the evaluated methods in both in-domain and cross-domain evaluations. On the cross-generator set, it adapts effectively with limited target-domain data, although zero-shot transfer remains challenging.
[CV-109] Privacy Leakage from a Thousand Words: Millipixel Location Recovery from Dot Maps CCS
链接: https://arxiv.org/abs/2609.07623
作者: Yuntao Du,Tanishq Pauskar,Hao Wang,Jing Su,Ninghui Li
类目: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to the ACM Conference on Computer and Communications Security (CCS), 2026. Code is available at: this https URL
Abstract:Dot maps, which visualize individual data points as dots over a geographic region, are widely used across diverse domains to represent spatial patterns in sensitive data. However, the understanding of the privacy risks associated with dot maps remains limited, particularly for maps covering large geographic areas. In this paper, we systematically analyze these risks and present AutoLocate, an automated framework for high-precision location recovery. At its core, AutoLocate exploits anti-aliasing artifacts introduced during map rendering, which inadvertently encode sub-pixel information about dot locations. AutoLocate formulates location recovery as a black-box optimization problem, iteratively refining estimated coordinates by minimizing perceptual discrepancies over these artifacts between the target map and rendered candidate maps. Extensive experiments on both real-world and synthetic datasets, across different attack scenarios and a broad range of map configurations (e.g., map scale, background, resolution), demonstrate the effectiveness of AutoLocate. In particular, it achieves average recovery errors as low as 1 meter (approximately 0.0002 pixel precision) on small-scale maps of the United States, over 200x more accurate than existing approaches. We also propose mitigation strategies and introduce a privacy risk assessment tool to help practitioners evaluate and reduce privacy leakage when publishing dot maps.
[CV-110] Solution for UCF UrbanTwin V2X-Real Track: Sim-to-Real Urban LiDAR 3D Object Detection
链接: https://arxiv.org/abs/2609.07608
作者: Pu Luo,Cong Xu,Yumei Li,Kexin Zhang,Licheng Jiao,Wenping Ma,Lingling Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 7 pages,2 figures
Abstract:Bridging the simulation-to-reality gap in roadside LiDAR requires addressing several coupled discrepancies, including scene geometry, sampling density, return patterns, and pedestrian scale. This report presents a multi-source collaborative training and class-aware fusion framework for Sim2Real 3D detection. The method organizes digital-twin scans, diffusion-redrawn scans, density-stabilized scans, and pedestrian morphology-aligned samples into a unified training pool with complementary roles. Within a common DSVT detection formulation, source-specialized expert branches preserve those roles while optimizing for the same detection objective. At inference, a predefined class-aware fusion pathway integrates geometry-stable and calibration-aware branches for vehicles, sampling-complementary branches for trucks, and morphology-consistent evidence for pedestrians. A label-free point-cloud center blend then refines geometric localization. On the UrbanTwin V2X-Real hidden test set, the unified system achieves a combined score of 0.7421, with 3D mAP@0.5 of 0.4518 and a realism score of 0.8871. The results indicate that a stable, interpretable collaboration among data sources is more valuable than unconstrained aggregation of model outputs.
[CV-111] Search-to-World: Evaluation of 3D World Delivery from User Request through Web Search
链接: https://arxiv.org/abs/2609.07605
作者: Zixiao Gu,Yabo Chen,Xunzhi Xiang,Yu He,Haibin Huang,Chi Zhang,Yunbo Wang,Xuelong Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Agentic systems can interpret user requests, search the live web, and use external tools, but their ability to transform retrieved web content into a usable 3D world has not been systematically evaluated. No established end-to-end pipeline or benchmark exists for this capability. We introduce Search-to-World, an end-to-end evaluation task covering request understanding, web visual-content retrieval, and 3D-world delivery. We define Observed Retrieval Rate (ORR) and World Delivery Rate (WDR) to distinguish observing relevant content from successfully delivering a request-aligned, perceptually acceptable world. We also present WorldSearcher, a reuse-then-reconstruction harness that connects existing search agents to world delivery: it first retrieves reusable 3D worlds and, when none are available, reconstructs a world from video. A structured recovery controller revises temporal grounding, replaces source videos, or reformulates queries after failure. Using WorldSearcher, we benchmark representative models on Search-to-World and study supervised fine-tuning (SFT) for recovery subagents. Results show that delivery depends on the underlying agentic model, and that relevant-content observation does not ensure world delivery. Jointly training recovery agents improves delivery success and action efficiency. Search-to-World makes agentic 3D-world delivery measurable, while WorldSearcher provides a practical evaluation harness with recovery capabilities.
[CV-112] BarkNet-Lite: A Lightweight Texture and Colour Network with the BarkBD Benchmark for Bark-Based Tree Species Recognition in Bangladesh
链接: https://arxiv.org/abs/2609.07600
作者: Aroshi Ali,Saad Ahmed,Md. Khalid Syfullah
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Tree species recognition supports forest inventory and biodiversity monitoring but still depends on scarce taxonomic expertise. Bark is visible year-round at ground level, yet bark recognition has concentrated on temperate floras and on large ImageNet-pre-trained backbones. We address both gaps. First, we release BarkBD, a bark dataset for Bangladesh: 14,258 uncropped smartphone photographs of 20 native species across four districts and three weather conditions, with a fixed stratified split. Second, we propose BarkNet-Lite, a 2.96M-parameter network trained from random initialisation, pairing a multi-scale texture pathway with a parallel colour-aware pathway. Over five seeds it reaches 96.64±0.66%accuracyunderstrict single-image inference, within 2.3 points of nine ImageNet-pre-trained backbones fine-tuned under an identical protocol and within one seed-level standard deviation of the smallest ofthem, andtransfers to public benchmarks (95.86% on BarkVN-50, 92.85% on BarkNet 1.0). Grad-CAM, validated by faithfulness and weight-randomisation checks, confirms its decisions rest on bark structure rather than background. The exported single-precision model classifies one photograph in 15.34ms on a commodity smartphone.
[CV-113] Solution for UCF UrbanTwin LUMPI Track: Sim-to-Real Urban LiDAR 3D Object Detection
链接: https://arxiv.org/abs/2609.07590
作者: Pu Luo,Cong Xu,Yumei Li,Kexin Zhang,Licheng Jiao,Wenping Ma,Lingling Li
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 5 pages,1 figures
Abstract:We present our solution to the LUMPI track of the UCF UrbanTwin Sim2Real LiDAR Challenge at the 6th DriveX Workshop, ECCV 2026. The detector must be trained only on synthetic data and is evaluated on 50 held-out real LiDAR frames; a separate 50-frame synthetic submission is evaluated for point-cloud realism. Our method addresses the Sim2Real gap at three levels. First, we align synthetic scans to the 50k-point test density and build a 30k-record training pool using UT-LUMPI geometry, RangeLDM-based sampling diversification, rare-class copy-paste, and pedestrian-oriented augmentation. Second, complementary DSVT detectors and Car/Bus PointPillars specialists are trained under the same synthetic-only constraint. Third, predictions are integrated by class-aware routing, asymmetric agreement fusion, constrained residual-recall supplementation, class-coverage auditing, and selective box-size calibration. The realism branch is optimized independently with radial-density matching, weak affine calibration, and calibrated set mixing. The final submission obtains a Combined Score of 0.4692, a Detection Score of 0.1797, a Realism Score of 0.9035, and 3D mAP@0.5 of 0.1258.
[CV-114] SphereSOD: Geometry-Structure Coupled Learning for 360 Salient Object Detection
链接: https://arxiv.org/abs/2609.07571
作者: Junsong Zhang,Zhijie Shen,Shuai Zheng,Feng Li,Runmin Cong,Yao Zhao,Chunyu Lin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages
Abstract:360° salient object detection (SOD) aims to accurately segment salient regions across a full field of view. However, equirectangular projection (ERP) introduces severe spatial distortion when mapping the spherical domain onto a planar representation. Existing methods mainly focus on compensating projection distortion while overlooking the interaction between panoramic geometry and salient object structure during feature perception and prediction refinement. To this end, we propose SphereSOD, an ERP-native framework that couples panoramic geometry with evolving salient structures. Specifically, spherical geometry governs feature sampling and spatial weighting, while coarse-grained saliency and contour prediction influence context aggregation during the progressive decoding process. SphereSOD first initializes deformable sampling based on spherical projection geometry and then employs bounded, content-adaptive offsets, yielding features that are better aligned with the underlying panoramic geometry. Subsequently, the decoder performs structure-guided context aggregation and progressive refinement to recover complete salient regions and accurate boundaries. Extensive experiments on three public 360° SOD benchmarks demonstrate state-of-the-art performance and a favorable accuracy-efficiency trade-off, supporting structurepreserving inference directly in ERP space as a promising alternative to projection-heavy panoramic pipelines.
[CV-115] Heat Kernel Textures: the Geodesic Gaussians That Do Not Splat ECCV2026
链接: https://arxiv.org/abs/2609.07557
作者: Simone Foti,Caner Korkmaz,Stefanos Zafeiriou,Tolga Birdal
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Machine Learning (cs.LG)
备注: ECCV 2026 (Long Oral)
Abstract:3D Gaussian Splatting has recently revolutionised novel view synthesis as well as many other 3D vision methods and applications. Drawing inspiration from this representation, we now rethink textures to overcome the main issues of UV mapping while considerably lowering their memory footprint. Heat Kernel Textures (HKTex) eliminate UV unwrapping as well as their persistent issues of wasted UV space, seams, distortions, vertex-duplication, and varying resolution. Grounded in discrete Riemannian geometry and intrinsically defined on any manifold surface discretised as a triangular mesh, HKTex uses anisotropic heat kernels as geodesic equivalents to Gaussians. Like our kernels, also the optimisation of their position and the adaptive densification strategies were redefined to operate on the surface of the object to be textureised. Our novel representation is also fully integrated with a physically based renderer and can be optimised either from existing textures or multi-view images. Our project page and code are available at this http URL.
[CV-116] Re-engineering SORT-based algorithms for low-cost small object tracking from omnidirectional footage
链接: https://arxiv.org/abs/2609.07547
作者: Xin Shu,Meegan Gower,Yvonne Buckley,Anil Kokaram
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by IEEE 28th International Workshop on Multimedia Signal Processing (MMSP)
Abstract:Multi-object tracking (MOT) has advanced rapidly in urban surveillance and autonomous driving, yet many trackers rely on ReID- and transformer-based appearance encoders and are designed for standard FoV cameras. These assumptions break down for low-cost omnidirectional deployments, where equirectangular projection introduces seam discontinuities and targets appear to be small and fast-moving. We address multi-object tracking of flying animals captured in remote environments using omnidirectional cameras. We propose a lightweight framework that re-engineers SORT-based tracking for this geometry, including (i) a Seam-Aware Motion Model that keeps the Kalman state continuous across the seam, (ii) a composite seam-aware association cost that pairs a wrapped Euclidean term with GIoU, and (iii) OmniSmall, a new benchmark of omnidirectional wildlife footage. On our new dataset, with ground-truth detections, our modifications improved over OCSORT by +8.51 HOTA, +9.41 MOTA, and +10.17 IDF1; with YOLOX detections the gain narrows to +1.95 HOTA. Our proposed methods improved tracking performance on OmniSmall and remained competitive on JRDB without adding appearance encoders while keeping the tracking stage CPU-only. Our dataset and source code are available at: this https URL.
[CV-117] Mo: Temperature Modulation for Multimodal Contrastive Learning
链接: https://arxiv.org/abs/2609.07540
作者: Dhimitrios Duka,Bernt Schiele,Hilde Kuehne,Anna Kukleva
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 6 figures, 11 tables
Abstract:Contrastive learning approaches achieve strong performance by training models to bring similar samples closer while pushing dissimilar samples apart. A crucial component of contrastive learning is the temperature hyperparameter \tau , which controls the penalty strength applied to negative samples. However, most existing methods either fix this hyperparameter or learn a global value during training. In this paper, we introduce TeMo, Temperature Modulation framework, a similarity-based modulation approach that adaptively adjusts the temperature for each positive-negative pair according to their similarity, enabling more fine-grained multimodal contrastive learning. Our approach seamlessly integrates temperature-modulated multimodal and unimodal losses with the standard multimodal contrastive loss by gradually transitioning between them. This design allows the model to capture both coarse- and fine-grained semantics at different training stages. Extensive experiments demonstrate that each component of TeMo consistently enhances performance across diverse zero-shot retrieval and classification tasks, establishing new state-of-the-art results.
[CV-118] When Semantically Consistent Encoding Meets View-Label Heterogeneity Modeling: A Unified Framework for Incomplete Multi-View Multi-Label Learning
链接: https://arxiv.org/abs/2609.07525
作者: Chengliang Liu,Bo Li,Bob Zhang,Yanghao Zhou,Jie Wen,Wenwu Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by IEEE TPAMI
Abstract:Incomplete multi-view multi-label learning requires not only robust semantic aggregation from partially observed views, but also label-aware exploitation of view-specific evidence. Existing approaches usually emphasize either shared representation learning or decision-level fusion. The former improves robustness against missing views, yet tends to compress label-discriminative view-specific cues into a single latent representation. The latter preserves individual view predictions, but often relies on fixed or globally learned fusion weights, ignoring that different labels of different instances may require different views. To address these limitations, this paper presents V2L, a unified representation-decision framework for incomplete multi-view multi-label classification. On the representation side, V2L constructs semantically consistent variational posteriors from incomplete views through a perturbation-aware encoding mechanism, which provides a stable shared semantic basis. On the decision side, V2L introduces an active view-label relevance modeling strategy that estimates instance-wise and label-wise view contributions, allowing each label prediction to adaptively select useful view-specific evidence. From the perspective of model architecture, these two important strategies are integrated into a unified framework through a hybrid fusion architecture, simultaneously meeting the requirements of cross-view semantic consistency and representational complementarity. Extensive experiments under both incomplete and complete settings show that V2L achieves leading performance on five benchmarks. Code is available at: this https URL.
[CV-119] opologically Consistent Agricultural Parcel Vectorization with Semantic-Guided Diffusion and Topology-Aware Polygonization
链接: https://arxiv.org/abs/2609.07520
作者: Weiqin Jiao,Xiaolong Zuo,Claudio Persello
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Agricultural parcel polygons play a fundamental role in geospatial applications such as precision agriculture, land administration, and crop monitoring. Beyond regular polygon geometry and low vertex redundancy, practical parcel maps should avoid topological conflicts and preserve common boundaries between adjacent fields. Yet this requirement remains largely unresolved: segmentation-based methods mainly produce parcel masks or raster boundary cues and rely on heuristic raster-to-vector conversion, instance- and contour-based methods reconstruct parcels independently, and recent vector-oriented methods improve polygon regularity but do not explicitly recover adjacent parcels from a shared topological structure. To address this gap, we propose a semantic-guided diffusion framework for topologically consistent agricultural parcel vectorization. It couples joint edge–vertex latent diffusion with supervised multi-cue conditioning to generate geometrically regularised parcel-boundary and vertex primitives while suppressing false-positive responses. A topology-aware parcel polygon reconstruction method then converts these primitives into regular polygons by reconstructing parcel faces from a common planar graph, enabling adjacent predicted parcels to reuse shared boundaries and avoid mutual interior intrusion. Extensive experiments on the AI4SmallFarms and iFLYTEK datasets evaluate parcel vectorization in terms of pixel-level coverage, geometric fidelity, object-level correctness, and topological consistency. The results show strong and competitive performance, with zero measured intrusion ratio and the highest shared-edge recall, demonstrating the potential of the proposed framework for accurate, regular, and topologically consistent agricultural parcel vectorization.
[CV-120] PICANet: Physics-Informed Cascaded Asymmetric Network for Infrared Small Target Detection
链接: https://arxiv.org/abs/2609.07515
作者: Jingjing Liu,Yinchao Han,Xianchao Xiu,Jianhua Zhang,Wanquan Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Infrared small target detection (ISTD) is an important research direction in image processing. However, existing methods are limited by severe background noise propagation and target degradation in high-level semantic features. To address these limitations, this paper proposes a plug-and-play physics-informed cascaded asymmetric network, named PICANet. Specifically, we construct a hierarchical prior decoupling module to explicitly extract low-level and high-level physical information, thereby characterizing target features at different levels rather than relying solely on convolutional extraction. Furthermore, a dual-prior interactive fusion module is developed to dynamically refine target representations while suppressing complex background clutter. Unlike previous work, a multi-level cross-feature attention module with the cascaded asymmetric mechanism is introduced to achieve precise alignment between high-level semantics and low-level spatial details. Extensive experiments demonstrate that the proposed PICANet outperforms state-of-the-art ISTD methods, showing satisfactory detection accuracy even against complex backgrounds. Our code is available at this https URL.
[CV-121] Mitigating Shortcut Learning: Texture-Penalized Prototype Networks
链接: https://arxiv.org/abs/2609.07504
作者: Akshay Anilkumar Girija,Elena Hoemann,Frank Köster,Sven Hallerbach
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 3 figures. Accepted at the 22nd IEEE International Conference on Advanced Visual and Signal-Based Systems (AVSS 2026)
Abstract:Standard Convolutional Neural Networks (CNNs) exhibit severe performance degradation due to a strong inductive texture bias that prioritizes local, high-frequency patterns over global structural shapes. This dependency causes confident misclassifications during textural changes or environmental effects. To address this flaw, this study introduces the Texture-Penalized Prototype Network (TPPN), a novel architectural framework that shifts this inherent bias without depending on resource-intensive augmented datasets. Specifically, a Texture-Penalization Branch (TPB) imposes a penalty to suppress the extraction of local texture proxies, forcing the network backbone to discard high-frequency cues and extract purified, shape-biased representations. By evaluating similarities within a prototype-based hypersphere derived from the final convolutional features, the approach enforces strict geometric constraints, treating objects as compositions of essential parts to achieve robust classification. Evaluations on texture-shape cue-conflict datasets and synthetic noise benchmarks demonstrate the stronger shape bias of this structural disentanglement. The proposed framework reduces the inherent texture bias of a baseline ResNet-50 from 55.11% to 29.73%, surpassing the texture-suppression capabilities of an off-the-shelf Vision Transformer (ViT-B/16). Furthermore, the approach demonstrates robust generalization under cue-conflict conditions, resisting textural shortcut learning when encountering Out-of-Distribution (OOD) shapes. The model maintains stronger shape accuracy against elevated perturbations. On clean validation data, the architecture incurs a minimal drop in accuracy of 0.90 percentage points. This provides a structural, efficient solution to CNN texture bias.
[CV-122] CrACK: Adversarial Attacks on Cross-Model Consistency in Collaborative Vision Foundation Models
链接: https://arxiv.org/abs/2609.07499
作者: Feifei Liu,Jintao Cheng,Chi Man Vong,Xiaoyu Tang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Training-free collaborative pipelines that integrate Vision Foundation Models such as CLIP, SAM, and DINO achieve strong open-vocabulary dense prediction and are increasingly deployed in safety-critical applications. The security of these systems is commonly assumed to follow from the robustness of their individual models. We challenge this assumption. We identify a vulnerability shared by every collaborative pipeline: each model consumes the intermediate output of another without verifying semantic consistency, an unverified premise that we term the semantic-spatial alignment dependency. Existing adversarial attacks target a single model and overlook this premise, leaving the inter-model interface entirely unguarded. We propose CrACK (Cross-model Adversarial Consistency attack), an inference-time attack that exploits this interface without modifying any input pixel, model weight, or training data. CrACK operates in two stages: Adversarial Affinity Contradiction Injection corrupts the cross-modal affinity matrix by inverting SAM encoder features under the guidance of CLIP patch-level semantics, and Semantic Interface Poisoning steers the prediction through a max-distance label permutation derived from CLIP text embeddings. Experiments on four collaborative pipelines across eight benchmarks show that CrACK causes catastrophic degradation while every individual model continues to produce its unchanged standalone output, rendering per-model defenses structurally blind. The corruption further cascades into large vision-language model reasoning, driving models such as LLaVA to produce erroneous responses from visually intact inputs. Our results show that the security of a collaborative AI system cannot be reduced to the robustness of its components, and that inter-model feature interfaces must be treated as first-class security boundaries.
[CV-123] CosmoH2G: A Hand-to-Gripper Transfer Dataset and Baseline Method for Object Manipulation with Complex Spatial Movements SIGGRAPH
链接: https://arxiv.org/abs/2609.07498
作者: Hongxiang Zhao,Mutian Xu,Zeyu Jin,Yiming Hao,Shuguang Cui,Xiaoguang Han
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: SIGGRAPH Aisa 2026; Project page: this https URL
Abstract:Transferring human hand demonstrations to robotic grippers has recently emerged as a cost-effective solution for robot learning. However, existing methods are largely confined to simple, planar tasks and fail to handle complex spatial movements (e.g., intricate trajectories involving rotations or flips) that are essential for robot manipulation. Motivated by this gap, we adopt an implicit, data-driven approach guided by fine-grained hand-pose motions. To this end, we introduce a scalable acquisition pipeline to collect hand-gripper paired demonstrations, governed by a rigorous protocol that prioritizes motion complexity and leverages a handheld gripper for seamless action mimicry. This yields a large-scale paired dataset comprising 6,189 episodes across 1,254 unique objects, exhibiting significantly higher spatial complexity than existing benchmarks. However, learning such complex mappings remains challenging. We observe that naive end-to-end generation of full gripper pose sequences is insufficient, as minor trajectory deviations compound rapidly under intricate dynamics. To address this, we propose a two-stage framework: Stage I predicts sparse gripper keyframes (initial and terminal) to simplify the mapping objective, while Stage II generates the full continuous action sequence conditioned on these keyframes. Furthermore, to mitigate cumulative drift, we keep the gripper’s orientation being learned while post-optimizing its translation based on the grasping heuristic and kinematic consistency. In both simulation and real-robot experiments, our framework enables stable and precise hand-to-gripper transfer of complex spatial manipulations, significantly outperforming traditional baselines. Project page: this https URL.
[CV-124] When Superpixels Fail on Documents: A Study of Segmentation for LIME Explanations IJCAI2026
链接: https://arxiv.org/abs/2609.07462
作者: Quentin Telnoff,Emanuela Boros,Mickaël Coustaty,Robin Jarry,Fabrice Crohas,Antoine Doucet
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 9 pages, 6 figures, 1 table, XAI Workshop @ IJCAI 2026
Abstract:Post-hoc explanation methods are widely used to inspect image classifiers, but their reliability depends on design choices that are often treated as implementation details. We study this issue for LIME on document image classification, focusing on the segmentation step that defines the interpretable units being perturbed. Standard image-based LIME typically relies on natural-image superpixels, which are poorly aligned with document structure such as text regions, layout blocks, and identification codes. Using RVL-CDIP, we compare Quickshift and SLIC with document-aware segmentations based on OCR bounding boxes and regular grids. Our results show that segmentation strongly affects explanation consistency, correctness, and local fidelity. Document-aware segmentations produce more stable and faithful explanations, require fewer perturbations to converge, and expose shortcut behaviour based on document identification codes, a known RVL-CDIP bias that superpixel-based LIME often obscures. These findings show that reliable post-hoc explanation requires domain-aware interpretable representations, and that segmentation should be treated as part of the explanation method rather than as neutral preprocessing.
[CV-125] Latent-to-Latent Flow for Volumetric Stochastic Segmentation MICCAI2026
链接: https://arxiv.org/abs/2609.07460
作者: Omar Todd,Sooha Kim,Raghav Mehta,Katherine Mackay,David Bernstein,Alexandra Taylor,Fabio De Sousa Ribeiro,Ben Glocker
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at MICCAI 2026
Abstract:Uncertainty arising from inter-observer variability in medical image segmentation plays an important role in developing treatment plans. Research in this area is inhibited by the lack of multiple annotations for large-scale medical datasets, especially for volumetric data, which suffers from additional scaling and computational complexity challenges. Flow matching has emerged as a powerful framework for generative modelling and has also been demonstrated to maintain strong performance when working with latent representations of images. In this work, we introduce a latent-to-latent flow technique for stochastic segmentation of medical volumes via encoded representations of both the image and label space. We evaluate our method on two challenging applications covering delineation uncertainty for radiotherapy planning and multiple organ structure segmentation, improving efficiency up to 14x compared with full resolution models while maintaining clinically relevant performance.
[CV-126] Parser-Free VLM Verification for Federated Weakly Supervised Video Anomaly Detection
链接: https://arxiv.org/abs/2609.07455
作者: Sébastien Thuau,Amira Gran,Siba Haidar,Rachid Chelouah
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 6 pages, 1 figure, AVSS 2026
Abstract:How can vision-language models help video anomaly detection (VAD) when surveillance data remain distributed, weakly labeled, and resource-constrained? Most weakly supervised VAD methods assume centralized training; recent VLM-based extensions further rely on dense inference, generated explanations, or additional adaptation. We introduce a lightweight federated MIL-VLM cascade in which only a compact MIL scorer is trained across clients, while a frozen VLM verifies high-scoring suspect segments post hoc. We study two VLM feedback interfaces: parsed text-generation decisions and a logit-based interface that extracts a continuous anomaly score from next-token Yes/No probabilities. Experiments on UCF-Crime with InternVL3.5-2B and Qwen3-VL-2B-Instruct show that text-generation verification can improve frame-level AUC after diagnostic temporal post-processing, but remains sensitive to prompts, parsers, model choice, and smoothing. In contrast, the logit interface provides a fixed parser-free signal that improves both frame-level AUC and frame-level AP over the MIL baseline across both VLMs, without temporal post-processing in its main configuration. Since suspect segments are updated independently once available, next-token logit feedback provides a simple segment-local alternative to text-generation verification.
[CV-127] AFID: A Unified Open Framework for Automated Fingermark Identification Quality Assessment and Feature Extraction
链接: https://arxiv.org/abs/2609.07439
作者: Tim Oblak,Rudolf Haraksim,Peter Peer
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Automated fingermark identification is the foundation of forensic investigation, yet progress in the field is held back by fragmented, closed-source solutions trained on private or discontinued data. We present AFID, a unified open-source framework for friction ridge image processing that performs recognition, quality assessment, and feature extraction based on a single shared encoder, trained exclusively on publicly available data. At its core is a fixed-length representation learned for identity discrimination, trained under heavy augmentation. Despite applying essentially no preprocessing beyond resizing and padding at inference, AFID sets a new state of the art in fixed-length fingermark recognition, leading identification across NIST SD 27 (67.6% rank-1) , SD 302 (54.9% rank-1), and SD 303 (67.6% rank-1), surpassing a commercial matcher on fingermarks. From the same frozen backbone, a quality assessment module predicts recognition utility more accurately than any compared baseline and generalizes across independent matchers, while lightweight decoders recover minutiae, ridge orientation, and segmentation competitive with dedicated methods. The framework proves that a single, efficiently trained encoder can support the full fingermark processing pipeline, from recognition through quality assessment all the way to feature extraction. To accelerate research on fingermark analysis even further, we release the code, models, and annotations to the community.
[CV-128] Unified Vision-Centric Pedestrian Crossing Action Prediction via Adaptive Patch Projection and Proactive Spatial Rectification
链接: https://arxiv.org/abs/2609.07420
作者: Yao Tian,Le Yang,Binglu Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision cues are available and informative for pedestrian action prediction, but obtaining stable target-centric representations from video frames remains challenging without frame-level external perception cues. Thus, most methods rely on additional perception modules or multi-source information fusion, leaving the reliability of vision-centric setting an open question. To this end, we propose ViCross, a vision-centric pedestrian crossing action prediction framework powered by multimodal large language models, which maintains target-centric reasoning from video frames without additional perception modules beyond first-frame target initialization. While multimodal large language models exhibit strong visual understanding, applying them directly to vision-centric action prediction faces two challenges. First, accurately perceiving target pedestrians often requires high resolution inputs and dense visual tokenization, making full-frame encoding computationally prohibitive. ViCross tackles this with Variable Resolution Patch Mapping module for efficient token allocation while preserving key pedestrian details. Second, missing spatiotemporal priors hinder consistent cross frame reasoning. ViCross mitigates this with a Spatial Constraint Enhancement Strategy that captures past motion, future locations, and action semantics for training-time proactive spatial rectification. Extensive experiments show that ViCross delivers clear gains in vision-centric prediction settings and is competitive with multi-source fusion approaches in several settings. Code is available at this https URL.
[CV-129] Self-Supervised Multi-View 3D Gaze Target Estimation via Probabilistic Ray Marching
链接: https://arxiv.org/abs/2609.07415
作者: Keqi Chen,Vinkle Srivastav,Nicolas Padoy
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We present a self-supervised approach, Self-MVGTE, for estimating 3D gaze targets from multiple camera views. Unlike existing methods that independently estimate 2D gaze targets per camera view, Self-MVGTE predicts gaze targets directly in 3D space for the first time. Moreover, it does not require any ground-truth annotations from the target scene and uses only the multi-view input images from a calibrated camera setup, pseudo 2D gaze target labels from a monocular gaze target estimation model, and 3D gaze vectors from a monocular 3D gaze estimation model. A key challenge is that these pseudo labels are inherently noisy and multi-view inconsistent. To address this, we propose a probabilistic ray marching framework, which models the uncertainty of these pseudo labels and exploits 3D gaze vectors as geometric priors. Specifically, these gaze vectors are first integrated into the monocular gaze target estimation model to improve its generalization to unseen scenes, producing higher-quality pseudo labels. Then, for 3D gaze target estimation, we construct a 3D gaze cone by casting a bundle of rays from the eye position around the gaze vector to strictly constrain the solution space. Within this cone, we propose a depth-guided feature sampling strategy using off-the-shelf DINOv2 and Depth-Anything-3 models, and estimate a spatial likelihood distribution of the gaze target. Finally, we convert the pseudo gaze target labels into a target distribution and softly optimize the network. Extensive experiments on the MVGT dataset show that Self-MVGTE achieves state-of-the-art performance, surpassing existing fully-supervised baselines.
[CV-130] RelightFormer: Feed-forward Generative Transformer for Multiview Object Relighting SIGGRAPH
链接: https://arxiv.org/abs/2609.07414
作者: Hejun Wang,Jinxi Li,Junwei Jiang,Shiwei Mao,Hu Cheng,Shouwang Huang,Bo Yang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG); Multimedia (cs.MM)
备注: SIGGRAPH Asia 2026. Hejun and Jinxi are co-first authors. Code and data are available at: this https URL
Abstract:Image relighting is traditionally tackled via complex inverse rendering pipelines, which suffer from ill-posed optimization, or single-image generative models that ignore crucial multi-view cues necessary for understanding 3D geometry and material interactions. To address these limitations, we introduce a feed-forward generative Transformer for direct single- and multi-view image relighting that entirely bypasses explicit intrinsic property estimation. Adapted from a video foundation model, our architecture features a latent illumination module that dynamically injects target environment maps into spatial features via cross-attention. Furthermore, we employ permutation-invariant positional encodings to symmetrically process unordered multi-view inputs without sequential bias. To train this robust data-driven model, we construct the massive Laval Objaverse Dataset (LOD), comprising 90K objects and 39K unique illuminations. Extensive experiments demonstrate state-of-the-art visual quality, photorealistic relighting quality, and strong zero-shot generalization across single-view, multi-view, and novel-view relighting tasks.
[CV-131] Multi-label versus multi-class classification of blood cells and their aggregates in microfluidic channels
链接: https://arxiv.org/abs/2609.07410
作者: Igor Zingman,Shada Abuhattum,Sara Kaliman,Maximilian Schlögel,Paul Müller,Markéta Kubánková,Nadine Ströhlein,Manuela Hauke,Lena Schnörer,Martin Kräter,Jochen Guck
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 26 pages, 5 figures
Abstract:Deformability cytometry (DC) is a type of imaging flow cytometry, which uses a camera-equipped device to measure cellular stiffness in addition to other cellular properties at high throughput. Cellular properties such as area and elongation can identify cell types, but this requires prior knowledge of distinguishing properties and cannot be applied to clinically important cell aggregates. Using DC data, we evaluated conventional multi-class (MC) classification and introduced a multi-label (ML) approach for identifying blood cells and their aggregates. In particular, an ML classifier can simultaneously assign multiple cell-type labels to a single imaged event. We show that, unlike MC classification, ML classification can identify cell aggregates not represented in the training data. It also avoids the need for exhaustive, strictly defined aggregate labels, thereby simplifying and speeding up annotation. Since automated blood analyzers do not reliably analyze cell aggregates, our approach may help address this clinical gap.
[CV-132] Impact of canny edge detection preprocessing on performance of machine learning models for Parkinsons disease classification
链接: https://arxiv.org/abs/2609.07408
作者: Sameer Bhat,Piotr Szczuko
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:This study investigates the classification of individuals as healthy or at risk of Parkinson’s disease using machine learning (ML) models, focusing on the impact of dataset size and preprocessing techniques on model performance. Four datasets are created from an original dataset: DS_0, (normal dataset), DS_1 (DS_O subjected to Canny edge detection and Hessian filtering), DS_2 (augmented DS_0), and DS_3 (augmented DS_1). We evaluate a range of ML models-Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), XGBoost (XBG), Naive Bayes (NB), Support Vector Machine (SVM), and AdaBoost (AdB)-on these datasets, analyzing prediction accuracy, model size, and prediction latency. The results show that while larger datasets lead to increased model memory footprints and prediction latencies, the Canny edge detection preprocessing supplemented by Hessian filtering (used in DS_1 and DS_3) degrades the performance of most models. In our experiment, we observe that Random Forest (RF) maintains a stable memory footprint of 61 KB across all datasets, while models like KNN and SVM show significant increases in memory usage, from 5.7-7 KB on DS_0 to 102-220 KB on DS_2, and similar increases in prediction time. Logistic Regression, Decision Tree, and Naive Bayes show stable memory footprints and fast prediction times across all datasets. XGBoost’s prediction time increases from 180-200 ms on DS_0 to 700-3000 ms on DS_2 (truncated)
[CV-133] Federated Binary Gating with Server-Side Vision-Language Inference for Surveillance Anomaly Classification
链接: https://arxiv.org/abs/2609.07403
作者: Côme-Alexis Puech,Sébastien Thuau,Amira Gran,Arthur Mennessier,Siba Haidar,Rachid Chelouah
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 7 pages, 2 figures, AVSS 2026
Abstract:Privacy-sensitive surveillance systems could benefit from large vision-language models (VLMs), but such models typically require centralized access to raw video. In federated learning settings, this challenge is amplified by non-independent and identically distributed (non-IID) client data, which can make direct multiclass anomaly classification unstable, especially for rare categories. We propose a hybrid two-stage architecture that combines a federated binary convolutional neural network (CNN) gate with server-side zero-shot VLM inference. The lightweight LiteCNN3D gate performs local anomaly screening and forwards only flagged videos to Qwen3-VL-8B, which assigns them to four anomaly metaclasses. We evaluate this design on UCF-Crime grouped into five coarse metaclasses and implement the federated stage in a real three-node heterogeneous deployment. In the studied setting, direct federated multiclass training collapses, whereas the proposed decomposition yields a better trade-off between classification quality and raw-video transmission. With fixed-threshold routing, the federated hybrid pipeline preserves nearly the same macro-averaged F1 score (F1-macro) as its centralized CNN+VLM counterpart while reducing the fraction of transmitted videos to 51.4%, although with a lower proxy macro receiver operating characteristic area under the curve (ROC AUC) than the centralized hybrid system. A complementary sensitivity-oriented routing operating point increases macro ROC AUC from 0.673 to 0.692 and reduces the false negative rate from 29.3% to 22.9%, but decreases F1-macro from 0.503 to 0.485 while increasing transmission from 51.4% to 57.9%. These results suggest that federation is better suited to coarse local screening, while routing rules can be adjusted to trade server-side VLM usage for higher anomaly sensitivity. Comments: 7 pages, 2 figures, AVSS 2026 Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2609.07403 [cs.CV] (or arXiv:2609.07403v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.07403 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-134] Social Intuition vs. Machine Reasoning : Anticipating Human-Robot Interaction from multiple modalities IROS2026
链接: https://arxiv.org/abs/2609.07394
作者: Raphael Lorenzo-Louis,Bertrand Luvison,Serena Ivaldi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 5 pages, 4 figures. Workshop paper accepted to The 4th Workshop on Nonverbal Cues for Human-Robot Cooperative Intelligence at IROS 2026
Abstract:Anticipating whether a person will interact from one’s own perspective is a highly intuitive task for humans, that relies on a combination of cues. We investigate how humans perform at predicting a person’s intention to interact from a service robot’s point of view, using pose-only or full video input, then benchmark different lightweight pose-based models and state-of-the-art vision-language models. We conducted our benchmark on the HUI360 dataset on a fixed pilot subset of 100 test tracks (25 positive, 75 negative). We found that with pose-only input, human annotators outperform lightweight trained pose models but not by large margins (+0.08 in F1-Score). But when given full egocentric video with a target bounding box, human annotators perform substantially better and largely outperform the Vision-Language Models (+0.2 in F1-Score). We also compared VLMs of different size and under different input conditions, and found that the best results do not correlate with model size. Our result confirms that predicting interactions is a challenging task for social robots and that reasoning-capable models are necessary but their actual reasoning capabilities alone do not suffice to match the social intuition of humans.
[CV-135] RAIL: Trajectory-Aware Visual Place Recognition against Unordered Databases ECCV2026
链接: https://arxiv.org/abs/2609.07373
作者: Dominik A. Kloepfer,Patrick Wenzel
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Published at ECCV 2026
Abstract:Modern Visual Place Recognition (VPR) methods excel on standard benchmarks yet remain brittle in feature-poor environments. By treating each query image in isolation, they discard the sequential context in any real trajectory. We formalize a task that exploits this context: given a query sequence, localize the final image against an unordered reference database – which, unlike sequence-to-sequence methods, requires no sequential structure in the database. We propose TRAIL (TRajectory-Aware Image Localization), a principled framework based on Conditional Random Fields (CRF) that combines learned functions for visual similarity and for camera-motion consistency, refining a distribution over candidate references as each query arrives. A lightweight post-processing layer atop any pre-trained VPR backbone, TRAIL improves a state-of-the-art baseline by up to 8.3 percentage points on our primary benchmark, transfers to unseen datasets without retraining, and delivers its largest gains where visual cues are scarce.
[CV-136] DF26: We Cannot Tell Fake From Real Anymore
链接: https://arxiv.org/abs/2609.07369
作者: Severyn Shykula,Andrii Yermakov,Ivan Samarskyi,Dmytro Mishkin,Jan Cech,Anastasiia Mishchuk
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We introduce DF26, a novel benchmark for detecting AI-generated videos containing fully synthetic clips produced by recent text-to-video and image-to-video models. The videos capture single-person public-speaking scenarios, spanning direct-to-camera recordings, official statements, and studio interviews - 271 real and 2,420 synthetic videos generated by seven modern video models. The study on DF26 shows that human performance in detecting AI-generated videos, as well as state-of-the-art deepfake detectors, is close to random chance. Our results highlight the limitations of current evaluation protocols and motivate the need for benchmarks that explicitly measure robustness to modern generative model distribution shifts.
[CV-137] From Explicit References to Scene Manifolds: Distributional Fidelity and Realism for Radiance Field Quality Assessment
链接: https://arxiv.org/abs/2609.07346
作者: Saeed Mahmoudpour,Gi-Mun Um,Hyon-Gon Choo,Peter Schelkens
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Radiance field representations such as 3D Gaussian Splatting (3DGS) enable high-quality novel view synthesis but can introduce complex, view-dependent artifacts from reconstruction, rendering, and compression. Reliable perceptual quality assessment (QA) is thus essential for evaluating rendered views and guiding the design of perceptually faithful scene representations. Existing full-reference QA metrics require an aligned reference image, while recent cross-reference metrics relax this requirement by comparing a test view with non-aligned references. However, under wide-baseline radiance field settings, selecting a reliable nearby reference can be difficult, particularly when evaluating views along arbitrary trajectories and poses. We propose SCODA, a lightweight scene-conditioned objective QA method that shifts QA from explicit image-to-image comparison to scene-manifold modeling. High-quality observations of each scene are represented as a multivariate Gaussian distribution in deep feature space, producing a semantic fidelity score that measures deviation from the scene distribution. A weakly-supervised distortion-aware patch discriminator provides a complementary realism signal, and both cues are combined through an unsupervised bounded fusion strategy. Experiments on multiple benchmarks show strong agreement with human judgments and robust generalization across GS- and NeRF-generated views and trajectories. Code is publicly available at this https URL.
[CV-138] CRISP: Corneal Confocal Microscopy Real-Time Image Stitching Pipeline
链接: https://arxiv.org/abs/2609.07336
作者: Qincheng Qiao(1),Puli Zhang(2),Jian Zhou(2),Xinguo Hou(1) ((1) Department of Endocrinology and Metabolism, Qilu Hospital, Shandong University, Jinan, China, (2) Department of Endocrinology and Metabolism, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 7 pages, 2 figures. Code: this https URL
Abstract:Morphology of the sub-basal nerve plexus (SNP) reflects peripheral nerve health, and corneal confocal microscopy (CCM) provides an important means for in vivo, real-time, non-invasive observation of the SNP. However, mainstream CCM devices offer a limited field of view per frame, whereas the SNP is spatially non-uniform; discrete image sampling is therefore sensitive to sampling location and frame selection, which limits the reproducibility and clinical adoption of CCM as a quantitative assessment tool. Wide-field stitching can reconstruct larger SNP mosaics by integrating sequentially acquired CCM images, but existing methods largely rely on offline post-processing, additional hardware, or specific acquisition protocols, and lack open-source real-time solutions for conventional CCM video streams. This paper presents CRISP (Corneal confocal microscopy Real-time Image Stitching Pipeline), an open-source real-time SNP wide-field stitching framework for conventional CCM examination video streams. CRISP excludes defocused and discontinuous segments via focus-aware gating, propagates poses through local pairwise registration, and maintains non-redundant spatial coverage with a sparse anchor map; when local temporal continuity is interrupted, the system completes relocalization and subgraph merging through global appearance retrieval followed by geometric verification. The framework prioritizes low-latency coverage feedback during examination while outputting accepted frames, poses, and anchor information to initialize offline fine stitching. To our knowledge, CRISP is the first open-source real-time SNP wide-field stitching framework released for conventional CCM video streams. By lowering the barrier to adoption and reproduction of wide-field stitching, CRISP may help move SNP wide-field imaging from a research tool into routine clinical examination workflows. Comments: 7 pages, 2 figures. Code: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2609.07336 [cs.CV] (or arXiv:2609.07336v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.07336 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-139] Weakly supervised neural network: segmentation of complex structures in X-ray microCT
链接: https://arxiv.org/abs/2609.07313
作者: Daniele Rusconi,Michela Ascolese,Stephanie Fest-Santini,Alberto Bravin,Maurizio Santini
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Segmentation of complex structures in X-ray tomographic data is a fundamental task in biomedical research, but it often requires large amounts of precisely annotated data, making fully supervised approaches costly and difficult to scale. In this study, weakly supervised deep learning is investigated as a strategy to reduce annotation effort while maintaining accurate segmentation. A two-dimensional convolutional neural network based on the nnU-Net framework was adapted to a weak supervision setting using sparse dot-based annotations, complemented by a limited number of fully segmented images. The approach was evaluated on high-resolution microCT slices of rat kidneys, targeting the segmentation of renal glomeruli, which are small, low-contrast anatomical structures. Results indicate that weak supervision provides a meaningful learning signal, enabling reliable localization of glomeruli even in the absence of dense labels. Incorporating a small set of high-quality annotations substantially improves segmentation performance, approaching that of a fully supervised model. These findings highlight the potential of weakly supervised learning as an annotation-efficient strategy for the analysis of complex structures in X-ray tomographic data, and suggest that alternative loss formulations tailored to sparse annotations may further enhance performance.
[CV-140] KODAMA: Multimodal Digital Twin Reconstruction for Urban RF Propagation Modelling
链接: https://arxiv.org/abs/2609.07298
作者: Maximiliano Wardle,A. Ryo Koblitz
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:3D reconstruction typically strives for geometric fidelity or visual plausibility. Radio frequency digital twins (RFDT) are instead judged by whether communication channels behave in them as they do in the real world. RFDTs promise site-specific channel prediction but current practice forces a choice between coarse automated scenes and hand-built, measurement-calibrated models that take weeks to construct per-site. We present KODAMA, an automated pipeline that reconstructs ray tracing-ready RFDTs at city scale from off-the-shelf geospatial data alone: aerial imagery, LiDAR, and photogrammetry yield terrain and watertight building meshes, while exposure-weighted multi-view fusion of street-level imagery recovers façade relief, electromagnetic materials, and clutter—all without site visits or calibration. Across three sites spanning 3.6 to 28 GHz, KODAMA’s uncalibrated predictions achieve single-digit RMSE, reducing point-to-point error by up to 5.35 dB over automated baselines and coming within 0.22 dB of a measurement-calibrated, hand-built RFDT.
[CV-141] LightSplat: Real-Time High-Fidelity 3D Gaussian SLAM with Loop Closure IROS
链接: https://arxiv.org/abs/2609.07274
作者: Junze Bao,Ye Gao,Yiming Huang,Xiaolong Yu,Chen Dong,Qing Gao,Wei Wang,Jinhu Lü
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Abstract:SLAM systems based on 3D Gaussian Splatting (3DGS) have recently demonstrated promising reconstruction accuracy for dense 3D scene representations. However, current 3DGS systems struggle to meet the strict demands of real-world deployments due to severe limitations in operational performance and map adaptability. To this end, we propose LightSplat, a hybrid-representation RGB-D SLAM framework. It synergizes local sparse features for robust and fast tracking with a dual-thread backend that progressively constructs dense Gaussian submaps. Crucially, we enable online loop closure through feature-accelerated 3DGS registration, refining overall map consistency through pose graph optimization. Ultimately, LightSplat achieves the online reconstruction of high-fidelity Gaussian map. Extensive experiments on multiple datasets and real-world robotic platform demonstrate that our method achieves near state-of-the-art reconstruction quality and the capability to accommodate practical camera motions, maintaining an average framerate of 8 FPS. Overall, LightSplat provides an efficient and robust foundation for deploying high-fidelity 3DGS in real-world environments.
[CV-142] MV-STRIDE: Enabling MLLM s to Master Multi-View Spatial Reasoning via Hierarchical Capability Modeling
链接: https://arxiv.org/abs/2609.07258
作者: Jin Xu,Xiaojian Huang,Zhuodong Luo,Zhihong Zhang,Xin Liu,Jiansheng Wei,Xinzhi Wang,Jie Zhao,Xuejin Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Despite the rapid progress of Multimodal Large Language Models (MLLMs) in 2D vision-language tasks, robust multi-view spatial reasoning remains a fundamental bottleneck due to the lack of structured 3D cognitive pathways in existing datasets. To address this, we introduce MV-STRIDE, a Multi-View hierarchical SpaTial Reasoning dataset with Interdependent and DEcomposed capabilitiEs. Moving beyond flat data structures, MV-STRIDE explicitly models the dependency relationships between foundational perception, scene understanding, and complex contextual reasoning, providing a coherent learning pathway aligned with human spatial cognition. We develop a systematic QA generation pipeline leveraging diverse 3D scene sources that enforces cross-view dependency constraints to prevent single-view solvability, generating multi-level spatial reasoning tasks supported by cognitively grounded chain-of-thought supervision for complex inference. Extensive evaluations demonstrate that our multi-stage training framework based on our hierarchical dataset achieves state-of-the-art performance across multiple spatial reasoning benchmarks, notably the multi-view oriented MMSI-Bench. Our approach enables MLLMs to maintain robust, 3D-consistent spatial reasoning across diverse viewpoints. The code and dataset are available at this https URL.
[CV-143] Proximity-CLIP: Text-Guided Semantic Proximity Learning for Zero-Shot Anomaly Detection ECCV2026
链接: https://arxiv.org/abs/2609.07229
作者: Manwen Yang,Leqian Ding,Yu Guo,Fei Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026. Including supplementary material
Abstract:Vision-language models offer a promising approach for zero-shot anomaly detection (ZSAD). However, due to object-centric bias, normal and anomalous text prototypes exhibit a high semantic overlap. While enforcing strict orthogonality between them improves discriminability, mapping highly contiguous visual inputs onto drastically orthogonal prototypes introduces a geometric dilemma, disrupting the pre-trained structural continuity. To address this problem, we propose Proximity-CLIP, a framework that visually calibrates the semantic margin to guide visual adaptation. First, we introduce a visually-calibrated semantic proximity learning mechanism that uses a bounded dynamic regularization to learn an appropriate semantic margin, ensuring discriminative separation while preserving structural alignment. Second, we design an Anomaly Query Module (AQM) driven by these text priors. Using the calibrated anomalous prototype as a semantic query, the AQM actively retrieves localized defect cues from contextual visual patches, mitigating the dilution of subtle anomalies during global pooling. Extensive experiments demonstrate that Proximity-CLIP outperforms current state-of-the-art methods across multiple ZSAD benchmarks with minimal architectural modifications.
[CV-144] he Price of Consistency: Exploiting Visual Anchors for Multimodal Jailbreaking in Video Generation ACM-MM2026
链接: https://arxiv.org/abs/2609.07216
作者: Peng Li,Qianqian Xu,Yangbangyan Jiang,Zhipeng Yu,Qingming Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This paper has been accepted to ACM MM 2026
Abstract:The rapid evolution of video generation has shifted the paradigm from pure text-driven to multi-conditional controllable generation, with reference images now widely adopted as conditional inputs to achieve superior spatiotemporal consistency. While these reference images serve as powerful visual anchors that significantly enhance controllability, their impact on safety remains largely unexplored. In this work, we reveal the visual anchoring effect: by enforcing consistency, the mechanism prevents the generated content from drifting away from the original harmful intent, thereby eliminating the model’s natural safety escape route from harmful to benign content. Consequently, visual anchors inherently increase the safety risk—this is the price of consistency. Building on this insight, we propose Decoupling Intent via Visual Anchors (DIVA), a training-free multimodal jailbreak framework for video generation that exploits this vulnerability. DIVA decouples harmful intent into a static visual anchor image and a dynamic motion text prompt, and employs dual-criteria selection to balance attack stealthiness with semantic preservation. Extensive experiments across various leading commercial platforms and mainstream open-source video generation models demonstrate that DIVA achieves a substantially higher Attack Success Rate than existing text-only methods. To facilitate future research, we additionally contribute TI2VSafetyBench, the first safety benchmark for multi-conditional video generation.
[CV-145] Unraveling the Real Working Mechanism and Inherent Flaws of GAE: A Method for Interpreting Transformer Processes from an Economic Perspective
链接: https://arxiv.org/abs/2609.07213
作者: Yongjin Cui,Xiaohui Fan
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We observe a phenomenon that current algorithmic research in the field of explainable artificial intelligence primarily pursues better performance on several proxy metrics. On the one hand, these proxy metrics themselves are more or less flawed and cannot properly measure the quality of methods. On the other hand, metric-oriented research approaches often lead to the neglect of the rationality and interpretability of the methods themselves. Explainable artificial intelligence is abbreviated as XAI. The metric-driven research paradigm has resulted in a lack of interpretability of the relevant XAI methods themselves. Accordingly, there is a need for interpretability research on XAI methods, which can be playfully referred to as XXAI. This paper is one of our works on XXAI. This paper takes Generic Attention-model Explainability (GAE), a widely influential model interpretation method , or rather, XAI method that represents an important technical route, as the research object, and explores the real working mechanism and flaws of this method as well as the technical route it represents. Based on the conclusions of this study, it may be necessary to re-examine or verify GAE-related methods and their domain applications. We argue that GAE is an interpretation method that focuses on the attention process. After pointing out the working mechanism and flaws of GAE, we propose Cumulative Asset Holdings (CAH), a more reasonable Transformer interpretation method integrating both process-based and feature-based ideas from an economic zero-sum games perspective. In addition, it is worth noting that our method is applicable to models with special tokens, where existing methods may suffer from limitations. The model simplification research method and the analysis of additive operations adopted in this study may provide inspiration for other research works in XAI.
[CV-146] CHILD: Human-in-the-Loop OOD Detection for Safe Clinical Deployment MICCAI2026
链接: https://arxiv.org/abs/2609.07188
作者: Jinlun Ye,Kaiyue Lu,Runhe Lai,Xinhua Lu,Jia-Xin Zhuang,Ruixuan Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages. Accepted at MICCAI 2026
Abstract:Out-of-distribution (OOD) detection is critical for safe deployment of medical AI systems. Recently, test-time adaptation (TTA) has emerged as a new paradigm for OOD detection, automatically adjusting detector behavior during deployment. However, such automatic adaptation mechanisms may raise safety concerns in safety-critical clinical environments. While physician oversight can mitigate these risks, it is resource-intensive and must be judiciously allocated. To reconcile safety with efficiency, we propose CHILD, a training-free framework designed to enhance streaming OOD detection via sparse human feedback. Operating under strict budget constraints, CHILD employs an adaptive risk-aware sample selection mechanism to pinpoint only the most decision-uncertain samples for review. Crucially, it maximizes the utility of this sparse feedback through a retrieval-based score calibration module, which refines model predictions using a compact feature cache without any parameter updates. Extensive experiments on four medical benchmarks demonstrate that CHILD turns limited supervision into significant reliability gains: with a sparse feedback budget of only 5%, it reduces the average FPR95 from 72.63% to 60.26% and improves AUROC from 75.53% to 81.85%, consistently outperforming state-of-the-art baselines. Our code is publicly available at this https URL.
[CV-147] Deep Learning for Biopsy-Free Subtyping of Basal Cell Carcinoma from Dermatoscopic Images
链接: https://arxiv.org/abs/2609.07180
作者: Alexandros Papadopoulos,Chrysa Episkopou,Ioannis Sarafis,Aimilios Lallas,Anastasios Delopoulos
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Basal Cell Carcinoma (BCC) is the most common type of skin cancer, accounting for nearly 80% of skin cancer di- agnoses. Its optimal clinical management is guided by the distinct histopathologic subtype, with aggressive variants requiring more drastic measures. In current clinical practice, subtyping relies on skin biopsies, a procedure both costly and invasive. In this paper, we conduct a preliminary investigation into using deep learning for BCC subtyping, solely from a single dermatoscopic image of the lesion. Given the limited data at our disposal, we employ pre-trained vision transformers (ViTs), a state-of-the-art family of models highly effective for challenging downstream tasks with limited labeled data. Through repeated stratified k-fold cross-validation, we demonstrate that ViTs can achieve superior performance (AUC 0.784 on a dataset of 1271 dermatoscopic images of various BCC subtypes) over standard CNN-based baselines as well as previously-reported human reader perfor- mance, on the task of differentiating aggressive BCCs from other subtype families. These initial findings highlight the potential of combining deep learning and dermatoscopy to provide a biopsy- free alternative for BCC subtyping, thus aiding in improving treatment planning and patient outcomes.
[CV-148] Weakly-supervised Kidney Tumor Classification from CT Scans with Multi-Instance Learning and Anatomical Filtering
链接: https://arxiv.org/abs/2609.07178
作者: Joonas Ariva,Dmytro Fishman
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 3 figures
Abstract:Deep learning models for CT scan analysis are often limited by the scarcity of precise pixel-level annotations, which require significant radiologist effort to produce. Training on scan-level labels alone reduces annotation requirements but introduces challenges: low supervision ratios and large input volumes make models prone to overfitting and shortcut learning. In this work, we investigate two complementary methods to address these challenges: multi-instance learning (MIL) and anatomical filtering. MIL divides CT volumes into 2D slice instances, enabling efficient 2D architectures with ImageNet pretraining rather than computationally demanding 3D models. Anatomical filtering uses Compass, our self-supervised body part regression model, to crop scans to pathology-relevant subregions without requiring segmentation masks. We evaluate two MIL frameworks - Attention-based MIL (ABMIL) and FocusMIL - on kidney tumor classification across one internal dataset (TUH) and two external datasets (KiTS23 and TCGA-KiRC). Our best models achieve F1 = 0.83 on the internal test set using only scan-level labels. We further show that anatomical filtering with the Compass model is critical for the out-of-distribution generalization of embedding-based ABMIL, while instance-based FocusMIL demonstrates greater inherent robustness to distribution shift. While evaluated on kidney tumors, we consider this a proof-of-concept for a broader weakly supervised CT classification pipeline applicable to other organs and pathologies.
[CV-149] Unsupervised Domain Adaptation for Symbol Spotting in Historical Encrypted Manuscripts
链接: https://arxiv.org/abs/2609.07159
作者: Giuseppe De Gregorio,Alicia Fornés,Lei Kang,Beáta Megyesi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The decipherment of historical encrypted manuscripts poses a fundamental challenge in Digital Humanities: before any transcription can begin, the symbol inventory of the underlying cipher alphabet must first be identified and characterized. We address this challenge through symbol spotting: given a candidate alphabet specified as a set of rendered font glyphs, the task is to determine whether and where its characters appear in an unseen handwritten document, without any labeled examples from the target script. The main difficulty lies in the domain gap between clean, digitally rendered font queries and degraded handwritten manuscript symbols. We propose a three-stage pipeline that bridges this gap without manual annotation, combining a joint SimCLR+DANN encoder for domain-invariant glyph representations with an embedding-space style-adaptation mechanism applied at retrieval time, requiring no re-training. Experiments on fourteen pages from seven encrypted manuscript collections show that our method outperforms zero-shot foundation models, including CLIP and DINOv2, by a large margin ( +0.194 P@1 over CLIP ViT-L/14), and surpasses task-specific trained baselines by +0.138 P@1. We further demonstrate that the Raw-Cover metric, computed in a fully unsupervised setting, provides a meaningful script-family fingerprint that identifies the underlying alphabet of an unknown document. This capability is of direct practical relevance to palaeographers, historians, and other researchers working with undeciphered manuscripts.
[CV-150] Mind the Approximation: Fisher-Weighted SVD Compression for ViTs
链接: https://arxiv.org/abs/2609.07155
作者: Moritz Thoma,Maximilian Groezinger,Maximilian Forstenhäusler,Emad Aghajanzadeh,Ryan Pegoud,Manoj Rohit Vemparala,Pierpaolo Mori,Alexander Frickenstein,Daniel Mueller-Gritschneder,Ulf Schlichtmann
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Model compression is key to mitigate deployment challenges of ever growing machine learning models. In this area of research, singular value decomposition (SVD)-based compression offers a compelling trade-off between computational efficiency and model accuracy. Fisher-weighted SVD in particular provides principled, loss-aware compression. However, we find that improving the fidelity of Fisher approximation used in the compression is poorly predictive of post-compression accuracy for Vision Transformers (ViTs). Motivated by this observation, we propose FACTS, a structured Fisher Approximation tailored to Compressing ViTs with Fisher-weighted SVD, which enforces token-local aggregation while preserving within-token activation-gradient dependence. Additionally, we introduce a fast Constrained Rank Search (CoRS), that optimizes layer-wise rank allocation while adhering to a fixed floating point operation (FLOP) constraint. Extensive experiments across ViTs and hybrid architectures demonstrate that FACTS consistently improves accuracy-efficiency trade-offs without requiring finetuning. Notably, it outperforms the strongest SVD baseline by up to +5.8 percentage points (p.p.) Top-1 on Swin-B, with further gains driven by our search method. Code is available at this https URL.
[CV-151] Ambient @ EgoLongQA 2026: Distilling Long-Video perception into a Sub-2B Model ECCV2026
链接: https://arxiv.org/abs/2609.07154
作者: Logesh Kumar Umapathi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Winning solution technical report for the EgoLongQA track of the ECCV 2026 Wearable AI Grand Challenge
Abstract:We describe our entry to the EgoLongQA track of the Wearable-AI Challenge in ECCV 2026, which placed first in the =2B parameter division with 0.8279 on the held-out test set. Our system is a single 2B vision-language model that answers multiple-choice questions about ten-minute egocentric videos in one greedy forward pass; It is obtained by distilling the junior perception module of a tool-using agentic pipeline, not the agent itself into a small student, using teacher traces filtered to those that answered correctly. it reaches 89% of the accuracy of the large agentic pipeline using 1.1% of its parameters. This raises a 27.1% base model to 81.4% on our held-out questions. The 2B backbone has 2.2132B parameters and therefore over the divisional limit, to make the entry admissable we prune the multilingual embedding table from 248,320 to 143,469 rows, reaching 1.9985B with provably identical logits on retained rows.
[CV-152] Stable-MM-R1: Anchoring Multimodal Reasoning Dynamics via Entropy-Guided Stratification
链接: https://arxiv.org/abs/2609.07148
作者: Yimeng Ye,Shuang Chen,Wenxuan Huang,Manyuan Zhang,Kaituo Feng,Zhangquan Chen,Jiayu Chen,Yucheng Zhou,Yicheng Xiao,Zhiyuan Feng,Tianyu Shi
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 3 figures
Abstract:While Reinforcement Learning (RL) effectively incentivizes reasoning in Large Language Models, current pipelines are hindered by training instability and rapid entropy collapse. These limitations often stem from “Rollout Silencing” and low-quality gradient signals in standard sampling procedures. In this work, we propose a robust, data-centric framework to stabilize RL training. We first introduce Potential-Aware Query Mining (PAQM), which filters data dynamically to focus on the “Distillation Zone”—samples with high potential for capability elicitation. Furthermore, we present Hybrid Stratified Replay (HSR), a novel mechanism that restructures batches by stratifying rollouts based on Path Entropy, a rollout-level confidence proxy, and outcome reward. Within each optimization step, HSR reuses current-policy “Stability Anchors” and “Hard Negatives” to construct high-contrast optimization groups, then clears its buffers before the next step. This approach mitigates entropy collapse while improving the utilization of learning signals under limited compute. Our method outperforms strong baselines on complex reasoning tasks, offering a principled solution for stable and efficient RL fine-tuning.
[CV-153] Flow3D-OPD: Multi-Teacher On-Policy Distillation for 3D Geometry Generation with Flow-Matching Diffusion Transformer
链接: https://arxiv.org/abs/2609.07137
作者: Zhiwei Ning,Zhen Zhou,Puhua Jiang,Xintong Han,Gengming Zhang,Jie Yang,Zhonglong Zheng,Yuanjie Zheng,Wei Liu,Chunchao Guo
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent image-to-3D generation models built on flow-matching diffusion Transformers (DiT) can produce high-fidelity meshes, yet their post-training strategy remains largely unexplored. There exist several critical bottlenecks in reinforcement learning: the inherent difficulty of defining comprehensive rewards for 3D geometric quality, and the gradient interference that arises when jointly optimizing heterogeneous objectives. Inspired by the practicability of on-policy distillation (OPD) in large language models and image generation, we propose \textbfFlow3D-OPD, a two-stage post-training framework that introduces multi-teacher distillation into 3D geometry generation. In the first stage, we utilize the semi-policy to enhance the foundational capability of the pretrained model and then design an agentic verifier for 3D geometric quality evaluation. Based on the verifier, we could cultivate domain-specialized teacher models via direct preference optimization (DPO). In the second stage, we consolidate heterogeneous expertise into a unified student model through on-policy distillation with hard task-routing sampling and gradient accumulation, which could mitigate the gradient interference in joint optimization. Without relying on elaborate modifications, our straightforward yet effective design achieves consistent improvements across all geometric quality dimensions and surpasses all teacher models in the average metric. Extensive experiments demonstrate that our approach provides an effective paradigm for reinforcement learning in 3D generation.
[CV-154] NutriBench-Kitchen: Benchmarking Embodied AI for Nutrition Management ECCV
链接: https://arxiv.org/abs/2609.07135
作者: Yulin Wei,Xiangchen Wang,Jianhui Pan,Jinyu Xiao,Zheng Tan,Ruozai Tian,Guanhua Chen,Feng Zheng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 4 figures, ECCV
Abstract:An embodied kitchen assistant must do more than recognize food in isolated frames. It must track ingredient states over time and integrate visual observations with recipe and nutritional knowledge to support constraint-aware decision-making. We formalize this capability as \emphEmbodied Nutrition Management: perceiving nutrition-relevant events, maintaining a persistent food state, and using it for knowledge-grounded planning. Existing benchmarks evaluate static food understanding or embodied cooking actions, but do not measure whether an agent can continuously update and use nutrition-relevant states in dynamic kitchens. To fill this gap, we introduce \textbfNutriBench-Kitchen, a benchmark containing 1,500 manually verified question–answer pairs from 160 cooking videos. It covers five task families: Ingredient Entry, Memory Management, Recipe Query, Long-Term Planning, and Short-Term Planning, spanning food-state construction, maintenance, knowledge retrieval, and decision-making across different planning horizons. Evaluations of proprietary and open-source large vision-language models reveal a substantial gap from human performance, particularly in quantitative ingredient estimation, long-term state tracking, and reasoning under interacting constraints. We further introduce \textbfNutri-Vgent, a diagnostic long-video agent with separate episodic, food-state, and recipe memories. Its consistent improvements demonstrate the value of explicit state representations and structured memory for nutrition management. Together, NutriBench-Kitchen and Nutri-Vgent provide a testbed for studying persistent state tracking and knowledge-grounded reasoning in dynamic kitchens. Code is available at this https URL.
[CV-155] A Two-Stage Framework for Ego-Centric Key Object Identification via Object State Prediction ICRA2026
链接: https://arxiv.org/abs/2609.07125
作者: Shihong Ling,Yue Wan,Xiaowei Jia,Na Du
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 3 figures, 3 tables. Accepted and presented at the 2026 IEEE International Conference on Robotics and Automation (ICRA 2026)
Abstract:This paper presents a novel framework designed to enhance key object identification in autonomous driving. Existing methods primarily focus on either detecting objects independently or leveraging visual relationships, but they do not explicitly consider the ego vehicle’s perspective in determining object importance. To address this gap, we propose a structured approach that integrates a virtual ego-vehicle representation and a modular object state predictor, enabling a more accurate estimation of object behaviors relative to the ego-vehicle. Subsequently, our framework employs spatial-temporal reasoning to refine key object identification, prioritizing objects based on their states and relative spatial information rather than relying solely on visual relationships. Experimental results on real-world driving datasets demonstrate the effectiveness of our approach in accurately detecting critical objects in complex traffic environments.
[CV-156] Discovering Natural Transformation Vulnerabilities in Black-Box Vision Models
链接: https://arxiv.org/abs/2609.07110
作者: Dongsu Song,DaeYun GO,Jay Hoon Jung
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: under review
Abstract:Natural adversarial examples (NAEs) reveal that vision models can fail under realistic semantic changes beyond norm-bounded perturbations. However, generating NAEs in a black-box setting remains challenging because existing generative attacks often rely on surrogate models, learned attack priors, or costly query-based optimization, whereas the natural transformations that expose model vulnerabilities are unknown a priori. We propose \textbfAdversarial Scenario Attack (ASA), a query-based black-box framework that searches over natural-language editing scenarios using a multimodal language model and a modern text-guided generative editor. ASA jointly explores background, weather, and material/color transformations through winner–loser feedback, and uses a greedy explorer to compose only attack-improving scenarios. Across diverse ImageNet classifiers, ASA achieves substantially higher attack success rates than prior query-based generative attacks while requiring fewer victim-model queries and preserving competitive perceptual quality. Moreover, ASA exhibits both image-level and prompt-level transferability: its adversarial images remain effective across victim-model architectures, while its discovered editing scenarios can be reused across same-class images and, in some cases, across architectures. These findings suggest that vision models possess reusable vulnerabilities to natural transformation patterns, which ASA can efficiently identify in a black-box setting.
[CV-157] Ambient @ EgoProactive 2026 : Proactive Egocentric Assistance with Visually Grounded Supervision ECCV2026
链接: https://arxiv.org/abs/2609.07099
作者: Logesh Kumar Umapathi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Winning solution technical report for the EgoProactive track of the ECCV 2026 Wearable AI Grand Challenge
Abstract:We present our submission to the EgoProactive track of the ECCV 2026 Wearable AI Challenge, which ranked first in the large-model division and second in the =2B division. The task requires a wearable assistant to decide after each eight-second segment of egocentric video whether to intervene or remain silent. Our approach has two main components. First, we reformulate intervention timing as single-token classification. Rather than generating either interrupt utterance or silent , the model predicts yes or no, and we derive the decision from the renormalised probabilities of these two tokens. This formulation improved macro-F1 by 0.249 and G-mean by 0.30 over free-form generation. Second, because labelled data were limited to the released validation set, we generated additional supervision using a tool-calling video agent that inspects each clip and assigns intervention timestamps. A narration-only alternative was four times larger and ten times cheaper, but transferred worse than supervision from an unrelated real corpus, suggesting that visual grounding is more important than annotation volume for this task. Comments: Winning solution technical report for the EgoProactive track of the ECCV 2026 Wearable AI Grand Challenge Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.07099 [cs.CV] (or arXiv:2609.07099v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.07099 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-158] CASCADE: A Spatio-Temporal-Causal Reasoning Representation and Dataset for Driving
链接: https://arxiv.org/abs/2609.07094
作者: Jenny Schmalfuss,Despoina Paschalidou,Simon Gerstenecker,German Ros,Jose M. Alvarez
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reasoning is a promising route to the generalization that autonomous driving requires in the long tail, as it can infer how the elements of a scene depend on one another and traverse those dependencies to conclusions beyond what is observed. Yet it is hard to tell whether a model’s conclusions follow the scene’s dependencies, because no driving representation makes them explicit enough to test against. Text-based reasoning traces lack spatio-temporal grounding, spatio-temporal scene graphs lack causal links, and reasoning annotations at scale are increasingly model-generated and hard to verify. To this end, we introduce CASCADE (Causal Spatio-Temporal Analysis of Driving Environments), which encompasses two components: (1) a structured scene representation for reasoning in driving scenes and (2) a human-annotated dataset built on it. For every actor that interacts with the ego vehicle, the CASCADE representation records frame-by-frame, for as long as the actor is visible, what action is taken, where it occurs, and how it depends on the actions and states of others. The resulting structure makes reasoning predictions machine-verifiable: they can be scored against it element by element, without relying on (M)LLM judges. The CASCADE dataset provides comprehensive human annotations for 2,066 driving clips of the PhysicalAI dataset, with over 34K elements that establish the spatio-temporal and causal context of each scene, including 8.6K time-stamped ego and agent actions, 3.7K causal links and 2.9K potential influences, and 6.1K annotations for agents, objects, traffic lights, and environments. Being entirely human-annotated, CASCADE provides the reference for this comparison: benchmarking the reasoning abilities of Physical AI models, and verifying the quality of automatically generated reasoning labels. The CASCADE dataset is available at this https URL.
[CV-159] Conditioned Initialization for Attention ICLR2026
链接: https://arxiv.org/abs/2609.07086
作者: Hemanth Saratchandran,Simon Lucey
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: ICLR 2026
Abstract:Transformers are a dominant architecture in modern machine learning, powering applications across vision, language, and beyond. At the core of their success lies the attention layer, where the query, key, and value matrices determine how token dependencies are captured. While considerable work has focused on scaling and optimizing Transformers, comparatively little attention has been paid to how the weights of the queries, keys and values are initialized. Common practice relies on random initialization or alternatives such as mimetic initialization, which imitates weight patterns from converged models, and weight selection, which transfers weights from a teacher model. In this paper, we argue that initialization can introduce an optimization bias that fundamentally shapes training dynamics. We propose conditioned initialization, a principled scheme that initializes attention weights to improve the spectral properties of the attention layer. Theoretically, we show that conditioned initialization can potentially reduce the condition number of the attention Jacobian, leading to more stable optimization. Empirically, it accelerates convergence and improves generalization across diverse applications, highlighting conditioning as a critical yet underexplored area for advancing Transformer performance. Importantly, conditioned initialization is simple to apply and integrates seamlessly into a wide range of Transformer architectures.
[CV-160] Single Image to Textured 3D Object Generation in Frequency Domain: From Theory to Pipeline
链接: https://arxiv.org/abs/2609.07085
作者: Qisen Wang,Yifan Zhao,Jia Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Single-view 3D reconstruction, also known as image-to-3D, is a persistently challenging task due to the extreme lack of information. Recently, diffusion models pre-trained on large-scale datasets served as 2D priors are used to solve the ill-posed task but suffer from color deviation and view inconsistency, which can be curbed by using diffusion models fine-tuned with 3D annotated data served as 3D priors. However, 3D priors lack high-frequency details, which cannot be solved by direct complementation with 2D priors in spatial domain for introducing erroneous low-frequency 2D prior guidance. In this paper, we revisit the characteristics of different diffusion priors from the frequency perspective. Based on our observations, we theoretically present a unified framework of hybrid optimization using multiple diffusion priors in frequency domain. Under this framework, we further propose Morpheus3D, a pipeline of 3D object generation from any single unposed image in the wild. Morpheus3D enhances 3D prior with high-pass image-prompt 2D prior guidance to reconstruct high-quality 3D objects while effectively suppressing view inconsistency, low-frequency color deviation, and high-frequency lacking problems. Both quantitative and qualitative experiments on the public and our collected datasets with complex textures show that our method exhibits significant improvements in generation quality.
[CV-161] SupGRPO: Enhancing GRPO with Matching-based Online SFT for Text Spotting ECCV2026
链接: https://arxiv.org/abs/2609.07081
作者: Xudong Xie,Yuzhe Li,Jing Shi,Zhifei Zhang,Curtis Wigington,Zhaowen Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV 2026
Abstract:Text spotting requires both accurate text recognition and precise spatial localization. Current specialised spotters excel at predicting tight bounding boxes in natural scenes, but falter on complex or artistic text, whereas multimodal large language models (MLLMs) possess strong recognition capabilities yet remain weak at localisation. To equip the text spotter with general and powerful recognition capabilities and to maximize its localization ability, we explore two MLLM-based fine-tuning methods: Supervised Fine-Tuning (SFT) and reinforcement learning fine-tuning based on Group Relative Policy Optimisation (GRPO). An interesting finding is that SFT is less effective than GRPO at enhancing recognition, while GRPO is less effective than SFT at enhancing detection. To compensate for each other’s shortcomings, we introduce a joint training strategy, SupGRPO, which simultaneously optimizes the model using both SFT and GRPO. SupGRPO employs the specially designed reward functions and develops a matching-based online SFT applied solely to coordinate tokens. It both mitigates the reward sparsity problem of GRPO and avoids the instance order dependency problem of SFT. To evaluate particularly challenging cases, we curate ATS, a dataset for artistic text spotting. Experiments demonstrate that SupGRPO improves both text recognition and detection, and attains superior performance. Our code and dataset will be released at this https URL.
[CV-162] One for All: Generalist Foundation Model for Cross-Sensor Skeleton Representation Learning
链接: https://arxiv.org/abs/2609.07078
作者: Jeonghyeok Do,Yun Chen,Munchurl Kim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Please visit our project page at this https URL
Abstract:For learning generalizable motion representations from large-scale unlabeled data, Self-supervised learning (SSL) has become a widely adopted methodology. However, existing approaches are primarily limited by the inherent heterogeneity of skeleton data—characterized by varying joint counts, indexing protocols, and topological structures across different sensors—which typically necessitates training separate, sensor-specific, or even entirely dataset-specific models. To overcome this, we introduce SOfA (Skeleton One for All), the first generalist foundation model designed to achieve sensor-unified skeleton representation learning across diverse sensors. To accommodate the dimensional gap caused by varying joint counts, we introduce a fixed-size set of learnable Canonical Joint Slots, acting as a universal vessel that seamlessly accommodates arbitrary skeletal topologies. SOfA fills these slots via an attention mechanism that dynamically aggregates skeletal information from sensor-specific inputs. Furthermore, we resolve joint index misalignment between various sensors by introducing a Semantic Joint Embedding derived from a pre-trained text encoder, rather than relying on absolute positional embeddings. To validate our approach, we standardized ten 3D skeleton datasets for unified training. Extensive experiments demonstrate that SOfA can serve as a truly universal encoder, achieving state-of-the-art (SOTA) performance across a wide range of downstream tasks and sensor types, often outperforming dataset-specific specialist models with a single foundation model.
[CV-163] Detect Anything in Graphic Design: Element-Level Rewards for Autoregressive Detection
链接: https://arxiv.org/abs/2609.07072
作者: Jiangning Zhu,Bowen Li,Shenyu Qiao,Yima Gu,Zhao Zhang,Yuhui Yuan,Shixia Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Graphic designs, such as posters, advertisements, and infographics, are an important medium for communicating information and shaping understanding. Unlike natural images, they consist of layered elements with explicit compositional order. However, existing object detection models treat these elements as an unordered set, leaving compositional order unexploited. To address this limitation, we present Detect Anything in Graphic Design (DAD), a model that formulates graphic design detection as compositional deconstruction. It decodes elements in compositional order, using lower-layer elements to better detect higher-layer ones. The key feature of DAD is amodal detection, which predicts the full bounding box of each element, including regions occluded by elements placed above it. Building on this formulation, we propose Element Relative Policy Optimization (EleRPO), which extends GRPO from sequence-level supervision to element-level optimization. EleRPO provides fine-grained training signals that capture how each detected element contributes to overall detection quality, and works synergistically with compositional order to improve detection performance. To support training and evaluation, we build a dataset of 10 million graphic designs. Experiments show that DAD outperforms all baselines and achieves human-level performance in amodal detection, supporting effective image-to-layer decomposition. EleRPO consistently improves over GRPO across nine detection benchmarks.
[CV-164] Continuous Token-Level Spatio-Temporal Context Modeling for Visual Object Tracking
链接: https://arxiv.org/abs/2609.07070
作者: Ding Xia,Meiqin Liu,Jing Zhou,Jian Lan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Spatio-temporal context has become increasingly crucial for visual tracking. However, most existing approaches extract spatio-temporal cues via discrete sampling strategies, which inherently deviate from the continuity of spatio-temporal context, thereby deteriorating tracking performance. To address this challenge, we propose TLCTrack, a novel tracking framework that models token-level spatio-temporal context through continuously updated salient tokens, enabling more accurate target representation. Specifically, TLCTrack incorporates three components: Masked Unidirectional Attention (MUA), Spatial Salient Token Collection (SSTC), and Temporal Salient Token Bank (TSTB) modules. By explicitly integrating spatio-temporal context, MUA extracts discriminative targetaware spatial features in the search region. To avoid the negative impact of background on feature learning, SSTC progressively suppresses background interference, thereby enhancing target spatial representation. Finally, TSTB captures high-quality spatio-temporal information through continuous salient token updates. Extensive experiments on five benchmarks demonstrate that our method achieves superior performance over state-of-the-art trackers. Code and models are available at this https URL.
[CV-165] SpatialBlock: Enhancing Spatial Intelligence in LVLMs via Synthetic Block-Stacking Problem
链接: https://arxiv.org/abs/2609.07064
作者: Soohyun Ryu,Sohee Kim,Eunho Yang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Large Vision-Language Models (LVLMs) have achieved strong performance on diverse visual tasks, yet their ability to reconstruct and reason about the 3D structure of the scene depicted in 2D images – referred to as spatial intelligence – remains limited. Existing approaches attempt to address this gap by using real-scene spatial question answering datasets that require dense geometric annotations. However, constructing such labels is costly, time-consuming, and often noisy due to reliance on external perception modules. In this work, we propose a novel paradigm inspired by human cognitive development: learning foundational spatial skills through structured block-manipulation tasks. We introduce SpatialBlock-15k, a synthetic dataset of 15,000 block-stacking problems covering 3D-to-2D projection, viewpoint transformation, and structural combination. The dataset further incorporates controlled color modulation as visual cues to encourage anchor-based reasoning in visually complex conditions. Experiments demonstrate that LVLMs trained on our dataset through either direct answering or reasoning-based prediction significantly outperform baselines and generalize to real-world spatial tasks, despite the dataset’s synthetic and compact nature. Code and data are available at this https URL.
[CV-166] CNsEMD: An Expert-Annotated Multi-Field-Strength MRI Dataset and a Hyperspherical Manifold Network for Multimodal Cranial Nerve Parcellation
链接: https://arxiv.org/abs/2609.07058
作者: Lei Xie,Junxiong Huang,Guoqiang Xie,Jiawei Zhang,Jiahao Huang,Tianling Lyu,Ye Wu,Mingchu Li,Shoujun Yu,Shanshan Wang,Qingrun Zeng,Yuanjing Feng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Cranial nerves (CNs) play essential roles in sensory, motor, and autonomic functions. Accurate CN parcellation from multimodal magnetic resonance imaging (MRI) is crucial for neuroanatomical analysis and neurosurgical planning. However, accurate CN parcellation remains extremely challenging because CNs are very small, exhibit low image contrast, and have slender tubular morphologies and complex anatomical trajectories. Moreover, the lack of publicly available, expert-annotated datasets has impeded the development and fair benchmarking of learning-based CN analysis methods. In this work, we introduce CNsEMD, an expert-annotated multimodal dataset for CN parcellation. It comprises data from 202 subjects acquired on 3T, 5T, and 7T MRI scanners. We further propose the projective hyperspherical manifold network (PHM-Net), which learns cross-modal representations by capturing angular relationships in a shared hyperspherical embedding space. Rather than performing multimodal fusion in Euclidean space, the proposed Hyperspherical cross-modal interaction (HCI) module enables bidirectional feature exchange between T1-weighted (T1w) and direction-encoded color (DEC) representations on a unit hypersphere. The Magnitude-preserving projective hyperspherical orientation representation (PHOR) captures the axial nature of DEC orientations while preserving diffusion magnitude. The hyperspherical prototype segmentation head (HPSH) further extends angular similarity to voxel-wise classification using normalized voxel embeddings and learnable class prototypes. Extensive experimental results on the CNsEMD dataset demonstrate the effectiveness of our PHM-Net against state-of-the-art methods. CNsEMD establishes a reproducible benchmark for multimodal CN imaging, while PHM-Net provides a geometry-consistent solution for CN parcellation across diverse MRI acquisitions.
[CV-167] Large Discrete Policy: Advancing Explicit Behavior Modeling with Stochastic Iterative Scoring
链接: https://arxiv.org/abs/2609.07049
作者: Zhenxin Li,Nadine Chang,Xinglong Sun,Jingde Chen,Wenhao Yao,Zi Wang,Maying Shen,Yu-Gang Jiang,Zuxuan Wu,Shiyi Lan,Jose M. Alvarez
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Behavior policies are often formulated as continuous generative models, whose iterative denoising processes are expressive but difficult to interpret and prone to producing implausible actions. We propose the Large Discrete Policy (LDiP), a fully discrete behavior modeling framework that selects actions from a large vocabulary of physically plausible candidates. Rather than perturbing actions, LDiP improves expressivity through stochastic iterative scoring: it progressively re-scores and prunes candidates with score-space stochasticity, enabling fine-grained ranking and exploration among plausible actions while preserving an explicit decision process. Across end-to-end planning, closed-loop driving, robotic manipulation, and vision-language-action settings, LDiP consistently outperforms strong discrete and continuous baselines in autonomous driving, and exceeds or matches continuous generative policies in robotic manipulation. These results show that discrete policies, when equipped with effective scoring mechanisms, offer an expressive, plausible, and interpretable alternative for behavior modeling. Project website: this https URL.
[CV-168] FreqDoor: A Hidden Trojan in the Frequency Domain for Backdoor Attacks on Vision-Language Models
链接: https://arxiv.org/abs/2609.07048
作者: Yasir Arafat Prodhan,Sadad Hasan,Mohammed Imamul Hassan Bhuiyan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 13 pages, 5 figures
Abstract:Vision-language models (VLMs) have recently shown excellent progress in open-ended image-to-text generation. However, their multimodal nature makes them persistently vulnerable to backdoor attacks. Existing backdoor triggers for VLMs are either spatial, textual, or bimodal, which may yield localized or recognizable trigger patterns. In this work, we explore a different attack surface and propose \ textsc FreqDoor, a training-time backdoor attack that implants triggers in the frequency domain. \ textsc FreqDoor mixes amplitude-spectrum components from a trigger-source image selectively while preserving the phase of a clean image to generate a spatially distributed and visually imperceptible trigger without modifying the textual input. We evaluate the attack on BLIP-2, InstructBLIP, and LLaVA for image captioning and visual question answering. On Flickr8k, \ textsc FreqDoor achieves attack success rates of 99.6% , 99.8% , and 98.4% on the three models, respectively, while preserving the semantic quality of the generated captions. On VQAv2, the corresponding attack success rates are 99.6% , 92.4% , and 79.6% .
[CV-169] LoGAN: Multilingual Font Localization with Generative Agents ECCV2026
链接: https://arxiv.org/abs/2609.07029
作者: Zhuoning Yuan,Ta-Ying Cheng,Benjamin Klein
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by ECCV2026
Abstract:Localizing a font into new languages is a highly intricate task requiring precise design adaptation of glyphs, color/texture, and spacing/kerning, from source to target languages. Most existing methods focus on single glyph generation with limited capability in handling multilingual font rendering. In this work, we propose LoGAN, a VLM-based agentic framework for few-shot multilingual font localization, which takes in a small number of individual glyphs from a font or letters from a logo and uses them to generate complete character sets in other languages. LoGAN breaks down this task into multiple components: a glyph-level diffusion model, a style finetuning module, a spacing and kerning transfer algorithm, and a texture expansion model, with a VLM agent coordinator. LoGAN achieves broad language coverage for font localization with various styles, including Chinese/Korean/Japanese (CJK). We evaluate our approach on both font and real-world logo datasets spanning more than 27 languages and compare it against both specialized font generation and state-of-the-art image editing models with strong text rendering capabilities (e.g., FLUX, Nano-Banana). Our approach yields higher glyph fidelity while maintaining better style, texture, and kerning consistency according to both quantitative and qualitative evaluations.
[CV-170] ARNAI: Artifact Removal Network based on Autoencoding and Inpainting for Robust Spinal Image Segmentation and Measurement
链接: https://arxiv.org/abs/2609.07013
作者: Sang-Jin Park,Jinyoung Choi,Seokwon Kim,Seungeon Song,Insu Park,Dougho Park,Taeyeon Kim,Youjin Lee,Donghoon Yang,Jaeman Cho,Joongwon Yang,Mansu Kim,Heumdai Kwon,Hong Gyu Baek,Dae Chul Cho,Injung Kim
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 12 pages 4 figures
Abstract:Purpose: This study aims to develop an AI framework applicable for postoperative imaging for automated measurement of spinopelvic parameters on radiographs with robustness to the presence of spinal implants. Materials and Methods: We retrospectively reviewed lateral lumbar spine radiographs from two institutions (Internal: January 2017–December 2024; External: October 2021–September 2025). We developed the Restore, Segment, and Measure (RSM) framework, incorporating a novel Artifact Removal Network based on Autoencoding and Inpainting (ARNAI) to mitigate implant-related artifacts in postoperative radiographs. Segmentation and spinopelvic parameter (PT, LL, SS, SCA) measurement performance were assessed using Wilcoxon signed-rank tests and intraclass correlation coefficients. Results: When ARNAI was added to a recent Transformer-based segmentation model, FCBFormer, the mean DSC increased to 0.870 from 0.814, with marked gains at L3–L5 and smaller improvements at L1–L2. On 91 radiographs with implants, the mean L4–L5 segmental Cobb angle error decreased to 4.7 ° from 15.6–16.2 °, an average error reduction of 70%. The ICC for L4–L5 segmental Cobb angle improved to 0.54 (Rater 1) and 0.59 (Rater 2) from 0.18, and ICCs for pelvic tilt, lumbar lordosis, and sacral slope all exceeded 0.70. The improvement in L4–L5 segmental Cobb angle error was statistically significant in the internal implant-containing cohort after correction for multiple comparisons. Conclusion: The proposed RSM framework improved automated spinopelvic parameter measurement in implant-containing postoperative radiographs. By mitigating implant-related artifacts, ARNAI improved segmentation and downstream measurement accuracy, with the greatest benefit observed for L4–L5 segmental Cobb angle estimation, where the mean error was reduced by approximately 70%. Comments: 12 pages 4 figures Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.07013 [cs.CV] (or arXiv:2609.07013v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.07013 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jinyoung Choi [view email] [v1] Mon, 7 Sep 2026 04:06:50 UTC (4,667 KB)
[CV-171] Fine-Grained Visual Preprocessing and Dual-Stream Temporal Modeling for Multimodal Sentiment Analysis on Social Media
链接: https://arxiv.org/abs/2609.07010
作者: Su Li,Yigong Zhang,Lei Xiong,Chune Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 35 pages, 10 figures
Abstract:Multimodal sentiment analysis often remains text-dominant due to raw-video noise and insufficient temporal modeling. Using CH-SIMS v2.0S, this study proposes three improvements: the NAPS pipeline—a seven-stage system integrating face tracking,identity embedding, and normalized lip-motion analysis to reduce visual noise;DS-TANet, combining an EfficientNetB2 static stream, RAFT optical-flow motion stream, motion-guided attention, and Bi-GRU temporal modeling; and DS-TAFNet, fusing visual and MacBERT-Base textual representations via concatenation fusion. With NAPS, the static visual baseline achieves 80.98% Macro F1, comparable to the text baseline of 80.55%; DS-TANet improves visual Macro F1 to 82.58%;and DS-TAFNet achieves 87.49% accuracy and 87.48% Macro F1. These results demonstrate that improving visual input quality and temporal representation is more effective than increasing fusion complexity under limited-data conditions.
[CV-172] NeuCME: Toward Dynamic Multimodal Continual Learning via Neural Combinatorics of Multiple Experts
链接: https://arxiv.org/abs/2609.07009
作者: Kai Guo,Chuanbin Liu,Peng Hu,Hao Wang,Xi Peng
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 5 figures
Abstract:Multimodal continual learning has recently shown great potential for developing agents with human-like intelligence by continuously learning new tasks across multiple modalities. However, existing methods typically assume that the set of modalities per task is predefined and fixed. In this paper, we investigate a more realistic learning setting, referred to as dynamic multimodal continual learning, in which the set of modalities may vary across tasks rather than remaining fixed. This setting involves two primary challenges: (i) spatio-temporal catastrophic forgetting and (ii) adaptive multimodal fusion. To address these challenges, we propose NeuCME (as shorthand for \textbfNeural \textbfCombinatorics of \textbfMultiple \textbfExperts), a novel framework designed to effectively learn and integrate knowledge across tasks with varying modalities. The proposed NeuCME model comprises three key components, namely modality-combinational rehearsal, multi-gated mixture-of-experts, and task relevance-guided distillation. Furthermore, we formulate an evaluation metric to quantify the dynamism of task sequences and then set up a comprehensive benchmark with different degrees of dynamism. Extensive experiments using four real-world datasets demonstrate that the proposed NeuCME outperforms state-of-the-art methods markedly.
[CV-173] GIFT: Goal-Injected Fine-Tuning for Efficient Manipulation Policy Adaptation EMNLP2026
链接: https://arxiv.org/abs/2609.07006
作者: Xiaoyuan Fang,Shuo Feng,Yuxuan Wang,Enhua Cheng,Peng Zhou,Piji Li
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 20 pages, 12 figures, accepted by EMNLP2026
Abstract:Compared with relying solely on initial observations and language instructions, predicting goal images with generative models as high-level visual guidance can significantly enhance the robustness of Vision-Language-Action (VLA) models. However, most existing foundation models have not systematically incorporated goal image conditioning due to the high computational training cost. To this end, we propose Goal-Injected Fine-Tuning (GIFT), a lightweight and efficient fine-tuning framework that seamlessly integrates generated goal images into multiple representative pretrained VLA models. Our approach introduces goal image features into observations via a zero-initialized convolution which progressively grows parameters from zero and prevents harmful noise from disrupting the pretrained policy during fine-tuning. As training proceeds, goal information is gradually incorporated, enabling efficient goal understanding without disrupting model stability. We further introduce a refined image editing method to generate semantically and visually consistent goal images from initial observations and task instructions. Experiments show that goal-aware VLA models achieve substantial performance gains across tasks: with only a single epoch of fine-tuning, GIFT outperforms the base model by 6.0% and 13.4% on two SIMPLER settings, and by 4.7% on LIBERO, demonstrating both efficiency and effectiveness.
[CV-174] CGSM: Concept-Guided Segmentation Model for Precise Pulmonary Lesion Delineation
链接: https://arxiv.org/abs/2609.07004
作者: Changheng Lin(1),Wenjie Zhang(1),Yushan Lu(1),Xinyue Yan(1),Xiao Jia(1),Wei Zhang(1) ((1) Shandong University, China)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Changheng Lin and Wenjie Zhang contributed equally to this work. Xiao Jia and Wei Zhang are corresponding authors
Abstract:Accurate segmentation of pulmonary lesions is essential for effective clinical diagnosis and treatment strategies. Existing segmentation approaches often lack task-specific semantic guidance, as text-based annotations typically offer coarse localization of lesions, leading to inadequate delineation of lesion boundaries and poor performance on small-scale lesions. To address this, we propose CGSM, a Concept-Guided Segmentation Model that integrates LLM-generated and clinically reviewed concepts into the segmentation process. Specifically, we design a Concept-Visual Alignment Module (CVAM) to activate relevant tokens within the concepts that align with visual features, enhancing the interaction between textual and visual information. In addition, we introduce a Concept Modulated Decoder (CM-Decoder), which uses concepts from CVAM as modulation signals to facilitate the adaptive fusion of image and text features, improving the segmentation accuracy. Extensive experiments on two public datasets show that CGSM achieves state-of-the-art performance, with results of 91.59% Dice and 84.49% mIoU on the QaTa-COV19 dataset, demonstrating its effectiveness in pulmonary lesion segmentation.
[CV-175] AV-SafetyBench: A Safety Benchmark for Text-to-Audio-Video Generation
链接: https://arxiv.org/abs/2609.06991
作者: Suah Choi,Tae-Young Lee,Gyeong-Moon Park
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 34 pages, 19 figures, 17 tables
Abstract:Recent text-to-audio-video (T2AV) models jointly generate video, speech, sound effects, and ambience from a single text prompt. This capability poses new challenges for safety evaluation, as unsafe content may be conveyed through the audio track or arise only when the visual and audio tracks are interpreted jointly. Existing safety benchmarks largely focus on either generated video or generated audio in isolation and are therefore not designed to capture these risks. To close this gap, we introduce AV-SafetyBench, the first safety benchmark developed specifically for T2AV generation. AV-SafetyBench comprises a four-axis, 13-category taxonomy and 5,200 manually reviewed prompts that specify visual scenes, speech, and non-speech audio. Our evaluation protocol assesses each output under three views: Full-AV, Video-Only, and Audio-Only. It then uses the Video-Only and Audio-Only judgments to assign Full-AV unsafe outputs to one of four risk sources: Video-Only, Audio-Only, AV-Both, or AV-Joint. We evaluate five open-source T2AV models and validate the automated Full-AV judgments against human annotations. Across the five models, Full-AV Unsafe Rates range from 25.1% to 49.4%. Beyond these aggregate rates, risk-source analysis reveals that, for four of the five models, Audio-Only and AV-Joint cases-unsafe outputs missed by video-only evaluation-account for 41.6-48.3% of Full-AV unsafe outputs for which a risk source could be assigned. In the Cross-Modal Harm Emergence category, AV-Joint accounts for 87.5% of unsafe outputs withan assigned risk source. Together, these findings demonstrate the value of AV-SafetyBench for evaluating T2AV safety across the visual and audio modalities and their interaction.
[CV-176] AnomalyCraft-700K: Component-Level Controllable and Verifiable Synthetic Anomalies for Fine-Grained Video Anomaly Understanding
链接: https://arxiv.org/abs/2609.06978
作者: Yuzhou Long,Haodong Zhang,Yunpeng Yang,Peng Wu,Guansong Pang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages,2 figures
Abstract:Progress in video anomaly understanding (VAU) has long been limited by inherent deficiencies of real-world anomaly videos, which are hard to collect and offer little control over their content. Synthetic anomaly approaches partially alleviate data scarcity, yet their generation remains largely controlled at the category or prompt level. They also lack component-level verification of video-text consistency and provide insufficient hard normal samples near the normal-anomaly boundary. To address this, we present AnomalyCraft-700K, a component-controllable synthetic anomaly dataset for fine-grained VAU, containing over 40K videos and over 700K task-level textual annotations. From fine-grained semantic components and a progressive three-stage pipeline, we craft anomaly events that are richly detailed, semantically controlled, and temporally structured, and additionally construct per-category hard normal samples to prompt the model to discriminate based on anomaly semantics rather than surface visual cues. Moreover, using the components as verification units, AnomalyCraft-700K further performs component-wise correction of video-text discrepancies introduced during generation, providing reliable annotations with verified cross-modal alignment for six tasks that progress from anomaly detection, through anomaly retrieval and captioning, to fine-grained anomaly reasoning. Evaluations of widely used methods under both traditional and MLLM-based protocols demonstrate that AnomalyCraft-700K serves as an effective source of supervision, from anomaly detection to fine-grained anomaly understanding.
[CV-177] Re-calibrated Contrastive Loss for Transformation-Aware Prompt Conditioning in Vision-Language Models BMVC
链接: https://arxiv.org/abs/2609.06967
作者: Seungmin Oh,Seunghun Kang,Jongbin Ryu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by British Machine Vision Conference (BMVC) 2026
Abstract:Ensuring effective transfer learning for vision-language models without compromising their generalization performance is crucial. However, many existing methods overlook data characteristics and simply reuse the training strategies adopted during pre-training. Specifically, they treat same-class samples as distinct instances and transform images independently of their paired text prompts, which makes model learning more difficult. We address these limitations through transformation-aware prompt conditioning and a re-calibrated contrastive loss. Fixed text descriptors identify the transformations applied to paired images, providing transformation-level consistency without altering class semantics. This design aligns the image and text branches at the transformation level, enabling richer representations while preserving the models’ ability to generalize. In addition, our loss function mitigates positive-gradient dilution in soft-target cross-entropy when each anchor has multiple valid positives. During transfer, our approach treats same-class samples as positives rather than distinct instances, enabling the model to learn domain-specific features more effectively. Experiments across distribution shift, transfer learning, and few-shot settings demonstrate consistent improvements over existing approaches. Source code for our method is available at this https URL.
[CV-178] DPSF-Net: A Dual-Prior Spatial-Frequency Network for Real-World Remote Sensing Image Dehazing
链接: https://arxiv.org/abs/2609.06962
作者: Mei Lu,Shangliang Shao,Shanliang Yao
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Real-world remote sensing image dehazing (RSID) remains challenging because atmospheric scattering, spatially non-uniform haze and colour distortion jointly degrade structural and spectral information. Most deep learning methods rely on RGB inputs and spatial-domain feature extraction, which limits their ability to separate global background haze from local surface details. Here, we propose DPSF-Net, a dual-prior spatial-frequency network built on MCAF-Net for real-world RSID. The network uses hazy RGB images and dark channel prior (DCP) maps as joint inputs, allowing physical degradation cues to guide end-to-end feature learning. A spatial-frequency residual interaction block introduces a FourierUnit branch into multi-directional spatial interaction to model large-scale haze components. A prior-guided feature attention module adaptively fuses prior and attention features to reduce colour shift and structural distortion. A selective kernel complementary fusion module screens multi-scale skip features through bidirectional residual complementary gating and selective kernel fusion. Extensive experiments demonstrate that DPSF-Net achieves state-of-the-art performance on the real-world RRSHID remote sensing image dehazing benchmark and remains competitive across multiple synthetic datasets. Moreover, the proposed method strikes a favourable balance among restoration quality, parameter count and computational complexity, supporting the effectiveness of dual-prior spatial-frequency modelling.
[CV-179] MSSP: Multi-Scale Spatially-Constrained Partition for Unsupervised Semantic Segmentation of 3D Point Clouds
链接: https://arxiv.org/abs/2609.06959
作者: Zhenghao Zhang,Xinjie Wang,Wei Wang,Jun Zhang,Hanyun Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by the International Conference on Image and Graphics (ICIG 2025), Springer LNCS
Abstract:3D point cloud semantic segmentation is essential for real-world spatial understanding, yet the prohibitive cost of human annotations motivates unsupervised approaches that require no labels. Existing superpoint-based methods typically rely on spectral analysis at a fixed granularity, failing to capture the hierarchical semantic structures inherent in complex indoor scenes. To bridge this gap, we present a Multi-Scale Spatially-Constrained Partition (MSSP) framework that combines multi-scale spectral analysis with spatially-constrained clustering. Multi-scale spectral analysis constructs enriched superpoint descriptors across multiple clustering granularities; however, the resulting high-dimensional feature space calls for a structural prior to translate into cleaner segmentation. Spatially-constrained clustering supplies this prior by restricting superpoint merging to physically adjacent regions, imposing the spatial coherence needed for multi-scale features to be effective. Extensive experiments on S3DIS and ScanNet show that MSSP achieves the best mIoU among unsupervised methods on the main benchmarks, with particularly significant gains on S3DIS. Notably, our ablation reveals a regularize-then-enrich interaction: multi-scale features alone do not improve final segmentation, yet become highly effective when coupled with spatial regularization, underscoring that spatial coherence is aprerequisite for multi-scale representations in superpoint clustering.
[CV-180] Joint-Conditioned Stereo Surface Reasoning for Interaction Field Estimation
链接: https://arxiv.org/abs/2609.06955
作者: Yanlin Jin,Yifan Yang,Bowen Yang,Kai Zhu
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Predicting hand–object interaction fields requires locating the nearest object-surface point for each hand joint, often from small and partially occluded image regions. We view this task as joint-conditioned surface-endpoint estimation: each joint has its own nearest endpoint, while endpoints from the same hand can draw on shared local surface evidence. This structure motivates Joint-Conditioned Stereo Surface Reasoning (JSSR). A temporal-stereo network jointly predicts 3D joints, a direct interaction field, and per-view endpoint evidence. Calibrated candidate search evaluates endpoint hypotheses using joint-specific image compatibility and cross-view correspondence. A hand-shared candidate support lets joints draw on common surface evidence, and a learned residual gate controls the geometric correction when observations are ambiguous. Our system built on this method ranked third on the SHOW3D Interaction Field Challenge leaderboard.
[CV-181] PRG-Fusion: Orchestrating Generative Priors with Reconstruction Evidence for Driving View Synthesis
链接: https://arxiv.org/abs/2609.06948
作者: Sipeng He,Jialei Chen,Zhen Fang,Dongchun Ren,Feng Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Synthesizing photorealistic driving videos along specified trajectories is essential for scalable closed-loop simulation. Reconstruction-based methods leverage neural rendering to synthesize geometrically consistent views, but often exhibit diverse artifacts and missing content when the viewpoint deviates from the training trajectory. In contrast, generative models can synthesize realistic views along arbitrary trajectories from vehicle sensor data, yet often struggle to maintain temporal and geometric consistency across frames. To combine the strengths of both, we propose PRG-Fusion, a framework for driving view synthesis that uses reconstruction evidence to orchestrate generative priors across regions. Specifically, we extract region-wise degradation evidence from reconstructed driving scenes and convert it into Preserve, Repair, and Generate (PRG) labels. At inference, these labels serve as a unified routing policy for region-aware spatiotemporal synthesis, orchestrating 3DGS appearance preservation, LiDAR-guided structural correction, and video-prior-driven content completion across Preserve, Repair, and Generate regions, respectively. We then follow a two-stage training paradigm, first establish geometric control from sparse LiDAR projections and subsequently learning appearance control from dense 3DGS renderings. Extensive experiments on Waymo demonstrate that PRG-Fusion achieves state-of-the-art overall performance in novel trajectory video synthesis, with superior visual quality and geometric fidelity while maintaining competitive view consistency under large trajectory shifts.
[CV-182] CARDEA: Auditable Reasoning Grounded in Spatial Evidence for End-to-End Coronary Angiography Interpretation
链接: https://arxiv.org/abs/2609.06931
作者: Jia-Jen Lee,Shih-Yen Hou,Kee Koon Ng,Wei-Chun Wang,Shih-Sheng Chang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 25 pages, 3 figures and 4 tables in the main text; supplementary appendix with 5 figures and 1 table. Model weights: this https URL . Code: this https URL
Abstract:Invasive coronary angiography (CAG) is the gold standard for diagnosing coronary artery disease, but interpretation varies substantially among observers. Existing AI systems can improve consistency but lack auditable decision processes and are limited in comprehensive open-ended assessment, undermining clinician trust and clinical adoption readiness. We developed CARDEA, a unified large vision-language model that serves as the inference core of a CAG pipeline. It was trained solely on public datasets and closed-ended tasks in three stages: visual feature alignment, a self-distilled Chain-of-Box (CoB) cold start, and reinforcement learning with verifiable rewards (RLVR) with a CoB reward encouraging bounding-box use in the reasoning trace. We assessed its two study-level diagnoses, dominance classification and complexity assessment, against a dedicated classifier and two interventional cardiologists. Report generation was excluded from training and evaluated zero-shot across stages on an external cohort using vessel-severity macro- F_1 . CARDEA trailed the classifier on in-distribution dominance but drew level under domain shift (accuracy, 0.91 [95% confidence interval (CI), 0.86 to 0.95]) and was comparable to the cardiologists on complexity assessment (accuracy, 0.90 [CI, 0.82 to 0.97]). Only RLVR improved zero-shot report generation, raising its vessel-severity macro- F_1 (0.686 [CI, 0.664 to 0.707]) above the untuned base model (0.513) and over twice the always-normal floor (0.312). CARDEA runs an end-to-end CAG pipeline from raw multi-view videos through keyframe selection to study-level diagnosis while exposing auditable spatial evidence behind its conclusions. RLVR on verifiable closed-ended tasks surfaced open-ended reporting ability that supervised imitation did not. Clinical use requires prospective validation against expert cardiologists.
[CV-183] Sub-Pixel Affine Registration of Space Debris Images via the Radon Point Spread Function
链接: https://arxiv.org/abs/2609.06929
作者: Shenshen Luan,Miaomiao Tian,Shuai Jiang,Yan Yang,Shuguo Xie,Zezhou Sun
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注:
Abstract:Inter-frame affine misalignment caused by platform jitter and attitude adjustments poses a fundamental challenge for multi-frame analysis of point targets in optical surveillance. Conventional registration methods rely on spatial intensity correlations or distinctive image features, both of which are largely absent in low-signal-to-noise-ratio point target imagery. We introduce the Radon Point Spread Function (RPSF) to characterize point targets in the Radon-transformed domain, and derive a closed-form framework that jointly estimates inter-frame translation and rotation from as few as four scalar RPSF samples per frame pair. The method requires no iterative optimization, feature extraction or interpolation, which is suitable for resource-constrained onboard processing. Simulation results confirm sub-pixel translation accuracy and a mean rotation error of 0.2556° at 1° Radon angular resolution. Validation on five real space debris datasets including both ground-based and in-orbit observations yields a mean calibration error below 0.5 pixels, substantially exceeding the precision required for reliable multi-frame processing.
[CV-184] Emo-DVS: A Multimodal Benchmark for Privacy-Aware Emotion Recognition with Event Cameras
链接: https://arxiv.org/abs/2609.06928
作者: Jiaqi Chen,Qinfu Xu,Hao Zhuang,Liyuan Pan
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 8 pages,5 figures,Accepted to ACM Multimedia 2026
Abstract:Emotion analysis is a fundamental task in computer vision, but its practical deployment remains constrained by the privacy risks inherent to conventional RGB cameras. Bio-inspired event cameras present a promising hardware-level solution because they capture asynchronous brightness changes, thereby reducing exposure of facial identity details while leveraging high dynamic range for robust perception under challenging illumination conditions. Despite these advantages, existing event-based methods struggle in complex real-world settings due to limited dataset scales, simple acquisition conditions, and reliance on single-modality visual cues. To address these, we establish a challenging tri-modal benchmark with event, audio, and text modalities and propose the Information-Guided Gated Fusion (IGF) framework, which first pre-trains an event encoder on the FAU subset of Emo-DVS to capture fine-grained facial dynamics, then employs adaptive modality gating to suppress modality-specific noise, and finally leverages mutual information maximization to align robust cross-modal representations. To alleviate data scarcity, we introduce Emo-DVS, the first large-scale event-based emotion analysis dataset, which couples dynamic illumination with the Facial Action Unit (FAU) subset and emotion subset. Extensive experiments demonstrate that IGF achieves state-of-the-art performance.
[CV-185] BEFORE THE FLIP: Measuring Hidden Score Shifts In Quantized Vision Language Models Before The Answer Changes for Visual Question Answering NEURIPS2026
链接: https://arxiv.org/abs/2609.06922
作者: Sourajit Saha,Shubhashis Roy Dipta,Shaswati Saha,Nobin Sarwar,Yuxuan Jiang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Under Review at VLM4RWD @ NeurIPS 2026
Abstract:Quantization makes vision language models (VLMs) cheaper to store and run by using fewer bits to represent their weights. While unchanged answers on visual question answering (VQA) after compression are an expected behavior, they can still hide changes in the underlying scores (log probabilities). For example, a model may still answer yes after compression, even as the score gap between yes and no shrinks. We introduce BEFORE THE FLIP to measure these hidden changes. Our method compares the score change caused by compression with the change caused by replacing the image’s internal representations, or image tokens, with one fixed average token. We then increase the precision of one weight group at a time to identify where extra bits help, and test whether choosing different groups for each question offers benefits beyond shuffled controls. Among 8,277 LLaVA questions where image token replacement measurably affects the scores, 4-bit compression shifts the yes or no score gap farther toward the replacement output than 8-bit compression. Qwen shows the same pattern, but with a smaller difference. Yet only 265 of 9,000 LLaVA answers change at 4 bits. In a separate study of 1,024 calibration questions, choosing weight groups separately for each question does not outperform both shuffled controls at any tested storage budget. These findings show that compression can alter the scores behind unchanged answers, but do not establish a reliable benefit from adjusting precision for each question.
[CV-186] Novel Methods for Catheter and Guidewire Segmentation in X-ray Fluoroscopy under a Federated Learning Setting
链接: https://arxiv.org/abs/2609.06876
作者: Chayun Kongtongvattana
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: PhD Thesis, University of Liverpool. 163 pages
Abstract:Endovascular procedures rely on real-time manipulation of thin instruments, catheters and guidewires, under X-ray fluoroscopy guidance, where accurate visual analysis is essential for procedural safety. Learning-based methods are constrained by structural complexity, data scarcity, and privacy regulations precluding centralised training across institutions. This thesis presents a structure-aware federated learning framework for catheter and guidewire analysis, with four contributions evaluated on real-animal and phantom data. A benchmark dataset, CathAction, is introduced for catheterisation analysis, with over 600,000 annotated frames and 40,000 segmentation masks. A shape-sensitive loss transforms masks into signed distance maps compared in a structural feature space, improving Dice coefficient by up to 2.9 points across five backbones. This is extended to federated learning with shape-sensitive loss, preserving geometric consistency under heterogeneous client data and outperforming federated averaging by up to three points in mean intersection-over-union as clients scale from four to eight. Federated learning with projected gradient descent adds adversarial optimisation, raising mean intersection-over-union by over ten points on real-animal data. Finally, a structure-aware diffusion framework synthesises catheter and guidewire video sequences, combining structural supervision with a domain-adaptive reconstruction objective, reducing Frechet video distance over a strong baseline while maintaining visual fidelity. Incorporating synthetic sequences into federated training raises the Dice score from 44 to 51 percent under data scarcity, with gains across four held-out sites. Together, these contributions advance privacy-preserving catheter and guidewire analysis, supporting collaborative training without centralising patient data or large amounts of manual annotation. Comments: PhD Thesis, University of Liverpool. 163 pages Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.06876 [cs.CV] (or arXiv:2609.06876v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.06876 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Chayun Kongtongvattana Dr. [view email] [v1] Sun, 6 Sep 2026 23:44:27 UTC (25,819 KB) Full-text links: Access Paper: View a PDF of the paper titled Novel Methods for Catheter and Guidewire Segmentation in X-ray Fluoroscopy under a Federated Learning Setting, by Chayun KongtongvattanaView PDFHTML (experimental)TeX Source view license Current browse context: cs.CV prev | next new | recent | 2026-09 Change to browse by: cs cs.AI References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[CV-187] Organization of Valence and Arousal in Vision-Language Representations of Built Environments: Insights from the EMOIS Dataset
链接: https://arxiv.org/abs/2609.06870
作者: Madoka Yonekura,Katsunori Kohda,Nobuhiko Muramoto,Takahiro Yamaguchi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visual perception of built environments contributes to the affective impressions that people form in everyday life. However, how these impressions are represented within vision foundation models remains largely unexplored. To support the systematic investigation of this subject, we introduce the Emotional Impression of Spaces (EMOIS) dataset, comprising 1,544 real-world built-environment images. Each image is annotated with image-evoked valence and arousal ratings collected from Japanese adults by conducting a large-scale web-based survey, with approximately 120 ratings per image. Using Contrastive Language–Image Pre-training (CLIP) representations, we perform predictive and geometric analyses to systematically investigate how valence and arousal are encoded and organized within the representation space. These analyses reveal that valence exhibited stronger and more coherent organization than arousal. Cross-dataset analyses with the Open Affective Standardized Image Set (OASIS), a benchmark dataset of general affective photographs, reveal differences in affective organization between the two datasets. Regression analyses demonstrate high predictive performance for valence and arousal within EMOIS, with mean coefficients of determination of 0.865 and 0.807, respectively, across repeated internal hold-out evaluations. Finally, we present an example-based interface illustrating how learned representations can support qualitative interpretation of predicted affective values. These findings can help elucidate affective representations of built environments and establish EMOIS as a densely annotated resource for future affective computing research in this domain.
[CV-188] RAIDAL: Redundancy-Aware Information Density Active Learning for CTC-Based Continuous Sign Language Recognition BMVC2026 BMVC RAID
链接: https://arxiv.org/abs/2609.06843
作者: Rafael A. Diniz Augusto,Gabriel L. Oliveira,Erickson R. Nascimento
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to BMVC 2026. This preprint includes one additional analysis not present in the BMVC version. Specifically, we apply RAIDAL’s filter to CTC-BADGE
Abstract:Continuous sign language recognition (CSLR) is a key technology for accessibility, yet its development remains limited by the high cost of annotating continuous video streams. Active learning offers a path toward mitigating this cost, but standard acquisition functions are not designed for weakly aligned sign language videos, where sign executions are interleaved with rest poses, irregular pauses, sign-like motion, and temporally redundant frames. This temporal redundancy can undermine sample selection, as acquisition scores may be influenced by timesteps from regions that are not associated with the decoded gloss sequence, distorting the video’s estimated informativeness. In this work, we show that modern CSLR models already contain a mechanism for identifying gloss-level temporal evidence: the CTC decoder. Although typically used only during inference, its alignment peaks indicate where the model localizes each predicted gloss in the feature sequence, providing a source of temporal structure for active learning acquisition functions at zero additional labeling cost. Thus, we introduce RAIDAL (Redundancy-Aware Information Density Active Learning), which repurposes the CTC decoder to restrict representation-based scoring to decoder-aligned gloss regions, rather than exposing the acquisition function to the entire unfiltered video. Across three datasets and two architectures, RAIDAL achieves its strongest data-efficiency gains over competing baselines in large-vocabulary, budget-limited settings, while remaining competitive in the smaller-vocabulary, large-budget setting. The code used in this work is publicly available at this http URL.
[CV-189] A Shared-Backbone Approach for Multi-Task MedMNIST Classification
链接: https://arxiv.org/abs/2609.06838
作者: Stefan-Dorian Gavril,Andrei Arhire,Adrian Iftene
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the International Conference on System Analysis Intelligent Information Technologies (SAIIT-2026)
Abstract:Multi-task biomedical classification requires models to generalize across disparate modalities and class distributions. We study 11 heterogeneous MedMNIST datasets using the harmonic mean of per-task macro-F1. We evaluate three backbones with task-specific linear heads. We identify a resolution domain shift between the MedMNIST API and evaluation environment. Resolving this inconsistency and optimizing architecture-specific regularization substantially improved performance. Our best configuration, a ConvNeXt-Tiny backbone with label smoothing, achieved a leaderboard harmonic-mean macro-F1 of 0.73294 in the Tensor Reloaded: Multi-Task MedMNIST competition, ranking sixth at the close of the official competition phase. Our implementation is publicly available at: this https URL
[CV-190] Generalist Open-World Temporal Perception
链接: https://arxiv.org/abs/2609.06823
作者: Cristian Sminchisescu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 42 pages, 4 figures
Abstract:The next generation of artificial intelligence systems will likely be natively temporal and multimodal in both inputs and outputs: able to converse, perceive, predict, reason, and synthesize through a shared world representation. Realizing this requires a temporal perceptual substrate integrating sensory streams, language, and structured outputs within a multimodal world model. We seek a generalist open-world perceptual system that represents biological forms, natural physical structures, and artifacts, and their interactions, as a coherent, temporally persistent process. The model should infer geometry, articulation, semantics, interaction structure, and uncertainty from raw multimodal streams; maintain identity through occlusion and viewpoint change; generalize across species, forms, mechanisms, and materials; and abstain or expand its ontology when encountering the unknown. The objective is a structured world state supporting understanding, prediction, counterfactual reasoning, and controllable synthesis. Recent work suggests that some cross-modal and reasoning-like capabilities can emerge from large-scale generative video pretraining, reminiscent of language-model scaling. Yet these capabilities are often accessed through language probes or expressed through photorealistic video, leaving explicit semantic, geometric, or temporal structure largely unexposed. This paper articulates an alternative and complementary paradigm: perception and synthesis as distinct conditionings within a shared Generalist Open-World Temporal Perception Architecture (GOWTPA). Recognition, structured prediction, and simulation arise as different conditionings of the same generative substrate, while reasoning and embodiment-specific policies build upon the resulting world state. This positions generalist temporal perception as a potential foundation layer for broader multimodal intelligence and physical AI.
[CV-191] Diagnosing and Dynamically Filtering Occupancy World Models for Active Mapping IROS2026
链接: https://arxiv.org/abs/2609.06820
作者: Jiahui Zhang,Gongbo Liang,Yu Zhang
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by IEEE IROS 2026 Workshop on WORLDS: World Models and Spatial Intelligence for Physical AI
Abstract:Active mapping requires a robot to select camera viewpoints that efficiently reconstruct an unknown 3D scene. To reason about unobserved regions, recent systems use pretrained occupancy networks as world models that complete missing geometry. The predicted structure contributes to expected coverage gain and constrains feasible robot motion. Consequently, occupancy errors can change both what the robot chooses to explore and where it is able to move. We diagnose these effects by holding the planner fixed and varying only the occupancy representation provided to it. We consider planning without completion, with learned occupancy, with false positives removed by a ground truth oracle, with false negatives restored by an oracle, and with ground truth occupancy. Our experiments show that correcting false positives or false negatives alone does not consistently improve final coverage. This finding reveals a gap between occupancy accuracy and downstream planning performance. Ground truth occupancy provides a much larger improvement in coverage efficiency than in endpoint coverage, suggesting that planning and reachability remain important bottlenecks even when the geometric world model is accurate. Based on these findings, we introduce a dynamic filtering strategy that preserves predictions in unexplored space while suppressing repeatedly unsupported occupancy using online observations. Preliminary examples show that this strategy can redirect viewpoint selection toward reachable surfaces that would otherwise remain unobserved.
[CV-192] Disparity Has a Sign: Stereo Matching Beyond the Zero-Disparity Plane
链接: https://arxiv.org/abs/2609.06809
作者: Jian Shi,Xinge Yang,Chaoyang Wang,Wolfgang Heidrich,Peter Wonka
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Modern stereo matching models fail when disparity crosses zero, with end-point error (EPE) rising by 4.6-37 \times . Yet stereoscopic content, from cinema 3D to VR, routinely contains objects behind the zero-disparity plane (ZDP), corresponding to negative disparities. The blind spot cascades through datasets, architectures, and evaluation protocols, all of which inherit the non-negative geometry. Rectified parallel cameras place ZDP at infinity, so every finite depth yields d=fB/z \ge 0 by construction, and nothing within the standard pipeline can violate, or even measure, a negative disparity. To measure it, we propose \textitZDPShift, a benchmark of 21,495 stereo pairs from seven cinematographer-authored open movies, each frame rendered at five zero-disparity-plane positions with dense signed ground truth. Six state-of-the-art image and video stereo matching models collapse once the plane moves. On identical scene content, FoundationStereo goes from 2.24 px EPE to 75.33 px, with every backbone leaving roughly half of all pixels exceeding a three-pixel disparity error. What is missing, however, is not the underlying matching capability. % The capability itself, however, is already present. Training on supervision synthesized from SceneFlow, which adds no new data or parameters, keeps the error flat across the signed range. Training only the decoder, with the pretrained matching features frozen, performs comparably across all six backbones, with EPE jittering within 0.2 px. Thus, the pretrained features already extend to the negative regime they were never trained on, and only the output convention discarded it. Meanwhile, positive-regime accuracy on KITTI, Middlebury, ETH3D, and Sintel is largely preserved.
[CV-193] Comparative Study of Anatomical and Learned Features in AI Models for Structural Brain MRI
链接: https://arxiv.org/abs/2609.06807
作者: Boyang Yu,Miquel Lopez Escoriza,Long Chen,Arjun V. Masurkar,Narges Razavian,Carlos Fernandez-Granda
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:In this work, we comprehensively evaluate three popular feature-extraction paradigms in AI-based neuroimaging modeling: (1) computation of anatomical surfaces and volumes, (2) supervised learning with convolutional neural networks (CNNs), and (3) unsupervised pretraining of vision transformer (ViT) foundation models, followed by supervised finetuning. Our study is based on 18 publicly available datasets containing 3D structural T1-weighted MRI scans from approximately 80,000 participants across seven distinct clinical tasks. We observe that a linear model based on anatomical features matches the diagnostic performance of complex nonlinear features learned by sophisticated AI frameworks, including foundation models trained on thousands of scans. Conversely, CNNs and pretrained ViTs learn features that implicitly capture relevant anatomical information, bypassing the need for explicit feature extraction. Building upon these insights, we propose Anatomy Segmentation Pretraining (ASP), a novel method to incorporate anatomical information during foundation-model pretraining, which outperforms existing models in biological age estimation.
[CV-194] When Speech Meets Lips: Interpretable Audio-Visual Synchronization for L2 Pronunciation Assessment
链接: https://arxiv.org/abs/2609.06788
作者: Bowen Yu,Mingyu Huang,Yishen Liu,Yue Zhao
类目: Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD)
备注:
Abstract:Automatic Pronunciation Assessment (APA) systems have achieved strong performance with transformer-based models and self-supervised speech representations. However, most methods rely only on acoustic signals and overlook temporal synchronization between speech and articulatory movements, limiting diagnostic feedback on timing mismatches important for L2 pronunciation training. We propose an interpretable audio-visual synchronization framework that explicitly models speech-lip temporal alignment through feature encoding, cross-attention fusion, lag estimation, stability quantification, and visualization. The framework introduces frame-level lag trajectories and a Lag Stability Index (LSI) to quantify synchronization robustness. We also interviewed 30 participants, including 10 instructors and 20 students with diverse first-language backgrounds, to assess its effectiveness. By transforming implicit alignment into interpretable representations, the framework connects automatic scoring with actionable Computer-Aided Pronunciation Training feedback. Datasets and supplemental materials are available at this https URL.
[CV-195] Unified Multi-Layer Subspace Modeling for Cross-Domain OOD Detection KR ECCV2026
链接: https://arxiv.org/abs/2609.06785
作者: Gerhard Krumpl,Henning Avenhaus,Horst Possegger
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026, Code: this https URL
Abstract:Out-of-Distribution (OOD) detection remains a fundamental challenge for neural networks, whose predictions can be overconfident on inputs that deviate from the training distribution. Most post-hoc OOD detection methods derive scores from a single representation level (eg., logits or penultimate features) or combine multiple layers via depth selection or OOD-calibrated weighting. However, because OOD shifts are diverse, the most informative representation level can vary strongly across OOD types and domains, making fixed-layer choices and OOD-calibrated aggregation brittle. In this paper, we propose PRISM (Projected Representation with Intermediate-layer Subspace Modeling), a model-agnostic post-hoc OOD detection method that models a unified multi-layer feature representation rather than aggregating independently scored layers. PRISM fuses intermediate and deep features into a single hierarchical embedding, estimates an in-distribution (ID) principal subspace, and then combines two complementary signals: (i) a class-conditional Mahalanobis distance in the projected subspace and (ii) the residual energy orthogonal to the learned manifold. This simple design avoids OOD-tuned layer weighting while capturing both in-subspace semantic deviations and off-subspace anomalies. Across diverse benchmarks spanning natural images, medical imaging, and industrial visual inspection, PRISM achieves consistent state-of-the-art cross-domain OOD detection performance with a single default configuration across all evaluated domains and architectures. We further show that PRISM incurs minimal inference overhead, making it practical for real-world deployment.
[CV-196] Agent ic Visual Generation: From Generative Models to Agent ic Control
链接: https://arxiv.org/abs/2609.06758
作者: Yinming Huang,Shuyuan Tu,Xi Yan,Jiahao Zhan,Zihan Yang,Zhen Xing,Hui Zhang,Tiehua Zhang,Yu-Gang Jiang,Zuxuan Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: project page: this https URL
Abstract:Visual generation is evolving from generative models used through a single invocation into agentic control processes that can plan, select tools, inspect intermediate synthesized outputs, revise failures, and reuse prior experience. In most existing systems, the controller is an LLM or VLM, while visual generation models serve as tools or executors. However, existing work lacks a consistent criterion for determining when a generation system becomes agentic. Planning depth, tool use, multi-role collaboration, and reinforcement learning are often treated as evidence of agenticity, even though none of them necessarily determines which generation decisions the controller can make. We organize the field according to what the controller can directly control in the generation process. At L1 Conditioning Control, the controller prepares the input to a predetermined generator but does not control which visual operation is executed. At L2 Execution Control, it selects and invokes actual generation, editing, rendering, or other content-modifying operations. At L3 Outcome-Adaptive Control, it observes an intermediate outcome and uses that observation to change a subsequent operation within the current task. At L4 Experience-Adaptive Control, it retains experience from completed tasks and uses that experience to change decisions on future tasks. L0 Fixed Support separately denotes generators, editors, evaluators, reward models, benchmarks, and fixed pipelines without a deployed controller that makes generation-level decisions. These levels describe a progressively broader decision-making scope rather than model size, system complexity, output quality, tool or role count, or training method. Applying this framework across image, video, editing, 3D, world, slide, and user-interface generation reveals how controller capabilities have evolved and how their mechanisms are distributed across levels.
[CV-197] LASSNet: Level-Aware Availability-Conditioned Spatial-Semantic Fusion for Brain Tumor Segmentation with Missing MRI Modalities
链接: https://arxiv.org/abs/2609.06733
作者: Haobin Chen,Ao Chang,Rundong Wang,Zhicheng Li,Zhihao Tang,Heqin Zhu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 2 figures, 3 tables
Abstract:Brain tumor segmentation from multimodal MRI relies on complementary evidence across four imaging sequences, yet one or more modalities may be unavailable because of acquisition cost, protocol variation, scan failure, or patient condition. Existing work has explored reconstruction, knowledge transfer, and direct feature fusion, but leaves open whether missing-modality fusion should change with representation level. High-resolution lateral features retain spatial detail, whereas compressed bottleneck features encode semantic and inter-modality context. We therefore hypothesize that fusion should be conditioned jointly on modality availability and feature hierarchy. We propose the Level-Aware Availability-Conditioned Spatial-Semantic Fusion Network (LASSNet), which contains two level-specialized modules. Hierarchical Availability-Conditioned Fusion (HACF) constructs four lateral representations using count-normalized aggregation of available modalities, mask-conditioned channel modulation, and local 3D refinement. Tri-Scale Relational-Spatial Fusion (TriRSF) models relations among available modality descriptors and spatial context across multiple bottleneck resolutions, followed by cross-scale aggregation and availability-conditioned global spatial attention. A shared coarse-to-fine decoder starts from TriRSF semantics and progressively injects HACF features, without reconstructing missing inputs. Across all 15 non-empty modality configurations, LASSNet obtains mean Dice scores of 76.7% and 83.2% over WT, TC, and ET on BraTS2019 and BraTS2023, respectively.
[CV-198] Uni-Light: An Ultra-Lightweight Framework via Uncertainty-Aware Knowledge Distillation for Brain Tumour Segmentation MICCAI2026
链接: https://arxiv.org/abs/2609.06729
作者: Libing Kuang,Soren Salehi,Ziling Wu,Ahmad P. Tafti,Armaghan Moemeni
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted as a poster paper at the Fourth Workshop in Data Engineering in Medical Imaging (DEMI), MICCAI 2026. 11 pages, 3 figures
Abstract:Accurate 3D brain tumour segmentation from multi-modal Magnetic Resonance Imaging (MRI) is essential for clinical diagnosis and treatment planning. Existing brain tumour segmentation methods often suffer from heavy computational demands, while current lightweight architectures frequently lack the capacity to maintain segmentation fidelity in complex tumour regions. To address these issues, we propose a novel ultra-lightweight framework (Uni-Light) that achieves high-fidelity segmentation with substantially reduced computational overhead. It combines multi-scale convolutions with an uncertainty-aware knowledge distillation scheme that directs the student model toward hard-to-classify regions, complemented by a Signed Distance Field boundary loss for geometric constraints. Experimental results on BraTS2023-GLI and MSD-BTS datasets demonstrate that Uni-Light reduces parameters by 97.56%, floating-point operations (FLOPs) by 73.03%, and inference memory footprint by 81.58%, while surpassing the state-of-the-art model by an average of 1.47% in Dice score, offering a highly competitive trade-off between segmentation accuracy and computational efficiency in resource-constrained clinical settings. This work also advances data engineering for medical imaging by demonstrating that teacher model uncertainty can be exploited as a data-driven supervisory signal, re-prioritising the training data distribution without requiring additional annotation.
[CV-199] Back to the Feature: Zero-Shot 6DoF Pose Estimation via Dense Local Features BMVC2026
链接: https://arxiv.org/abs/2609.06726
作者: Ali Rafiaei,Michael Greenspan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at BMVC 2026
Abstract:We present B2TFPose, a training-free zero-shot method for 6DoF pose estimation of unseen objects from RGB images. Using a single frozen DINOv3 vision transformer as its only pretrained component within the pose estimation pipeline, B2TFPose extracts dense patch-level features that generalize across the synthetic-to-real domain gap without any task-specific fine-tuning, revisiting the classical local feature matching paradigm through the lens of large-scale self-supervised foundation models. Three contributions advance the training-free state of the art. A geodesic non-maximum suppression strategy retrieves a viewpoint-diverse template set for coarse-to-fine correspondence matching. Render-guided Re-Correspondence (RRC) synthesizes object-specific views at the estimated pose and re-establishes dense 2D-3D correspondences to sharpen the initial estimate without additional learned parameters. A multi-mask hypothesis selection strategy jointly scores competing segmentation candidates to resolve segmentation ambiguity. On the seven core datasets of the BOP Benchmark, B2TFPose achieves 40.7 mean AR without refinement and 56.4 with refinement, establishing state-of-the-art performance among training-free RGB methods and outperforming trained counterparts including GigaPose and GenFlow, at competitive inference speed.
[CV-200] ADELE - Adaptive Delaunay Grids for High-Fidelity Mesh-Native Reconstruction SIGGRAPH
链接: https://arxiv.org/abs/2609.06723
作者: Johannes Weidenfeller,Shaofei Wang,Philipp Fürnstahl,Siyu Tang
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: Accepted to SIGGRAPH Asia 2026 Conference Papers | Project page: this https URL | Code: this https URL
Abstract:Meshes remain the most practical representation for geometry reasoning and integration into graphics pipelines, yet existing reconstruction methods struggle to produce high-quality meshes. Most state-of-the-art approaches initially learn an intermediate representation (NeRF/3DGS) and treat mesh extraction as a post-processing step, which often leads to oversmoothed surfaces or poor quality meshes with excessive triangle this http URL mesh-native optimization methods alleviate some of these issues but suffer from fixed-resolution discretizations and unstable optimization behavior. In this paper, we introduce an adaptive mesh-based optimization framework and a practical mesh rendering technique to address these challenges. Our representation combines an optimizable Delaunay-triangulated tetrahedral grid with a multi-resolution hash grid. The former is refined through point pruning and insertion, while the latter provides latent features for SDF/appearance value predictions. We use volumetric rendering to bootstrap a coarse geometry while leveraging mesh-based rendering for recovering fine-grained details. Additionally, we propose a differentiable, rasterization-based depth-offset rendering formulation, reducing geometric artifacts and improving reconstruction quality. Our method significantly outperforms existing mesh optimization approaches across a variety of object-centric benchmarks while being competitive with state-of-the-art NeRF/3DGS methods.
[CV-201] Companion-style QA Assistance in Ego-Vision
链接: https://arxiv.org/abs/2609.06721
作者: Hangyu Qin,Junbin Xiao,Shenglang Zhang,Angela Yao
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Preprint. Under Review
Abstract:AI companions are envisioned as always-on assistants that support users in daily life. With this regard, we introduce BuddyVQA, a benchmark for companion-style question answering (QA) on egocentric streaming video. BuddyVQA contains 21.6K questions linked to 6K highlight moments across 1,012 long, egocentric videos. It features two key characteristics that are common in daily first-person QA assistance but are largely overlooked in existing VideoQA benchmarks: ego-deictic expressions and interactively chained questions (e.g., “Where is it?”, “How to get there?”). These require models to infer a user’s in-situation intent by resolving visual pronouns in the context of egocentric visual and QA contents, with both grounded in a long-form streaming setting. To tackle the challenges, we propose MyBuddy, a companion-style QA assistant that highlights a multimodal chain-of-thought reasoning mechanism to infer the final answer based on the historical QA and visual content. An additional question filter and multi-level memory are designed to facilitate efficient QA and visual information retrieval under streaming QA settings. Experiments show that MyBuddy significantly enhances the performance of foundation models on BuddyVQA. Moreover, these gains generalize to other streaming and common video QA benchmarks, demonstrating the applicability and effectiveness of our approach. Our code and dataset are available at this https URL
[CV-202] RoLA: Rotary-Positioned Low-Rank Linear Attention for Efficient Diffusion Transformers
链接: https://arxiv.org/abs/2609.06712
作者: Zekun Zhang,Yixiang Cai,Yuxi Liu,Tengxu Sun,Tianle Liu,Zhoutong Wu,Haoyu Li,Baole Ai,Ang Wang,Jiamang Wang,Lin Qu,Kun Yuan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Diffusion Transformers (DiTs) achieve strong video generation quality, but their dense spatiotemporal self-attention scales quadratically with sequence length and quickly becomes the dominant inference bottleneck. Sparse low-rank hybrids alleviate this cost by combining a local sparse branch with a global compressed branch. In video DiTs equipped with 3D Rotary Position Embeddings (RoPE), the global branch faces a structural compatibility issue: when RoPE is applied before a nonlinear feature map, the rotation and nonlinearity generally do not commute, making it difficult to keep a query-independent linear summary while preserving relative rotary geometry. Existing work often sidesteps this issue by replacing genuine cross-token global aggregation with coordinate-conditioned surrogates or learnable absolute positional modules. These compromises can be effective, but they approximate relative decay from absolute coordinates and introduce extra positional parameters. We propose \textbfRoLA, a rotary-positioned low-rank linear-attention branch that keeps genuine cross-token aggregation while remaining compatible with a reusable linear summary. The design applies RoPE \emphoutside the nonlinear low-rank feature map and reuses a truncated subset of the pre-trained rotary schedule matched to the low-rank bottleneck. This yields a linear-time low-rank global branch with relative positional behavior by design and no additional positional parameters; the full sparse–low-rank module still includes the fixed-sparsity sparse branch. Experiments on open-source video DiTs show that the resulting method remains competitive in generation quality at 90% sparsity while achieving 2.63 \times end-to-end inference speedup on Wan2.1-14B (720p, 81 frames, measured on an NVIDIA H100 GPU). Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2609.06712 [cs.CV] (or arXiv:2609.06712v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.06712 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-203] Attention-Enhanced Deep Features with Heterogeneous Ensemble Learning for Glaucoma Detection ALT
链接: https://arxiv.org/abs/2609.06699
作者: Abdullah Al Shafi,Nishat Sadaf Lira,Abrar Hasan,Kazi Saeed Alam,Swapnil Kundu Argha
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted and presented at 2026 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON)
Abstract:Glaucoma is a progressive optic neuropathy characterized by irreversible damage to the optic nerve, making timely diagnosis critical to prevent permanent vision loss. Although deep learning has demonstrated promising performance in automated glaucoma detection, existing approaches often overlook feature refinement, suffer from class imbalance, and rely on individual classifiers that limit prediction robustness. To address these challenges, this paper proposes a hybrid glaucoma detection framework that integrates attention-enhanced deep feature extraction with heterogeneous ensemble learning. Specifically, deep representations are extracted using InceptionV3 and subsequently refined by incorporating the Convolutional Block Attention Module (CBAM) to enhance discriminative retinal features. To improve classification robustness, the extracted features are classified using multiple machine learning models together with Single-Level Ensemble (SLE) and Double-Level Ensemble (DLE) strategies, while SMOTE combined with Tomek Links (SMOTE+TL) is employed to alleviate class imbalance. Furthermore, a systematic comparison of handcrafted, deep, and attention-enhanced deep feature representations is conducted. Experimental evaluation on two public retinal fundus datasets demonstrates that deep feature-based methods consistently outperform handcrafted feature-based methods, while the proposed attention-enhanced framework achieves the best overall performance. Furthermore, Grad-CAM visualizations confirm that the proposed model focuses on clinically relevant retinal regions, providing interpretable evidence on the model’s prediction process.
[CV-204] ransNormal-2: Geometry-Grounded Rectified Flow with Edge-Aware Decoding for Precise Normal Estimation
链接: https://arxiv.org/abs/2609.06665
作者: Mingwei Li,Yi Yang,Hehe Fan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:Diffusion-based models enable monocular geometry estimation, yet their pixel-space precision is limited by a shared, under-studied error source: VAE reconstruction degradation. The 8x spatial compression in the VAE encoder-decoder degrades surface normals at object boundaries; even encoding and decoding ground-truth normals introduces 1.3–8.5° of mean angular error (MAE), with edge MAE reaching 2.8x the global MAE. We present TransNormal-2, a FLUX.2-based rectified-flow framework with single-step deterministic inference that addresses this degradation on both sides of the VAE decoder: in how latent predictions are supervised during training, and in how decoded normals are corrected at inference. First, geometry-aware pixel-space losses, including inverse rendering self-consistency, von~Mises-Fisher angular loss, and wavelet edge-aware regularization, complement latent MSE by enforcing spherical normal geometry and diffuse image-formation cues after VAE decoding. Second, a lightweight Geometric Refinement Module (GRM) applies an RGB-guided residual correction to reduce boundary-localized decoding errors without freely rewriting the coarse prediction. On general-scene benchmarks, TransNormal-2 matches or exceeds MoGe-2 on all eight reported metrics while using only 1.4% as many task-specific normal annotations. The gains are clearest for transparent objects, reducing MAE by 4.2° on ClearGrasp and 3.1° on ClearPose over the strongest prior baselines. Code will be released at this https URL.
[CV-205] FSAN: Flow State Attention Network for Aerodynamic Prediction
链接: https://arxiv.org/abs/2609.06660
作者: Wenxuan Jin,Jianguo Yao,Haibing Guan,Xijun Li
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Accurate aerodynamic prediction is critical for designing fuel-efficient and safe transportation systems such as aircraft and automobiles, yet traditional computational fluid dynamics (CFD) simulations remain computationally expensive and expertise-intensive, severely limiting their use in iterative design and real-time analysis. Existing deep learning surrogates suffer from two major limitations: (i) they are evaluated on datasets with narrow flow-condition ranges, leaving their performance under complex flow conditions undemonstrated; (ii) they treat global flow conditions as a single vector injected uniformly across all surface points, ignoring that different geometric regions experience distinct local flow phenomena, which degrades prediction accuracy under complex flow conditions. To address these limitations, we propose the Flow State Attention Network (FSAN). FSAN separately encodes point cloud and flow conditions, then partitions the geometry into multiple flow states via learnable soft assignments, and uses flow features to update these state representations, which in turn influence point cloud features through state changes. This enables fine-grained, state-specific interaction between geometry and flow information. Extensive experiments on two well-recognized aerodynamic benchmarks demonstrate that FSAN achieves the highest accuracy among the methods compared in this work at a higher computational cost. On Emmi-Wing, FSAN reduces the Relative L2 (REL-L2) error by over 20% compared to the strongest baseline (Transolver), and on DrivAerNet++, it achieves a 10% reduction compared to the strongest baseline (AdaField). These results establish FSAN as a promising neural surrogate on public benchmarks with diverse flow conditions and geometries.
[CV-206] VidaForge: Open Research Infrastructure for Video Pretraining Data Recipes
链接: https://arxiv.org/abs/2609.06652
作者: Yan Ma,Jiadi Su,Zhulin Hu,Ethan Chern,Linhao Zhang,Tiantian Mi,Pengfei Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: this https URL
Abstract:Video foundation models increasingly rely on large-scale pretraining data, yet the end-to-end data pipelines behind them remain largely closed and difficult to inspect or reuse. Researchers seeking to understand how video data recipes affect model pretraining often need to build substantial infrastructure before testing even a focused hypothesis. We present VIDAFORGE, an open research infrastructure that represents a video data recipe as an executable five-stage workflow from raw videos to training datasets. A decision in this workflow can be varied to construct alternative datasets while preserving how every sample was produced. To demon strate this research workflow, we compare data recipes with different coverage and quality in early from-scratch pretraining of Wan 2.1 and V-JEPA 2.1. Across both learning objectives, the broader-coverage recipe achieves the highest downstream benchmark scores, while loss-based evaluation favors different recipes. This study demonstrates how VidaForge connects data-recipe choices to downstream model performance. We further release VIDAFORGE-3M, containing 3.14 million scene level clips totaling 6,475 hours, with fine-grained annotations and curation signals for video data-recipe research.
[CV-207] When Does a Laugh Begin? Structured Annotator Disagreement in Temporal Laughter Localization ECCV2026
链接: https://arxiv.org/abs/2609.06646
作者: Eyal Hanania,Daniel Arkushin,Naveh Ayal,Jonathan Benvenisti,Amos Bercovich,Elie Zemmour,Sahar Froim
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to the Workshop on Affective Behavior Analysis in-the-wild, ECCV 2026
Abstract:Annotators routinely disagree on laughter boundaries and subtle chuckles, yet temporal laughter localization typically evaluates against a single reference annotation. We show that this disagreement is structured rather than random noise. Re-annotating the SMILE-Temporal benchmark (672 videos, 1,683 events) with 3-5 annotators per video (alpha = 0.757), we find systematic patterns: disagreement is 1.73 times larger at offsets than onsets, far more common for chuckles than full laughs (77% vs. 20%), and predictable from event attributes (AUC = 0.831). Evaluating against a single annotator breaks down under this structure: system scores shift by 0.246 F1 depending on the chosen ground truth, correctly ranking systems only 69.7% of the time (vs. 80% against all annotators). We propose a disagreement-calibrated evaluation that scores predictions against the full annotator distribution using conformally calibrated tolerance bands (wider at offsets, 0.727s, than onsets, 0.5s). The per-annotator annotations and analysis code are available at this https URL .
[CV-208] MARR: Decoupling Policy Execution and Calibration for All-in-One Medical Image Restoration
链接: https://arxiv.org/abs/2609.06645
作者: Haobin Chen,Ao Chang,Heqin Zhu,Rundong Wang,Ting Liu,Shaohua Kevin Zhou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 4 figures, 2 tables
Abstract:All-in-one medical image restoration seeks to recover heterogeneous clinical images with a single model, but PET, CT, and MRI differ substantially in degradation statistics, anatomical contrast, and output-space bias. A fully shared network can entangle modality-specific residual errors, whereas separate modality-specific networks sacrifice the practical advantages of unified deployment. We therefore recast all-in-one restoration as a question of where limited adaptation should be placed: policy selection, feature execution, or output calibration. We propose MARR, a compact restoration framework that constrains multi-modality adaptation into degradation-aware policy routing, modality-private residual execution, and image-domain residual correction without requiring degradation labels or separate modality-specific models. The policy branch forms a routing prompt from input statistics, latent content, and modality identity, and uses it only as a control signal. Prompt-gated modality-private adapters then perform lightweight residual refinement at intermediate decoder stages, while zero-initialized modality-specific output heads calibrate the final image-domain residual without perturbing the initial shared prediction. On an all-in-one PET, CT, and MRI restoration benchmark, MARR outperforms thirteen methods re-trained under the same protocol, achieving PSNR values of 37.34 dB, 33.85 dB, and 32.09 dB on PET, CT, and MRI, respectively, and the best modality-average PSNR of 34.43 dB. The code is publicly available at this https URL.
[CV-209] RGB Input Pipelines: Throughput GPU Memory and Transformation Coverag e
链接: https://arxiv.org/abs/2609.06635
作者: Vladimir Iglovikov
类目: Performance (cs.PF); Computer Vision and Pattern Recognition (cs.CV)
备注: 26 pages, 8 figures. Benchmark code: this https URL
Abstract:An image-augmentation pipeline must deliver a complete batch before a model can use it. We compare seven input paths from five libraries, starting with RGB JPEG files and ending with a synchronized CUDA float16 batch. We manually matched transformation recipes and parameters across libraries to make the workloads as comparable as possible. The experiment uses 57 selected recipes, a batch size of 256, and one NVIDIA L4 machine. Throughput and peak process GPU memory are recorded together in 759 measurements. On the 11 recipes shared by all paths, DALI and AlbumentationsX have median throughputs of 5,029 and 4,679 images/s, with median peak GPU memory of 2,086 and 1,852 MiB. Broader pairwise comparisons favor AlbumentationsX on 26/26 TorchVision recipes, 50/51 Kornia recipes, and 25/26 Pillow recipes. DALI is faster than AlbumentationsX on all 22 shared recipes, with a median throughput ratio of 1.18x. A separate census reports coverage of the 118 entries in a selected AlbumentationsX RGB catalog. The study measures input preparation at fixed settings; it does not measure model training, numerical equivalence, or the best attainable configuration of each library. Benchmark code: this https URL.
[CV-210] Physico-Geospatial Grounded Scene Interpretation for Mobile Robotics
链接: https://arxiv.org/abs/2609.06629
作者: Nicolas Schuler,Janik Kurtz,Lea Dewald,Marcel Sauber,Félicia Teferle,Jürgen Graf
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 9 pages, 3 figures, 3 table; accepted for International Conference on FutureTech 2026 (ICFT), AMMAN, JORDAN October 18-22 2026
Abstract:Recent advancements in deep learning allow robotic agents to interact with dynamic and unstructured environments. Of special interest is the integration of physico-geospatial world knowledge into such systems, either by using physics-aware machine learning models, knowledge graphs to model relationships or spatio-temporal and logical reasoning. In the present work, we introduce an approach to augment the output of pre-trained, unmodified VLMs used for scene interpretation by integrating semantic descriptions, OpenStreetMap building data and street information with positional, temporal and metric information obtained from our sensory systems, fusing this information using LLMs. We apply this concept to an outdoor recording within a university campus, achieving an F1-Score of 0.83 in the task of grounding buildings and 0.64 for path surface grounding on our pilot evaluation set. The results demonstrate the conceptual capability of the proposed solution to deliver physico-geospatial grounded natural language descriptions. Code and results are available at this https URL
[CV-211] GeoCo-SAVi: Geometry-Consistent Slot Attention for Explicitly Editable Object Representations
链接: https://arxiv.org/abs/2609.06628
作者: Haoxiang Huang,Zhekai Wang,Xiang Liu,Sen Cui,Changshui Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 30 pages, 4 figures. Code is available at [ this https URL ]( this https URL )
Abstract:Object-centric video models represent scenes with slots, yet exposed geometry can vary in meaning with appearance. In Invariant Slot Attention (ISA), explicit position and scale can disagree with the decoded center and extent; edits can yield unexpected motion or resizing, and replacing appearance can shift geometry. GeoCo-SAVi promotes geometric authority and semantic alignment. Its spatially equivariant, object-wise decoder makes position and scale effective commands: changing them moves or resizes the rendered support. Factual position alignment ties position to the decoded center, and normalized attention overlap discourages duplicate allocation. Appearance transplantation aligns geometry semantics across objects, so recipient geometry governs layout while donor appearance supplies shape. A temporal initializer propagates calibrated slots across frames. On Obj3D, GeoCo-SAVi matches ISA reconstruction, reduces p-centroid error by over 80%, and cuts appearance-induced size variation by over 50% while producing the expected translation and scale responses. On 250 MOVi-C videos, it also improves reconstruction, instance grouping, and fixed-identity geometry control over two same-protocol references. GeoCo-SAVi transforms explicit geometry into compositional control, making both position and scale more readable and editable. Comments: 30 pages, 4 figures. Code is available at [this https URL](this https URL) Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2609.06628 [cs.CV] (or arXiv:2609.06628v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.06628 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-212] GAN-Blot: A Controllable Structure-Style Synthesis Benchmark for Western Blot Forensics
链接: https://arxiv.org/abs/2609.06619
作者: Hao-Chiang Shao,Fong-Yi Lin,Te-An Chien,TianYu Chen,Da-Jhong Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Western blot (WB) images are widely used as key evidence in biomedical research. Recent scientific misconduct cases reveal that WB imagery is increasingly fabricated, making WB forensics a major concern for research integrity. However, while the progress of forensic detection techniques often relies on advances in forgery-generation techniques, the development of WB forensic techniques has been hindered by the lack of standardized appearance attribute definitions, image datasets, and controllable generation frameworks for WB imagery. To address this limitation, we present a controllable WB image synthesis framework, named GAN-Blot, for generating realistic synthetic WB images. We introduce a formulation that decomposes a WB image into a structure component and a style-reference component, enabling independent control over local protein-band geometry and the global visual appearance of a synthetic WB image. GAN-Blot integrates a dual-path autoencoding design with several style-alignment loss terms to enable implicit control over structure-style synthesis without predefined semantic appearance attributes. We further contribute a synthetic WB dataset containing more than 46K images and propose four evaluation protocols for controllable WB synthesis. Extensive experiments show that GAN-Blot can generate WB images with high fidelity in both protein-band structure and visual style. Under blind inspection, the generated images can fool domain experts and are not reliably distinguished from authentic WB images by existing detectors and screening platforms. These results demonstrate their utility as challenging controlled cases for validating and developing WB forensic methods.
[CV-213] Multi-History-Step SDE Inversion for Image Editing with Superior Regional Awareness ECCV2026
链接: https://arxiv.org/abs/2609.06602
作者: Haiyan Wei,Yunlong Wang,Huaibo Huang,Zhenan Sun,Kunbo Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV 2026
Abstract:In recent years, diffusion stochastic differential equation (SDE) inversion and inversion-free methods have become prevalent for training-free image editing, as they can achieve faithful reconstruction without tuning. However, existing approaches remain inefficient, exhibit limited plasticity, and struggle to accurately preserve unedited regions. To address these issues, we propose MIEdit, a training-free editing framework based on SDE inversion. MIEdit introduces a predictor-corrector multi-history-step scheme to achieve superior editing quality with fewer steps. We further mitigate heterogeneity and conflict between the multi-conditioned noise residuals and gradient terms during sampling, improving stability and editing plasticity under large edits. MIEdit also includes Inversion-Time Automatic Semantic Angle Masking (IASM); it leverages classifier-free guidance to automatically generate semantic angle masks during inversion and applies them throughout the sampling process for regional constraints, without extra user inputs. We additionally construct EditEval++ (30 fine-grained tasks, 1,000+ image-text-mask triplets) for comprehensive evaluation; experiments show that MIEdit outperforms state-of-the-art techniques. Project page: this https URL.
[CV-214] Layer-Wise Gate-Controlled Prompt Truncation in a Multimodal Chest X-Ray Classifier
链接: https://arxiv.org/abs/2609.06590
作者: Jingtao Lei,Hongji Li,Dexiang Shu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 3 figures, 3 tables
Abstract:Mixture of Prompt Experts (MoPE) adapts multimodal transformers through input-dependent prompt composition, while retaining a fixed prompt length. We investigate a layer-wise gating extension in a binary chest X-ray classification pilot study. The controller predicts a retention ratio for each sample, averages these ratios within a mini-batch, and uses the resulting integer length to truncate the static and mixed visual prompts. Retained mixed prompts are also scaled by the individual ratios. In one recorded run per configuration, the gated model reached a best validation accuracy of 0.8996, compared with 0.8969 for the fixed-length baseline; the corresponding final values were 0.8963 and 0.8802. The exported gate statistics imply a retained length of one at all recorded training points, relative to a configured maximum of six. This reduces the complete visual sequence from 210 to 200 tokens, but no direct runtime measurements establish an acceleration benefit. Report-derived labels, report text as input, sequential data partitioning, and the absence of repeated controlled experiments limit interpretation. The findings document prompt shortening under the configured gate penalty; they do not establish sample-specific length allocation, superiority over fixed short prompts, or clinical utility. Code is available at: this https URL.
[CV-215] Reading Decoder Trajectories: Training-Free Counterfactual Query-Trajectory Reliability for Small-Object Detection
链接: https://arxiv.org/abs/2609.06581
作者: Zhaoning Shi,Bo Ma
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Small-object detection remains challenging because limited pixels cause information loss and suppress the scale knowledge encoded in pretrained detectors. Existing approaches mainly improve representations through multiscale training, architecture redesign, or parameter adaptation, implicitly assuming that frozen models lack the required capability. We challenge this assumption and hypothesize that small-object knowledge already exists in frozen detectors but remains underactivated and unstable during query evolution. To test this hypothesis, we propose Counterfactual Query-Trajectory Reliability (CQTR), a training-free framework that elicits latent responses through counterfactual scale interventions and interprets candidate reliability from decoder-internal spatial convergence, semantic persistence, and cross-scale conflicts. A small unlabeled training subset selects the appropriate correction mechanism for each model-data stream, without parameter updates or target-domain annotations. Across 27 combinations of nine frozen detectors and three datasets, CQTR consistently improves average precision (AP) and average precision for small objects (APs). Closed-loop analyses further show that scale intervention activates latent responses, trajectory evidence predicts ground-truth support, and unlabeled routing selects the more effective branch. CQTR therefore reframes small-object detection from external scale augmentation to the activation and reliability assessment of latent scale knowledge.
[CV-216] Learning to Use Imagination: Progress-Conditioned Future Utilization for World Action Models
链接: https://arxiv.org/abs/2609.06578
作者: Yijie Zhu,Zitong Yu,Wei Li,Hui Ma,Wen Li,Rui Shao,Liqiang Nie
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). Project page: this https URL
Abstract:World Action Models (WAMs) extend Vision-Language-Action (VLA) models by incorporating future visual dynamics into action generation. However, existing WAMs often utilize imagined futures with limited adaptation to evolving execution progress, potentially introducing distracting or unreliable predictive cues. This limitation arises from two empirically identified forms of non-uniformity in future utility: (i) at the inter-progress level, the utility of imagined futures varies across execution stages as control demands change; and (ii) at the intra-progress level, individual future latents exhibit heterogeneous relevance within the same progress state. To address these limitations, we propose ProWAM, a Progress-Conditioned World Action Model that introduces execution progress as an explicit intermediate representation for adaptive imagination utilization. ProWAM comprises two tightly coupled components: (1) To obtain a reliable representation of execution progress, we propose the Self-Supervised Dual-Temporal Progress Encoder (SS-DTPE). SS-DTPE couples short-term action-observation interaction modeling with long-term recurrent progress aggregation to capture recent execution feedback and accumulated task history. (2) Conditioned on the progress representation from SS-DTPE, we propose the Hierarchical Progress-Conditioned Imagination Modulation (HPIM) to adapt imagination utilization to execution progress. HPIM operates at two complementary levels: an inter-progress global modulation mechanism adapts future utilization across execution stages, while an intra-progress relevance mechanism differentiates individual future latents within each progress state. Extensive experiments demonstrate consistent gains over strong VLA and WAM baselines.
[CV-217] 3DHarnessBench: Probing Agent ic 3D-to-Code Capabilities of Frontier Vision-Language Models
链接: https://arxiv.org/abs/2609.06535
作者: Ling Liu,Bingchen Gong,Amal Dev Parakkat,Maks Ovsjanikov
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We introduce 3DHarnessBench, a benchmark that evaluates the agentic ability of frontier vision-language models (VLMs) to recover 3D geometry as Blender Python code from a variety of inputs. Unlike previous frameworks that prompt the VLMs with a fixed input (e.g., a single rendering or a text description), 3DHarnessBench evaluates four separate harness settings that progressively enable active agentic exploration, facilitated by recent Blender MCP functionality. Our hierarchy from Single-view, Multi-view, Active Visual (arbitrary viewpoint access), and Full 3D Interaction (complete access to the target object through Blender function calls) probes the models’ abilities in both visual perception and active inference, tool calling, and self-correction. We observe that the ability of all frontier models to recover 3D geometry improves significantly with richer function call access, although the improvements are strongly model-dependent, revealing highly uneven agentic 3D-to-code capabilities. We will release the benchmark, code, outputs, and agent trajectories for reproducible 3D evaluation.
[CV-218] Selective Knowledge Control for Continual GUI Agent Learning over Application Streams
链接: https://arxiv.org/abs/2609.06530
作者: Zirui Shang,Xin Shu,Yang Liu,Zhi Gao,Xinxiao Wu,Lifeng Fan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Continual learning is a crucial capability for Graphical User Interface (GUI) agents to adapt to evolving applications while retaining knowledge acquired from previous applications. Such application streams pose a challenging knowledge modeling problem: new applications often share underlying knowledge with past ones, yet also introduce specific knowledge that must not interfere with historical knowledge. In this paper, we propose activation-conditioned selective knowledge control, a lightweight method that achieves selective knowledge retention via neuron-level gradient manipulation. Our method maintains a compact historical knowledge state to protect highly activated MLP neurons that preserve previous knowledge. When a new application arrives, it performs real-time gradient surgery conditioned on forward activation. Concretely, the protected neurons are categorized into two types: unactivated neurons holding specific knowledge, whose gradients are truncated to prevent interference; and activated neurons holding shared knowledge, whose gradients are orthogonally projected to preserve stability while enabling adaptation. After each application stage, newly identified critical neurons are merged into the historical state for future learning. Empirical evaluations on multi-app sequential benchmark demonstrate that our method effectively mitigates catastrophic forgetting on prior applications while sustaining robust adaptation to new ones.
[CV-219] A Cloud-Based Hybrid Model for Real-Time Detection of BRTA-Approved Licence Plates Using YOLO Tiny and Haar Cascade
链接: https://arxiv.org/abs/2609.06507
作者: Debashis Kar Suvra,Tahsina Farah Sanam
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 3 figures, 2 tables. Published in 2024 27th International Conference on Computer and Information Technology (ICCIT), Cox’s Bazar, Bangladesh, 2024, pp. 2488-2493, doi: https://doi.org/10.1109/ICCIT64611.2024.11022598
Abstract:Accurate vehicle license plate detection is essential for applications such as intelligent transportation systems, toll collection, parking management, and law enforcement. In Bangladesh, this task presents distinct challenges due to the complexity of localized license plates and environmental factors like lighting, occlusion, motion blur, and obstructions such as dirt or mud. These challenges often render conventional methods ineffective. This paper introduces a novel hybrid approach, combining the YOLO Tiny deep learning model with the Haar-Cascade classifier, for enhanced detection and localization of Bengali license plates. A key innovation of our system is the integration of a dynamic retraining pipeline, which allows the model to adapt to evolving real-world conditions. This retraining mechanism significantly boosts performance in low-confidence scenarios by continuously improving the model’s accuracy as new data is encountered. Additionally, a publicly accessible dataset of BRTA-compliant license plates, captured under diverse and challenging conditions, has been developed to support this approach. Experimental results demonstrate that our approach not only achieves superior detection accuracy and computational efficiency over conventional models but also ensures consistent performance in resource-constrained environments, particularly in Bangladesh.
[CV-220] CAM: Question Answering on Entity-Centric Videos with Continuous Extraction and Adaptive Querying
链接: https://arxiv.org/abs/2609.06504
作者: Yizhou Tian,Zizhe Chen,Shiyuan Deng,Garry Yang,Zijie Dai,Luohao Pan,Hao Lin,Peiqi Yin,Xiao Yan,James Cheng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Memory facilitates question answering over long videos by extracting and retrieving facts to fit within the limited context windows of multimodal LLMs (MLLMs). Existing solutions typically extract independent memory entries from fixed-length video clips and thus cannot capture high-level semantics that need to be summarized over extended time periods, such as character traits and relations. Moreover, they rely solely on similarity-based retrieval and may fail to retrieve the fine-grained details required for question answering. To tackle these problems, we propose CAM, featuring continuous extraction for high-level semantics and adaptive querying for fine-grained details. In particular, CAM stores the entities and relations extracted from video clips in a knowledge graph. To capture the high-level semantics of each entity or relation, CAM summarizes the local subgraph of the target entity or relation once the subgraph reaches a predefined size. To retrieve the fine-grained details required for question answering, CAM supports multiple search methods, including knowledge graph traversal, video re-watching, and audio listening. It utilizes a planner-executor-verifier pipeline to adaptively compose these search methods according to question intent. Evaluations on three benchmarks show that CAM outperforms SOTA baselines and improves their accuracy by up to 23 percentage points. Code is available at this https URL.
[CV-221] Vision-Guided Text Prompt Tuning for Multimodal Sentiment Analysis
链接: https://arxiv.org/abs/2609.06497
作者: Xiaoran Kou,Jingyi Wu,Peng Sun,Yang Liu,Hong Chen
类目: Multimedia (cs.MM); Computer Vision and Pattern Recognition (cs.CV)
备注: This paper has been accepted to IEEE MMSP 2026
Abstract:Multimodal sentiment analysis requires effective modeling of both verbal semantics and non-verbal affective cues. A central challenge is to calibrate text-centered sentiment understanding with visual facial evidence in a controlled, adaptive, and parameter-efficient manner. Text usually serves as the semantic anchor, whereas visual cues provide complementary evidence for ambiguous or implicit expressions; however, indiscriminate fusion may introduce visual noise and distort textual semantics. Moreover, fully fine-tuning large visual and textual encoders is costly and prone to overfitting on limited and scenario-dependent MSA benchmarks. To address these issues, we propose Vision-Guided Text Prompt Tuning (VG-TPT), which formulates visual-text sentiment modeling as controllable visual calibration of frozen text representations. VG-TPT injects visual affective cues into a frozen BERT encoder through layer-wise adaptive prompts, rather than relying on late-stage feature fusion or full backbone tuning. A co-guided router composes prompts from a trainable prompt bank according to both the evolving text state and the visual guidance feature, enabling sample-specific and layer-specific modulation. Experiments on CMU-MOSEI and CMU-MOSI show that VG-TPT consistently improves over text-only baselines and achieves competitive or superior performance compared with several full-modality methods, while updating only 2.4M trainable parameters. The code is available at this https URL.
[CV-222] Diffuse2Seg: Diffusion Models Can Segment Anything Without Supervision
链接: https://arxiv.org/abs/2609.06491
作者: Christoph Hümmer,Joachim Sicking,Fabian Hüger,Hanno Gottschalk
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Open-world entity segmentation aims to predict masks for arbitrary objects across domains and at multiple granularities, from parts to whole objects. In this setting, SAM sets a strong standard: trained on SA-1B, comprising 11M images and over 1B carefully annotated masks, it achieves remarkable zero-shot performance. Collecting such labels is expensive and time-consuming, however, which limits how far this recipe can scale. Text-to-image diffusion models offer a way around this. Their intermediate features transfer well across perception tasks, and since object structure emerges as the model denoises a noise sample into an image conditioned on a text prompt, that structure is already encoded in these representations. They can therefore be exploited for open-world entity segmentation without retraining or supervision. Building on this observation, we introduce Diffuse2Seg, which repurposes generative diffusion models for automatic mask generation by propagating a grid of point prompts through their self-attention representations in an edge-preserving manner. Diffuse2Seg produces multi-granular instance masks and outperforms prior state-of-the-art label generators by 4.3-7.1 p.p. in AR_1000 across five domains. Training an instance segmentation model on these generated masks advances detector-free open-world segmentation by 7.4 and 7.7 p.p. on “things” and “stuff+things” datasets and surpasses the detector-based UnSAM on “stuff+things” by 2.1 p.p. in AR_1000. Finally, we show that a model trained on Diffuse2Seg labels provides a strong initialization for semi-supervised learning, outperforming its fully supervised counterpart with already 5k labeled images.
[CV-223] One MLLM One Call: Efficient Zero-Shot Vision-and-Language Navigation via Spatial-Aware Waypoints
链接: https://arxiv.org/abs/2609.06476
作者: Shiqi Pan,Qi Zheng,Hanqin Sun,Youjian Zhang,Daquan Feng,Xu Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires an embodied agent to navigate unseen environments by following natural language instructions. Current zero-shot VLN-CE methods either rely on pre-trained waypoint predictors or require multiple queries to large models per step. To address prohibitive inference latency and computational overhead, we propose O2C-Nav, an efficient zero-shot navigation framework that calls only a single large model once per decision step. Our approach introduces a training-free structured waypoint generator and a novel abstract representation that projects sparse, history-aware candidate waypoints directly onto RGB images as visual markers. The MLLM selects a waypoint or generates a fallback target bounding box at each step, while a low-level Fast Marching Method (FMM) planner converts the selected target into an executable collision-free path. This paradigm provides the model with concrete spatial perception and explicit memory while significantly reducing the visual processing load. Extensive evaluations on the R2R-CE and RxR-CE benchmarks demonstrate that O2C-Nav outperforms current state-of-the-art zero-shot methods, highlighting its great potential for real-time robotic deployment. Code is available at this https URL.
[CV-224] hinking with Cameras: Active Visual Reasoning via Dynamic Viewpoint Control for Surveillance Video Understanding
链接: https://arxiv.org/abs/2609.06475
作者: Xiao Zhang,Wang Zeng,Sheng Jin,Wentao Liu,Chen Qian,Shichao Kan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Large vision-language models (LVLMs) have recently achieved remarkable progress in general-purpose video understanding. However, their application to surveillance videos remains challenging due to the lack of large-scale domain-specific datasets and the limitation of passive observation from fixed viewpoints. In surveillance scenarios, critical visual evidence can be easily missed when targets are distant, small, occluded, or move beyond the current camera view. In this work, we introduce CamVLM, a new framework for Thinking with Cameras, which enables LVLMs to actively acquire visual evidence through dynamic viewpoint control rather than passively analyzing fixed video streams. We first construct CCTV-Anomaly, a large-scale surveillance video understanding dataset containing 14,459 videos across 10 anomaly categories, with detailed captions and event annotations. We further formulate viewpoint control as an active visual perception problem and build CamTrack-53K, an object-centric viewpoint trajectory dataset for learning camera actions. Moreover, we propose a reinforcement learning based viewpoint policy optimization framework, which models camera control as a sequential decision-making process and learns long-horizon observation strategies beyond supervised trajectory imitation. Extensive experiments demonstrate that CamVLM achieves state-of-the-art performance under both passive observation and dynamic viewpoint settings, validating the effectiveness of active camera-based reasoning for surveillance video understanding. Our datasets, model, and code will be available at this https URL .
[CV-225] Large-Scale Pretraining for Improving Deep Learning-Based Geometric Distortion Correction of Diffusion-Weighted Imaging
链接: https://arxiv.org/abs/2609.06437
作者: Saroj Khanal,Yashawant Kumar Yadav,Kritam Bhattarai,Jeevan Neupane,Shristi Subedi,Saship Gwachha,Manish Kumar Tiwari,Dong Zhang,Confidence Raymond,Aondona Moses Iorumbur,Udunna Anazodo,Surendra Maharjan,Bishesh Khanal,Mahesh Shakya,Pralhad Kumar Shrestha
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Diffusion-weighted imaging (DWI) is widely used in clinical settings but remains vulnerable to geometric distortion. Conventional correction methods often require additional acquisitions or vendor-specific solutions, limiting their feasibility in high-throughput, resource-constrained settings. This study investigates whether large-scale pretraining strategies can improve deep learning-based distortion correction for single-phase-encoding DWI. We formulate the task as image reconstruction, and compare a non-pretrained baseline against a self-supervised and a generative pretrained model, evaluated using both quantitative image-similarity metrics and qualitative expert assessment. The best-performing model was further tested for transferability on data collected in an LMIC setting with acquisition shift. Pretrained models outperformed the non-pretrained baseline, with cWDM achieving the strongest results across both quantitative and qualitative evaluation. However, application to LMIC data revealed transferability challenges, including contrast alteration and over-reliance on T1-weighted anatomical structure. Registering images to a common standard space improved predictions, suggesting that harmonized preprocessing may enhance cross-domain deployment.
[CV-226] PLSR: Progressive and Localized Super-Resolution of 3D Objects via Localized Latent Voxel Diffusion
链接: https://arxiv.org/abs/2609.06436
作者: Yuxin Liu,Minshan Xie,Jiawen Liang,Runsong Zhu,Chi-Wing Fu,Tien-Tsin Wong
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注:
Abstract:High-resolution 3D asset generation is vital in various 3D applications. Existing state-of-the-art diffusion-based models remain constrained by fixed resolutions, limiting their ability to produce details. In this paper, we tackle the challenge of generating more detailed, higher-resolution 3D objects by introducing a 3D super-resolution (SR) framework built on existing 3D generative foundation models. To this end, we design PLSR, a progressive and localized super-resolution solution to achieve this goal effectively and memory efficiently. Technically, given a coarse geometry from a pretrained 3D generator, we decompose the global SR task into localized sub-tasks via an associative input decomposition scheme, adapt a flow-based 3D generator into a localized super-resolution model through low-cost finetuning, and unify them in an iterative patch-wise denoising pipeline for seamless high-resolution output. Experiments on challenging objects show that our approach is able to generate 3D details with new strong fine-detail fidelity while significantly reducing the computational cost, offering a new and practical solution for high-resolution 3D asset generation.
[CV-227] Separating Capability from Confidence: Grounded Dual-State Calibration for GRPO-Trained Medical Vision-Language Models
链接: https://arxiv.org/abs/2609.06419
作者: Yangyang Xie,Ke Hao,Jiaqi Liu,Yun Gu,Xinglin Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Medical vision-language models (VLMs) require confidence that reflects both answer correctness and patient-specific visual evidence. Recent GRPO-based methods optimize verbalized confidence together with answer generation. However, this joint optimization may interfere with answer learning and drive confidence toward near-binary values. Verbalized confidence also provides no explicit assessment of visual support. We therefore separate capability learning from confidence estimation and propose \textbfDualRead. DualRead builds on the insight that reliability can be read from the actor’s internal states at critical moments in the answering process. It freezes the GRPO-trained actor and combines pre-answer solvability with a post-answer assessment of the generated answer and its visual support. To further assess whether confidence reflects visual grounding, we introduce \textbfCounterfactual Confidence Grounding AUC (CCG-AUC). It measures whether confidence decreases when real-image substitution changes the actor from correct to incorrect. Across two VLM backbones and both in- and out-of-distribution medical VQA benchmarks, DualRead improves correctness discrimination and calibration over verbalized confidence while preserving answer accuracy. CCG-AUC reveals whether confidence responds to answer-relevant visual evidence rather than primarily to non-visual cues.
[CV-228] Scaling 3D Generative Priors to Large-Scale Scene Meshes from Multi-View Images
链接: https://arxiv.org/abs/2609.06385
作者: SangEun Lee,Wonseok Chae,Hoyoung Yoo,Geunyong Kim,NackWoo Kim,Hyeonjin Kim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Pretrained 3D generative models produce detailed geometry and appearance but are primarily designed for object-centric generation within a limited spatial extent. Recent approaches address this limitation by partitioning large scenes into smaller spatial regions and applying pretrained 3D generative priors to each region. However, scaling tiled generation to large multi-view scenes makes it challenging to maintain local geometric continuity and global appearance consistency. We present a training-free framework for large-scale textured mesh generation from multi-view images. Our key idea is to scale tiled generation to large scenes with increased spatial detail while coordinating generation both locally and globally. We introduce local context tiled generation to improve geometric continuity between neighboring regions and global appearance alignment to reduce appearance discrepancies across distant regions. An adaptive scene decomposition further determines the number of tiles according to the input scene geometry. Experiments demonstrate improved geometric and appearance fidelity over existing approaches while enabling fine-grained generation of large-scale scenes. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2609.06385 [cs.CV] (or arXiv:2609.06385v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.06385 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-229] An Integrated Video-AI Platform for Action-Level Microanastomosis Training and Performance Feedback
链接: https://arxiv.org/abs/2609.06380
作者: Yan Meng,Daniel A. Donoho
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Developing microanastomosis skill requires repeated practice with timely, action-specific feedback, yet expert review of lengthy microscope videos does not scale to frequent or distributed training. We present an integrated video-AI platform that turns a complete simulated procedure into inspectable, interactive feedback through three connected modules. First, a proposed transformer segments the video into six surgical actions. Second, object detection and tracking localize instrument tips within each action; the resulting kinematic features and action statistics drive supervised classification of five NOMAT-aligned performance dimensions. Third, a grounded large language model (LLM) uses these structured outputs to answer user questions about the current scene, actions, motion, and predicted performance through a unified interface. In a two-site study, 17 participants completed 72 procedures comprising 576 suture placements. The action-segmentation module achieved 87.66% accuracy and 82.86% F1, increasing to 93.62% and 88.32% after workflow-aware refinement. The five performance classifiers achieved 76.0% mean accuracy, with Cohen’s \kappa from 0.63 to 0.93. Although the language interface and educational benefit require prospective evaluation, these results establish the technical basis for an expert-supervised platform that can shorten review, expose the evidence behind performance estimates, and support scalable formative microsurgical training.
[CV-230] Multi-Grid Post-Training for Long-Form Multi-Shot Video Generation
链接: https://arxiv.org/abs/2609.06373
作者: Jiawei Mao,Haoqin Tu,Hardy Chen,Yuhan Wang,Keyang Xu,Jieru Mei,Hongliang Fei,Ruogu Fang,Wei Shao,Cihang Xie,Yuyin Zhou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 13 figures. Project page: this https URL
Abstract:Generating long-form multi-shot videos requires coherent within-shot motion and visually consistent narratives across shots. Existing video generators favor continuous motion and struggle to present complete shot sets when an entire narrative is packed along one temporal axis. We propose MovieGrid, a Multi-Grid Post-Training paradigm that decomposes a long video into shorter, temporally ordered chunks and arranges them on a spatial grid for joint modeling. This design reduces the number of shots handled by each temporal axis while enabling global information exchange across chunks. We construct the Multi-Grid Long Video (MGLV) dataset from 1,000 long-form videos using source video collection, hierarchical segmentation, grid video construction, and character-aware story annotation, producing 54K grid videos paired with story prompts. Our Noise-Free Random-Grid Training retains a random subset of chunks as clean visual context for denoising the remaining chunks. Grid Embedding encodes grid structure, character-aware Story Prompts link recurring entities, and Grid Boundary Loss stabilizes layouts. Under the same token budget, MovieGrid generates 6.05 times more shots than Temporal Packing in a 1,616-frame video. On a benchmark spanning five real-world categories, it achieves state-of-the-art intra-shot consistency (0.9131 versus 0.8086 for HoloCine) and inter-shot consistency (0.5914 versus 0.5384 for StoryMem). MovieGrid can further scale video length with minimal compromise through single or multiple generations.
[CV-231] DualPathOcc: Dual-Resolution BEV Encoder for 3D Occupancy Prediction
链接: https://arxiv.org/abs/2609.06370
作者: Lihao Qiu,Jian Chen,Ruihao Wang,Ramu Gautam,Mei Yang,Yingtao Jiang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 6 figures, 4 tables
Abstract:Predicting 3D occupancy from multi-view images requires preserving geometric detail during 2D-to-3D lifting while reasoning over sparse, volumetric scene representations. We present DualPathOcc, a camera-based framework that combines a Spatial Enhancer for high-resolution feature aggregation before BEV compression, a SENet-augmented dual-path BEV encoder for local-global context modeling, and height-aware weighted cross-entropy for near-ground occupancy. The final model is optimized with occupancy supervision and no explicit depth loss. On single-frame Occ3D-nuScenes, DualPathOcc achieves 37.37 mIoU. We further analyze how surface-centered depth targets interact with volumetric occupancy learning.
[CV-232] NOVA: Normal-Side Modeling for Training-Free Zero-Shot Video Anomaly Detection
链接: https://arxiv.org/abs/2609.06360
作者: Wei-Chih Yin,Yun-Ching Kao,Cheng-Kuan Lin,Yu-Chee Tseng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Training-free zero-shot video anomaly detection (ZS-VAD) leverages vision-language models (VLMs) to localize anomaly instances from a predefined anomaly vocabulary, without providing any video. Existing CLIP-based methods often emphasize anomaly-side semantics, while the competing normality side remains less carefully formulated. We identify two key limitations in existing solutions: (i) blurred decision boundary: normal prompts may contain ambiguous verbs, such as running, that are semantically close to anomalies, reducing normal and abnormal separation in the VLM embedding space; and (ii) modality gap: poor alignment between features of textual normal anchors and visual frames. We propose NOVA, a training-free ZS-VAD framework that strengthens the normal side at both linguistic and visual levels. NOVA introduces Normality-Aware Prompt Construction (NA), which excludes anomaly-adjacent verbs and biases normal descriptions toward static, low-motion scenes. To overcome the text-vision modality gap, NOVA constructs a Visual Normality Anchor (VNA), which creates a weighted visual normal anchor from the initial frames of each test video, providing a video-specific normal reference without task-specific training or annotations. NOVA achieves 89.86 percent AUC on UCF-Crime and 95.07 percent AUC and 84.82 percent AP on XD-Violence, reaching state-of-the-art performance among comparable training-free zero-shot methods.
[CV-233] AGSA-Net: Abundance-Guided Self-Attention Network for Spectral Unmixing-Aware Hyperspectral Remote Sensing Image Classification
链接: https://arxiv.org/abs/2609.06359
作者: Nafisa Anjum,Satavisa Dey Borno,Ananna Saha,Mir Faiyaz Hossain,Sifat Momen,Nabeel Mohammed,Shafin Rahman
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: This paper has been accepted at IEEE Transactions on Geoscience and Remote Sensing (TGRS)
Abstract:Hyperspectral image (HSI) classification plays a vital role in remote sensing applications, including agriculture, environmental monitoring, and urban analysis. However, its performance remains challenged by high spectral redundancy, noise sensitivity, and the difficulty of jointly modeling local material composition and long-range spectral dependencies. To address this, we propose AGSA-Net, an abundance-guided self-attention network that explicitly integrates spectral unmixing priors into the classification process. AGSA Net first estimates physically meaningful subpixel abundance maps subject to non-negativity and sum-to-one constraints, regularized by hybrid linear-nonlinear reconstruction decoder. The learned abundances are then used to construct an abundance affinity prior that guides a spectral transformer to emphasize class-discriminative interactions, and the resulting transformer features are fused with compact abundance descriptors for final prediction; in contrast to existing approaches that use abundance as auxiliary or concatenated features. Experiments on Indian Pines, Augsburg, and Berlin demonstrate the benefit of incorporating abundance- guided contextual modeling, particularly in heterogeneous urban scenes. The source code and trained models are available at: this https URL
[CV-234] MSCA-UNet: Multi-Scale Context and Attention U-Net for Image Segmentation
链接: https://arxiv.org/abs/2609.06356
作者: Sheng-Wei Chan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 5 pages, 2 figures, 1 table
Abstract:U-Net remains a practical baseline for image segmentation because of its simple encoder-decoder structure and skip connections. However, the bottleneck representation is still dominated by a limited set of receptive fields, while decoder features are propagated without explicitly emphasizing the most informative channels and spatial locations. This paper presents MSCA-UNet, a U-Net-based segmentation architecture that combines multi-scale contextual aggregation at the bottleneck with channel-spatial attention refinement in the decoder. The multi-scale module uses parallel atrous convolutions to capture contextual features at different receptive fields, while Convolutional Block Attention Modules (CBAMs) progressively recalibrate decoder features. Under identical experimental settings, the baseline U-Net achieves 96.9% mIoU on a held-out test set. Adding multi-scale context improves mIoU to 97.5%, while attention alone reaches 98.4%. Combining both mechanisms yields 99.1% mIoU, a 2.2 percentage-point improvement over the baseline. Parameter analysis further shows that the attention-only variant adds approximately 0.044M parameters, whereas the multi-scale module contributes most of the additional model capacity. The results support the view that multi-scale context enrichment and attention-based feature refinement provide complementary benefits within a U-Net framework.
[CV-235] ChildGaze: A Benchmark Dataset for Collaborative Behavior Understanding in Children
链接: https://arxiv.org/abs/2609.06353
作者: Sindhuja Penchala,Saketh Reddy Kontham,Prachi Bhattacharjee,S. Nima Mahmoodi,Daniel Fonseca,Sareh Karami,Mehdi Garemani,Sudip Mittal,Shahram Rahimi,Noorbakhsh Amiri Golilarz
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Understanding collaborative behavior in children is important for analyzing social participation, peer interaction, shared attention, and engagement during play and learning activities. Reliable recognition of these cues can support research in child development, educational analysis, and human-centered computer vision. However, estimating where a child is looking does not necessarily reveal whether the child is actively participating in a shared activity. To support this higher-level analysis, we introduce ChildGaze, a child-centered behavioral annotation dataset built on the ChildPlay video collection [1]. ChildGaze introduces two behavioral labels, collaborative and non-collaborative, assigned independently to each child within a frame. The dataset provides face, left-hand, and right-hand bounding boxes for children and adults and organizes the annotations at the row, person, and frame levels. The current release contains 27 annotated video files, 10,641 frames, and 73,268 body-part annotation rows. Annotation reliability was evaluated on 1,187 frames using independent annotations from two annotators. The collaboration labels achieved 93.16% raw agreement and a Cohen’s kappa of 0.8631, while bounding-box annotations achieved an overall mean IoU of 0.808. Baseline experiments with pretrained ViT and Swin Transformer models achieved up to 97.44% child-person-level accuracy and 96.80% frame-level accuracy, respectively. These results show that ChildGaze provides a reliable benchmark for studying collaborative behavior in naturalistic child-adult and peer interactions.
[CV-236] Radiation Rotation and Scale Invariant Feature Descriptor for Multimodal Image Matching
链接: https://arxiv.org/abs/2609.06343
作者: Yuanxin Ye,Tengfeng Tang,Tao Peng,Zhiqiang Han,Jiayuan Li,Mi Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 15 figures, 5 tables
Abstract:Multimodal image matching is a fundamental task for multi-source information fusion. However, geometric distortions and nonlinear radiometric differences (NRD) severely limit performance, especially under radiometric, rotation, and scale variations. To address this issue, we propose a radiation, rotation, and scale invariant (RRSI) feature descriptor. First, a dual-head regional sampling (DHRS) module simultaneously performs Cartesian and Log-Polar sampling on keypoint neighborhoods, retaining spatial structural properties while enhancing robustness to rotation and scale variations. We then jointly encode geometric and radiometric relations between multimodal images in a unified deep feature space, enabling feature encoding, interaction, and fusion across intra-modal, dual-head sampled, and inter-modal regions. Furthermore, we introduce a bidirectional cross-modal generative reconstruction constraint during training. By decoding implicit features into structural patches of the counterpart modality, this mechanism anchors modality-invariant geometric topologies without additional inference overhead. Experiments on optical-infrared and optical-SAR datasets demonstrate highly competitive matching performance and strong robustness to rotation and scale variations. RRSI supports the full rotation range from 0 to 360 degrees and scale factors up to four. Its generalization ability is further validated on multimodal images from computer vision, remote sensing, and medical imaging. The implementation will be made publicly available at this https URL .
[CV-237] One Shared LoRA Weight for MRI Reconstruction across Acceleration Factors
链接: https://arxiv.org/abs/2609.06338
作者: Zhiwei Zhao,Weikang Gong,Zhongnian Li,Xinzheng Xu
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Accelerated MRI reconstruction recovers images from undersampled k-space. However, different acceleration factors produce distinct artifact patterns. Existing methods often train separate models for each factor, leading to poor cross-factor generalization and high training and storage costs. We propose Shared LoRA, a parameter-efficient framework that freezes the pretrained SHFormer backbone and trains a single shared set of LoRA adapters together with a lightweight gating network. During training, undersampled inputs are generated by randomly sampling acceleration factors and their corresponding sampling masks, enabling the shared adapters to learn reconstruction knowledge across factors. Given the acceleration factor, GateNet generates layer-wise coefficients to dynamically modulate the residual strength of each adapter. Experiments show that Shared LoRA achieves the best or competitive PSNR and SSIM across acceleration factors, while its trainable parameters account for only about 5.3% of the total model parameters. Its performance at lower acceleration factors remains largely unaffected as the jointly trained factor set expands, and it generalizes stably to unseen neighboring factors.
[CV-238] CST-WM: A Causally Structured World Model for Embodied Visual Tracking
链接: https://arxiv.org/abs/2609.06302
作者: Junyi Hu,Shuaihang Yuan,Yi Fang
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 15 pages, 8 figures
Abstract:Embodied visual tracking requires a robot not only to react to the current view, but to choose actions that preserve or recover future evidence of a moving target under ego-motion, occlusion, and distractors. It is therefore a predictive decision problem over future target observability and apparent scale. A central difficulty is a task-specific form of causal hallucination: in action-conditioned prediction, a model can exploit the strong correlation between robot control and target-related observations by hallucinating a direct causal effect from the current action to target evidence, rather than letting action influence that evidence only through robot motion and the resulting observation change. The shortcut yields plausible futures with the wrong semantics for tracking-oriented planning and re-acquisition. We propose CST-WM, a causally structured world model that decomposes the latent state into target-evidence, robot, and observation branches and factorizes the transition so that direct action injection into the target-evidence branch is blocked, while action remains available to robot motion and observation updates. Combined with rollout-based model-predictive control, CST-WM supports both stable following and temporary target re-acquisition in one planning framework. On EVT-Bench and Habitat 3.0, covering standard tracking, target-loss recovery, and cross-dataset transfer, it improves following quality, distance-range control, safety, and re-acquisition over reactive and world-model baselines; offline diagnostics show better multi-step rollout fidelity, stronger planning-value consistency, and substantially reduced direct action leakage. For embodied visual tracking, future prediction alone is not enough: the predictive structure itself must align with how target evidence enters planning.
[CV-239] Object-Aware Background-Controlled Editing via Weighted Velocity Guidance
链接: https://arxiv.org/abs/2609.06288
作者: Wuji Wang,Yue Wu,Chenhao Yi,Shuhui Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Training-free image editing steers diffusion or flow-matching generative models at inference time by modifying prompt-conditioned denoising velocities. Existing velocity-based editors often apply prompt-induced residuals globally over the latent space and rely on the model to localize semantic changes implicitly. For object-centric edits, these residuals are rarely zero outside the target object, so small non-target components can accumulate during multi-step integration, causing background drift and unstable object boundaries. We propose Object-Aware Velocity Control (OAVC), a training-free framework that introduces object-level control into the velocity-integration process. OAVC decouples where semantic residuals are allowed to act from how they are injected into the dynamics. It constructs a background-anchored reference interface under the source prompt and then performs object-localized safe semantic injection under the target prompt. A constrained injection operator suppresses drift-inducing velocity components, while time-adaptive spatial weighting stabilizes the transition near object boundaries. OAVC requires no training or modification of pretrained model parameters. Experiments on object-centric image and video benchmarks with image and video rectified-flow backbones show improved background preservation, structural fidelity, boundary stability, and temporal consistency while retaining effective localized editability.
[CV-240] IM-ENGINE: Image Editing for Embodied Data Generation
链接: https://arxiv.org/abs/2609.06279
作者: Yian Wang,Junyi Cao,Xiaowen Qiu,Chuang Gan
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 26 pages, 19 figures, 3 tables
Abstract:Learning-based manipulation requires supervision that is both semantically meaningful and physically executable, but current data pipelines often provide only one of these properties. Human demonstrations capture intent but are costly to collect and constrained by the human-robot embodiment gap, while simulation can scale data generation but often under-specifies functional behavior. We present IM-ENGINE, a simulator-grounded pipeline that uses image editing as an intermediate representation for embodied data generation. Given a rendered scene with known geometry, depth, segmentation, and camera parameters, IM-ENGINE edits the image to inject task-relevant semantics, recovers explicit 3D state using simulator priors and an unchanged anchor object, refines the state in physics, and converts it into robot-executable supervision. We instantiate the pipeline for dexterous grasp synthesis and goal-state generation. For grasping, IM-ENGINE generates a human grasp in image space, recovers the hand-object interaction, retargets it to a robot hand, and refines it into physically validated robot grasps. For goal generation, it edits a rendered scene into a desired outcome, recovers the target-object pose, and refines it into physically valid, semantically meaningful goals and trajectories. This combination of generative semantic priors and simulator grounding enables scalable task-relevant supervision for robot learning.
[CV-241] A Comparative Study of GAN-Based Deep Learning Models for Pneumonia Detection in Chest X-Rays
链接: https://arxiv.org/abs/2609.06276
作者: Roshan Paudel,Aashish Ghimire,Pramod Acharya
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:This study evaluates pneumonia classification in chest X-rays using VGG19, MobileNetV2, ResNet50, and a custom CNN, and explores Generative Adversarial Network (GAN)-based synthetic data augmentation. MobileNetV2 achieved the highest reported accuracy of 88% with balanced class-wise performance. The custom CNN achieved pneumonia recall of 92.67% and precision of 79.43%, highlighting a precision-recall trade-off. Accuracy, F1-score, precision, recall, confusion matrices, and training curves were used to assess performance. Synthetic pneumonia images were combined with real images to investigate whether augmentation could improve classification performance. In the reported VGG19 comparison, augmented-data training accuracy reached approximately 100%, while validation accuracy remained near 50%, below the real-data validation accuracy. This experiment therefore did not demonstrate a validation-performance benefit from GAN augmentation. The classifier comparison highlights differences in accuracy and pneumonia recall, while the augmentation experiment indicates the need for further evaluation of synthetic-image quality and training settings. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2609.06276 [cs.CV] (or arXiv:2609.06276v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.06276 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-242] Adapting Vision Foundation Models to Acoustics for Pose-Free 3D Sonar Reconstruction
链接: https://arxiv.org/abs/2609.06261
作者: Kevin Zhang,Jingxi Chen,Mohamad Qadri,Russell Shomberg,Michael Kaess,Jia-Bin Huang,Adithya Pediredla,Christopher Metzler
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages. Jingxi Chen and Mohamad Qadri contributed equally as co-second authors
Abstract:Vision foundation models trained on Internet-scale RGB datasets enable remarkable capabilities across a range of tasks, from text-to-video generation to few-shot 3D scene reconstruction. An acoustic foundation model trained on large-scale sonar datasets could enable similar capabilities in the underwater domain, where turbidity and low-visibility conditions make conventional RGB foundation models inapplicable. Unfortunately, a lack of freely available large-scale sonar datasets makes training such a model from scratch impractical. In this work, we demonstrate that vision foundation models can be efficiently adapted to the sonar setting by (1) exploiting the geometric relationship between the two sensing modalities and (2) employing accurate physics-based noise models for synthetic data generation. The resulting sonar adaptation models enable new capabilities: For the first time, we experimentally demonstrate sonar-based pose-free 3D reconstruction.
[CV-243] One Model Two Worlds: Bidirectional Sonar-Optical Translation
链接: https://arxiv.org/abs/2609.06253
作者: Shengji Jin,Trung Tien Dong,Ahmed Lamidi,Chen Chen,Xiaomin Lin,Yi Sheng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 5 figures, 3 tables
Abstract:Translating between imaging sonar and optical cameras is valuable for underwater perception, but supporting both directions with separate models duplicates storage and computation. A unified bidirectional model is therefore attractive, yet existing approaches largely treat the two directions symmetrically despite their fundamentally different image-formation physics. We argue that sharing a generative model does not require sharing the physics. We introduce the Direction-Asymmetric Realism Bridge (DARB), which retains a shared diffusion-bridge trunk while routing direction-specific physical priors through asymmetric pathways: range-aware modulation for sonar-to-optical translation and polar ray-dependent processing for optical-to-sonar translation. We further show that symmetry in training is also costly: applying a common realism schedule reduces sonar-to-optical PSNR by 2.60 dB. Our Adaptive Realism Supervision (ARS) instead determines when, where, and how strongly perceptual supervision is applied from reconstruction quality and gradient balance. Together, DARB and ARS enable one bidirectional model to match the sonar-to-optical specialist within 0.11 dB PSNR, outperform the optical-to-sonar specialist by 0.70 FID, and surpass two independently trained BBDMs on seven of eight metrics.
[CV-244] MobileVLA-R1 2.0: RL-Enhanced Reasoning for Mobile Robot Control
链接: https://arxiv.org/abs/2609.06251
作者: Ting Huang,Yue Huang,Zeyu Zhang,Shuicheng Yan,Hao Tang
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Code: this https URL . Website: this https URL
Abstract:Grounding natural-language instructions into reliable and executable actions remains a fundamental challenge for vision-language-action (VLA) systems on mobile robots, due to the persistent gap between high-level semantic reasoning and low-level locomotion and manipulation control. Existing approaches often rely on implicit reasoning or monolithic action prediction, making it difficult to maintain coherent long-horizon decision making while producing precise and adaptable robot actions. To address this challenge, we propose MobileVLA-R1 2.0, an RL-enhanced VLA framework that explicitly couples structured embodied reasoning with executable mobile robot control. The framework learns multi-granularity reasoning over embodied trajectories through supervised Chain-of-Thought (CoT) alignment and reinforcement learning, improving reasoning-to-action consistency beyond purely behavioral supervision. To support both locomotion and manipulation, we further introduce a reasoning-conditioned action decoder that maps multimodal reasoning representations to task-level action targets, which are subsequently translated into embodiment-specific commands by robot controllers. This design provides a unified perception-reasoning-action interface while decoupling high-level action generation from robot-specific actuation. We conduct extensive evaluations on language-guided navigation, quadruped control, and humanoid mobile manipulation, covering VLN-CE, QUARD, and real-world deployments on Unitree Go2 and G1 robots. MobileVLA-R1 2.0 consistently outperforms strong VLA baselines, achieving an average 1.6 point improvement in SR on VLN-CE and a 10.0 point improvement in full-task success on real-world G1 mobile manipulation tasks over MobileVLA-R1, while demonstrating robust long-horizon instruction following and closed-loop execution across different robotic platforms.
[CV-245] ask-Oriented Semantic Communication for Hazard Warning and Remote Operation in Connected Vehicle Platoons
链接: https://arxiv.org/abs/2609.06242
作者: S M Sabit Bananee,Shahriar Hasan,Muhammad Mahbub Alam,Nafiul Rashid
类目: Networking and Internet Architecture (cs.NI); Computer Vision and Pattern Recognition (cs.CV)
备注: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
Abstract:Connected and automated vehicle platoons require reliable dissemination of task-relevant hazard information to enable appropriate downstream responses, with unresolved situations additionally requiring remote human intervention. However, the limited channel bandwidth of V2X communication makes high-volume sensor data transmission challenging, motivating the exchange of only task-relevant information rather than raw sensor data. This paper proposes an integrated semantic and task-oriented communication framework for cooperative hazard response and remote operation in automated vehicle platoons. The framework combines onboard instance segmentation and monocular depth estimation for task-oriented hazard decision-making with Stable Diffusion Variational Autoencoder (SD-VAE)-based semantic scene compression. Task-relevant hazard information and the compact semantic representation are jointly disseminated through a broadcast-based Emergency Semantic Message (ESM) over the existing V2X protocol stack. Evaluation demonstrates that SD-VAE consistently outperforms JPEG and JPEG 2000 on task-relevant reconstruction metrics, exhibiting graceful degradation rather than a sharp cliff effect under low-SINR conditions, demonstrating its feasibility for remote scene reconstruction when human intervention is required. Moreover, an extensive campaign of 7,290 simulation runs shows a low overall inter-vehicle collision rate of 0.99%, with collisions occurring mainly under extreme high-speed and short-gap conditions.
[CV-246] Spatial Attention Supervision for Defect Localization: Exploiting Ground-Truth Masks as Training Signal in Diffusion-Augmented Defect Detection
链接: https://arxiv.org/abs/2609.06232
作者: Sajjad Rezvani Boroujeni,Muskan Saraf,Gnana Tulasi Makineni,Tom Bush,Hossein Abedi
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
备注: 20 pages, 5 figures, 5 tables. Code: this https URL
Abstract:Ground-truth defect masks in industrial inspection datasets are typically reserved for evaluation. This paper repurposes them as spatial supervision signals during training of classification networks, teaching a model not just what to predict but where to look. The method adds an activation-based attention alignment loss that steers convolutional feature maps toward defect regions, in a mixed-supervision formulation that also accommodates samples without masks, such as diffusion-generated images. Combined with DDPM augmentation, synthetic images contribute quantity while masks contribute spatial precision. We evaluate 85 models (four CNN backbones under a 2x2 data/training factorial over five seeds, plus a Swin-V2-T transformer baseline) on the MVTec-AD bottle benchmark, with localization measured on held-out defect images excluded from classifier gradient updates. Main findings: (1) attention-guided training improves activation-based localization (Pixel-AUROC) by +18.0% for EfficientNetB0 with augmentation (p=0.005, Cohen’s d=2.6) and +18.7% for ResNet50 (p=0.008), significant in four of eight CNN settings (uncorrected for multiple comparisons) with no significant change in classification; (2) for EfficientNetB0 a data x training-mode interaction is significant (p=0.002), consistent with a super-additive effect (+13.6% combined vs +1.6% summed individual effects); (3) architectures with weaker spatial representations benefit most, whereas ConvNeXt-T shows no effect, apparently because its depthwise-convolution activations yield spatially uninformative channel-mean maps; (4) unsupervised PatchCore remains the strongest localizer (Pixel-AUROC=0.983), contextualizing the supervised gains. These results show that existing evaluation masks can act as practical training signals that measurably and reproducibly improve where defect classifiers attend.
[CV-247] Branch-Centric Tokenization and Test-Time Augmentation for Skeleton Generation
链接: https://arxiv.org/abs/2609.06218
作者: Zhengyuan Li,Chuanyu Pan,Yuanming Hu,Raymond Yeh
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Automatic skeleton generation involves predicting both joint positions and skeletal connectivity. However, existing approaches struggle to encode branch structures into token sequences and do not use test-time computation effectively. We study these choices within a unified autoregressive framework. First, we introduce branch-centric tokenization, a branch-aware representation that places structurally related elements next to each other and encodes connectivity directly in the sequence. Compared with standard BFS-style serialization, this representation yields more compact sequences. Second, we introduce view-augmented generation, a test-time augmentation procedure that applies axis-aligned rotations to the input mesh, maps all predictions back to a common frame, and selects the final skeleton based on mesh coverage and consistency among predictions from different views. Experiments show that our method achieves better skeleton prediction accuracy than state-of-the-art methods. In particular, our method reduces the CD-J2B error by 16.9% on the Articulation-XL2.0 dataset compared to the strongest directly comparable baseline, Auto-Connect. Qualitative results on in-the-wild meshes further demonstrate generalization across diverse inputs.
[CV-248] From Gaze to Meaning: A Training-Free AI Agent for Unified Grounding and Explanation ECCV2026
链接: https://arxiv.org/abs/2609.06208
作者: Shayan Nasiriboukani,Sara Atito,Mohammad Nezamipour,Muhammad Awais
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV 2026
Abstract:Understanding human attention is fundamental for scene interpretation, yet existing approaches often rely on heavily trained models that lack interpretability. Prior methods struggle to jointly reason about gaze targets, attended objects, and visual grounding without extensive supervision. To the best of our knowledge, this work introduces the first training-free Gaze Target Agent (GTA) for gaze-guided reasoning across tasks such as gaze target prediction, attention localization, and object identification. This is achieved by leveraging pretrained vision-language models, augmenting them with visually guided prompts, and employing a memory-based retrieval strategy for high-uncertainty samples to improve performance without additional training. We evaluate our approach using both quantitative metrics and qualitative results. Quantitatively, our method achieves state of the art performance on the GazeFollow and GazeHOI benchmarks. Qualitatively, our agent provides detailed semantic predictions, predicts the correct targets even when ground truth labels are wrong, and remains flexible without vocabulary constraints.
[CV-249] PhysWeep: Does a Video Generator Realize the Physics You Ask For?
链接: https://arxiv.org/abs/2609.06207
作者: Rasul Khanbayov,Hasan Kurban
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Image-to-video generators are often credited with absorbing physical dynamics as implicit world models, a claim the community currently checks with plausibility scores that ask whether a clip looks consistent with real-world motion. Plausibility is the wrong test on its own, because a clip can look natural while encoding the wrong value of the governing physical parameter, and no existing benchmark measures this gap directly. PhysWeep closes it with a fixed, label-free audit, treating a frozen generator as a black box, recovering the realized parameter from generated pixels, and reporting how often generation is trackable at all, how far the realized value sits from the requested one, and which, if either, of the literature’s two proposed failure mechanisms the data support. A deterministic-simulator positive control confirms every score is exactly checkable. Applied to three open generators across six sweep axes, PhysWeep finds a specific, reproducible, previously undocumented failure. Conditional on producing trackable motion, two of the three generate confident, well-fit dynamics that converge to one of a small number of fixed, wrong values selected by the sampling seed rather than by the request, reproducing across two independent model families, two physical systems, and an independent tracker. It matches neither the prior reversion nor the case-based clamping the literature anticipates, because the reversion target is seed-conditional rather than a single global default, and a leave-one-out selection rule rejects both; the in-range faithfulness slope is statistically indistinguishable from zero wherever a response is estimable at all. A benchmark averaging over seeds would never see this: each sample is confidently locked to a wrong constant, exactly the failure a plausibility score is structurally blind to. We release the protocol, suite, and analysis code.
[CV-250] One Perturbation Is Not Enough: Identifiability and Blind Baselines for Behavioral AI Evaluation
链接: https://arxiv.org/abs/2609.06190
作者: Rasul Khanbayov,Mariam Sohail,Ahmed Abdala,Hasan Kurban
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Behavioral evaluations perturb an input and read the induced change in the output in order to certify that a system uses that input. We show that the number of perturbations such a certificate requires is fixed, and that reporting a single perturbation cannot supply it. Where a response ratio is a property of the policy rather than of the test items, the behavioral record is a linear measurement of an exponent vector recording how much the output depends on each input, so perturbations identify input use exactly when their logarithms span the input space. At least n are needed for n inputs, an incomplete design confuses precisely the policies differing along the kernel of its design matrix, and sharpening a perturbation never substitutes for adding an independent one. We also derive in closed form the score such a test awards a policy that reads nothing, which is far from zero and which none of the probes we survey reports. Instantiating this where the correct response is fixed by dimensional analysis, we run a complete identifying set of three perturbations on three vision–language models reporting a physical quantity from video. All three score far below their own blind bound rather than above it, because each defaults to one of a small set of round calibration values that never matches what the scale asserts; none moves its relabeling response by a single exponent, and none is separable from the same model instructed to ignore the video.
[CV-251] SolarBench: A global solar energy nowcasting benchmark
链接: https://arxiv.org/abs/2609.06187
作者: Yuhao Nie,Stephen Campbell,Quentin Paletta,Liwenbo Zhang,Tao Jing,Samer Chaaraoui,Jonathan Giezendanner,Andea Scott,Tao Sun,Cong Feng,Max Aragon,Jacques Camier,Adam Jensen,Florian Kotthoff,Yuexing Yang,Yang Ming,Mengying Li,Stefanie Meilinger,Yupeng Wu,Adam Brandt,Sherrie Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:As the share of solar power grows, nowcasting weather-driven solar variability becomes critical for reliable energy system operation. State-of-the-art approaches increasingly apply deep learning to sky camera and geostationary satellite observations, but fragmented datasets and inconsistent evaluation make it difficult to determine whether reported improvements generalize across climates, cloud regimes, and photovoltaic (PV) systems. Here we introduce SolarBench, an open global benchmark for image-based solar nowcasting. SolarBench harmonizes more than six million sky and satellite images from 11 diverse sites spanning a decade, together with irradiance or PV output and auxiliary atmospheric data. An accompanying toolbox supports reproducible data access, processing, model development, and evaluation. Using SolarBench, we benchmark representative models and reveal a gap between average forecasting accuracy and the ability to capture rapid solar fluctuations. We further quantify predictability across cloud regimes and demonstrate data-efficient adaptation to new PV systems. SolarBench provides an extensible foundation for fair comparison and methodological innovation in solar nowcasting.
[CV-252] Multiple Myeloma Lesion Segmentation on Whole-Body Diffusion-Weighted Imaging via Efficient Anatomical Anticipation and Multimodal Confirmation
链接: https://arxiv.org/abs/2609.06165
作者: Mengmeng Zhang,Shengqian Huang,Junde Zhou,Xiaoping Wu,Hao Luog,Jing Wanga,Yicheng Sun,Jiao Li,Haibo Zhang,Sheng Xie,Fan Wangg,Qin Wangc,Huadan Xue,Yisheng Lv,Fei-yue Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Whole-body diffusion-weighted imaging (WB-DWI) is widely used for multiple myeloma (MM) assessment, yet automated lesion segmentation remains challenging due to limited anatomical delineation and the low specificity of marrow hyperintensity. Existing studies have introduced bone region-of-interest (ROI) information and apparent diffusion coefficient (ADC) maps to mitigate these ambiguities, but practical limitations remain. Bone ROI construction often relies on costly manual annotation, image registration, or dedicated bone models, while ADC is usually incorporated only through simple channel fusion, limiting its ability to provide complementary structural and lesion-discriminative cues. To address these limitations, we propose a two-stage framework for MM lesion segmentation on WB-DWI. In the first stage, we train a bone ROI generation model from ADC images without dedicated bone labels, providing an efficient and practical anatomical prior for lesion analysis. In the second stage, we propose Anatomy-guided Multimodal U-Net (AMU-Net), which leverages ADC in a manner consistent with clinical lesion assessment rather than treating it as a generic auxiliary modality. Extensive experiments demonstrate the effectiveness and practicality of the proposed method. It achieves the best overall performance among the evaluated methods, with a mean Dice score of 76.2%.
[CV-253] BDub: Production-Oriented Visual Dubbing
链接: https://arxiv.org/abs/2609.06144
作者: Bihan Li,Xinyang Li,Zeran Xu,Meiguang Jin,Junfeng Ma
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visual dubbing must synchronize mouth motion with replacement speech while preserving identity, appearance, and temporal consistency. Although X-Dub provides a strong mask-free video-editing baseline, its application to livestream and generated-video content reveals limitations in production-domain robustness, temporal and motion stability, identity and oral-detail preservation, and inference efficiency. We present \textbfTBDub, a production-oriented extension of X-Dub that combines task-adaptive post-training with task-aware few-step distillation. Post-training adapts the video DiT using production-domain data, production-specific conditioning and filtering, and enhanced audio features to obtain a 30-step Teacher. Distillation adapts DMD/DMD2 to conditional video editing and compresses the Teacher into a two-step Student. On 38 TalkVid clips, the Teacher improves all eight reported reconstruction, perceptual, identity, and synchronization metrics over X-Dub. In the MOS evaluation, it improves lip-sync consistency, identity consistency, and visual quality over X-Dub by 0.14, 0.95, and 0.90 points, while the Student achieves the highest lip-sync and visual-quality scores and remains close to the Teacher in identity consistency. In paired end-to-end generation timing from the first VAE encode through the final VAE decode on a single NVIDIA H20 GPU at 512\times512 , the Student reaches 7.13 effective FPS and reduces total latency by 13.93\times ; the DiT stage alone is accelerated by 42.49\times . The Student largely retains the Teacher’s generation quality and audiovisual synchronization. The code is available on GitHub at \this https URL, and the 30-step Teacher and two-step Student weights are available on Hugging Face at this https URL.
[CV-254] PASTEL: Panoramic Alignment for Monocular 4D Scene Reconstruction ECCV2026
链接: https://arxiv.org/abs/2609.06099
作者: Yuankun Yang,Yi Wei,Bo Bai,Wenyang Zhou,Li Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026. 11 figures
Abstract:Reconstructing 4D scenes from casually captured monocular video is vital for applications in virtual reality (VR) and embodied AI. Recent advances in 4D reconstruction and novel view synthesis have substantially propelled this capability. However, existing reconstruction methods generally cannot recover regions beyond visible camera limits. Consequently, we introduce a new paradigm that achieves 4D scene synthesis by combining visible-region reconstruction from monocular input with invisible-region generation beyond observable camera boundaries. We present Panoramic Alignment for Strategic Exploitation of Generative Priors (PASTEL). Specifically, PASTEL proposes panoramic scene alignment, a novel representation that reformulates the intractable 3D “invisible region” exploration into a tractable 2D directional trajectory planning. This is achieved by reducing the viewpoint planning from 6-DoF search to a 2D directional search with explicit visibility boundaries. By operating within this panoramic space, our method strategically identifies camera trajectories that maximize exploration beyond observable boundaries while minimizing viewpoint deviation. Experimental results show that PASTEL can not only extrapolate plausible scene content beyond the observable boundaries of input monocular videos, but also substantially boost monocular 4D reconstruction performance. PASTEL outperforms the previous state-of-the-art method by 0.9dB in full-image PSNR on the DyCheck IPhone dataset.
[CV-255] Automatic Red Teaming for Implicit Vulnerabilities of Text-to-Image Models ECCV2026
链接: https://arxiv.org/abs/2609.06094
作者: Chang Ma,Junlin Han,Shuo Chen,Runjia Li,Philip Torr,Jindong Gu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026
Abstract:Red-teaming Text-to-Image (T2I) models is essential for safe deployment, yet it remains particularly challenging against implicit adversarial prompts. Unlike explicit adversarial prompts that can be readily identified and blocked, implicit ones are much harder to detect: the prompts appear benign on the text surface yet still lead to inappropriate visual content. To address this, we propose Adversarial Probing for Implicit VulnErabilities (AdvPIE), a multimodal agentic framework to expose implicit vulnerabilities without requiring access to the parameters of target models. AdvPIE adopts a policy agent to generate and refine implicit adversarial prompts based on the feedback from a judge agent. To construct informative feedback, the judge agent provides modality-specific safety evaluation at both global and relative levels across iterations. To effectively leverage the feedback, we propose a novel Cumulative Adversarial Decoding strategy for the policy agent, which dynamically reweights token distributions to favor tokens that lead to more harmful images while preserving sampling diversity. Extensive experiments on standard and safety-aligned T2I models show that AdvPIE1 effectively uncovers implicit vulnerabilities, outperforming various baseline methods.
[CV-256] Report of the 8th LSVOS Challenge: Complex and Multimodal Video Object Segmentation ECCV2026
链接: https://arxiv.org/abs/2609.06078
作者: Chang Liu,Henghui Ding,Lingyi Hong,Ning Xu,Linjie Yang,Yuchen Fan,Canyang Wu,Jinrong Zhang,Xusheng He,Ce Bian,Xianjing Han,Jianlong Wu,Mingqi Gao,Sijie Li,Jungong Han,JeongRae Kim,Chaehyun Kim,Changwon Lim,Jungyoon Lee,Gyuil Lim,Doeon Kim,Seong-heum Kim,Pranjal Aggarwal,Sean Welleck,Yiwen Ren,Jianing Liu,Yingxin Wang,Kexin Zhang,Licheng Jiao,Lingling Li,Xu Liu,Jinxing Zhou,Suiyi Zhao,Yanghao Zhou,Ruohao Guo,Liangtao Shi,Jinxia Xie,Xiantao Hu,Ting Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 3 figures (6 panels), 3 tracks; report of the 8th LSVOS Challenge held in conjunction with ECCV 2026
Abstract:This report summarizes the 8th Large-scale Video Object Segmentation (LSVOS) Challenge, held in conjunction with ECCV 2026. The challenge evaluates video segmentation in three complementary settings: complex semi-supervised video object segmentation on MOSEv2, text-guided referring video object segmentation on MeViSv2-Text, and audio-guided referring video object segmentation on MeViSv2-Audio. We describe the tasks and evaluation protocols and review the methods of the top three teams in each track. Across the nine leading solutions, foundation segmentation models are combined with target-aware memory, multimodal reasoning, explicit target-existence verification, agentic interaction, and corrective tracking. These systems illustrate a broader transition from single-model mask propagation toward modular pipelines that reason about object identity, query validity, and temporal reliability.
[CV-257] NSFlow: End-to-End Differentiable Neuro-Symbolic Optical Flow for Visual Odometry
链接: https://arxiv.org/abs/2609.06074
作者: Yicheng Lin,Yuxiu Xu,WenDong Chen,Cong Li,Bin Han
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 5 figures
Abstract:Sparse optical flow provides stable inter-frame correspondence, playing a key role in Visual Odometry (VO) and Visual-Inertial Odometry (VIO). Classical optimization-based methods, such as Lucas-Kanade (LK), perform well under small displacements but are sensitive to large motions and illumination changes. Modern regression-based learning methods, while more robust in complex scenes, are often computationally heavy and lack explicit geometric consistency, making them less suitable for efficient VO/VIO front-ends. To bridge this gap, we propose a hybrid neuro-symbolic framework that combines the strengths of both paradigms. Our method uses a Convolutional Neural Network (CNN) to extract robust feature representations, which is fed into a differentiable LK optimizer to estimate optical flow in an end-to-end trainable manner. Through implicit differentiation, gradients are propagated across the iterative solver, enabling joint optimization of feature extraction and flow estimation. The resulting system integrates seamlessly into existing VO/VIO pipelines and runs in real-time on embedded platforms. Experiments show that our method outperforms conventional optimization-based flow in challenging conditions such as dynamic lighting and low texture, while also achieving higher accuracy and lower latency than purely regression-based alternatives. When deployed in a VIO system, our method demonstrates significant performance improvement, achieving an average error reduction of 42% on challenging datasets while enhancing tracking stability. The code is publicly available.
[CV-258] DriveZero: End-to-End Driving Beyond Human Demonstrations
链接: https://arxiv.org/abs/2609.06055
作者: Hao He,Chengcheng Hu,Zirun Su,Heng Zhang,Haisong Liu,Jinke Li,Haochen Tian,Zhenwei Shen,Hongyang Li,Zhichao Li,Yunchen Yang,Bochao Huang,Siyu Zhang,Kuangye Chen,Xiongjie Zhang,Wentao Dai,Hengchen Dai,Siyuan Liu,Zehao Huang,Naiyan Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Most end-to-end autonomous-driving systems learn by imitating human driving logs, leaving their learned behavior constrained by the quality and behavioral coverage of the recorded trajectories. This report presents DriveZero, an end-to-end system that learns driving behavior beyond human demonstrations. It decomposes driving into a perception model and an action model, pretrains each in the regime best suited to it, and combines them into one end-to-end planner. The two models call for different learning recipes: perception must understand the world, and benefits from massive and diverse visual data; action must interact with it, and requires closed-loop feedback. On the action side, we introduce DriveRL, a mixed-agent closed-loop reinforcement-learning framework. It converts real driving logs into interactive worlds, where a privileged teacher policy is trained with PPO through closed-loop rollouts. For the perception model, DriveVFM consolidates multiple frozen vision foundation models, including DINOv3, SigLIP2, SAM and Depth Anything V2, into a single backbone from raw images alone, requiring no task-specific annotations. DriveZero then unifies the two: a camera-only planner that distills the frozen DriveRL teacher through its rolled-out trajectories. The goal-conditioned teacher can moreover be queried under augmented driving intents, yielding diverse, goal-consistent supervision that logged data cannot provide. On nuPlan, DriveRL with value-guided test-time action search achieves a mean score of 93.57 across the Val14, Test14-hard, and Test14-random community splits in both non-reactive and reactive modes, exceeding the Log-Replay expert on all three splits. DriveZero achieves state-of-the-art performance on NAVSIMv1, NAVSIMv2 and the closed-loop HUGSIM benchmark without any human trajectory supervision.
[CV-259] Image-Scale Robustness and Visual Recognition Performance: A Cross-Architecture Analysis
链接: https://arxiv.org/abs/2609.06051
作者: Anish Monsley Kirupakaran
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:The sensitivity of visual recognition models to changes in image scale is well established, yet the factors governing this sensitivity across heterogeneous architectures remain unclear. In this work, we investigate whether scale robustness exhibits a common quantitative structure across modern vision models. We evaluate 20 pretrained ImageNet-1K classifiers spanning seven architectural families, including convolutional, mobile, efficient, and Transformer-based architectures. By systematically reducing input image scale, we construct scale-accuracy response curves and define a characteristic scale as a compact measure of the onset of substantial recognition degradation. We then examine the relationship between characteristic scale and baseline recognition accuracy, model parameter count, architectural family, and representation stability. A strong inverse association is observed between baseline accuracy and characteristic scale (Pearson r = -0.890, R^2= 0.792, p 10^-6). This relationship remains stable under bootstrap resampling, leave-one-architecture-out analysis, and leave-one-family-out analysis. In contrast, parameter count provides negligible additional explanatory power after controlling for baseline accuracy (p = 0.80), while architectural family does not provide significant incremental explanatory power. Furthermore, characteristic scale shows essentially no association with representation stability (r = -0.003, p = 0.991). These results indicate that, across the studied models, scale robustness is strongly organized by baseline recognition performance rather than simply by model size, architectural family, or representation stability. The study provides an empirical framework for characterizing scale robustness across vision architectures and identifies a reproducible accuracy-scale regularity that warrants further theoretical investigation.
[CV-260] What Does Animal Re-Identification Learn? Linear Biological Concepts and Their Origins in Visual Representations
链接: https://arxiv.org/abs/2609.06020
作者: Robert Nolting,Alexandra Schild,Moritz Weckbecker,Maximilian Schall,Gerard de Melo
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Conservation increasingly relies on camera traps that collect more wildlife imagery than experts can manually analyze, making animal re-identification (Re-ID) essential for monitoring individuals and populations. Yet understanding which cues drive model decisions is challenging for ViT-based Re-ID models, whose metric-learning objectives provide no explicit supervision for biological concepts. We ask whether such models nonetheless organize their representations along biologically meaningful axes. Using a DINOv3 backbone fine-tuned for Western lowland gorilla Re-ID with triplet-margin loss, we find that sex and age emerge as linear directions that generalize to held-out individuals, reaching up to 0.91 AUROC and being recoverable from a single image per individual. Activation steering further shows that the sex direction is causally used by the model, flipping a significant fraction of predictions to the opposite sex. Comparing off-the-shelf and fine-tuned backbones shows that Re-ID training does not create these concepts, but relocates them across the network. Finally, data attribution reveals that the representation we find reflects a graded biological axis, is redundantly encoded across the population and shaped by visually ambiguous individuals. Together, these findings show how interpretability can uncover both the biological structure and failure modes of Re-ID representations, providing a step toward auditable computer vision for wildlife monitoring.
[CV-261] FujinSplat: Seeing Through Smoke with RAW-Domain Gaussian Splatting
链接: https://arxiv.org/abs/2609.06017
作者: Gengjia Chang,Ziteng Cui,Shuhong Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 20 pages, 11 figures, including supplementary material
Abstract:The appearance of a smoky scene is shaped by two processes that a camera records together: the participating medium alters scene radiance in a view-dependent way, and the image signal processor (ISP) then remaps the result through a nonlinear tone and color transformation. Recovering a clean 3D scene requires separating both. Per-view sRGB dehazing acts only after the ISP has entangled them; standard 3D reconstruction ignores the medium and absorbs it into scene geometry and radiance. FujinSplat addresses the problem in the RAW domain, where the two processes remain separable. A per-scene Base ISP is fitted from the scene’s hazy RAW captures to its own camera renderings and then frozen, providing a fixed photometric anchor that performs no dehazing. Analyzing expert corrections reveals a compact, low-dimensional correction space identifiable from RAW alone. FujinSplat therefore fits per-view action answers at the training poses and trains a single scene-agnostic controller to regress them from RAW; the corrected views supervise one static 3D Gaussian representation, jointly with a bounded per-view residual that reconciles cross-view photometric inconsistencies. On the RealX3D real-world smoke benchmark FujinSplat clearly outperforms the strongest comparable baseline, ahead of both physics-based reconstruction and restoration-then-3DGS pipelines.
[CV-262] Depth-to-Image Synthesis-Driven Generative Unguided Depth Completion
链接: https://arxiv.org/abs/2609.06007
作者: Jiayi Yuan,Na Zhao,De Wen Soh
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Guided depth completion methods heavily depend on RGB quality and alignment, while unguided ones often suffer from limited precision due to the absence of explicit visual cues. In this paper, we present Depth-to-Image Synthesis-Driven Generative Unguided Depth Completion (GUDC), a new completion paradigm that innovatively bridges advanced 2D generative models with unguided depth completion, enabling semantics-aware depth inference without real RGB inputs. Our key idea is to exploit ControlNet’s powerful depth-conditioned generation capability to synthesize pseudo-images directly from sparse depth, effectively converting the original unguided setting into a semantics-guided one. To address the potential image-depth misalignment caused by depth sparsity, we propose a multi-level dense-to-sparse representation distillation strategy for ControlNet fine-tuning, where dense-depth features act as teacher signals to distill consistent structural representations for sparse-depth inputs. Furthermore, during pseudo-image-guided completion, we propose a pseudo-image semantic attention fusion module to adaptively extract informative semantic cues from pseudo-images while suppressing artifacts (e.g., texture hallucinations). Extensive experiments on KITTI and NYUv2 validate that our GUDC achieves superior accuracy and robustness over existing methods.
[CV-263] Geometry-Aware Test-Time Learning for Quantitative Spatial Reasoning ACM-MM2026
链接: https://arxiv.org/abs/2609.06004
作者: Gege Zhang,Shuaicheng Niu,Gang Dai,Lei Sun,Shuangping Huang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by ACM MM 2026
Abstract:Quantitative spatial reasoning in visual-language models (VLMs) aims to infer spatial distances and directional relationships among objects in 3D space from a 2D image and a natural language query. Despite recent progress, VLM spatial reasoning remains brittle under distribution shifts, largely due to the high cost of 3D supervision. As a result, models often produce inconsistent or contradictory predictions when faced with novel object configurations or rephrased spatial queries, revealing a misalignment between learned representations and underlying geometry. To address this, we propose TTL-SR, a geometry-aware Test-Time Learning framework for quantitative Spatial Reasoning that leverages geometric consistency constraints and unlabeled test data to adapt models to target domains. Specifically, TTL-SR augments the input query with geometrically coupled auxiliary queries, filters unreliable predictions via adaptive geometric triggering to construct structured token-level pseudo-labels, and updates model parameters under a geometry-aware multi-objective loss using only test data. Experimental results demonstrate that TTL-SR significantly boosts spatial reasoning performance, yielding 6.47% and 9.41% accuracy gains for Qwen3-VL-4B-Instruct and SpatialRGPT-VILA-1.5-8B on Q-Spatial-ScanNet dataset, respectively.
[CV-264] MORPHA: Morphology-Constrained Training and the Limits of Cross-Acquisition Transfer in Low-Resource Malaria Microscopy MICCAI2026
链接: https://arxiv.org/abs/2609.05990
作者: Favour Okechukwu Igwezeke,Chikodili Helen Ugwuishiwu,Joseph Uzochukwu Emesiani,Samuel Ifebuche Agada,Ekenechukwu Lilian Anozie,Mary Ofuru Kama,Adaobi Chiazor Emegoakor
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 2 figures. Author-submitted manuscript prior to peer review. The work was accepted for poster presentation at MIRASOL 2026, a MICCAI 2026 workshop
Abstract:In low-resource malaria microscopy, a model trained on one smear preparation routinely meets images from another, and how well morphology-based constraints transfer across this acquisition gap is unclear. We study this on real African field microscopy from Uganda (Lacuna), asking where encoding measured parasite morphology as a training constraint improves cross-acquisition transfer and where generic regularisation suffices. We present MORPHA, a morphological consistency constraint that derives stage-conditional statistics from the stage-annotated BBBC041 dataset and penalises predictions that deviate from them. Defined uniformly across binary, object-level, and stage-aware regimes without changing architecture or inference, it shapes training in the binary regime. The detection regime is a mapped boundary. On transfer from thin-smear cells to thick-smear field images, the constraint reduces the binary-classification generalisation drop by 30.8% (F1 0.578 to 0.699) at negligible within-domain cost and lowers in-distribution calibration error by 49% (ECE 0.0162 to 0.0082). A content-free control applying the identical constraint to random statistics recovers less of the drop (25.3% vs 30.8%), indicating the measured content, not constraining alone, contributes to the gain. Two standard confidence regularisers exceed the constraint on raw transfer, locating where morphology adds value and where generic regularisation suffices. We map two deployment-relevant boundaries: thin-smear statistics do not transfer to thick-smear detection (trophozoite AP@0.50 falls to 0.000), and cross-acquisition pseudo-labelling fails before filtering applies. Together these yield a morphology-grounded consistency signal and evidence-based guidance for malaria dataset and model design in low-resource settings.
[CV-265] Accelerating Diffusion Transformers with Gaussian Process Rectified Feature Cache ECCV2026
链接: https://arxiv.org/abs/2609.05981
作者: Zhirong Shen,Rui Huang,Chang Zou,Shikang Zheng,Jiacheng Liu,Peiliang Cai,Zhengyi Shi,Yaosong Du,Liang Feng,Xiaobing Tu,Jinkui Ren,Xiantao Zhang,Linfeng Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted by ECCV 2026
Abstract:Diffusion Transformers have become the dominant paradigm in generative AI, but their high computational costs severely hinder real-time applications. Prediction-based feature caching is widely used to accelerate diffusion transformers; however, as the number of steps increases, the deviation between its predictions and the reference full-compute trajectory gradually grows. An intuitive idea is to use an online regression model to dynamically correct this deviation, but it faces the issue of label data being unavailable during the acceleration process. This paper presents a statistical observation that the residuals between the features of full computation steps using caching methods and reference full-compute trajectory locally exhibit a zero-mean Gaussian distribution. By treating the features of full computation steps as noisy observations of reference features, the data acquisition problem is resolved. Based on this observation, a plug-and-play GP-Refiner correction framework is proposed. This method utilizes Gaussian Process Regression for correction and, leveraging the properties of GPR, introduces an uncertainty-adaptive computation strategy that triggers necessary full-computation calibration by monitoring the posterior variance in real time. Experiments demonstrate significant improvements across different models when combined with various state-of-the-art methods. Integrating the proposed framework with TaylorSeer reduces the computational load by 19.3% while improving PSNR by 0.9 dB and reducing LPIPS from 0.46 to 0.29. Code is available in this https URL.
[CV-266] Efficient and Robust Camera-independent Multiview 3D Geometric Reconstruction from Noisy Monocular Depth Estimation and Multiple Point Matching
链接: https://arxiv.org/abs/2609.05972
作者: Marius Leordeanu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages
Abstract:We present an efficient and robust method for 3D geometric reconstruction that is based solely on the camera-independent linear relationships among a given set of points, which are stable over time and robustly estimated using multiple point matches. We essentially learn, from correspondences between points across several frames, a linear geometric auto-regression matrix \mathbfW , which establishes how a point in 3D can be expressed as a linear combination of all the others. This matrix is constant and does not depend on the world coordinate system or the camera pose—it is an intrinsic property of the point set. We also show that the principal eigenvectors of \mathbfW , which all have eigenvalue 1 , provide a homogeneous representation of the 3D point configuration. The first version of our method takes advantage of noisy monocular depth maps in order to obtain, from multiple frames, a robust geometric auto-regression matrix \mathbfW of linear relationships between the 3D points. Thus, we build on recent advances in deep learning, which now provide monocular depth estimation models that are fast but very often noisy. Our approach handles noise through robust linear estimation over several frames. The second version of our method does not need monocular depth estimation maps. It applies in cases of weak-perspective projection, when the linear combinations between the 3D points can be robustly estimated from their 2D projections in the image. Note that the camera projection matrix is never used in our derivations. Consequently, our method does not recover camera pose, but only 3D structure. This is a key difference between our method and the related literature on 3D geometric reconstruction. Comments: 12 pages Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2609.05972 [cs.CV] (or arXiv:2609.05972v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.05972 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Marius Leordeanu [view email] [v1] Sat, 5 Sep 2026 08:20:53 UTC (18 KB)
[CV-267] st-Time Weak-to-Strong Alignment: Transferring Implicit Rewards from Weak to Strong Flow Models
链接: https://arxiv.org/abs/2609.05968
作者: Xin Xie,Fan Zhang,Dong Gong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Aligning a text-to-image generation flow model with a reward makes it follow objectives that the training data alone does not provide. Alignment fine-tuning delivers this by reinforcement learning (RL) or preference optimization, but it must be repeated for every checkpoint and returns a model fixed at the reward and strength it was trained with. Test-time alignment instead steers a frozen model during sampling, allowing task-specific and sample-specific guidance. Existing methods obtain this only by drawing the per-step signal from the reward function itself, through its gradient, or through a separately trained value function. We propose changing the supervision source: let a pair of weak models, not a reward function, supply the supervision. A source aligned model, kept together with its base as a source alignment pair, stores its training reward as an implicit, step-wise, KL-anchored signal expressed in the sampler’s own coordinates. We explore whether this model-form supervision can cross scale, and show that it does: our method, AlignGraft, aligns a larger, frozen, never-tuned model by adding the pair’s velocity difference during sampling. The transport is exact under a shared noising kernel and needs neither the reward nor its gradient at test time. The method has no schedules, only a single scalar that controls the alignment strength and can extrapolate it beyond that of the source alignment pair. Across image and video flow models (Stable Diffusion 3.5, FLUX, and Wan), the transfer lifts the frozen large model on preference, compositional, and text-rendering rewards, can exceed the source aligned model itself, and preserves the large model’s fidelity at a small constant sampling overhead. Extensive experiments show that one alignment run on a weak model produces supervision that the whole model family can reuse at test time.
[CV-268] FineHOI: Part-Aware Dense Representations for Zero-Shot Human-Object Interaction Detection
链接: https://arxiv.org/abs/2609.05959
作者: Francesco Tonini,Lorenzo Vaquero,Mohammad Mahdi Derakhshani,Cees Snoek,Elisa Ricci,Cigdem Beyan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ACM Multimedia 2026
Abstract:Human-Object Interaction (HOI) detection aims to localize humans and objects in images and classify their interactions. Zero-shot HOI focuses on recognizing interactions that are not observed during training, requiring models to generalize beyond seen verb-object compositions. Recent approaches leverage Vision-Language Models (VLMs), benefiting from rich semantic representations. However, they often rely on global or detector-centric features that compress interaction cues and hinder fine-grained spatial reasoning. To overcome this limitation, we propose FineHOI, a zero-shot HOI framework that explicitly models interactions from dense patch-level features. Our approach is motivated by the observation that human-object interactions are defined by localized spatial relationships, which are not preserved by global and detector-centric representations. To this end, we introduce an Adaptive Part-Level Attention module that decomposes humans and objects into semantically coherent parts via unsupervised clustering, and re-weights them based on their interaction relevance. These representations are then integrated through a Region-Aware Interaction Transformer that integrates part-aware and global features and produces the final HOI embedding. Extensive experiments demonstrate that FineHOI consistently outperforms existing zero-shot HOI methods, achieving particularly strong gains on unseen interactions. Code is available at this https URL.
[CV-269] STP-BENCH: A Unified Systematic Benchmark for Virtual Spatial Transcriptomics from Histopathology Images
链接: https://arxiv.org/abs/2609.05956
作者: Youngmin Chung,Ji Hun Ha,Andrew H. Song,Cristina Almagro-Pérez,Chaeyoung Seo,Won Jun Suh,Jeong Won Beom,Kyoung Bin Oh,Eytan Ruppin,Faisal Mahmood,Joo Sang Lee
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Spatial transcriptomics (ST) provides unprecedented insights into tumor heterogeneity by capturing spatially resolved gene expression, yet its high experimental cost hinders large-scale adoption. Consequently, computational approaches that predict spatial gene expression directly from hematoxylin and eosin slides, termed virtual ST, have rapidly emerged. Despite this progress, assessing advances in the field remains difficult due to insufficient benchmarking: prior studies rely on small, heterogeneous datasets, inconsistent training and inference pipelines, and limited evaluation of biological interpretability and model robustness. To address these gaps, we present STP-BENCH, a standardized benchmark for virtual ST models. STP-BENCH comprises six cancer types spanning two ST platforms (Visium and Xenium), with each training dataset containing more than 30,000 spots and at least 15 slides to ensure statistical reliability. We evaluate 21 predictive approaches, re-implemented with a unified pathology foundation model as the morphological encoder when architecturally applicable. Beyond conventional benchmarks that report average predictive accuracy on highly variable genes, we systematically examine which genes and gene sets are recoverable from histomorphology. We further evaluate the downstream biological utility of predicted profiles through cell-type deconvolution and spatial domain identification, and assess model reliability under domain shifts and data scaling. Notably, unified morphological encoding substantially re-orders model rankings established in prior studies, indicating that architectural innovations and image encoding have been conflated in previous evaluations. We publicly release STP-BENCH to support reproducibility and serve as a community benchmark at this https URL.
[CV-270] ProtoRAG : Prototype-Based Retrieval Augmentation for Few-Shot Fine-Grained Remote Sensing Object Detection
链接: https://arxiv.org/abs/2609.05953
作者: Jian Wang,Yuxiang Hong,Chufeng Zhou,Chao Pang,Xiaokang Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Few-shot fine-grained object detection (FGOD) in remote sensing imagery is challenging because limited annotations must support both object localization and discrimination among visually similar subcategories. Although multimodal large language models (MLLMs) provide strong coarse object localization, they lack explicit visual evidence for reliable fine-grained recognition. To address this limitation, we propose ProtoRAG, a prototype-based retrieval-augmented framework that decouples coarse localization from fine-grained recognition by equipping MLLMs with an external object-level visual memory. To construct a reliable visual memory from limited support samples, we introduce Discriminative Prototype Space Learning (DPSL), which encourages discriminative and prototype-stable representations through supervised contrastive learning and prototype-consistency regularization. We further develop an uncertainty-guided candidate-constrained reasoning strategy that augments MLLMs with retrieved candidate-specific visual references and invokes multimodal reasoning only for ambiguous instances. Extensive experiments show that ProtoRAG consistently surpasses representative baselines in nine few-shot settings, outperforming the strongest baselines by 14.80, 2.27, and 4.04 mAP _50 on MAR20, HRSC2016, and FAIR1M-2.0, respectively.
[CV-271] Beyond Classification: Structured Supervision Aligns Visual Evidence with Medical Semantics BMVC2026
链接: https://arxiv.org/abs/2609.05937
作者: Hexiang Bai,Hanyang Xu,Xiaoxue Li,Xiaoliang Wu,Shangde Gao,Hongxia Xu,Ke Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 22 pages,7 figures,Accepted to BMVC 2026
Abstract:Vision Transformers (ViTs) have shown immense potential in medical image analysis. However, standard pre-training via global image classification suffers from spatial collapse, where models rely heavily on background shortcuts rather than localising critical foreground lesions. To overcome this limitation and align visual evidence with precise medical semantics, we systematically investigate alternative pre-training this http URL, we evaluate three independent forms of structured supervision: topological priors via graph self-supervision, dense pixel-level constraints via segmentation, and cross-modal semantic grounding via image-text pairs. Notably, our empirical analysis reveals that while all three forms of structured supervision successfully alleviate the global pooling bottleneck and steer visual attention towards foreground regions, image-text alignment achieves the most superior performance. By embedding high-dimensional diagnostic logic, the cross-modal approach not only anchors attention on precise visual evidence but also enables profound abstract reasoning. Extensive experiments demonstrate that this semantically enriched pre-training fundamentally enhances the model’s feature representation. Consequently, when fine-tuned for downstream clinical classification tasks, our models achieve superior accuracy and yield highly interpretable attention maps focused on true pathological features, vastly outperforming vanilla classification baselines.
[CV-272] AVSplat: Dense-View Feed-Forward 3D Gaussian Splatting with Assist-View Preconditioning
链接: https://arxiv.org/abs/2609.05925
作者: Muyu Xu,Fangneng Zhan,Yu Wei,Hanspeter Pfister,Shijian Lu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Pose-free feed-forward 3D Gaussian Splatting enables novel view synthesis from uncalibrated multi-view images. Although more views should improve performance, existing methods often degrade with dense-view inputs because global aggregation spreads attention over many tokens, and naive voxel fusion averages many Gaussians into overly smooth representations. We present AVSplat, a framework that turns additional views into reliable signals for both aggregation and representation. Before global attention, each view performs a single lightweight interaction with a small set of Assist Views chosen for relevance and diversity, and the cached features provide a focused scene context that stabilizes correspondence. For representation, we use adaptive temperature-aware voxel fusion that sharpens attribution under high occupancy, guided by occupancy and point confidence. Crucially, AVSplat restores positive view scaling where performance remains stable or improves as more input views are added, instead of degrading in the dense-view regime. Ablations show that Assist View Preconditioning is primarily responsible for preventing dense-view degradation, while Occupancy-guided Voxel Fusion contributes most of the single-point image-quality gains.
[CV-273] PAI-Actor: Cinematic Multi-Character Replacement in Dynamic Scenes
链接: https://arxiv.org/abs/2609.05918
作者: Bangxun Tang,Heyuan Gao,Yiren Song,Guian Fang,Zijian He,Jie Yang,Mike Zheng Shou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We present PAI-Actor, a cinematic multi-character animation framework for character replacement in dynamic movie scenes. Unlike conventional animation systems that mainly drive a single static image or a single subject, our goal is to replace and animate multiple characters within real video clips while preserving the original scene dynamics, camera motion, and background content. This setting is particularly challenging because the generated characters must remain consistent with the source performance in motion and interaction, while also matching the surrounding background in lighting, shadow, composition, and overall cinematic appearance. To address this, we formulate multi-character animation as a structure-guided human recovery problem and build a movie-driven training pipeline from high-quality film data. Furthermore, to support practical cinematic production, we introduce a bidirectional-to-autoregressive distillation framework: we first train a bidirectional diffusion transformer for high-quality short-clip generation at 1080P resolution, and then distill it into an autoregressive video-to-video model for efficient inference and longer video generation. Experiments show that PAI-Actor enables high-fidelity multi-character animation with strong scene consistency, cinematic visual quality, and efficient long-form generation.
[CV-274] STAR-Pro: Stage-Wise Token Adaptive Reduction with Progressive Refinement for Efficient Large Vision-Language Models
链接: https://arxiv.org/abs/2609.05916
作者: Yichen Guo,Tinghao Wang,Qizhe Zhang,Lingbei Meng,Yuan Zhang,Jiajun Cao,Hao Jiang,Chenwei Wu,Jixian Wu,Sixiang Chen,Tao Luo,Hongyang Cheng,Kai Tang,Chenxi Li,Renyuan Li,Xiande Huang,Wenya Wang,Shanghang Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 26 pages, 7 figures
Abstract:Large vision-language models (LVLMs) achieve strong multimodal understanding, but the hundreds to thousands of visual tokens they process impose substantial computational overhead, motivating training-free visual token pruning. In this work, we conduct two complementary analyses of visual token pruning. First, we measure the feature-space coverage of tokens retained before cross-modal fusion and find that aggressive pruning discards substantial visual information. Second, we track text-to-visual attention across decoder layers and find that the visual tokens considered important change substantially with depth, making one-shot pruning decisions unreliable. Together, these findings show that effective pruning should preserve broad visual coverage before fusion and progressively refine the retained tokens as cross-modal evidence evolves during fusion. We therefore propose STAR-Pro (STage-Wise Adaptive Token Reduction with Progressive Refinement), a training-free two-stage framework. Its Adaptive Stage applies pivoted QR to construct an over-budget feature-coverage candidate pool, while its Progressive Stage uses evolving text-to-visual attention at selected decoder layers to prune a nested survivor set under a target layer-average token budget. Extensive experiments across seven LVLMs spanning multiple architectures and 18 image and video benchmarks demonstrate the effectiveness of STAR-Pro under aggressive pruning. On LLaVA-Video-7B, STAR-Pro reduces visual tokens by 90.5%, retains 92.7% of baseline performance, and achieves a 2.24\times measured inference speedup. Code is available at this https URL.
[CV-275] FreeTransformSR: Efficient Lightweight Image Super-Resolution via Free Low-Rank Learnable Transform
链接: https://arxiv.org/abs/2609.05912
作者: Hongji Li,Yunhui Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 5 figures
Abstract:Single image super-resolution aims to reconstruct high-resolution images from low-resolution inputs. This paper proposes FreeTransformSR, a novel lightweight super-resolution network based on a channel-wise free low-rank learnable transform. The transform learns task-adaptive basis functions in a data-driven manner, enabling adaptive feature modulation with minimal parameter overhead. To further enhance high-frequency detail recovery, we introduce a local feature modulation branch that complements transform-domain processing with depthwise convolution. In addition, a soft complexity adaptive module dynamically fuses the outputs of local convolution and window self-attention branches through a lightweight gating network, adaptively adjusting the fusion ratio based on regional texture characteristics. An adaptive intensity modulation strategy is also incorporated to adjust transform-domain response strength at the sample level, enabling the network to dynamically adjust processing intensity according to input features. Extensive experiments on five benchmark datasets demonstrate that FreeTransformSR achieves competitive PSNR/SSIM performance with significantly fewer parameters and FLOPs. Specifically, FreeTransformSR achieves 32.41 dB on BSD100 x2 and 27.00 dB on Urban100 x4 with only 595K parameters, while delivering faster inference speed than competing methods, making it well-suited for deployment in resource-constrained scenarios. Source code is available at: this https URL.
[CV-276] GenPuzzle: Benchmarking Visual Reasoning in Image Generation Models
链接: https://arxiv.org/abs/2609.05902
作者: Changpeng Zhao,Yiren Song,Jinpeng Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent image generation systems increasingly combine multimodal understanding, reasoning, and synthesis, suggesting that they may do more than render plausible scenes. Yet existing evaluations emphasize aesthetics, prompt alignment, compositionality, or text-based answers, leaving unclear whether these systems can solve visual problems and faithfully express solutions in pixels. We introduce GenPuzzle, a benchmark for reasoning-centric image generation. GenPuzzle contains 2,005 problems across 12 tracks, spanning pattern completion, spatial construction, mazes, Sudoku, nonograms, tangrams, board games, matchstick puzzles, orthographic projection, and mathematical visual proof. Each task provides a visual puzzle and requires an image output that preserves the input state while executing a logically valid solution. GenPuzzle uses task-specific evaluation protocols: discrete grid outputs are transcribed and verified programmatically, while visually complex outputs are assessed with tiered, multidimensional, or binary multimodal large language model (MLLM) rubrics. We further select the automatic judge by measuring agreement with human reference scores. Across three frontier generators, the strongest model reaches only 40.57 Macro Overall, revealing frequent failures in logic, geometry, state preservation, and instruction execution. GenPuzzle provides a testbed for measuring progress from image rendering toward visual problem solving.
[CV-277] Multimodal Resource-Exhaustion Attacks on Vision-Language Models via Joint Pixel-Prompt Optimization CCS2026
链接: https://arxiv.org/abs/2609.05889
作者: Zhaoxiong Ni,Yatie Xiao,Chi-Man Pun,Fei Peng,Qingxiao Guan,Keke Tang
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
备注: Extended version of the paper accepted by ACM CCS 2026
Abstract:Resource-exhaustion attacks against autoregressive vision-language models (VLMs) typically assume unimodal threat models, treating the image branch as the primary optimization surface while holding user-visible prompts fixed. Even recent loop-centric variants remain confined to this single-channel paradigm, leaving the exploitation of availability unexplored as a cross-modal optimization problem over jointly controllable input surfaces. We introduce Joint Pixel-Prompt Optimization (JPPO), the first compound adversarial framework elevating the visible prompt to a first-class adversarial variable alongside image perturbations. Under a restricted joint-input threat model, JPPO performs coupled, stagewise optimization over both the pixel and prompt surfaces. This produces synergistic cost amplification, mechanistically distinct from loop-dependent failures, exhibiting negligible loop incidence in our experiments. Evaluating five open-source VLM families on MS COCO and ImageNet under an 8/255 infinity-norm budget, JPPO achieves over 4.6x latency and 5.3x energy amplification on Qwen2.5-VL-7B, and over 36.6x latency with 32.7x energy amplification on BLIP-2. This represents the strongest cost amplification among directly compared baselines while requiring substantially fewer optimization iterations. Ablations confirm this amplification arises from multimodal coordination rather than prompt length or isolated modalities. These findings reveal structural blind spots in current VLM serving defenses, motivating cost-aware robustness evaluation as a first-class security requirement for multimodal deployments. Comments: Extended version of the paper accepted by ACM CCS 2026 Subjects: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV) ACMclasses: K.6.5; I.2.6; I.2.7 Cite as: arXiv:2609.05889 [cs.AI] (or arXiv:2609.05889v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.05889 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-278] UniFusion: Sparse-View 4D Reconstruction via Unified Spatio-temporal Depth Alignment
链接: https://arxiv.org/abs/2609.05888
作者: Yongzhe Lyu,Shaofei Wang,Yixin Chen,Siyuan Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:In this paper, we address the challenging problem of 4D reconstruction from sparse-view videos. This setup usually relies on monocular depth estimation to provide priors for the reconstruction model. A key challenge arises from limited cross-view overlap and temporal variation, making monocular depth predictions inconsistent across views and time. Existing methods align spatial and temporal dimensions in separate stages, requiring foreground segmentation masks while failing to leverage temporal cues for cross-view alignment. Contrary to these methods, we propose a unified spatial-temporal depth alignment framework that jointly resolves cross-view and cross-time inconsistencies without distinguishing foreground/background. Our method represents depth maps across views and time as a set of spatio-temporal neural fields. This representation not only yields fast convergence, but also captures spatio-temporal correlation among depth maps implicitly, without dependence on external segmentation/tracking models. We also propose a multi-view depth-order loss while leveraging the classic scale-and-shift-invariant loss to further improve the final depth quality. The aligned depths initialize and supervise Gaussian splatting models for 4D reconstruction. Experiments on Ego-Exo4D and EgoHuman demonstrate that our improved depth alignment substantially benefits dynamic Gaussian-splatting-based reconstruction methods for novel-time/view synthesis and geometry accuracy/consistency.
[CV-279] FACT: A Forensic Agent with Compiled Tool-Use Trajectories for AI-Generated Image Detection
链接: https://arxiv.org/abs/2609.05876
作者: Jiaoyang Chen,Bin Hu,Jingyu Hu,Kun Zhou,Qin Zhang,Zhengzhe Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:AI-generated image detection is increasingly open-world: new image generators produce highly realistic images that make visual artifacts harder to identify. Existing detectors usually rely on a fixed set of forensic cues, so a detector that works well for one generator family may fail on another. We introduce FACT (Forensic Agent with Compiled Tool-use Trajectories), which learns an image-conditioned tool-use policy for forensic analysis. Instead of applying a fixed detector, FACT decides which forensic tools to call, interprets the returned evidence, and stops when sufficient evidence has been collected. FACT follows an Evolve–Distill–Refine pipeline: it evolves an execution-verified forensic skill, compiles the skill into action–observation tool-use trajectories, distills them into a compact agent, and refines the policy with cost-aware GRPO. Across two internal and four public benchmarks, FACT achieves the best performance among all compared methods, including on recent unseen generators, deepfakes, and manipulated images.
[CV-280] Hierarchical Prompt Injector for Domain Generalization Segmentation ECCV2026
链接: https://arxiv.org/abs/2609.05864
作者: Xin Kun Lin,Ruoyu Guo,Jiaqi Guo,Maurice Pagnucco,Yang Song
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV2026
Abstract:Domain Generalized Semantic Segmentation (DGSS) is a challenging task, as vision models often rely on low-level appearance cues that change across domains. In contrast, structural attributes exhibit cross-domain stability, motivating the use of structural priors for DGSS. Existing methods use prompt learning to transfer such priors into DGSS models, but typically encode each class as a single holistic prompt. Moreover, these methods apply prompts uniformly to all pixels, offering no mechanism to adapt when only a subset of object regions is visible due to viewpoint changes, occlusion, and environmental variation. We address this with \textbfSpatial Hierarchical Prompts (SHP) that enrich each class with region-level geometric anchors capturing structural appearance from distinct viewing angles, ensuring complementary coverage under arbitrary viewpoints. Additionally, we propose the \textbfHierarchical Prompt Injector (HPI), which enables spatially adaptive prompt injection in foundation models. HPI spatially grounds prompts by modeling their semantic relevance and spatial influence with visual features. Considering the difficulty of learning spatially and semantically aware prompt injection, we further introduce auxiliary supervision to align hierarchical prompts with their corresponding object regions. We achieve 70.62% and 72.74% mIoU on synthetic-to-real and real-to-real benchmarks, respectively. Code and checkpoints are released at this https URL
[CV-281] Selective Posterior Margin Regularization for Forward-Corrected Classification
链接: https://arxiv.org/abs/2609.05859
作者: Zexing Zhang,Jichao Li,Tianyang Lei,XiongYi Lu,Yang Kewei
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
备注:
Abstract:Learning with class-conditional label noise often relies on a transition model from latent clean classes to observed annotations. Forward correction embeds this transition in the likelihood, yet finite-sample networks may still memorize corrupted labels. The corrected likelihood also induces a reverse posterior over the clean classes that could explain each annotation. When its leading class differs from the annotation, the model and transition matrix provide evidence against that annotation, but the leading alternatives can remain nearly tied. We introduce Selective Posterior Margin Regularization (SPMR), which preserves the Forward objective and converts this disagreement into a graded update on the clean classifier. SPMR selects the leading reverse-posterior class, scales a detached pairwise margin by the separation between the two leading posterior classes, and assigns correspondingly little influence to diffuse conflicts. The gap factorizes into transition- adjusted pairwise separation and the posterior mass carried by the leading pair. The active margin follows the locally minimum-norm logit direction that enlarges the selected pairwise margin. Across five known-transition benchmarks, SPMR improves full-length Forward by 2.5-7.0 percentage points and remains 0.7-2.5 percentage points above Forward with Mixup and early stopping. Matched interventions support distinct gains from the posterior-space coefficient, transition-adjusted target, and pairwise action. The same design transfers to estimated transitions, human annotations, architectural changes, and stronger Forward recipes. The formulation uses latent-class evidence already available inside Forward correction without promoting every posterior conflict to a corrected label.
[CV-282] Map the Possibilities: Spatial Belief Fields for Language-Goal Aerial Navigation
链接: https://arxiv.org/abs/2609.05841
作者: Haotian Xu,Yue Hu,Zhengqiu Zhu,Yong Zhao,Wenguan Wang,Quanjun Yin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Language-goal aerial navigation requires an agent to local- ize a potentially unobserved target from relational instruc- tions and partial observations, and translate this inference into metric actions in large-scale continuous environments. Existing methods often reduce language grounding to one single waypoint or action, prematurely collapsing the spatial uncertainty inherent in incomplete evidence and ambiguous relations. To address this limitation, we introduce SBFNav, a closed-loop navigation framework centered on a language- conditioned Spatial Belief Field (SBF). Unlike ego-centric maps that primarily record what has been observed, SBF rep- resents a task-conditioned distribution over plausible target locations, preserving multiple spatial hypotheses under par- tial evidence. At each step, this distribution is updated from accumulated observations as new evidence becomes avail- able. Built on this representation, SBFNav selects the goal that best aligns with the instruction and observations as a met- ric waypoint for control. Experiments on both the original and revised CityNav benchmarks achieve the best reported overall performance. On the Test Unseen split, our method improves SR from 25.91% to 32.29% and SPL from 19.63% to 30.43%. Ablation studies further confirm the advantages of spatial-belief modeling over single-point prediction.
[CV-283] CoRe-SAM3: Conditional Semantic–Visual Reconciliation for SAM3 Crack Segmentation
链接: https://arxiv.org/abs/2609.05816
作者: Shipeng Liu,Liang Zhao,Dengfeng Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Code will be available at: this https URL
Abstract:Crack segmentation requires a model to recognize target semantics while accurately recovering thin, low-contrast, and topologically continuous local structures. Although SAM3 provides strong open-concept segmentation, its direct application to the crack domain still misses weak cracks, activates crack-like background regions, and produces local boundary errors. We first diagnose the functional differences between the internal prompt-conditioned semantic representation and native visual representation of SAM3 on five crack datasets. The results show that the semantic representation already carries most task information for crack prediction, whereas the utility of the visual representation depends on the current semantic state. Directly combining the two representations does not yield consistent gains. Based on this finding, we propose Conditional Semantic–Visual Reconciliation, termed CoRe. CoRe retains semantic prediction as the primary decision path, applies lightweight semantic calibration to adjust the target-domain decision mapping, and uses spatially aligned native visual evidence to generate a zero-initialized, bounded, and regularized conditional residual that selectively corrects existing predictions. Across five domains, CoRe-SAM3 improves the average Crack IoU from 62.34% to 70.47% and clDice from 81.98% to 89.24%, while introducing only 18.914 K trainable parameters. Prediction-transition analysis further shows that CoRe corrects an average of 34.38% of native errors, with a damage rate of only 0.23% on pixels correctly classified by native SAM3. These results demonstrate that constrained prediction correction based on the functional differences between internal representations provides an effective and parameter-efficient target-domain adaptation strategy for vision foundation models with strong task-specific semantic priors.
[CV-284] MolParser-Mobile: Ultrafast OCSR System for Large-Scale Chemical Literature Mining
链接: https://arxiv.org/abs/2609.05807
作者: Xi Fang,Haocheng Lu,Han Lyu,Chengxiang Luo,Linfeng Zhang,Guolin Ke
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Optical Chemical Structure Recognition (OCSR) is a fundamental component of chemical literature mining, enabling molecular database construction, reaction extraction, and AI-driven scientific discovery. Despite substantial progress in recognition accuracy with recent deep learning-based methods, inference throughput remains a critical bottleneck that limits web-scale deployment. To address this challenge, we propose MolParser-Mobile, an AutoML-optimized lightweight end-to-end OCSR framework. MolParser-Mobile contains only 9.98M parameters, while reaching a throughput of 1,520 molecules per second on a single NVIDIA RTX 4090D GPU. Despite its compact design, it maintains competitive and, on several benchmarks, superior recognition accuracy.
[CV-285] Closed-Loop Evaluation of Birds-Eye-View Maps from Cross-View Transformers as Inputs to Behavior-Cloning Policies
链接: https://arxiv.org/abs/2609.05783
作者: Felipe Carlos dos Santos,Eric Antonelo,Gustavo Claudio Karl Couto
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In autonomous driving, Bird’s-Eye View (BEV) representations provide a structured, top-down abstraction of the vehicle’s surroundings and have become a key input modality for Behavioral Cloning (BC) policies. While ground-truth BEV maps are readily available in simulation, real-world deployment requires replacing them with camera-predicted counterparts - a substitution that introduces perceptual errors whose downstream impact on closed-loop driving performance is not well understood. In this work, we investigate the use of Cross-View Transformer (CVT)-predicted BEV maps as direct policy inputs for a BC agent in the CARLA simulator. We propose a six-channel BEV representation covering road surface, planned route, lane boundaries, vehicles, pedestrians, and traffic lights, and introduce a Kernel Density Estimation (KDE) weighting scheme that rebalances the segmentation loss towards underrepresented driving maneuvers such as curves and intersections. Closed-loop evaluation across two CARLA towns shows that the KDE-weighted model is the only predicted-BEV agent to complete a full episode without infractions, despite not achieving the highest aggregate IoU. This discrepancy reveals that global segmentation metrics are poor proxies for driving performance: what determines navigation success is prediction quality at geometrically critical locations, and the route channel emerges as the primary bottleneck for reliable agent navigation under predicted BEV inputs.
[CV-286] AAMBERS-UAV: Acquisition-Aware Multimodal Backbone Evaluation and Ranking for UAV Weedy Rice Segmentation
链接: https://arxiv.org/abs/2609.05762
作者: Tarek Rahman,Nazim-E-Alam,Md Kishor Morol,Jannatun Noor
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:UAV image collections contain spatially and temporally related frames, yet semantic-segmentation benchmarks commonly split them at image level. Such splitting can place samples from one acquisition in both model development and testing, obscuring transfer to a genuinely new survey. Using the 734-sample WeedyRice-RGBMS-DB, we fix a 124-image target-acquisition test set and compare two protocols with identical train, validation, and test counts: target-held-out, which excludes the target acquisition from development, and target-exposed, which admits its remaining images. SegFormer-B0 is evaluated with RGB, four-band multispectral (MS), and seven-channel RGB+MS input over two fixed-split seeds. RGB is strongest under complete acquisition holdout ( 0.7317\pm0.0201 IoU), whereas RGB+MS becomes strongest after target exposure ( 0.7822\pm0.0269 ). A fixed-split U-Net/ResNet18 replication confirms positive exposure gains for all three inputs, but retains RGB as the best modality under both protocols. Acquisition exposure therefore increases measured performance across both evaluated backbones, while its effect on modality ranking is architecture-dependent. A supplied-split audit reveals strong near-sequential dependence, and corruption tests show that early fusion is substantially more sensitive to RGB–MS displacement than to moderate radiometric scaling. These results support acquisition-aware same-test evaluation as a necessary complement to ordinary image-level splitting in multimodal UAV benchmarks. The code and supporting the findings of this study will be publicly released upon acceptance of the paper.
[CV-287] GeoContext: One Context Ladder Two Failure Modes in Vision-Language Geolocation: Flat Reliance on User-Provided Location Context and False Confirmation of Location Claims
链接: https://arxiv.org/abs/2609.05761
作者: Yifan Zhang,Kai Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Code and data: this https URL
Abstract:Visual geolocation benchmarks typically ask a model where an image was captured without accounting for the location context that users often provide. We introduce GeoContext, a resource supporting two complementary tasks: GeoHint, open-ended localization given a true but coarse location hint, and GeoVerify, binary verification of whether an image was taken within 150 m of a claimed place. GeoContext constructs a context ladder by stratifying nearby reference points according to distance and referenceability, allowing the image to remain fixed while the supplied context varies. The benchmark covers 109 sites in 30 cities and evaluates five vision-language models using 21,933 GeoHint responses and 6,270 GeoVerify responses. Our evaluation reveals three main patterns. First, hint repetition varies by only 1.5 percentage points across referenceability tiers and by less than 3 points across distance bands, while the resulting localization error increases steadily with hint distance. Second, behavior depends strongly on no-context performance: at sites with low no-context accuracy, the median ratio between localization error and hint distance is approximately 1.00, whereas at higher-accuracy sites it ranges from 0.24 to 0.69. After correcting for bias introduced by the site grouping procedure, only one of the five models retains a negative accuracy estimate when given a nearby hint. Third, in GeoVerify, no model reaches d’ = 1 for decoys immediately beyond the 150 m tolerance. Model rankings also change when sensitivity is separated from response bias, and 83.8% of false acceptances are reported with confidence of at least 0.8. We release the benchmark, construction pipeline, audit decisions, and scoring code. Comments: Code and data: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.05761 [cs.CV] (or arXiv:2609.05761v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.05761 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-288] Gaussian Linear Functional Manifold Method for Massive Point Cloud Data
链接: https://arxiv.org/abs/2609.05744
作者: Hong Zhao,Tonglin Zhang,Baijian Yang,Jin Wei-Kocsis,Songlin Fei
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Machine Learning (stat.ML)
备注:
Abstract:Reconstructing continuous terrain manifolds from massive, unstructured airborne LiDAR point clouds remains challenging in complex Wildland-Urban Interface (WUI) environments, where deep neural networks require costly point-wise annotations and nonparametric surface reconstruction methods often lack structural interpretability. This paper introduces the Gaussian Linear Functional Manifold (GLFM), a physics-informed statistical framework that represents continuous surface topography using deterministic linear functional bases while modeling microscale diffuse laser backscatter as an isotropic Gaussian process. To avoid the quadratic computational cost of exact constrained maximum likelihood estimation, we develop an algebraic singular value decomposition (SVD) rank-reduction algorithm that enables linear-time parameter estimation and closed-form quadric classification. Evaluated on 35.2 km^2 of real-world aerial LiDAR data, GLFM automatically filters ground points and extracts morphological features, achieving an adjusted Rand index (ARI) of 0.9933 against field-verified ground truth and outperforming four leading baselines while maintaining an out-of-core memory footprint. The framework provides a rigorous, interpretable, and scalable foundation for large-scale point cloud analytics.
[CV-289] SeRV: Semantic-Aligned Residual Vector Quantization for American Sign Language Generation
链接: https://arxiv.org/abs/2609.05742
作者: Hongyu Wu,Xu Wu,Tianhao Wu,Jiawei Yu,Phuc Nguyen,Jian Liu,Yi Wu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:American Sign Language (ASL) generation remains challenging due to limited paired text-ASL motion data and the difficulty of learning motion representations both precise for reconstruction and predictable from linguistic input. Existing methods rely on motion tokenizers optimized for reconstruction, without explicit semantic supervision from paired text. As a result, the learned tokens remain limited in supporting semantically consistent and fine-grained ASL motion generation. To address this limitation, we propose SeRV (Semantic-Aligned Residual Vector Quantization), a semantic-aligned RVQ tokenizer for ASL generation. SeRV learns a semantically structured residual token space by combining sentence-level motion-text alignment with token-level text-conditioned supervision. Building on this tokenizer, a Hierarchical GPT predicts residual motion tokens in a coarse-to-fine manner, generating structurally coherent and semantically aligned 3D ASL motion. We further construct a large-scale reconstructed 3D ASL motion-text benchmark by recovering paired 3D motion from YouTube-ASL videos. Experiments across 375 hours of ASL video show that SeRV achieves state-of-the-art pose accuracy on both How2Sign and YouTube-ASL datasets, while producing semantically consistent 3D ASL motion directly from text.
[CV-290] RenderFormer-V2: Neural Rendering with Heterogeneous Scene Primitives ECCV2026
链接: https://arxiv.org/abs/2609.05738
作者: Chong Zeng,Yue Dong,Pieter Peers,Lvmin Zhang,Maneesh Agrawala
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Machine Learning (cs.LG)
备注: Accepted to ECCV 2026. Project page: this https URL
Abstract:We present ‘RenderFormer-V2’, a unified learned transformer-based neural rendering model, complementary to modern physics-based rendering systems, that can handle diverse light-transport effects such as caustics, volumetric scattering, environment lighting, textured and displaced surfaces and out-of-distribution materials without per-scene training or specialized code. RenderFormer-V2 models global light transport as a sequence-to-sequence transformation. Following its predecessor, RenderFormer-V2 also employs a two stage process: a view-independent stage that resolves intra-scene primitive to primitive transport, and a view-dependent stage that transforms the internal neural scene representation into image pixels. Different from RenderFormer, our model employs a novel combined windowed-attention and rendering-informed attention sink in the view-independent stage to improve scalability while maintaining render accuracy. To further improve versatility, RenderFormerV2 supports heterogeneous scene primitives, including environment maps and participating media, and it employs a material encoding independent of the underlying surface reflectance model that encodes material appearance via a novel neural embedding. We demonstrate the versatility of RenderFormer-V2 on a variety of scenes and perform an extensive ablation of the improved attention mechanism.
[CV-291] Bigger Text Encoders Can Hurt CLIP Zero-Shot Performance
链接: https://arxiv.org/abs/2609.05730
作者: Samir Char,Carles Domingo-Enrich,Randall Balestriero
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Contrastive Language-Image Pretraining (CLIP) is a building block of many machine learning applications. Scaling laws have guided resource allocation for large-scale training, yet prior work treats total CLIP model size as a single variable, without exploring how the capacity split between encoders impacts downstream performance. Here, we train multiple CLIP models with different vision and text encoder sizes, revealing that for most vision encoders, there is an optimal text encoder size beyond which zero-shot performance degrades—even as total parameter count increases. Exploiting this behavior yields efficient configurations that match the zero-shot performance of the standard ViT-B/16 architecture with up to 55% fewer parameters. We further show that this degradation stems from overfitting induced by the oversized text encoder, and that using modality-specific weight decay coefficients not only recovers but improves performance across all degraded configurations. A geometric analysis reveals a trade-off in which scaling the text encoder improves embedding uniformity but worsens cross-modal alignment; we further show that these metrics are predictive of zero-shot performance. We hope these findings motivate CLIP architectures and training methods that counteract this degradation, a prerequisite for scaling CLIP reliably and efficiently.
[CV-292] Full-Page Optical Music Recognition of Handwritten Monophonic Scores
链接: https://arxiv.org/abs/2609.05662
作者: Adrian Rosello,Antonio Ríos-Vila,David Rizo,Jorge Calvo-Zaragoza
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Full-page end-to-end Optical Music Recognition seeks to transcribe entire music pages directly into symbolic notation, avoiding the limitations of traditional pipelines that rely on accurate staff segmentation. Recent Transformer-based architectures have achieved strong performance on typeset scores, relying on large-scale synthetic data for pretraining. However, their applicability to handwritten music remains largely unexplored. In this work, we study full-page transcription on handwritten monophonic collections and analyze the impact of synthetic pretraining in this setting. To investigate which factors are most relevant during pretraining, we introduce a generator capable of producing visually coherent full-page scores in both typeset and handwritten styles. Experiments on three real handwritten datasets provide a comparative evaluation of several full-page pipelines and different synthetic pretraining strategies. The results suggest that the benefits of synthetic pretraining are primarily associated with learning structural layout conventions rather than with visual similarity to the target handwriting.
[CV-293] Srijika: OpenType-Layout-Reusing Font Restyling for Nine Indic Scripts
链接: https://arxiv.org/abs/2609.05661
作者: Anil Pai
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Machine Learning (cs.LG)
备注:
Abstract:We present Srijika, a system for producing installable OpenType fonts for nine Brahmic scripts: Devanagari, Tamil, Bengali, Telugu, Kannada, Malayalam, Gujarati, Gurmukhi, and Odia. Rather than generating fonts from scratch, Srijika restyles glyph outlines from shaping-complete template fonts. It preserves the template’s cmap and GSUB closure and its GPOS data under a documented metric policy, making every output a complete font by construction. This addresses a central challenge of Indic font generation: hundreds to thousands of conjuncts, half forms, and matra variants must remain mutually consistent under OpenType shaping. Srijika produces 66 TTFs: 57 curated presets and nine open-vocabulary showcase fonts. All pass the OpenType Sanitizer, while HarfBuzz and CoreText reproduce the template glyph-ID sequences on conjunct-heavy probes. A full-closure audit covering 80,915 glyphs and 54,812 anchors quantifies metric changes. Natural-language style selection uses Lipika, a retrieval index over approximately 650 open-license font families. A reference-conditioned latent diffusion model redraws template glyphs in the selected style, followed by content gating, harmonization, and shaped-cluster verification with fallback to template outlines. We evaluate against no-learning baselines. On diffusion-training-family-held-out SSIM gates, template copying outperforms generation on 50 of 56 faces. Style movement is measurable only with an internal same-model embedding whose training corpus includes the held-out families, so these results require caution. A learned baseline, independent style metric, and human study are outside this report’s scope. Our contributions are the layout-reusing formulation and pipeline, its nine-script audit and benchmark, and a negative-results catalogue covering failed conditioning, objective choices, and data-hull limits of reference-guided restyling. Subjects: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Machine Learning (cs.LG) Cite as: arXiv:2609.05661 [cs.CV] (or arXiv:2609.05661v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.05661 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-294] Facial Age Estimation for Age Fraud Detection in National ID Systems
链接: https://arxiv.org/abs/2609.05638
作者: Sharib Athar,Arka Koner,Chetan Naik,Barada P. Sabut,Tanusree Deb Barma,Anoop M. Namboodiri,Anil K. Jain
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Identity fraud during biometric enrollment and updates remains a major challenge for large-scale national identity systems. A common fraud vector is misrepresenting one’s age to access age-restricted services or welfare schemes. In this work, we present SwinAge, a facial age estimation system designed for use within the Aadhaar biometric enrollment pipeline, to assist quality-check (QC) operators to flag potential age-related fraud. This is critical for a system like Aadhaar (the world’s largest national identity programme), that holds about 1.5 billion unique identities, with 22.4 million new enrollments and 283 million updates in the last year. Building upon the SwinFace architecture with landmark-based similarity (warp affine) alignment, we train on a large in-house dataset of 1.45 million face images and evaluate on an independent, age-stratified test set of 283K images, both drawn from an ethnically diverse population of 716K unique subjects. We investigate three Aadhaar-specific operational thresholds (5, 18, and 60 years) and propose a deployment triage framework that flags suspected cases for manual review. Following NIST FATE, we report false acceptance/rejection rates (FAR/FRR) at each threshold rather than aggregate accuracy: at 1% FAR the model achieves an FRR of 3% (5yrs), 0.4% (18yrs) and 11.0% (60yrs). SwinAge achieves a mean absolute error (MAE) of 2.94 years on the same test set, outperforming three zero-shot vision language models on all benchmarks, and improving the state-of-the-art on 5 out of 7 public benchmark datasets. We further report per-gender errors and distill lessons for national identity programs.
[CV-295] Unsupervised Transfer Clustering for Mitigating Cold Start in Active Prompt Learning
链接: https://arxiv.org/abs/2609.05636
作者: André Camargo Portella,Samuel Felipe dos Santos,Jurandy Almeida
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-Language Models (VLMs) are able to achieve impressive zero-shot classification performance by aligning visual and textual representations, but each new task still demands handcrafted prompts. Active Prompt Learning (APL) combines Active Learning (AL) and Prompt Learning (PL) into a single framework, allowing for the usage of the VLM prior knowledge for iteratively querying the most informative images to be labeled. However, the cold-start problem is still relevant for APL methods, where the performance of the initial query can be worse than random sampling. While recent state-of-the-art APL methods mitigate with balanced sampling and multimodal features, they rely on rigid, distance-based clustering to group these features. This simplistic approach can struggle to capture the complex, high-dimensional semantic distributions inherent to VLMs, leading to suboptimal query representativeness. Unsupervised transfer can be applied to these features as a possible alternative, since it is capable of inferring the underlying human labeling of a task without any form of supervision. This way, samples can be grouped in semantically coherent clusters. This paper proposes Unsupervised Transfer Clustering with Selective Querying (UTC+SQ), a framework that enhances a recent APL approach by leveraging state-of-the-art unsupervised transfer model. These models generate high-fidelity pseudo-labels that establish semantically meaningful clusters, allowing for the selection of more relevant samples. Experimental evaluations demonstrate that shifting from distance-based to projection-based clustering improves the representativeness of the queried subset, achieving accuracy gains in 6 of the 8 datasets tested.
[CV-296] me-Aware Assistive Navigation ICRA2026
链接: https://arxiv.org/abs/2609.05596
作者: Masaki Kuribayashi,Zhongkai Shangguan,Eshed Ohn-Bar
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: ICRA 2026
Abstract:Can interactive vision-and-language agents learn not just what to say but also \textbf\textitwhen to say it? Current language models rarely plan over whether and when to realize a real-time response to a user. However, providing accurate and timely support for human decision-making, such as when guiding visually impaired individuals through urban environments, requires careful real-time responsiveness–poorly timed responses can distract users or add unnecessary cognitive load. As a machine intelligence challenge for Multimodal Large Language Model (MLLM)-based agents, we introduce a large-scale multimodal benchmark for an egocentric, assistive navigation task in complex outdoor environments. Using this benchmark, we uncover a fundamental limitation of off-the-shelf MLLMs in delivering safe and time-sensitive navigation instructions, even with model fine-tuning on substantial amounts of data. We then demonstrate that a simple yet effective modification of the model, including direct supervision to predict the underlying reason for each instruction, yields significant performance gains across open-loop, closed-loop, and sim-to-real generalization settings. However, our analysis highlights persistent challenges in temporal reasoning, safety-critical object awareness, and relational and distance understanding. To advance the development of scalable assistive agents, we will release our simulation, benchmark, and code (available at the project website: this https URL).
[CV-297] SceneMosaic: Efficient and Diverse Simulation-Ready Scene Generation via Hybrid Agent ic Layout Evolution
链接: https://arxiv.org/abs/2609.05594
作者: Xingjian Ran,Xiaoye Mo,Sihao Liu,Jianyu Zhang,Li Luo,Bo Dai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: this https URL
Abstract:Diverse and simulation-ready indoor scenes are essential for interactive entertainment and embodied AI, yet their scalable generation remains challenging. Recent agentic text-to-3D scene pipelines that rely on vision-language models (VLMs) can generate scenes of high fidelity but require costly iterative object placement and refinement. Another mainstream paradigm, parametric image-to-3D scene models, produces scenes efficiently from strong priors learned from 2D images but often leads to imprecise and physically invalid scenes. More importantly, both paradigms struggle to output diverse scenes for a single input, making it hard for them to reflect the dynamically changing nature of real scenes. In this paper we propose \textbfSceneMosaic, a framework that combines the merits of both paradigms. It obtains the initial candidate from the learned image-based prior, and subsequently evolves the result through VLM agents, ensuring both efficiency and physical validity. Within the evolution process, SceneMosaic exploits the locality of natural scenes and decomposes a scene into independent local units, allowing separate evolution within each unit before composing the global scene via Cartesian product. On SceneEval-100, SceneMosaic matches the strongest agentic baseline in semantic layout quality with a 24x speedup, substantially reduces physical violations, and receives the highest human ratings. Our code is publicly available at this https URL.
[CV-298] GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation
链接: https://arxiv.org/abs/2609.05588
作者: AgiBot Research Team,Renhang Liu,Wenzhi Zhao,Zhuo Yang,Liliang Chen,Pengfei Zhou,Shengcong Chen,Guanghui Ren,Youlun Peng,Rongjun Jin,Nan Wang,Sukai Wang,Xindong He,Jinyuan Feng,Ziyu Xiong,Linqing Zhong,Yifei Wei,Feng Han,Long Zhang,Da Huang,Nanshu Zhao,Chenghao Yin,Mo Wu,Zhaodong Yan,Kongtao Hu,Yuxiang Yan,Aogelijiang Niyazi,Yu Fang,Jia Zeng,Lizhu Meng,Daizhen Lv,Haoyu Cao,Zhiwen Hou,Lianjin Ye,Yuehan Niu,Zhikai Cai,Xuan Hu,Hui Min,Xiongfeng Cai,Yue Liao,Jing Wu,Soujanya Poria,Ye Li,Sanping Zhou,Maoqing Yao
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Technical report by the AgiBot Research Team. Project page: this https URL
Abstract:World-action models (WAM) predict future states to guide robot actions, enabling learning from both action-free video and action-labeled interaction. Most inherit pretrained video generators, leaving WAM pretraining and scaling underexplored. We introduce Genie Envisioner Act 2.0 (GE-Act 2.0), a world-action model whose trainable generative and action components are all initialized from scratch on manipulation data. It combines a control-oriented autoencoder (CoAE), a single-step visual planner (SVP), and an inverse dynamics model (IDM). CoAE retains action- and instruction-relevant information under aggressive compression, while SVP produces a complete future state in one differentiable pass, so visual planning and inverse dynamics can be pretrained separately on complementary data. The components are then jointly trained with knowledge-aligned selective optimization (KASO), which reduces mismatched supervision by selecting only predicted futures judged behaviorally compatible with the recorded action. We evaluate pretrained checkpoints directly, without per-task fine-tuning, on 100 tasks across 20 manipulation skill groups with held-out scenes, backgrounds, lighting, and object instances. Scaling co-training data from 300 to 30,000 hours raises success from 17.1% to 44.1% on G1-OP and from 13.4% to 31.1% on G2-90D; despite comprising less than 2% of the co-training data, G2-90D improves by 17.7 points, suggesting cross-embodiment transfer. Gains span 19/20 and 18/20 skill groups, and skill-specific coverage strongly correlates with zero-shot out-of-distribution (OOD) success (Pearson r=0.80; Spearman rho=0.85). Under the same protocol, the model grounds object, color, shape, and position references in at least 90% of trials and follows explicit instructions even when they conflict with an already-committed behavior or a conventional scene association.
[CV-299] An overview of 3D Vision-Language Models
链接: https://arxiv.org/abs/2609.05583
作者: Márcus Lobo,Vitor Matias,Afonso Paiva,Jeová Farias,Tiago Novello,Moacir Ponti
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted for presentation as a tutorial at SIBGRAPI 2026. Tutorial page: this https URL
Abstract:Vision-Language Models (VLMs) are reshaping computer vision by aligning visual and textual embeddings, allowing models to recognize visual concepts and reason about them using natural language. Traditional 3D deep-learning models, however, are typically trained for specific tasks, such as classification, segmentation, or detection, and do not naturally support cross-modal retrieval from their embedding spaces using text or images as queries. To address this issue, Contrastive Language-Image Pretraining (CLIP)-based methods align 3D embeddings with pretrained image and text representations, giving rise to 3D Vision-Language Models (3D VLMs) that support zero-shot classification, cross-modal retrieval, and open-vocabulary recognition of 3D shapes. This tutorial provides an overview of 3D VLMs, ranging from basic definitions of 3D representations and their encoding into embeddings to cross-modal contrastive alignment, modern multimodal frameworks, and 3D Vision-Large Language Models (3D VLLMs). We present the main definitions of contrastive learning for multimodal embedding alignment and highlight recent advances in language-guided 3D Gaussian splatting, 3D shape generation, and embodied AI for robotics.
[CV-300] ViT3Flow: A Test-Time Training Transformer MeanFlow for Postoperative Radiograph Synthesis in Scoliosis
链接: https://arxiv.org/abs/2609.05579
作者: Rui Tang,Sicheng Yang,Moxin Zhao,Hongqiu Wang,Guankun Wang,Lei Zhu,Hongliang Ren,Menglin Cong,Nan Meng
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
备注:
Abstract:Predicting postoperative spinal morphology from preoperative radiographs could provide valuable support for scoliosis surgical planning, but remains challenging because surgical correction induces large spatial changes while anatomical structures must be faithfully retained. We formulate this problem as postoperative scoliosis radiograph synthesis and construct ScoliSurg, the first paired dataset for this task, comprising 632 preoperative–postoperative whole-spine radiograph pairs with structured morphology information. We further propose ViT ^3 Flow, a single-NFE conditional MeanFlow framework for efficient postoperative radiograph synthesis. ViT ^3 Flow models surgical correction as finite-interval generative transport and replaces conventional self-attention with test-time-training token mixers that perform sample-specific inner adaptation to the anatomy and deformity pattern of each case. In addition, a Spinal Morphology Extraction Agent extracts distributions of dominant-curve region and direction from the preoperative radiograph. These distributions guide Diagnosis-Routed Interval Cross-Attention (DRICA), which performs interval-dependent vertical, horizontal, joint, and global retrieval from a separate preoperative token stream. This design enables the evolving postoperative representation to incorporate spatially corresponding anatomical evidence throughout the transport process. Extensive experiments on ScoliSurg demonstrate that ViT ^3 Flow achieves the best performance among the compared methods in perceptual image quality, anatomical fidelity, and clinically relevant geometric accuracy, while requiring only a single network evaluation. These results highlight the potential of ViT ^3 Flow for efficient and anatomically faithful postoperative radiograph synthesis in scoliosis surgical planning.
[CV-301] Capsule Lens: Locating and Tracking Concept Geometry in Model Representations
链接: https://arxiv.org/abs/2609.05575
作者: Yiming Tang,Harshvardhan Saini,Samyak Jha,Huaming Chen,Xufeng Duan,Dianbo Liu
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Understanding how concepts are encoded in the internal representations of machine learning models is a central problem in mechanistic interpretability, essential both for the science of deep learning and for the trustworthy deployment of increasingly capable models. Existing approaches to interpret model representations mainly map representations onto more interpretable spaces and do not directly characterize how concepts occupy representation space; various hypotheses have been proposed, but often lack of rigorous validation and largely focus on static representations. In this work, we introduce Capsule Lens, a framework that matches the region a concept occupies with a simple, trackable geometric form, a capsule, defined by several interpretable parameters, fitted in closed form to each concept’s geometry and validated on held-out samples. We apply Capsule Lens in two major settings: static and dynamic representations. On static representations, we demonstrate how to locate concept geometry across various models, and how the span and norm curves uncover important geometric characteristics. On dynamic representations, we present three case studies tracking representation drifts induced by distinct training settings, CLIP pretraining, RL post-training on visual question answering, and RL post-training on mathematical reasoning. These analyses reveal qualitatively different geometric dynamics, ranging from broad network-wide restructuring in CLIP pretraining to localized and concept-specific changes in RL post-training. Our results include findings aligned with existing literature as well as novel observations. We believe Capsule Lens stands as a promising tool for locating, analyzing, and tracking concept geometry in both static and dynamic representations.
[CV-302] Subject-Relative Micro-Motion and Sleep Dynamics for Near-Infrared Video Sleep Staging
链接: https://arxiv.org/abs/2609.05550
作者: Kunmin Jang,You Rim Choi,Hun Heo,Heonjun Lee,Suahn Bae,Dongik Park,Hyun-Woo Shin,Hyung-Sin Kim
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Near-infrared (NIR) video is a promising modality for contactless sleep monitoring, but recent video-based sleep staging methods often use it as a route to reconstructed respiratory/cardiac proxies or cross-modal physiological representations. We study video-only sleep staging under labels defined by polysomnography (PSG), where the model infers sleep stages from NIR video alone without explicit physiological proxy reconstruction or auxiliary physiological signal supervision. This tests whether NIR video itself can provide informative sleep-stage evidence, rather than only serving as an input for recovering physiological proxies. We propose ViNUSS (Video-Native Unmediated Sleep Staging), a framework that combines subject-relative micro-motion learning with full-night sleep dynamics modeling. Spatially anchored pre-spatial micro-motion encoding preserves localized temporal variation together with its spatial context. Within-subject stage contrast learns stage cues with respect to each subject’s night-specific baseline. Two-scale sleep dynamics modeling captures within-epoch motion evolution and organizes epoch-level evidence into a coherent full-night sleep-stage trajectory. On 475 overnight NIR recordings (~3,250 hours), ViNUSS achieves 0.80 accuracy and 0.78 macro-F1 for four-class sleep staging. Interpretability analysis suggests attention to thoraco-abdominal periodic motion and gross body movements associated with arousals and position changes. These results support NIR video as an independently informative and complementary modality for PSG-defined sleep-stage estimation
[CV-303] Video Compression with Graph-inspired Neural Representation
链接: https://arxiv.org/abs/2609.05541
作者: Changqi Wang,Ge Gao,Fan Zhang,Yue Li,Kai Zhang,Li Zhang,David Bull
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 6 Figures
Abstract:Implicit Neural Representations (INR) provide a compact and content-adaptive paradigm for video compression, typically representing a video through shared network parameters and frame-indexed embeddings. Compared to conventional or autoencoder-based codecs, these approaches exploit temporal redundancy within videos in an implicit manner, which potentially results in sub-optimal compression performance. In this paper, we propose G-NeRV, a graph-inspired INR that explicitly improves temporal redundancy exploitation in the implicit latent space. Motivated by the total correlation principles in information theory, we construct a temporal neighborhood over frame embeddings and perform message passing to aggregate reusable information from neighboring frames through an adaptive gate controlling the injection of neighboring information. Inspired by the reference frame buffer in conventional video coding, a memory bank mechanism has been further designed to enable efficient temporal-neighbor retrieval under random frame-index sampling in INR training. This new representation model has been integrated into an advanced representation compression framework and compared with existing conventional and neural video codecs. The results show that the G-NeRV codec outperforms the state-of-the-art INR-based codec, NVRC, and the latest standard video codec, VVC VTM, by 8.86% and 14.68% (in BD-rate), respectively, measured by PSNR on the UVG dataset.
[CV-304] Knowing When Not to Answer: Abstention and Refusal Reasoning in Vision–Language Models
链接: https://arxiv.org/abs/2609.05540
作者: Karan Dua,Amit Agarwal,Hitesh Laxmichand Patel,Hansa Meghwani,Jyotika Singh,Ranjeet Gupta,Graham Horwood,Tao Sheng,Avi Sil,Sujith Ravi,Dan Roth
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Many medical conditions require diagnosis through detailed, multi-context clinical assessment rather than from visual appearance alone. Despite this, vision-language models (VLMs) are increasingly queried to interpret images in ways that touch on medical or diagnostic judgments, raising safety concerns when such inferences are unsupported. ASD diagnosis requires behavioral and developmental evidence, not static facial photographs. We audit whether VLMs abstain from this unanswerable paired-image query, and whether expressions sway non-abstaining choices. We introduce PARITY (Paired Assessment with Reused Identity), a synthetic, demographically balanced set of identity-controlled neutral/expression portrait pairs with neutral-neutral controls. All identities are synthetic and have no ASD status; because the query is unanswerable from images, any non-abstaining selection is treated as a harmful attribution. Across contemporary VLMs, we find a clear split between refusal-first models and speculative models; in the latter, certain expressions disproportionately trigger harmful selections. Clinical guardrails and single-image framing substantially increase abstention, suggesting actionable mitigations in both prompting and interface design Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) ACMclasses: I.2.7; I.2.10 Cite as: arXiv:2609.05540 [cs.CV] (or arXiv:2609.05540v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.05540 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-305] Dual-Latent Memory Routing for Vision-Language Reasoning ICML2026
链接: https://arxiv.org/abs/2609.05539
作者: Hao-Xuan Ma,Jin-Fei Qi,Yicheng Xiao,Han-Jia Ye
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted as a Spotlight at ICML 2026; 17 pages, 7 figures
Abstract:Multimodal large language models (MLLMs) have recently made strong progress in vision-language reasoning, yet their performance often degrades as generations grow longer. A key factor is that they frequently lose track of earlier visual evidence and intermediate constraints under a monolithic growing context. Inspired by how humans separately recall what they see and what they infer when solving complex tasks, we propose DLMR, a parameter-efficient mechanism that equips MLLMs with Dual Latent Memories: a visual memory that compresses image evidence and a reasoning memory that tracks intermediate conclusions and constraints. A Router then dynamically decides which memory and how much to reuse during inference, preserving visual grounding while maintaining coherent long-horizon reasoning. DLMR is trained in three stages, from latent memory construction to selective router learning, while keeping the base MLLM frozen, yielding substantial gains on both general and reasoning benchmarks with only a small number of additional trainable parameters. Analyses further show interpretable, state-dependent routing with specialized memory roles and reduced decoding tokens over long generations. Code is available at this https URL.
[CV-306] VIS-DICT: A Visual Dictionary for Missing Modality Imputation in Social Network Depression Detection
链接: https://arxiv.org/abs/2609.05537
作者: Hamed Marvi,Mohammad Mehdi Keikha,Abolfazl Nadi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Tracking social media posts can help spot early signs of depression. Recent studies show that combining text and images works better for detecting depression than using text alone. However, many social media posts do not have images, which makes it hard to use multimodal models. Most existing methods fill in missing images using retrieval or generative models that need extra training. In this paper, we introduce Vis-Dict, a dictionary-based method that builds missing visual features by linking words to average image vectors from complete training posts. These estimated visual features are then combined with text to track changes in user behavior over time. We tested Vis-Dict on a social media dataset using user timelines of up to 512 posts and compared it with other missing-data methods. The results show that Vis-Dict performs on par with generative networks, reaching an F1-score of 0.9454 and an ROC-AUC of 0.9890. Most importantly, Vis-Dict achieves this strong performance with zero trainable parameters for image generation. These findings show that directly connecting words to visual features is an effective and practical way to handle missing images in depression detection systems.
[CV-307] Beyond the Verdict: Evidence-Aligned Evaluation of Visual Prompt-Injection Guardrails ECCV2026
链接: https://arxiv.org/abs/2609.05535
作者: Suyoung Lee,Myungsub Choi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at the Second Workshop on Benchmarking Evidence-Aligned Multimodal Reasoning (BEAM2) at ECCV 2026 (Oral Presentation)
Abstract:Verdict-only evaluation does not reveal whether a vision-language model (VLM) used the visual evidence that should support its decision. We study this problem in web-agent guardrails, where a VLM judges whether on-screen text conflicts with a user instruction. We introduce Mind2Web-Injection, a benchmark of 9,954 instruction-screenshot pairs with instruction-relative labels, pixel-exact evidence boxes, and matched image-side counterfactuals. Across six VLMs, two models with nearly identical average precision differ ninefold in Evidence-Aligned Detection (EAD), the fraction of attacks both detected and correctly localized. To test whether a verdict depends on the command cited as evidence, we replace the instruction with one that endorses that command. Qwen3-VL-32B, the strongest open-weight localizer, returns aligned in only 58.7% of cases, whereas GPT-5.6-luna does so in 99.9%. To diagnose these failures, we propose two training-free interventions. ReadGate improves grounding without changing verdicts, while CmdCompare tests whether explicit instruction-command comparison resolves instruction-side inconsistency. These results motivate reporting verdict correctness, evidence localization, and counterfactual responsiveness separately.
[CV-308] SimpleMemVLA: A Simple but Effective Native-Video Memory for Vision-Language-Action Models
链接: https://arxiv.org/abs/2609.05533
作者: Cheng Yin,Wang Xu,Junpeng Yang,Sikyuen Tam,Hanyu Liu,Yuan Yao,Xiangrui Zeng,Junbo Cui,Yequan Wang,Zhouping Yin,Yankai Lin
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Robotics (cs.RO)
备注: 29 pages, 12 figures
Abstract:Long-horizon manipulation is partially observable: the information needed to choose the next action may appear only in observations from minutes earlier. Existing memory mechanisms: retrieval banks, learned compressors, recurrent states must decide what to keep from the past before knowing what a future decision will require. This was motivated by the assumption that minute-scale history is too large to process directly, which modern VLM backbones no longer make true. In this work, we introduce SimpleMemVLA, a VLA without a dedicated memory module. It keeps the sampled history intact and passes it to the backbone in the timestamped video format the backbone was pretrained to process; the hidden states of a generated sub-task then form the only channel from history to a standard flow-matching action head. Since consecutive decisions share most of their history, prefilling the shared prefix during action execution keeps latency close to a single-frame VLA. SimpleMemVLA sets a new state of the art on four memory benchmarks without cost on general-purpose control. Holding the backbone and training setup fixed, it outperforms retrieval, compression and recurrent-state mechanisms by a wide margin, and causal interventions confirm that the policy genuinely reads its history. Code available at this https URL
[CV-309] A Specialized Large Multimodal Model for Interpreting PET/CT in Head and Neck Cancer
链接: https://arxiv.org/abs/2609.05532
作者: Haengbok Chung,SunGyu Kim,Joo hyun Lee,Sangjin Bae,Min Jeong Cho,Minseok Suh,Jae Sung Lee
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Background: Diagnosing head and neck cancer using PET/CT is clinically challenging and time-consuming due to the anatomical complexity of the region, motivating computer-aided diagnosis (CAD). Generalist Large Multimodal Models (LMMs) remain limited in medical contexts by insufficient domain-specific knowledge, privacy and security concerns, and verbosity, motivating specialized standalone LMMs. Purpose: We evaluated the feasibility of a specialized LMM for automated PET/CT interpretation in head and neck cancer using a large-scale multi-institutional PET/CT dataset, a tailored training curriculum, and autoregressive training. Methods: LLaVA-NeXT was fine-tuned using a two-level curriculum with image-conversation pairs curated by two radiologists from public data. The dataset included clinically important annotations such as primary tumor presence and metastatic lymph node location. Level 1 used 28,000 image-conversation pairs to learn basic information, including modality type and hypermetabolism. Level 2 used 12,975 pairs to learn primary tumor presence and the existence and anatomical location of cervical lymph node metastases. External validation included four institutions with diverse imaging devices. Results: The specialized LMM substantially outperformed ChatGPT and LLaVA-NeXT. In Level-2 external validation, ROUGE-L, ROUGE-S, Cosine Similarity, Precision, Recall, and F1 were 0.8751, 0.8794, 0.8324, 0.8794, 0.8711, and 0.8751, while generalist models consistently scored below 0.1. Primary tumor classification accuracy was 83.14 +/- 1.15% internally and 69.03 +/- 0.81% externally. For lymph node localization, the corresponding scores were 0.6389, 0.6257, 0.5287, 0.5782, 0.6371, and 0.6648. Conclusion: Specialized LMMs show promising results for fast, accurate PET/CT-based diagnostic support and medical education, highlighting their potential for clinical translation.
[CV-310] DIVA: Exploiting Cross-Step Conditional Propagation for Visual Jailbreaks in Discrete Diffusion Vision-Language Models EMNLP2026
链接: https://arxiv.org/abs/2609.05525
作者: Guorui Song,Runqing Tang,Jingye Zhang,Luyuan Zhang,Feice Huang,Cong Ray,Guocun Wang,Dake Zhong,Choo Sin Wai,Bingquan Dai,Chuming Wang,Tongxu Lin,Wanyu Guo,Haoqian Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to EMNLP 2026 Main. Code: this https URL
Abstract:Large vision-language models (VLMs) are increasingly deployed in safety-critical settings, yet existing visual jailbreak research has focused almost exclusively on autoregressive architectures, leaving an important emerging family unstudied: multimodal discrete diffusion vision-language models (dVLMs). We identify a vulnerability specific to diffusion generation: because the visual embedding conditions every reverse denoising step rather than acting as a one-time prefix, adversarial visual semantics are repeatedly propagated and amplified across the generation trajectory, a phenomenon we term cross-step conditional propagation. We provide empirical evidence through stage-sensitivity analysis, prompt-level switch rates, and pairwise denoising-bin disagreement metrics, confirmed by bootstrap resampling. We propose DIVA (Discrete-diffusion Vision-language model Attack), a white-box visual jailbreak framework using cross-modal intent obfuscation and diffusion-aware multi-timestep adversarial optimization. Across three dVLMs, DIVA reaches 58.8%, 67.7%, and 69.1% HADES ASR under the Beaver reward-model metric, outperforming visual jailbreak baselines designed for autoregressive models. Code: this https URL
[CV-311] Infrastructure-based Monocular 3D Vehicle Localization Framework with Experimental Validation
链接: https://arxiv.org/abs/2609.05523
作者: Akos T. Kopeczi-Bocz,Tian Mi,Gabor Orosz,Denes Takacs
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
备注:
Abstract:This paper presents a one-stage learning framework that maps monocular roadside-camera images directly to vehicle states in a ground-fixed coordinate frame. Unlike conventional approaches that first detect vehicles in the image plane and subsequently apply geometric post-processing, the proposed method leverages features from a pretrained object detector to jointly estimate each vehicle’s ground-plane position, dimensions, and yaw angle. The framework therefore uses visual features not only for vehicle detection but also for direct spatial and orientation estimation. To support model training and evaluation, we develop a data-collection and label-generation pipeline based on synchronized video from a roadside camera and an unmanned aerial vehicle (UAV). Acting as a temporary top-view sensing platform, the UAV provides vehicle trajectories, dimensions, and orientations, which are transformed into the ground-fixed coordinate frame and temporally aligned with the roadside-camera images to generate ground-truth labels. The framework is evaluated using data collected during multiple experiments at the Mcity Test Facility. Results show that the proposed method can recover vehicle trajectories and orientations from monocular roadside imagery without a separate geometric post-processing stage, demonstrating its potential as a scalable approach to infrastructure-based perception at urban intersections.
[CV-312] Diffusion models for eye-gaze trajectory generation using position and velocity representations
链接: https://arxiv.org/abs/2609.05522
作者: Laxman Basnet,Alexander Szorkovszky,Pedro G. Lind,Anis Yazidi,Shailendra Bhandari
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
备注: 14 pages, 7 figures
Abstract:Eye-tracking data are expensive to collect, requiring specialized hardware and controlled laboratory conditions, and difficult to share because of privacy constraints. We address this using two complementary denoising diffusion probabilistic models (DDPMs) for unconditional generation of eye-gaze dynamics from visual-search data. Both use an identical FiLM-conditioned one-dimensional U-Net with self-attention (19.35,M parameters), trained on 8,s sliding-window sequences from 28 participants. One model generates raw two-dimensional gaze-position sequences, while the other generates two-component velocity sequences; each uses representation-specific preprocessing, training settings, data partitions, and evaluation protocols. Both are evaluated across three independent training seeds, with aggregated metrics reported as mean, \pm ,SD. The position-space model achieves a mean Jensen-Shannon (JS) divergence of 0.016\pm0.004 across nine kinematic features, with the highest feature-wise mean below 0.030 , fixation duration within 2% of real data, and a Fr’echet Gaze Distance more than an order of magnitude below statistical and Markovian baselines. Under a Train-on-Synthetic-Test-on-Real protocol, synthetic-only training achieves R^2=0.66\pm0.02 , or 82.7% of the real-data R^2 point estimate. The velocity-space model achieves a mean JS divergence of 0.0065 across velocity components, speed, log-speed, and turning angle, with a maximum of 0.015\pm0.005 . Reconstructed path length is less accurate ( 0.21\pm0.02 versus 0.03\pm0.01 in position space), although the protocols differ. Overall, unconditional diffusion captures local gaze kinematics and short-range temporal and directional structure, while long-range properties such as saccade counts and cumulative path geometry remain targets for future conditioned models.
[CV-313] Situation Awareness for Intelligent Data Distribution in Connected Vehicles
链接: https://arxiv.org/abs/2609.05521
作者: Falk Dettinger,Akshay Narla,Michael Weyrich
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Networking and Internet Architecture (cs.NI); Systems and Control (eess.SY)
备注: Peer-reviewed and Published in the 2025 IEEE 101st Vehicular Technology Conference (VTC2025-Spring)
Abstract:The limitations of on-board sensors and blind spots caused by occlusion cause the reduction of perception quality in autonomous vehicles. In such cases, cooperative perception provides additional data via Vehicle-to-Everything communication to enhance local perception, causing a large volume of data transmission. The vehicle can focus on acquiring and utilizing relevant data according to the prevailing road context by identifying the current traffic situation. To achieve this, we propose a concept for the situation identification of the vehicle using Bird’s-Eye-View images. Firstly, the situation around the vehicle is identified using object detection with semantic segmentation, followed by understanding the context of the traffic using a situation identification module consisting of an open-source projective transformation network Cam2BEV and a situation identification neural network. The concept was evaluated and validated by running the software on the CARLA simulator using the in-built RGB camera and the semantic segmentation camera. Additionally, the portability of the situation identification module for real-world applications was verified on Cityscapes and nuScenes urban driving datasets. Overall, the proposed situation identification approach enables efficient sensor data management by prioritizing relevant data to the current traffic situation. The source code is available in the following link: this https URL
[CV-314] Contrastive Knowledge Distillation for Anomaly Detection in Multi-Illumination/Focus Display Images
链接: https://arxiv.org/abs/2609.05520
作者: Jihyun Lee,Hangil Park,Yongmin Seo,Taewon Min,Joodong Yun,Jaewon Kim,Tae-Kyun Kim
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: MVA 2023 (Oral)
Abstract:In this paper, we tackle automatic anomaly detection in multi-illumination and multi-focus display images. The minute defects on the display surface are hard to spot out in RGB images and by a model trained with only normal data. To address this, we propose a novel contrastive learning scheme for knowledge distillation-based anomaly detection. In our framework, Multiresolution Knowledge Distillation (MKD) is adopted as a baseline, which operates by measuring feature similarities between the teacher and student networks. Based on MKD, we propose a novel contrastive learning method, namely Multiresolution Contrastive Distillation (MCD), which does not require positive/negative pairs with an anchor but operates by pulling/pushing the distance between the teacher and student features. Furthermore, we propose the blending module that transforms and aggregate multi-channel information to the three-channel input layer of MCD. Our proposed method significantly outperforms competitive state-of-the-art methods in both AUROC and accuracy metrics on the collected Multi-illumination and Multi-focus display image dataset for Anomaly Detection (MMdAD).
[CV-315] CrossModalQA: A Cross-modal and Multi-hop Benchmark for Multimodal Retrieval-augmented Generation
链接: https://arxiv.org/abs/2609.05518
作者: Jiacheng Cai,Zijin Hong,Zheng Yuan,Huachi Zhou,Qinggang Zhang,Xiao Huang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 20 pages, 7 figures
Abstract:Despite the strong capabilities of multimodal large language models (MLLMs), their parametric knowledge remains incomplete and difficult to update, motivating multimodal retrieval-augmented generation (RAG) to ground responses in external text and images. However, existing benchmarks face two major limitations: (i) they typically emphasize single-hop retrieval or reasoning over a small set of provided contexts rather than open-domain evidence discovery; and (ii) they provide fragmented coverage of cross-modal reasoning paths, leaving complex multi-hop and multi-image reasoning underexplored. In this paper, we introduce CrossModalQA, an open-domain benchmark for evaluating multimodal retrieval and reasoning over heterogeneous corpora. CrossModalQA contains 1,863 question-answer pairs constructed from 4,987 Wikipedia articles and 4,431 Wikimedia Commons images. It covers five complementary reasoning paths: vision-to-text, text-to-vision, vision-to-text-to-vision, multi-image intersection, and image-set reasoning. Every question requires retrieving and composing distributed textual and visual evidence, with an average reasoning depth of 3.50 hops. We construct the benchmark through multimodal knowledge graph-guided subgraph sampling and apply rule-based consistency checking and LLM verification to ensure multimodal dependence and traceable evidence. Extensive experiments demonstrate that existing multimodal RAG systems struggle to recover complete evidence chains and can underperform closed-book models when incomplete retrieval introduces distracting context. Further analysis reveals that complete cross-modal retrieval contributes more to answer accuracy than generator scaling, while multi-image retrieval and reasoning remain the primary bottlenecks limiting end-to-end performance.
[CV-316] An Exploratory Study of Frequency-Aware Task Weighting for YOLOv8-Based Unified Driving Perception
链接: https://arxiv.org/abs/2609.05516
作者: Zhiyuan Nie,Zixi Zhou,Xianbin Gu
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 13 pages, 4 figures, 1 table
Abstract:Unified perception enables autonomous driving systems to perform object detection, drivable-area segmentation, and lane segmentation within a single network, improving efficiency and reducing deployment complexity. Jointly optimizing multiple perception tasks remains challenging because tasks exhibit different convergence rates, loss scales, and optimization stability. Existing task-weighting methods use loss magnitude, learned uncertainty, short-term loss changes, or gradient statistics; here, we explore the frequency structure of a recent loss-history window as a complementary signal. We implement and examine Frequency-aware Task Weighting (FTW), a dynamic task-balancing rule that estimates a loss-trajectory stability proxy from the low-frequency energy ratio of recent loss histories. FTW assigns larger weights to tasks whose mean-centered loss trajectories contain a larger proportion of low-frequency power. We document FTW and two baselines under full-network static training and progressive freezing using a unified YOLOv8-based perception framework with three task-specific heads. Experiments on Mapillary Vistas compare FTW with fixed and uncertainty-based weighting under both configurations. Final holdout metrics are reported for the checkpoint with the lowest per-epoch validation loss in each run. Across six single-run configurations, static FTW has the largest derived overall score and lane mIoU, progressive FTW has the largest detection mAP, and static uncertainty weighting has the largest drivable-area mIoU. Without repeated-seed estimates, single-task baselines, or FTW ablations, these rankings are descriptive. The evidence supports the feasibility of loss-frequency-based weighting in this pipeline, but does not establish improvement over the baselines or generalization beyond the reported runs. Comments: 13 pages, 4 figures, 1 table Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) Cite as: arXiv:2609.05516 [cs.CV] (or arXiv:2609.05516v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.05516 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-317] When and What to Teach: Budget-Aware Online Adaptation for Web Agents
链接: https://arxiv.org/abs/2609.05513
作者: Jianwei Zhang,Sihan Cao,Pengcheng Zheng,Ya Wen,Pei Ke,Kuien Liu,Shen Gao,Wei Dong,Yang Yang,Chaoning Zhang
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 16 pages
Abstract:Web agents have achieved significant success in automating complex internet tasks but deploying them in real-world environments requires continuous online adaptation. Given that deploying powerful proprietary models remains commercially cost-prohibitive, practitioners must rely on lightweight local models that evolve post-deployment via online teaching from a stronger teacher. However, standard interactive feedback imposes prohibitive costs. We show that conventional trajectory-level preference optimization wastes budget on both unresolvable episodes and redundant execution turns. To resolve these inefficiencies, we propose \textbfScore-Guided Online Teaching with Budgeted Trajectory Trimming, a budget-aware framework that systematically orchestrates \textbfwhen and \textbfwhat to teach. Specifically, our framework integrates a solvability-aware teacher gate to dictate \textbfwhen to query the teacher model and a score-guided turn selection mechanism to decide \textbfwhat informative turns to retain. Extensive experiments on MiniWoB and TimeWarp demonstrate that our method achieves comparable first-pass success while reducing teacher calls by 22.6% and student training compute by 52.1% on average. Our code is available at this https URL.
[CV-318] ECHO: Dyadic 3D Facial Motion Generation with Asymmetric Deterministic Articulation and Stochastic Reaction
链接: https://arxiv.org/abs/2609.05506
作者: Zhuoqiang Cai,Yujie Sun,Chaoyue Niu,Hongyun Yu,Zhiwen Chen,Chengfei Lv,Fan Wu
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD)
备注: 10 pages, 4 figures, 4 tables. Accepted to ACM Multimedia 2026 (MM '26) as a poster presentation
Abstract:We propose ECHO for dyadic 3D facial motion generation under a strict dual-stream audio-only setting, formulating the problem as an asymmetric task involving speech-constrained articulation and one-to-many listener reactions. To address this asymmetry, ECHO decomposes motion into a deterministic anchor that captures stable speech-correlated structure and a stochastic residual that models the remaining one-to-many interaction dynamics. On top of this backbone, Motion Memory acts as a training-only regularizer during brief late-stage fine-tuning to provide local priors for weakly conditioned listening windows, while semantic-group scaling controls residual injection across expression, jaw, and neck. This design balances speaking-side articulatory fidelity with listening-side realism and diversity in a single generation process. Results from unified, state-wise, and ablation evaluations show that conversational 3D motion benefits from decomposing stable and uncertain components rather than applying stochasticity uniformly. ECHO provides a practical formulation and technical basis for deployable conversational digital humans under strict audio-only conditions.
[CV-319] A Survey on Adversarial Attacks and Defenses for Diffusion Models Across Multiple Modalities ECCV2026
链接: https://arxiv.org/abs/2609.05503
作者: Ozgur Kara,Tarik Can Ozden,Furkan Horoz,Zeqian Long,Haotian Xue,Yipu Chen,Oguzhan Akcin,Yongxin Chen,James Matthew Rehg
类目: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR)
备注: Accepted into Life-Cycle Intellectual Property Governance of Visual Generative Models Workshop at ECCV 2026
Abstract:Diffusion models have become the dominant family of generative models in the visual domain. However, their widespread public availability enables misuse at scale, motivating a rapidly growing body of research on adversarial attacks and defenses. This survey provides, to our knowledge, the first unified review of this literature across three visual modalities: image, video, and 3D. We introduce a comprehensive, task-centric taxonomy: we first divide the literature by modality; within each modality, we separate methods into attacks and defenses, and then group them by the generative task they target, presenting them chronologically within each task. Moreover, we provide an in-depth analysis of their evaluation settings, consolidating the datasets, metrics, and benchmarks used to assess them. We conclude by identifying several open challenges and outlining concrete future research directions. Project Webpage: this https URL
[CV-320] Architectural and Regularization Components in Deep Learning Medical Image Registration: Systematic Ablation Study
链接: https://arxiv.org/abs/2609.05484
作者: Nabira Rashid
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 15 pages, 1 figure
Abstract:Deep learning registration methods routinely stack two kinds of enhancement on a base network: architectural additions such as affine pre-alignment stages, and training-objective additions such as regularization losses. Papers tend to adopt both at once, so it is unclear which is doing the work. I ran a controlled ablation to separate them. Using the OASIS brain MRI dataset (394 training subjects, 20 test subjects), I trained four variants of the same registration pipeline: a baseline 3D U-Net with basic similarity losses, the same U-Net with a full regularization suite, an affine-plus-deformable architecture with basic losses, and the affine architecture with the full suite. I evaluated registration accuracy (MSE, NCC, SSIM), deformation quality (Jacobian determinant preservation, displacement statistics, an anatomical plausibility score), and computational cost. Regularization alone accounted for most of the gain: a 21.3% relative gain on the MSE-improvement metric (1.78% to 2.16%, P.001) and a 21.8% relative gain in NCC improvement, while cutting maximum deformation from 53.1 to 0.51 units, a 99.0% reduction, at essentially no computational cost (-0.06% inference time). The combined model produced the largest accuracy gain, 25.8% (1.78% to 2.24%), and raised anatomical plausibility from 0.596 to 0.930, at a moderate +9.8% inference-time cost. Gradient correlation rose from 0.742 at baseline to 0.980 for the fully enhanced model. All enhanced variants reached sub-voxel accuracy under plausible deformation constraints. Regularization losses are the primary driver in this setting, delivering the accuracy gains and almost all of the deformation control for free at inference time, while the affine architecture adds a smaller complementary benefit at acceptable cost. The 99% reduction in unrealistic deformations addresses a known barrier to clinical deployment.
[CV-321] C-Next: Zero-Shot Multimodal Cyclone Forecasting
链接: https://arxiv.org/abs/2609.02085
作者: Zhe Wang,Sijie Chen,Yiming Luo,Daehyun Kim,Chien-Yi Chang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages
Abstract:We present TropicalCycloneNext (TC-Next), a multimodal deep learning model that forecasts tropical cyclone track and intensity at 6 - 24 h leads by leveraging a foundation model’s forecast fields of atmospheric kinematic and thermodynamic fields and GridSat infrared satellite imagery. Trained only on GraphCast forecasts over the Western Pacific (WP), yet reliant only on generic atmospheric variables, TC-Next on GraphCast lowers track error by 15 - 44% and intensity error by a factor of 3 - 6 relative to a conventional, rule-based tracker, TempestExtremes; applied without retraining to the forecast fields of Pangu-Weather and IFS HRES, it stays ahead of TempestExtremes on both. Applied zero-shot to the generic weather fields of WeatherNext Cyclones on the 2025 WP season, TC-Next attains lower intensity error at every lead time, and lower or comparable track error, compared to that model’s specialized direct tracker in a deterministic comparison. Our ablation studies show that our multimodal model is able to utilize the additional modality to improve performance in tracking errors at every lead time and in intensity prediction at longer lead times.
[CV-322] Neural Posterior Estimation for Tomographic Weak Lensing Mass Mapping
链接: https://arxiv.org/abs/2609.07833
作者: Tim White,Shreyas Chandrashekaran,Camille Avestruz,Jeffrey Regier, theLSST Dark Energy Science Collaboration
类目: Instrumentation and Methods for Astrophysics (astro-ph.IM); Cosmology and Nongalactic Astrophysics (astro-ph.CO); Computer Vision and Pattern Recognition (cs.CV); Applications (stat.AP)
备注: 17 pages, 10 figures, 1 table
Abstract:Weak gravitational lensing shear and convergence trace the distribution of baryonic and dark matter across space, making them a powerful probe of cosmic structure. Inferring shear and convergence from images is a challenging inverse problem. The prevailing approach to this task estimates shear from weighted averages of galaxy ellipticities, calibrates these estimates to account for systematic biases, and transforms them to reconstruct convergence, a multistage procedure that requires substantial computational resources and meticulous handling of statistical uncertainties. As an alternative, we propose a probabilistic approach to field-level weak lensing inference in which we train a deep neural network to directly map a multiband image to a variational distribution over the underlying tomographic shear and convergence fields. This neural posterior estimation (NPE) procedure implicitly marginalizes over nuisance variables in the cosmological forward model and does not require evaluating the likelihood function. It is also amortized, so it enables rapid posterior inference for astronomical surveys once the neural network is trained. When evaluated on synthetic images from the LSST-DESC DC2 Simulated Sky Survey, NPE produces well-calibrated variational distributions for shear and convergence that are consistent with the ground truth. We describe how maps sampled from these variational distributions could be used in a subsequent simulation-based inference procedure to approximate the posterior distribution over cosmological parameters.
[CV-323] Spatial Feature-wise Linear Modulation (SpFiLM) for Contrast Agent -Aware Brain Parcellation MICCAI
链接: https://arxiv.org/abs/2609.07718
作者: Pushpendra Singh(1),Joshua R. Astley(1),Roman Rodionov(2),John Duncan(2),Tom Vercauteren(1),Rachel Sparks(1) ((1) School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK, (2) Department of Epilepsy, Queen Square Institute of Neurology, University College London, London, UK)
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 10 pages, 2 figures. Accepted at MLCN 2026 (MICCAI workshop)
Abstract:Most automated brain parcellation tools are developed and validated on T1-weighted (T1w) MRI. Yet, some clinical workflows for which parcellation is relevant only use contrast-enhanced T1w (T1ce) MRI, on which T1w-trained models are less accurate. We present a unified network that parcellates both pre- and post-contrast agent T1w MRI reliably, trained on a combination of the two with conditioning that spatially modulates its response differently for each. Feature-wise Linear Modulation (FiLM) is a known approach for input-based modulation in networks. It applies a per-channel scale and shift uniformly across the input. However, the appearance change between pre- and post-contrast varies locally across the brain, making FiLM suboptimal for our use case. In this work, we introduce Spatial FiLM (SpFiLM), a conditioning layer whose modulation varies spatially, assembling a voxel-wise scale and shift from image-derived spatial patterns. Using a cohort of 134 patients with paired T1w and T1ce MRI parcellated into 106 classes, the addition of SpFiLM layers in a UNet increased the mean Dice on the test set of 25 patients from 80.2% to 84.1%, a 4.9% relative improvement. Adding SpFiLM layers led to the best performance on both pre- and post-contrast MRI, even when controlling for network parameter counts.
[CV-324] AstraMoE-SR: Trajectory-Guided Diffusion for Blind Satellite Jitter Deblurring and Super-Resolution
链接: https://arxiv.org/abs/2609.07012
作者: Yi-Chung Lai,Chin-Tien Wu,Yu-Chih Chen
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 12 pages, 4 figures
Abstract:Pushbroom satellite imaging couples limited spatial resolution with platform attitude instability. Platform jitter produces spatially varying motion blur because each scan line is acquired under a different instantaneous attitude, while perspective geometry causes the same perturbation to induce different pixel displacements across the field of view. Existing blind restoration methods that assume a spatially invariant kernel and satellite jitter correction methods that rely on auxiliary observations are therefore not directly applicable. We present AstraMoE-SR, a single-image framework that jointly restores motion blur and spatial resolution without auxiliary measurements. Rather than estimating a blur kernel, we infer how the camera moved by reparameterizing degradation as a local exposure trajectory under pushbroom geometry. A conditional diffusion model estimates the trajectory distribution, mitigating the over-smoothing of high-frequency jitter by deterministic point estimation. The predicted trajectory conditions a pretrained latent diffusion backbone through trajectory-guided geometric alignment and spatially adaptive reconstruction. We further show that the remaining point-wise trajectory error is consistent with intrinsic jitter-phase ambiguity that is not resolved by increasing estimator capacity. On all 1,411 DOTA-v1.0 images degraded using our physically motivated forward model, AstraMoE-SR is the only evaluated method to outperform the no-restoration baseline across every fidelity metric, improving on StableSR by 0.64 dB PSNR, 15.2% LPIPS, and 0.091 DINO feature similarity. Reconstructions conditioned on predicted trajectories differ negligibly from those using ground-truth trajectories, indicating that the estimates retain the degradation information required for effective restoration.
[CV-325] MedGSSR: Generalizable Medical Image Super-Resolution 3D Reconstruction via Hierarchical Feed-forward Gaussian Splatting ECCV2026
链接: https://arxiv.org/abs/2609.06874
作者: Chengkai Wang,Luoyu Hong,Yiting Zhao,Jiamin Wang,Xiang Feng,Feiwei Qin,Zhenzhong Kuang,Xuefei Yin,Ali Bashashati,Yanming Zhu
类目: Image and Video Processing (eess.IV); Computational Engineering, Finance, and Science (cs.CE); Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM)
备注: ECCV 2026
Abstract:High-resolution volumetric medical imaging is critical for clinical diagnosis, yet acquisition is often limited by scanner hardware, scan time, and for CT, radiation dose. Medical 3D Super-Resolution (Med3DSR) offers a computational alternative, but existing methods commonly rely on per-subject optimization, pretrained priors, or coordinate-based implicit representations, which compromise anatomical fidelity and limit efficiency. To address these limitations, we present MedGSSR, a fully end-to-end feed-forward framework that represents volumes as an explicit 3D Gaussian field for Med3DSR. Unlike coordinate-based implicit functions, our explicit 3D Gaussian representation naturally enhances signal continuity and local high-frequency fidelity. Specifically, MedGSSR explicitly decouples the reconstruction process into coarse-grained structural preservation and fine-grained textural refinement through the proposed Pyramid Anatomical Encoder and a Hierarchical Gaussian Projector. To support arbitrary-scale super-resolution, we introduce sub-voxel Gaussian decomposition and a Differentiable Gaussian Voxelizer that directly queries the continuous 3D intensity field, reducing discretization artifacts. Extensive experiments on MRI and CT benchmarks demonstrate that MedGSSR significantly outperforms state-of-the-art methods. Notably, our framework exhibits robust generalizability across unseen datasets without requiring per-subject optimization, enabling fast inference and high-fidelity volumetric super-resolution in practical clinical settings. Our project webpage, including code, is at this https URL
[CV-326] NEO-BENCH: A New Multi-Source Benchmark for Generalizable Astronomical Streak Detection
链接: https://arxiv.org/abs/2609.06774
作者: Jiayou He,Jessica Yao
类目: Instrumentation and Methods for Astrophysics (astro-ph.IM); Computer Vision and Pattern Recognition (cs.CV)
备注: The code and data used in NEO-BENCH are publicly available on GitHub at this https URL and on Hugging Face at this https URL
Abstract:Near-Earth Objects (NEOs) can appear as faint streaks in long-exposure astronomical images. Detecting these streaks across diverse observatories requires methods that remain reliable despite differences in image quality, orientation, sky background, and noise. However, existing detectors are commonly evaluated using data from only one source, providing limited evidence of cross-source generalization. We introduce NEO-Bench, a multi-source benchmark containing 8,376 images from five astronomical-image datasets. The sources include the Hubble Space Telescope, a Stellina smart telescope, the United Arab Emirates Meteor Monitoring Network, a TETRA1 telescope using a Celestron C14 with Fastar, and the Roboflow Asteroid dataset. We converted the data to a common YOLO format, audited a sample of labels, and defined within-source and leave-one-source-out evaluation protocols. We evaluated four approaches: Hough, Radon, Gaussian PSF, and YOLO26L. Leave-one-source-out F1 decreased in 14 of 20 image-level method-source pairs and 13 of 20 IoU@0.50 localization pairs. Across the datasets categorized as medium or hard, F1 decreased in 11 of 12 image-level pairs and 9 of 12 localization pairs. These results show that cross-source performance remains inconsistent and that reliable generalization across astronomical imaging sources remains an open challenge. The benchmark, code, and data are publicly available. Comments: The code and data used in NEO-BENCH are publicly available on GitHub at this https URL and on Hugging Face at this https URL Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2609.06774 [astro-ph.IM] (or arXiv:2609.06774v1 [astro-ph.IM] for this version) https://doi.org/10.48550/arXiv.2609.06774 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-327] Deep learning from the crowd Fundamentals of morphological galaxy classification
链接: https://arxiv.org/abs/2609.06316
作者: Luis Enrique Sucar,Carlos del Burgo,Jonathan Serrano-Pérez
类目: Astrophysics of Galaxies (astro-ph.GA); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Aims. The objective of this work is to adapt a deep neural network model to perform galaxy morphological classification trained from crowd annotations, considering the training scheme, the agreement between the annotators, and the hierarchy. Methods. We use Galaxy Zoo 1 as our experimental testbed and trained a convolutional neural network (CNN) for the automatic classification of galaxies’ morphologies. We analyze the impact of the following aspects on the classification accuracy and training efficiency: (i) Training only the last layer vs. training all the network; (ii) Classification with only the CNN vs. considering the hierarchy; (iii) Comparing the models trained with different amounts of data and levels of agreement between the annotators; (iv) Training by stages, transferring knowledge from one model to another; and (v) Combining several models as an ensemble. Results From the experiments, we derive the following results: (i) Training all the layers in the network significantly improves the accuracy (10% increase in exact match), compared to training only the last layer; (ii) There is a tradeoff between the amount of data and the level of agreement between the annotators used for training; (iii) Using the hierarchy can improve accuracy when the amount of training data is reduced; (iv) Training by stages through transfer learning (curriculum learning) produces higher accuracy for limited data; (v) Ensembles can improve accuracy; (vi) Models achieve a low accuracy for the most difficult cases, but, if we consider hierarchical measures, we can derive useful results for upper levels in the hierarchy. An accuracy above 99% is achieved when training all layers of the network and considering a high agreement between the annotators. Conclusions. Training deep learning models from crowd annotations involves additional challenges than learning from hard annotations. Subjects: Astrophysics of Galaxies (astro-ph.GA); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) Cite as: arXiv:2609.06316 [astro-ph.GA] (or arXiv:2609.06316v1 [astro-ph.GA] for this version) https://doi.org/10.48550/arXiv.2609.06316 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jonathan Serrano [view email] [v1] Sun, 6 Sep 2026 00:36:30 UTC (1,086 KB)
[CV-328] Reliable iToF Depth Sensing via Sensor-Intrinsic Uncertainty Modeling and State-Space Restoration
链接: https://arxiv.org/abs/2609.05507
作者: Yansong Du,Yutong Deng,Yuting Zhou,Zhancong Xu,Yingjia Lu,Mengdi Wang,Feiyu Jiao,Bangyao Wang,Zhaoxiang Jiang,Xun Guan
类目: Optics (physics.optics); Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 7 figures
Abstract:Indirect time-of-flight (iToF) cameras provide compact and cost-effective dense depth measurements, but their ranging accuracy is often degraded by sensor-intrinsic uncertainty under practical imaging conditions. Spatially uniform or range-only Gaussian perturbations cannot accurately reproduce the range-dependent and signal-dependent noise characteristics of real iToF measurements, leading to a synthetic-to-real gap for learning-based restoration. To address this problem, we propose a joint depth-uncertainty modeling and restoration framework for reliable iToF sensing. A sensor-intrinsic depth-uncertainty model is first developed from calibrated tap responses, returned-signal levels, and sensor noise statistics through a depth-oriented weighted least-squares formulation. The resulting pixel-wise uncertainty is used for heteroscedastic depth synthesis and uncertainty-aware restoration supervision. Based on this heteroscedastic data synthesis, we further develop a U-shaped restoration network with Depth Visual State Space (DVSS) blocks, which combine long-range state-space modeling with convolutional spatial-channel refinement for structure-preserving depth recovery. Experiments on synthetic data and measurements captured by an in-house iToF prototype validate the proposed uncertainty model under varying range and returned-signal conditions. Controlled comparisons with fixed and range-aware Gaussian noise, together with evaluations on U-Net, Restormer, and DVSS, further demonstrate that the proposed synthesis consistently benefits different restoration backbones. The complete framework achieves 40.85~dB PSNR and 2.54 mm MAE on the synthetic test set, and 35.42 dB PSNR and 4.87 mm MAE on real iToF measurements.
[CV-329] Representation learning of human cortical folding to reveal long lasting neurodevelopmental signatures
链接: https://arxiv.org/abs/2609.05438
作者: Julien Laval,Robin Guiavarch,Antoine Dufournet,Racim Menasria,Barthélémy Drabczuk,Cristobal Mendoza,Saeb Tounsi,Chikh Abdelghani Baroud,Merieme Bourenane,Vanessa Troiani,William Snyder,Marisa A Patti,Mylène Moyal,Marion Plaze,Arnaud Cachia,Federica Santacroce,Giorgia Committeri,Claire Cury,Kevin De Matos,Olivier Colliot,Zhong Yi Sun,Clara Fischer,Vincent Frouin,Pietro Gori,Denis Rivière,Joël Chavas,Jean-François Mangin
类目: Quantitative Methods (q-bio.QM); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:The human brain folds in utero, primarily during late gestation. Shortly after birth, cortical folding patterns are established and remain stable thereafter, making them promising early neurodevelopmental markers. Yet it is unclear whether representations given by current neuroimaging foundation models capture cortical folding variability. Here, we introduce Champollion, a self-supervised learning framework that learns interpretable local representations of cortical folding from structural MRI. Optimized on representative folding-related tasks, Champollion accurately captures known folding patterns across cortical regions and external datasets. In a comprehensive benchmark, it consistently outperforms neuroimaging and general-purpose foundation models. Furthermore, Champollion reveals richer genetic associations than conventional morphometric descriptors and identifies localized folding signatures associated with incomplete hippocampal inversion, prematurity, and maternal smoking. These results establish cortical folding as a rich and largely untapped source of neurodevelopmental information and illustrate how pre-processing and architectural inductive biases can recover biologically meaningful signals overlooked by current generalist foundation models.
人工智能
[AI-0] ANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model
链接: https://arxiv.org/abs/2609.09158
作者: Anqi Li,Yuxin Chen,Zhaobo Li,Zhuo Cao,Junli Ren,Masayoshi Tomizuka,Dhruv Shah
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:We study the problem of navigating cluttered indoor environments with a humanoid robot. Unlike conventional methods that model navigation as a 2D path planning problem, humanoid traversal in cluttered environments requires continuous geometry-aware whole-body adaptation, including coordinated arm placement, torso adjustment, and gait modulation for collision-free movement through complex 3D spaces. We introduce TANGO, the first whole-body vision-language navigation framework for language-conditioned humanoid traversal in cluttered environments. Given a natural-language instruction and egocentric RGB observations, TANGO directly predicts 29-DoF joint-space actions for downstream whole-body control. We train TANGO entirely in simulation by synthesizing diverse collision-free traversal behaviors via global path planning, kinematic whole-body motion generation, obstacle-aware motion editing, and RL-based tracking. This pipeline provides dynamically feasible action supervision for learning language-conditioned whole-body policies. In extensive simulation experiments, TANGO demonstrates state-of-the-art performance in vision-language navigation, while outperforming strong modular baselines in navigating challenging scenes requiring obstacle negotiation. Lastly, we deploy TANGO zero-shot on a Unitree G1 humanoid robot, and observe robust language-guided traversal in cluttered real-world scenes without training on any real-world navigation data.
[AI-1] NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting
链接: https://arxiv.org/abs/2609.09140
作者: Tobias Susetzky,Raphael Rehms,Dmitrii Seletkov,Özgün Turgut,Michelle Espranita Liman,Lisa Steinhelfer,Rickmer Braren,Daniel Rueckert
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The digitization of healthcare has generated vast, longitudinal, and multimodal patient records over a lifetime, yet fully exploiting these data to represent and predict patient state trajectories remains a critical challenge. Current AI models often struggle to capture the complex, irregular temporal dynamics and inherent stochasticity of real-world multimodal patient data. Existing AI approaches for modeling longitudinal patient records are predominantly discriminative, limited to a few modalities, constrained by closed categorical vocabularies, treating time as a monotonic inductive bias, or they are limited in forecasting future patient states. We introduce NOAH, a time-aware, task-agnostic, generative transformer model representing and forecasting the full multimodal patient journey. NOAH features a novel bidirectional time integration and a variational latent space to capture the continuous evolution of patient states and the stochasticity of clinical trajectories. Built from over 559 million clinical events from 431,000 hospital visits of 299,000 patients across the MIMIC dataset family, NOAH natively processes medical images, time-series and numeric signals, categorical events, as well as structured and unstructured clinical records. NOAH is the first truly holistic generative model in its field, enabling autoregressive forecasting with optional time control, zero-shot classification, and counterfactual intervention simulation. It generates highly informative and predictive patient state representations that demonstrate strong performance in probing for clinical outcomes, 15 ICD chapters, and 29 comorbidities, as well as in time-to-event prediction. Seamlessly handling diverse modalities and complex temporal dynamics, NOAH provides a versatile, task-agnostic, scalable foundation for intelligent predictive systems in personalized clinical care and digital medicine.
[AI-2] Co-Evolving Harnesses and Models: On-Policy Correction Helps Weaker Models Catch Up Where Imitation Fails
链接: https://arxiv.org/abs/2609.09134
作者: Zhou Yu,Bin Bi,Shiva Kumar Pentyala,Shubham Mehrotra,Sougata Chaudhuri,Shilpa Bhagavath,Zeyuan Chen,Ran Xu,Phil Mui,James Zhu,Sitaram Asur
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Agent harnesses (the system prompt, tool set, execution hooks, and context-management scaffolding around a model) are a critical determinant of agentic task success. Automated harness evolution can enable smaller models to perform well on domain-specific tasks at a fraction of frontier-model cost. Since both the harness and model weights shape behavior, we ask how harness evolution and lightweight fine-tuning should be combined. Across seven enterprise agent tasks, we first evolve a harness with the weaker model, then find that a stronger expert often uses it more effectively, suggesting expert supervision could close the remaining gap. However, training the weaker model on the expert’s complete trajectories under the evolved harness backfires: performance regresses on all seven tasks by 4 to 30 points across Qwen3-Coder and Gemma 4, even though the same procedure helps under the unevolved harness. Our analysis shows that imitation transfers knowledge and increases scaffold usage, but disrupts model-harness fit: the weaker model adopts the expert’s planning strategy without the competence to execute it and no longer matches the harness evolved around its native planning style. We therefore develop an on-policy expert-correction pipeline, automated by a meta-level MLE agent, that localizes the failing turn in the weaker model’s own rollout and asks the expert to rewrite only that turn. This preserves the model’s planning style and combines the gains of harness evolution and model adaptation. Our results identify and resolve a source of contention between harness and weight updates, yielding a compatibility-preserving recipe for economical co-evolution on domain-specific enterprise tasks.
[AI-3] A Generalization of Amaris Bayesian Duality
链接: https://arxiv.org/abs/2609.09126
作者: Mohammad Emtiyaz Khan,Thomas Möllenhoff
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)
备注:
Abstract:Amari’s contributions to information geometry and machine learning are well known. Here, we revisit Amari’s work on Bayesian duality which has not received as much attention. We connect Amari’s Bayesian duality to a convex duality of Bayes’ rule. Using this connection, we present a generalization of Amari’s Bayesian duality and discuss its relevance for modern artificial intelligence.
[AI-4] DeCAL: Towards Physically-Grounded Dexterous Vision-Language-Action Models via Contact-Aware Latent Co-Imagination
链接: https://arxiv.org/abs/2609.09119
作者: Yankai Fu,Ning Chen,Junkai Zhao,Heng Zhang,Guocai Yao,Pengwei Wang,Zhongyuan Wang,Shanghang Zhang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Dexterous manipulation involves contact-rich and fine-grained interactions with the physical world, posing significant challenges for existing vision-language-action (VLA) models due to severe visual occlusions and complex contact dynamics. While recent works have incorporated tactile sensing into robotic manipulation, most approaches still rely on homogeneous multimodal fusion, lacking adaptive tactile integration and explicit modeling of physical dynamics. In this work, we present DeCAL, a physically-grounded dexterous vision-language-action model that unifies understanding, imagination and action generation for contact-rich dexterous manipulation. Built upon a Mixture-of-Transformers (MoT) architecture, DeCAL leverages specialized experts for each capability while enabling efficient information flow among them. To effectively leverage tactile information, we introduce Adaptive Visuo-Tactile Fusion that dynamically regulates tactile interactions via a contact-aware gating strategy. Furthermore, we propose Visuo-Tactile Latent Co-Imagination to jointly model visual and tactile dynamics, equipping the policy with implicit physical world knowledge. Experimental results show that DeCAL consistently achieves state-of-the-art performance across all tasks, attaining a 71% average success rate and an 83.4% progress success rate, while also demonstrating strong generalization to unseen scenarios. The website is available at this https URL.
[AI-5] MeClear: Cooperative Game-Theoretic Attribution and Risk-Aware Memory Clearance for Long-Horizon LLM Agents
链接: https://arxiv.org/abs/2609.09115
作者: Boyu Yang,Jiazheng Sun,Zilong Lu,Zhi Qiu,Xin Peng,Jun Zheng
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 15 pages, 12 figures
Abstract:Long horizon Large Language Model (LLM) agents rely on external memory systems to preserve user preferences and task knowledge across extended interactions. Conventional retrieval mechanisms optimize semantic compatibility rather than downstream utility, frequently introducing outdated, misleading, or conflicting evidence into the active context. We present MeClear, a task conditioned memory clearance framework that identifies memories featuring negative downstream utility through cooperative attribution and selectively suppresses them from agent execution. MeClear combines Leave One Out screening with sampled cooperative Shapley attribution to distribute utility across interacting evidence, effectively resolving redundant conflict masking where single removal evaluations fail. Utilizing attribution rankings, MeClear executes a query scoped minimal clearance strategy over a nested filtration, verifying task recovery on the cleared context without permanently altering the persistent memory bank. Comprehensive experimental evaluations across ten long dialogue memory pools demonstrate that MeClear achieves a target recall of 85.9% and an overall task recovery rate of 82.3%, representing a 25.5 percentage point improvement over Leave One Out (LOO) baselines.
[AI-6] he Surprising Effectiveness of Approximate Value Iteration in Self-Play
链接: https://arxiv.org/abs/2609.09094
作者: Raphael Boige,Amine Boumaza,Bruno Scherrer
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Combining search with function approximation has driven major advances in game-playing programs, making self-play algorithms more competitive than ever. Still, the computational overhead of the most popular methods, based on Monte Carlo Tree Search (MCTS), can be substantial. In this work, we investigate whether simpler methods remain competitive in non-trivial, moderately sized games such as Connect Four, Hex(7x7) and synthetic games. We train a minimal self-play implementation of Approximate Value Iteration (AVI) and use ground-truth oracles for exact evaluation. Contrary to expectations, our results demonstrate the surprising effectiveness of AVI: it learns more accurate value functions than those learned by AlphaZero, while its one-step-lookahead greedy policies remain competitive with MCTS-based policies at substantially lower training and inference costs. Preliminary experiments on Othello and Go(9x9) show that AVI trains stably on larger games and learns effective value functions. These findings suggest that the success of MCTS-based methods may have eclipsed simpler approaches that have become increasingly practical with modern deep-learning tools.
[AI-7] Everything in Moderation: Per-Domain Coverag e Optima and Alignment-Resistant Domain Gaps in Multi-Domain Mid-Training ICLR
链接: https://arxiv.org/abs/2609.09081
作者: Yunpeng Xu,Kun Zheng
类目: Artificial Intelligence (cs.AI)
备注: About to commit to ICLR
Abstract:Mid-training, the stage between pre-training and alignment, is where a model’s per-domain data composition is typically set by data availability rather than principled design. We ask what that decision buys, and whether a later alignment pass can undo it. In a controlled logical-reasoning setting (Qwen3-8B-Base, with a 4B replication; five semantically rule-disjoint KOR-Bench domains) we train 30 allocations spanning the five-domain simplex, 24 sweep configurations plus six withheld from the fit, at five seeds each. Three findings emerge. First, every domain has an interior coverage optimum: the moderate band ( 10% - 40% ) is best for all five domains, and a calibrated permutation test for quadratic interiority gives P\approx0.010 ; the fitted mid-training-only curves, with 8B peaks between 9.9% and 35.1% , reproduce for curve shape but not peak location. Second, the gaps survive a fixed-budget alignment pass: compensatory SFT raises 116/120 cells (mean +4.32% ) yet bridges 0/240 pairs at a 5% threshold and 30/240 at a 10% ratio, an equal-budget uniform control behaves almost identically, and a permutation null would bridge 13.8\pm3.3 and 77.9\pm8.5 pairs ( P0.001 ). Third, zero coverage collapses mid-training-only accuracy, though a FineWeb-Edu-only control shows the collapse is commingled with generic drift. An exploratory \theta^* allocation attains the largest full-pipeline gain ( +4.36% vs. +0.80% / +0.64% ,pp) but is marginal under Welch test.
[AI-8] hinkPrior: Zero-Rollout Difficulty Priors for Cold-Start Prompt Selection in RLVR
链接: https://arxiv.org/abs/2609.09075
作者: Tommy Sha,Skylar Zhai,Siqi Zhao
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 13 pages, 3 figures, 5 tables. Project page: this https URL
Abstract:In reinforcement learning with verifiable rewards (RLVR) trained with group relative policy optimization (GRPO), the KL-free reward-advantage term studied here depends on within-group reward variation. If all rollouts in a group are correct or all are wrong, their group-relative advantages are identically zero; these zero-advantage silent groups provide no reward-advantage gradient, yet uniform sampling spends 39% of a run’s rollouts on them. History-based prompt selection must first spend target-policy rollouts to estimate difficulty, creating a cold start with rollout waste; ThinkPrior instead uses an external anchor in one offline pass to construct a zero-rollout difficulty prior before the first target-policy rollout. The verifier-scored anchor pass rate supplies an external-anchor initialization for a Beta posterior; ThinkPrior selects by expected learnability and then updates from training outcomes, changing neither the loss nor the optimizer. On Qwen2.5-Math-7B across sixteen seeds, ThinkPrior more than halves early silent groups and cuts wasted rollouts through step 30 by nearly a fifth, while we detect no difference in final accuracy. On this 250-prompt pool the fixed-budget result is a reallocation rather than a net saving. The measured ThinkPrior+DAPO composition reduces generated rollouts by 10.6% while both arms retain the same 3840-rollout update budget. The prior requires no target-policy rollout before the first selection, but the posterior thereafter uses target-policy outcomes.
[AI-9] raining-Free Task Vectors for LLM Behavioral Control
链接: https://arxiv.org/abs/2609.09054
作者: Gabriel J. Perin,Lucas Boscaini,André Araujo,Nina S. T. Hirata
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Task vectors enable post-training model editing by identifying semantically meaningful directions in weight space, typically computed as the difference between a fine-tuned model and its pretrained initialization. However, this reliance on fine-tuning makes discovering such directions costly and limits the practicality of post-training model editing. To address this limitation, we introduce Training-Free Task Vectors (TFTVs), a novel method to compute task-vector-like directions without requiring fine-tuning. Our method maps activation steering vectors to rank-one weight-space edits using only forward-pass statistics, while satisfying arithmetic properties that directly support learning via addition, forgetting via subtraction, and the composition of multiple edits. Empirically, we evaluate TFTVs on large language model behavioral control tasks and show that they consistently amplify, suppress, and compose target behaviors while preserving general knowledge and problem-solving skills. We also validate our method against other editing and steering baselines, experimentally demonstrating that TFTVs achieve stronger trait control with better or competitive utility preservation. We hope our work opens new directions for the community in post-training model editing and broader training-free model control. Code is available on the project website: this http URL.
[AI-10] It Is Not My Code Anymore
链接: https://arxiv.org/abs/2609.09022
作者: Augusto Camargo
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 7 pages, 2 figures, 1 table. Research note
Abstract:AI-assisted programming raises distinct questions about who produces code, who feels ownership of it, and who is responsible when it fails. This research note examines these distinctions through a hypothetical enrollment failure and a selective reading of the literature. Identifying the producer of a defective expression does not, by itself, determine the duties of reviewers, release decision-makers, or service operators. Collective ownership likewise leaves those duties to be specified. The discussion then considers how quality engineering can evaluate both generated implementations and the processes that produce them. Acceptance criteria should be justified by the required service outcome, with component checks contributing evidence toward that outcome. This perspective also permits comparison with systems that perform a task without generating a separate program for it. Such substitution would change the object of authorship while leaving the service obligation intact. The note reports no new empirical results; it proposes distinctions and evaluation questions for AI-assisted software production.
[AI-11] Let It Go or Learn to Self-Correct: Continuous Diffusion for Constrained Discrete Tasks
链接: https://arxiv.org/abs/2609.09009
作者: Mariia Drozdova,Stéphane Liem Nguyen,François Fleuret
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Preprint. The main results of this work were obtained by May 2026
Abstract:Denoising Diffusion Probabilistic Models (DDPMs) generate samples by starting from noise and repeatedly denoising while keeping each update close to the current noisy state. This behavior is effective in many continuous domains, but its role is less clear for globally constrained discrete tasks, such as Sudoku, graph connectivity, Latin squares, and N-queens. In such settings, early discrete errors can be difficult to undo. As a result, standard diffusion sampling may preserve early mistakes, even when the model’s clean predictions are informative. We compare standard samplers to sampling directly from the model’s clean prediction. Without retraining, this single change improves Sudoku validity from 31% to 95%, with consistent gains across the other discrete tasks. We hypothesize that staying close to the current noisy state is harmful because the reverse trajectory can drift off the forward noising distribution the model was trained on. To reduce this train-test mismatch, we further introduce self-correction training, which exposes the model to its own predictions, improving robustness to errors that arise during inference. This substantially improves the performance of standard samplers. Our results suggest that continuous diffusion models can learn nontrivial global constraints, but discrete reasoning tasks require better alignment between training and inference: either through samplers that reduce commitment to early decisions, or through training that teaches the model to correct its own inference-time errors.
[AI-12] Deposon: An Auditable Conservation-Guaranteed Game-Theoretically Tested Scattering Layer over LLM Reasoning Paths
链接: https://arxiv.org/abs/2609.09001
作者: Qihao Yuan
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 23 pages, 5 figures. Author affiliation: School of Chemistry and Life Resources, Renmin University of China. Code: this https URL
Abstract:Multi-step LLM reasoning lacks a machine-recheckable ledger: discarded reasoning paths leave no auditable record. We propose the Deposon scattering layer, which binds each node of an LLM-generated concept-decomposition graph to a two-parameter Deposon state; paths undergo three-channel scattering – transmission, reflection, irreversible dissipation – obeying T+R+A=1 for arbitrary parameters, with a maximum per-path energy-audit deviation of 2.2E-16 (machine epsilon). We report all three evidence tiers honestly. On synthetic trap benchmarks the path-filtering gain is closed (pre-registered): unified reaches 100% versus a decoy-capture baseline at 7%/10%. On real benchmarks the layer is indistinguishable from a trivial six-keyword rule filter (GSM8K 0.87 = 0.85, McNemar p=0.5; StrategyQA 0.899 = 0.899); no difference is detected here, so we sharpen the claim to “the differential value lies solely in machine verifiability.” Fusion yields a second negative result: convex combinations with a semantic prior never improve (physics 0.484 - 0.452), and the apparent lambda=2 gain is an anti-field artifact; any fusion gain must be nonlinear. Modeling the reverse dynamics as a potential game on the graph, we evidence an auditable scalar’s monotonicity and near-gradientness and quantify the empirical coordination ratio (ECR). The three formalized dynamical-equivalence propositions (P1a/P1b/T-P1c) are falsified under the pre-registered kill protocol, and the potential-game claim is downgraded to approximate (cyclic-graph median residual 0.669): only consistency-level evidence survives at the dynamical level. Code: this http URL.
[AI-13] ransformers as In-Context Samplers: From Closed-Form Diffusion to Estimation-Free Sampling
链接: https://arxiv.org/abs/2609.08981
作者: Arman Adibi,Alireza Jafari,Mohammad Ghavamzadeh,Hadi Daneshmand
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Applications (stat.AP); Computation (stat.CO); Machine Learning (stat.ML)
备注:
Abstract:A growing body of work establishes that large language models are not mere statistical memorizers, but are capable of in-context learning: performing inference at test time using only examples provided in the prompt, without any parameter updates. Prior theoretical work has shown that this capability extends to supervised learning tasks such as linear regression. We prove that in-context learning extends further to \emphdata generation: frozen transformers can simulate iterative generative samplers from in-context samples. We first show that transformers can realize closed-form and smoothed closed-form diffusion samplers. The construction identifies a concrete generative role for softmax attention: it computes responsibility weights and weighted empirical averages, while feedforward layers implement Euler updates. To empirically relate these constructions to pretrained language models, we study \emphsemantic-topic sampling: prompts consisting of words drawn from a common semantic category, such as animals, foods, or cities. Across transformer layers, the normalized hidden states exhibit a two-stage geometry: they move toward a uniform spherical reference in intermediate layers and then return to structured, topic-dependent representations near the output. We further measure an interacting-particle energy on these hidden-state clouds and observe the same U-shape pattern. We then prove that transformers can approximate an energy-based sampler, constructing the same U-shape energy across the layers. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Applications (stat.AP); Computation (stat.CO); Machine Learning (stat.ML) Cite as: arXiv:2609.08981 [cs.LG] (or arXiv:2609.08981v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.08981 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-14] GraphFAS: A Distributed System for Automated Graph Feature Generation and Selection in Industrial Transaction Networks CIKM2026
链接: https://arxiv.org/abs/2609.08970
作者: Yice Luo,Yun Zhu,Xi Chen,Yongchao Liu,Xintan Zeng,Chengying Huan,Kai Zhang,Jinrui Zhang,Juelu Zhang,Jiajun Zheng
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: Accepted by CIKM 2026
Abstract:Industrial fraud detection often relies on costly expert-crafted features that overlook graph-structured relational signals, while GNNs often do not meet the interpretability and deployment requirements of financial risk control. We propose GraphFAS (Graph Feature Automated Selection), a distributed feature selection procedure based on Boruta that bridges this gap through: (1) a non-parametric graph feature generation module that constructs explicit, interpretable structural features via multi-hop subgraph extraction and multi-scale aggregation without learned parameters; and (2) an automated distributed feature selection algorithm extending Boruta with median-based aggregation across partitions to robustly identify informative features at scale with minimal domain expertise. Compared with end-to-end GNN pipelines, GraphFAS decouples feature aggregation from model training, enabling direct integration with tabular models and direct compatibility with TreeSHAPbased explanations. Deployed in Alipay, GraphFAS delivers orderof-magnitude improvements in engineering efficiency while showing strong performance against expert-driven and graph-learning baselines on large-scale graphs.
[AI-15] SQLMorph: Query Mutation and Fine-Grained Metrics for Text-to-SQL Evaluation
链接: https://arxiv.org/abs/2609.08950
作者: Mohammadhossein Malekpour,Mohamed Riahi,Maxime Lamothe,Amine Mhedhbi
类目: Databases (cs.DB); Artificial Intelligence (cs.AI)
备注:
Abstract:Text-to-SQL systems translate natural language queries into executable SQL, democratizing access to structured data. Despite recent advances driven by large language models (LLMs), evaluation remains a major bottleneck: public benchmarks fail to capture the complexity of enterprise schema, while building private evaluation sets is costly and nondeterministic, making evaluation results difficult to reproduce. To address this issue, we present SQLMorph, a framework for Text-to-SQL evaluation via query mutation. SQLMorph introduces two techniques to automatically generate and expand evaluation sets: Join Query Expansion (JQE), which systematically increases structural complexity through valid join additions, and Textual Query Augmentation (TQA), which generates controlled natural language perturbations to assess robustness to linguistic variation. JQE and TQA create targeted choke points to challenge specific system components. When applied to state-of-the-art systems, JQE increases query coverage and reveals accuracy degradation as the number of joins grows. Meanwhile, TQA shows that linguistic brittleness induced by heavy abbreviation can reduce accuracy by up to 17%. Beyond evaluation sets, SQLMorph introduces a family of execution-level metrics that address the limitations of current binary measures, such as Execution Accuracy. We define Execution Precision (EXP) and Execution Recall (EXR) to quantify the fraction of correct and recovered results, respectively, and combine them via F1 for unified scoring. Our experiments show that these relaxed metrics enable fine-grained analysis of over- and under-prediction, revealing differences across systems that binary metrics obscure. Together, SQLMorph’s query mutation and fine-grained metrics support debugging and better align Text-to-SQL evaluation practices with real-world deployments. Subjects: Databases (cs.DB); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.08950 [cs.DB] (or arXiv:2609.08950v1 [cs.DB] for this version) https://doi.org/10.48550/arXiv.2609.08950 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: 2026 IEEE 42nd International Conference on Data Engineering (ICDE), pp. 2628-2640 Related DOI: https://doi.org/10.1109/ICDE65706.2026.00196 Focus to learn more DOI(s) linking to related resources
[AI-16] SkillAdam: Stable and Efficient Skill Evolution for Agents
链接: https://arxiv.org/abs/2609.08944
作者: Gaoyuan Li,Meihao Fan,Yizhe Liu,Shaolei Zhang,Ju Fan,Siyi Wang,Jiaheng Hou,Xudong Weng,Honghan Tian,Zang Li
类目: Artificial Intelligence (cs.AI)
备注: 17 pages, 4 figures, 6 tables
Abstract:Agent skills provide a lightweight way to equip frozen language-model agents with domain knowledge and procedural guidance, yet obtaining high-quality skills remains costly and difficult to scale. Expert-written skills require substantial human effort. Recent skill self-evolution methods automate an iterative loop that uses execution feedback to revise skills, but their heuristic update strategies often yield unstable optimization and low iteration efficiency. We identify two challenges in realizing stable and efficient skill self-evolution. Direction Stability requires effective corrections to accumulate rather than be overwritten by iteration-local feedback. Update Adaptivity requires the scope of each revision to reflect the consistency of recent case-level improvements. We introduce SkillAdam, an Adam-inspired framework for optimizing discrete and non-differentiable skill documents. As a functional analogue of Adam’s first moment, an optimization memory records identified problems and the outcomes of prior solution attempts to stabilize the update direction. As a functional analogue of Adam’s second moment, a volatility-driven edit budget tracks the history-weighted variation of recent case-level improvements and adaptively controls the update magnitude. Across seven benchmarks that span short- and long-horizon tasks, SkillAdam achieves state-of-the-art performance with more stable optimization dynamics. It also obtains stronger skills with substantially fewer optimization iterations and lower cost than prior methods. Code repository: this https URL
[AI-17] OntoKG-EQ: A provenance-grounded competency-question-governed knowledge graph for auditable analyst querying
链接: https://arxiv.org/abs/2609.08869
作者: Furqan Nasir,Muhammad Atif Saeed,Muhammad Ehsan,Sher Jeel Ahmad,Abdul Moiz Altaf
类目: Emerging Technologies (cs.ET); Artificial Intelligence (cs.AI); Databases (cs.DB)
备注: 36 pages, 2 figures
Abstract:Analysts in emerging equity markets keep answering the same questions. Did fundamentals match the market’s response? How does the local currency co-move with returns? Which firms outperform sector and benchmark, and which disclosures coincide with abnormal trading? These answers come from ad-hoc spreadsheets that are hard to reproduce, audit, or trust. We present OntoKG-EQ, a knowledge-based system that makes such queries reproducible, evidence-linked, temporally explicit, valid, and inspectable. It couples a bounded, competency-question-governed core ontology with a provenance-aware knowledge graph in which every class, property, shape, and metric is justified by one of five frozen questions. The system materialises market data into the graph, computes the metrics, validates its structure against declarative shape constraints, answers each competency question with a graph query, derives typed findings, and generates an explanation tracing each result to its observations, evidence, sources, and provenance. We evaluate on curated datasets from three emerging markets (Pakistan, Malaysia, Indonesia). Once each market’s data is mapped into the common schema, the ontology, shapes, queries, and rules are reused unchanged. A relational-database baseline shows the graph changes no analytics. Its value is governance, provenance, and self-explaining structure. Because answers are rendered deterministically from the validated graph, their consistency with it is guaranteed by construction. Used as a reference, the system measures how consistently eight open language models transcribe the same evidence (provenance coverage 0.00 to 1.00). A study with a 17-participant convenience panel finds the evidence bundle significantly increased perceived trust and completeness. Code and data are openly released.
[AI-18] API Benchmark Scores Do Not Reliably Transfer to Chatbot Interfaces
链接: https://arxiv.org/abs/2609.08861
作者: Jennifer Wang,Joachim Baumann,Daniel E. Ho,Sanmi Koyejo
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Benchmark scores are a central currency in model releases: they inform purchasing decisions, shape public trust, and influence policy. Yet, a key assumption underlying benchmark scores is that the model performance measured through APIs faithfully reflects the behavior of deployed systems. We challenge this assumption by auditing ChatGPT, Claude, and Gemini across seven systems and nine benchmarks spanning general capability, social bias, and sycophancy. We find systematic API–interface differences in both accuracy and consistency. On average, API evaluations score 3.4 percentage points higher in accuracy and 2.1 percentage points higher in test–retest agreement than corresponding interface evaluations. For ChatGPT, the performance difference between API and interface access exceeds the API-only difference between GPT 5.3 and GPT 5.4. Put differently, switching access surfaces can degrade performance as much as downgrading a full model generation. We further test whether exposed API controls can reproduce interface behavior by varying system prompts, sampling parameters, and reasoning settings. These controls shift behavior in some cases but do not reliably eliminate the gap. Our findings document a context-validity gap: measurements obtained through APIs do not necessarily generalize to corresponding deployed interfaces, complicating the use of API evaluations as proxies for deployed systems. Subjects: Artificial Intelligence (cs.AI); Software Engineering (cs.SE) Cite as: arXiv:2609.08861 [cs.AI] (or arXiv:2609.08861v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.08861 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-19] Earth System World Model for What-If Simulations: A Case Study for Terrestrial Ecosystems
链接: https://arxiv.org/abs/2609.08855
作者: Zhihao Wang,Ruichen Wang,Ruohan Li,Lei Ma,George Hurtt,Xiaowei Jia,Gengchen Mai,Shaowen Wang,Yiqun Xie
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted in SIGSPATIAL’26
Abstract:Machine learning emulators have become essential for accelerating expensive Earth-system simulations, but most existing approaches remain passive forecasters: they reproduce simulator trajectories under prescribed forcings without an explicit interaction mechanism for user-specified interventions. This limits their use in interactive scientific workflows and Earth-system digital twins, where users often need to explore how a system would respond if selected state components were changed. We propose an action-conditioned world-modeling framework for Earth-system emulation that reformulates simulator trajectories as supervision for controllable state-transition learning. The key idea is transition-action pretraining: naturally observed state changes are treated as label-free action supervision, allowing the model to learn both prescribed dynamics and action-conditioned responses without manually annotated interventions. We further introduce masked response learning to infer unobserved variables under partial state edits and learn coupled system dependencies. We test this framework on ecosystem dynamics across six global regions and multiple stand ages. Experiments show that the model preserves competitive long-horizon emulation accuracy while enabling controllable structural interventions and coherent responses in coupled ecosystem-cycle variables. These results suggest a practical route from passive Earth-system emulators toward interactive, intervention-aware scientific surrogates.
[AI-20] Closing the Consistency Gap: Self-Evolving Agents That Learn to Stay on Course
链接: https://arxiv.org/abs/2609.08832
作者: Evelyn Duesterwald,Benjamin Elder,Lilian Ngweta,Shashanka Ubaru,Malgorzata Zimon
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM)-powered agents can be accurate on average yet unreliable in production, a discrepancy that has been observed but remains largely unaddressed. When given the same task five times, a ReAct agent on the AppWorld benchmark using GPT-4.1 succeeds in all five runs only 53% of the time, even though its per-run pass rate averages 77%. We call this 24-point shortfall the consistency gap, and we argue that addressing it is a precondition for trustworthy AI agent deployment. We present a self-evolving agent framework that reduces this gap by identifying unstable, low-consistency steps in agent trajectories and converting them into episodic memory the agent can draw on in future runs. At its core is a Consistency Analyzer that pinpoints where and why a trajectory is likely to flip across executions, and a Guideline Generator that converts the diagnosis into targeted guidelines, committed to memory and injected into future agent executions on similar tasks. On AppWorld with ReAct/GPT-4.1, our framework raises the fraction of tasks that succeed in all five runs by +16 points on same-task evaluation and +13 points on similar-task generalization.
[AI-21] Evidence-Grounded Retrieval for Investigation Hunt Lead Generation from CTI Reports
链接: https://arxiv.org/abs/2609.08790
作者: Akash Prakash,Boubakr Nour,Makan Pourzandi,Chadi Assi,Mourad Debbabi
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Accepted for presentation and publication at the 2026 IEEE Conference on Dependable and Secure Computing (DSC) - Workshop: Cyber Resilience Attack Intelligence (CRAI)
Abstract:Threat hunting increasingly depends on converting unstructured knowledge (e.g., Cyber Threat Intelligence reports) into actionable hunt leads: concise, investigable hypotheses grounded in observable artifacts and adversary techniques. Producing such leads manually is a tedious and hard-to-scale task. Existing automated approaches stop at the entity layer, ignore the defender’s operational environment, and analyze each report in isolation. To address these gaps, we introduce AHLERT, a system that automatically extracts relevant, environment-aware, and hunt leads from threat reports through (i) a hybrid retriever that combines dense vector search with multi-hop traversal over a knowledge graph seeded with MITRE ATTCK; (ii) an ontology-grounding retrieval-augmented generation method that constrains each lead to the defender’s own assets and controls; and (iii) an LLM-agnostic framework that emits structured, directly actionable leads rather than loose indicators of compromise. We evaluate AHLERT on public CTI reports for well-known APTs across multiple proprietary and open-weight models. Hybrid evidence retrieval with ontology grounding raises mean F1 by ~2x (0.44 to 0.85) over a single-route flat-RAG baseline, and AHLERT attains the highest effectiveness score (~86.95%) compared with off-the-shelf LLM models.
[AI-22] Silent Revision: Measuring Undisclosed Change in the Safety Frameworks of Frontier AI Developers NEURIPS2026
链接: https://arxiv.org/abs/2609.08789
作者: Louis Yiven Zhu
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 8 pages of main text plus appendices, 3 figures. Under review at the AI Science workshop (AISciK), NeurIPS 2026. Corpus and code: this https URL
Abstract:Frontier AI developers publish safety frameworks that commit them to evidencing whether their models are dangerous. The European Union and California now treat these documents as instruments of accountability, and both already impose duties on their revision. Neither requires the revision to be legible, in the sense that a reader could learn from the developer’s own account what changed. We introduce the silent revision rate, the share of material changes to a framework’s commitments that the developer’s published account does not identify, and we release the versioned, hash-pinned corpus needed to compute it. The corpus contains every public version of the safety frameworks of the twelve developers that have published one, together with each provider’s changelog, redline or announcement. We trace 710 commitment instances across twelve consecutive version pairs, code them against a frozen codebook, and adjudicate 244 individually. Three findings follow. First, 67% of material changes (95% CI 62 to 72) are silent under a strict standard and 53% under a lenient one, falling to 49% at section granularity. Second, silence appears to track the form of the account, since narrative announcements run at 74% against 63% for itemised changelogs, whereas account length in words barely matters; on the test that respects nesting the difference is suggestive. Third, 77% of traced changes weaken or remove a commitment, and in seven of eight pairs weakenings are more often silent than strengthenings. The statutory remedy therefore exists and specifies the wrong artefact. A justification explains why a framework changed, an enumeration states what changed, and only the latter makes revision auditable. We argue that publication duties should carry an enumeration duty, which one provider already meets, voluntarily and incompletely.
[AI-23] Adaptive Anisotropic Attention for Axis-Structured Signals
链接: https://arxiv.org/abs/2609.08788
作者: Mahir Jain,Parshva Runwal,Aditya Ray Mishra,Arvasu Kulkarni,Sandeep Singh,Siddharth Panwar
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 24 pages, 9 figures, 21 tables
Abstract:Dense self-attention treats all token pairs as equally plausible before learning, an interaction-isotropic prior that can be mismatched to structured signals. For structured, low signal-to-noise ratio (SNR) signals such as EEG, dependencies are organized along the electrode and time axes, and this uniform prior exposes each token to many irrelevant interactions. We introduce Adaptive Anisotropic Attention (AAA), which splits attention into two paths: a temporal path, where each token attends to the tokens of its own electrode across time, and a spatial path, where it attends to the tokens of the other electrodes at the same time step. A small gate predicts, for every token, a convex combination of the two path outputs: two non-negative weights that sum to one. On six EEG downstream tasks, the resulting model, AXON (AXis-factorized Operator Network), improves mean balanced accuracy over a dense baseline under both linear probing and full fine-tuning. We show that both paths (temporal and spatial) are necessary and that the weighted sum beats a hard choice of one path; most of the benefit comes from the gate learning a different temporal/spatial balance at each layer of the network. Controlled audio spectrogram experiments show that axis factorization transfers beyond EEG. These results suggest that aligning attention with the natural axes of structured signals provides a useful inductive bias.
[AI-24] Its All in the Way You Say It: The Role of Information Representation in LLM -Based Glycemic-Event Prediction
链接: https://arxiv.org/abs/2609.08772
作者: Andrea Apicella,Pasquale Arpaia,Matteo Orefice,Andrea Pollastro,Roberto Prevete
类目: Artificial Intelligence (cs.AI)
备注: Submitted to a journal for peer-review
Abstract:Large Language Models (LLMs) are increasingly being investigated for physiological time-series prediction, yet their effectiveness may depend not only on the model itself, but also on how physiological information is represented and presented at inference time. This study investigates prompt-based general-purpose LLMs for postprandial hyperglycemia and hypoglycemia prediction in individuals with type 1 diabetes. Using the OhioT1DM dataset, we evaluate multiple open-weight LLMs under zero-shot and few-shot inference across prediction horizons of 30, 60, and 90 minutes. The analysis varies both the textual representation of the available physiological information and the amount of information exposed to the model, ranging from glucose observations alone to derived descriptors and additional contextual variables related to insulin, meals, carbohydrates, and physical activity. Performance is compared with conventional patient-specific supervised models and with Gluco-LLM, a language-model-based architecture explicitly adapted to glucose time-series forecasting. Results show a marked task-dependent behavior. Conventional supervised models achieve the strongest performance for hyperglycemia prediction, whereas the best observed prompt-based LLM configurations improve performance for hypoglycemia across all investigated horizons. The effectiveness of prompt-based inference is also strongly influenced by how physiological information is represented, while providing additional contextual information does not lead to a systematic improvement. Overall, these findings highlight physiological information representation as a central design factor in prompt-based LLM approaches to glycemic-event prediction.
[AI-25] When Can One Obtain Certificates of Optimality Using Positivstellensaetze?
链接: https://arxiv.org/abs/2609.08736
作者: Nayoon Kim,Allen Gehret,Shenyuan Ma,Jakub Marecek
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We study certificates of positivity and optimality for learning problems whose objectives and constraints need not be polynomial. We isolate an axiomatic core of Fischer’s constructive strict and weak Positivstellensätze and prove the resulting theorems for abstract function algebras over ordered fields. The framework separates two roles that can otherwise be conflated: objective and constraint functions may be built from broad classes of continuous or definable operations, while the auxiliary primitives used to construct a certificate satisfy explicit scalar and closure axioms. We give instances over continuous and definable function algebras, including ordered fields not closed under square roots, derive lower-bound and global-optimality certificates, and analyze both expanded term length and shared computation-graph complexity.
[AI-26] Application of curiosity driven exploration methods for hardware interference identification
链接: https://arxiv.org/abs/2609.08729
作者: Ludovic Matar,Clement Moulin-Frier,Pierre-Yves Oudeyer
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The transition from single-core to multi-core architectures in safety-critical embedded systems introduces significant challenges due to inter-core interference caused by contention for shared hardware resources. Such interference affects execution times and complicates the verification of strict temporal requirements, particularly in domains such as avionics where standards require comprehensive identification of interference sources. Existing interference analysis approaches, whether manual or model-based, struggle to capture the full range of behaviors arising from the complex interactions among micro-architectural components. In this paper, we frame multi-core interference analysis as the exploration of a complex system behavior space. We propose the use of curiosity-driven exploration algorithms from artificial intelligence to systematically and efficiently cover the space of possible interference behaviors. Using a simulator-based environment, we show that the proposed approach achieves broader and more uniform behavioral coverage within a limited experimental budget compared to traditional pseudo-random program generation methods.
[AI-27] GoAnt: Quality-Diversity Multi-Agent Search for Alpha Factor Discovery in Market Microstructure Data
链接: https://arxiv.org/abs/2609.08719
作者: Stella Zhao,Tommy Sha
类目: Artificial Intelligence (cs.AI)
备注: 14 pages, 1 figure, 8 tables, including supplementary appendices
Abstract:Automated alpha factor discovery searches symbolic trading signals from price-volume panels and order-book data under a fixed evaluation budget. Existing single- and multi-agent program-search systems can overfit predictive proxies that fail after execution costs and repeatedly explore redundant factor families, limiting execution robustness and behavioral diversity. We introduce GoAnt, a quality-diversity multi-agent search framework that combines non-communicating Explorer, Exploiter and Connector workers with a shared adaptive Mental Map and a compact Queen dispatcher. The Mental Map organizes candidates by leakage-free execution profiles and retains one elite per niche, while the Queen reallocates the evaluation budget from explicit search-state summaries. We also define a map-independent effective-yield protocol that counts high-quality, mutually nonredundant factors directly from each method’s evaluation records, giving archive-based and map-free systems the same ruler. On real A-share microstructure data spanning 2023–2026, GoAnt reaches quality-weighted yields of 41.8 and 47.6 in price-volume and order-book settings, improving the strongest baseline by 57% and 97% under matched budgets. Its locked populations retain 0.64 and 0.67 of in-sample quality out of sample, compared with 0.61 and 0.63 for a static map.
[AI-28] Neither Adversarial Training Nor Purification: Emergent Adversarial Robustness from Oscillatory Predictive Learning
链接: https://arxiv.org/abs/2609.08683
作者: Mohammed-Yassine Habibi,Klea Ziu,Martin Takáč,Makoto Yamada
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 9 pages, 3 figures, 6 tables
Abstract:Adversarial robustness in computer vision is still largely achieved through adversarial training or test-time adversarial purification, both of which introduce significant computational overhead by generating adversarial examples during training or performing iterative denoising at test time. We study whether empirical robustness can instead emerge from architectural and representation-learning inductive biases. We introduce Oscillatory Predictive Learning (OPL), a two-stage framework that combines Artificial Kuramoto Oscillatory Neurons (AKOrN) with predictive self-supervised pretraining using X-PhiNet. Because our default checkpoint uses randomized initial oscillator states, we compare it with other randomized adversarial defense methods that provide precise, reproducible, and strong attack protocols. Experiments on CIFAR-10 and CIFAR-100, with additional corruption evaluation on CIFAR-10-C, demonstrate that our method achieves competitive results under the AutoAttack-rand evaluation protocol. On CIFAR-10 and CIFAR-100, OPL attains 76.63 \pm 0.76 % and 50.44 % robust accuracy, respectively, under \ell_\infty , \epsilon=8/255 , AutoAttack-rand with EoT K=20 .
[AI-29] BIFTA: Brain-Inspired Few-Shot Tactile Adaptation for Unknown Sensors
链接: https://arxiv.org/abs/2609.08673
作者: Boheng Liu,Ziyu Li,Xia Wu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Advances in tactile sensing have made contact-rich perception possible, accelerating progress in robotic manipulation, material understanding, and embodied interaction. However, because optical design, elastomer mechanics, and imaging geometry differ substantially across tactile sensors, models trained on known sensor types can suffer an abrupt performance collapse on unknown sensors. To address this problem, we propose the Brain-Inspired Few-Shot Tactile Adaptation (BIFTA) framework; it draws on the brain’s rapid sensory adaptation mechanism to adapt a frozen encoder to an unknown tactile sensor from a small labeled support set. BIFTA preserves pretrained representations through dual-view statistical memory, constructs support-conditioned spectral graphs to repair sensor-dependent feature neighborhoods, and applies uncertainty-gated recurrent propagation to strengthen reliable cross-query evidence. Extensive benchmarks across three tactile datasets show that BIFTA substantially improves adaptation to unknown sensors: with only 10% labeled target data on SITR, it raises mean Sparsh accuracy from 6.86% for the frozen source classifier to 87.09%, exceeding the strongest implemented prior comparison by 47.22 percentage points, and these gains generalize across datasets, pretrained backbones, and tactile tasks. These results validate BIFTA for data-efficient adaptation to unknown tactile sensors and offer a promising route toward tactile models that transfer across heterogeneous hardware.
[AI-30] X2Streaming-ASR: wait when uncertain emit when ready for streaming ASR
链接: https://arxiv.org/abs/2609.08672
作者: Zhiwei Lin,Kaiqi Fu,Rime Wen,Zehan Liu,Shawn Qin,Roy Gan,Hao Wang,Qian Wang
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注:
Abstract:Streaming automatic speech recognition (ASR) for real-time voice agents and full-duplex dialogue must provide accurate partial transcripts with low commit latency. Existing systems commonly use a fixed chunk size, look-ahead, or target delay, or encourage emissions near estimated acoustic boundaries. These approaches do not directly optimize how much additional context to use at each output position under a single-pass, hard-commit constraint. We propose X2Streaming-ASR, which decomposes streaming recognition into when to commit and what to commit. Its three-stage training procedure first establishes streaming recognition ability, then warm-starts the commit policy with automatically probed trajectories, and finally refines the policy using character-level, segment-assigned group-relative rewards for recognition accuracy and latency. Across AISHELL-1/2/3 and WenetSpeech, X2Streaming-ASR achieves a mean character-level commit latency of 27-84 ms relative to forced-aligned character endpoints, compared with 409-585 ms for the evaluated streaming baselines. It achieves the best streaming CER among the evaluated systems on AISHELL-1 and AISHELL-3 with substantially lower latency.
[AI-31] MoEMB: Scaling Universal Multimodal Embeddings with Efficient Mixture-of-Experts Models
链接: https://arxiv.org/abs/2609.08663
作者: Xuanming Cui,Shlok Kumar Mishra,Wentao Bao,Aashu Singh,Zihao Wang,Xiangjun Fan,Jun Xiao,Ser-Nam Lim,Jianpeng Cheng
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Universal multimodal embedding (UME) increasingly demands encoder’s capacity for handling a broad range of tasks and modalities with increased complexity. Prior scaling methods either increase the representation size, retrieval effort, or scales the encoder into a heavy multimodal LLM. Recent works, such as Think-Then-Embed (TTE), explore scaling via reasoning tokens. However, embedding models are hard to scale up: increasing parameters directly tradeoffs for the large training batch size that contrastive learning needs, and retrieval has to be served under tight latency. Moreover, UME tasks are diverse in complexity, where scaling up embedders can bring significant redundant computation. In this work, we propose MOEMB, which instead scales UME along the expert axis through mixture-of-experts (MoE), growing encoder capacity while preserving single-vector, non-autoregressive encoding. Through a systematic study of the design space and training recipes for MoE-based UME, MoEMB sets a new state of the art on both MMEB-V2 and MRMR among models trained on public MMEB-family data: with only 3B active parameters, MoEMB surpasses TTE-based methods with 4x active parameters, using significantly less computes. To further improve the scalability and efficiency, we conduct the first comprehensive study of adaptive computation for MoE-based embedding, spanning diverse strategies across training-based and inference-only methods. Together, these results support expert scaling as an effective and efficient direction for UME, with adaptive computation further improving efficiency for MLLM-based embedding models towards large-scale retrieval and recommendation systems.
[AI-32] SUN: Reaching for Novelty in Reinforcement Learning
链接: https://arxiv.org/abs/2609.08642
作者: Wenyan Yang,Arsenii Mustafin,Dominik Baumann,Joni Pajarinen,Simone Parisi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: 39 pages. Accepted at the 19th European Workshop on Reinforcement Learning (EWRL 2026)
Abstract:Exploration in reinforcement learning (RL) remains a fundamental challenge. Recent goal-conditioned RL strategies (which select goals to encourage broader state coverage) have shown promising results, but none scores a goal by novelty and reachability jointly: the two signals are traded off by hand, applied in sequence, or one is neglected outright. In this paper, we introduce a reachability-aware goal-selection framework that explicitly integrates these two aspects, and that can be seamlessly incorporated into any off-policy RL algorithm. To this aim, we propose SUccessor-to-Novelty (SUN), an indicator derived from successor value functions to identify goals that are both novel and reachable. We prove that SUN recovers count-based bonuses in the limit, bounds short-horizon hitting probabilities, and provably rejects unreachable goals. We further present an adaptive goal-selection strategy that leverages these properties, and an accurate yet lightweight pseudocount to avoid the overhead of classic methods. We back up all our claims with thorough benchmarks: SUN consistently outperforms state-of-the-art methods in standard and novel environments with unreachable or hard-to-reach states, irreversible transitions, obstacles, mazes, and unbounded spaces.
[AI-33] CASD: Chunk-Aligned Semantic Distillation for Multi-StageRobot Manipulation
链接: https://arxiv.org/abs/2609.08638
作者: Tinghe Ding,Jiahao Li,He Wang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:An action chunk can span several stages of a manipulation task, yet a label for its first step describes only the current stage. We introduce Chunk-Aligned Semantic Distillation (CASD), which derives semantic targets for entire action chunks. An offline vision–language model segments demonstrations into described stages. Their occupancy within each action chunk determines a weighted semantic target, including transitions between stages. A CASD generator learns to predict this target from the current observation, robot state, and task instruction. We then freeze the generator and train a policy conditioned on its predictions. The semantic branch runs once per policy query, without online VLM calls or reasoning-trace decoding. Teacher matching on annotated LIBERO training episodes is above chance for both single-stage and boundary-crossing chunks. We evaluate three Fast-WAM variants and a DreamZero integration across four benchmarks, including distribution shifts on LIBERO-Plus. Compared with published references, IDM+CASD reaches 98.9% versus 98.0% average success on LIBERO, while Uncond falls below its reference. Joint+CASD reaches 93.0% versus 90.6% on RoboTwin 2.0, and DreamZero+CASD reaches a 47.9% four-category MolmoSpaces manipulation average versus 40.7%. Performance varies across backbone integrations.
[AI-34] Suan: Rectifying Direct Preference Safety Alignment in Large Language Models
链接: https://arxiv.org/abs/2609.08634
作者: Oleksandr Cherednichenko,Roman Klypa
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Integrating robust safety guardrails into Large Language Models (LLMs) is essential for delivering helpful yet harmless responses. While proprietary systems exhibit reliable safety controls, their underlying methodologies and trade-offs remain largely undisclosed. Achieving comparable security in open-weight models remains a persistent challenge, as post-trained variants frequently suffer from over-refusal and degraded general quality. To overcome these drawbacks, we introduce Suan, a novel preference optimization algorithm. Unlike existing methods, we formulate the optimization objective directly at the gradient level, bypassing the standard variational derivation. As a result, we obtain more interpretable and robust training dynamics. Extensive evaluations across a diverse suite of competitive baselines and benchmarks demonstrate that Suan achieves superior safety alignment while fully preserving response utility.
[AI-35] Leverag ing contextual events on structure-aware next activity prediction
链接: https://arxiv.org/abs/2609.08622
作者: Alessandro Mele,Claudia Diamantini,Domenico Potena
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Predictive process monitoring aims at forecasting various aspects of running processes. Among the different tasks, next activity prediction represents the most extensively investigated. However, only a limited number of existing approaches explicitly encode contextual information, i.e., the environmental conditions in which the process is executed, typically modeled through event log attributes or aggregated measures. In this paper, an approach based on the concept of Instance Graphs is introduced. To incorporate contextual process instances, several encoding strategies are proposed and evaluated by measuring their impact on prediction performance. For each encoding strategy, a set of prefix-Instance Graphs is generated and subsequently provided as input to a Graph Neural Network for the classification task. The proposed approach is evaluated on multiple real-world event logs, and the experimental results demonstrate that incorporating contextual process instances benefits prediction performance.
[AI-36] CLAMP: Constrained Decoding for Vision-Language Embodied Planning EMNLP2026
链接: https://arxiv.org/abs/2609.08602
作者: Tianyi Ma,Parisa Kordjamshidi
类目: Artificial Intelligence (cs.AI)
备注: Accepted to Findings of the Association for Computational Linguistics: EMNLP 2026
Abstract:Embodied planning increasingly relies on vision-language models (VLMs) to translate instructions and visual observations into executable action sequences. However, fluent plans are not always executable. A VLM may refer to objects that are not visually observed, select actions whose required affordances are unavailable, or violate syntax and action constraints. We introduce CLAMP, a multimodal constraint-grounding framework that turns scene evidence into decoding-time constraints for a frozen VLM planner. CLAMP uses the initial observation to restrict object references to those supported by the scene, while a provided symbolic action model specifies state transitions and goals. During decoding, hard masks eliminate invalid next-token candidates, while a Hidden Markov Model (HMM)-based world-state lookahead module reweights the probabilities of the remaining feasible candidates based on action preconditions and goal reachability. This allows the planner to retain the VLM’s language prior while preventing visually unsupported, unsafe, or infeasible candidates from entering the plan. For unseen tasks and environments, CLAMP adapts the HMM at test time using label-free continuations sampled from the frozen VLM. Experiments on VLABench, SafeAgentBench, and TaPA show that scene-grounded constraints improve object grounding and safety, while most remaining failures stem from perception errors or misaligned constraint specifications.
[AI-37] Graph-Based Personalized Memory for LLM Agents : Representation Evolution Retrieval and Evaluation
链接: https://arxiv.org/abs/2609.08599
作者: Dac Duy Anh Nguyen,Zhangchi Qiu,Shigeng Chen,Alan Wee-Chung Liew
类目: Artificial Intelligence (cs.AI)
备注: Accepted by ICKG 2026
Abstract:Large Language Model (LLM) agents are evolving from single-session tools toward long-term personal assistants that must adapt to individual users across tasks, contexts, and interactions. This shift makes memory a core requirement for personalization, since user preferences, goals, constraints, relationships, and past experiences are accumulated gradually and often change over time. Graph-based personalized memory provides a structured way to model such user information through explicit relations, temporal context, and evidence links. Such representations can model not only what an agent remembers about a user but also how memories are connected, revised, and retrieved to support personalized decisions. However, existing work remains fragmented across personalized agents and generic graph memory frameworks, making it difficult to understand the design space as a whole. This survey develops a lifecycle-oriented view of graph-based personalized memory for LLM agents. We organize existing studies around memory representation, memory evolution, memory retrieval, and memory evaluation. We further compare key design choices, discuss current evaluation practices, and open challenges in building reliable long-term personalized agents. This survey aims to clarify how graph-based memory can support adaptive, controllable, and user-centric LLM agents.
[AI-38] Leverag ing Cardiac Imaging to Improve ECG-Based Detection of Chagas Disease in Resource-Constrained Settings
链接: https://arxiv.org/abs/2609.08582
作者: Laura Alvarez-Florez,Daniel Uyterlinde,Samuel Ruipérez-Campillo,Lukas P. A. Arts,Folkert W. Asselbergs,Fleur V. Y. Tjong
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
备注:
Abstract:Chagas disease is a major cause of cardiomyopathy in Latin America. Cardiac magnetic resonance (CMR) imaging can characterize its structural abnormalities, but scanners and expert readers remain scarce in endemic regions. Electrocardiography (ECG) is inexpensive and widely available, yet structural disease must be inferred indirectly from electrical signals. We propose to transfer CMR-derived structural knowledge to ECG through contrastive pre-training. Using 63,193 paired ECG-CMR examinations from the UK Biobank, we align an ECG encoder with a clinically grounded CMR embedding space using an asymmetric InfoNCE objective. Despite seeing no Chagas cases during pre-training, the resulting representation improves ECG-based Chagas detection. Across CODE-15% and SaMi-Trop, a frozen linear probe achieves an AUROC of 0.851 and sensitivity at the top 5% of predicted risk (Top5%-TPR) of 0.427 in five-fold cross-validation, compared with 0.827 and 0.377 for an unaligned ECG-FM baseline. On the PhysioNet/CinC 2025 Challenge test set, our model obtains the highest AUROC on SaMi-Trop-3 and the best ELSA-Brasil challenge score among the three top-performing methods, indicating that imaging-supervised ECG representations can generalize to populations and settings beyond the pre-training distribution.
[AI-39] Agent Grad: Intervention-guided Prompt Optimization for Multi Agent Systems
链接: https://arxiv.org/abs/2609.08572
作者: Jaewon Chu,Jinwoo Seo,Jaewon Cho,Jeehye Na,Yunyang Xiong,Youngdae Kim,Hyunwoo J. Kim
类目: Artificial Intelligence (cs.AI)
备注: 13 pages
Abstract:Large language model (LLM)-based multi-agent systems (MAS) achieve strong performance by employing specialized multiple agents, yet their performance depends on the prompt design of each agent. For MAS prompt optimization, textual gradient methods that guide prompt updates using natural-language feedback have emerged as a leading paradigm. In this paper, we identify limitations in two stages of existing textual gradient approaches: gradient extraction and gradient aggregation. In gradient extraction, previous works select a target prompt without verifying whether modifying it resolves the failure, and derive gradients without agent-level supervision over the corresponding agent’s intermediate output. In gradient aggregation, individual gradients are randomly grouped and concatenated, often mixing unrelated failure modes and producing prompts that fail to generalize. To address these limitations, we propose \textbfAgentGrad, a prompt optimization framework for multi-agent systems based on sequential intervention and semantic textual gradient abstraction. For each failure, sequential intervention modifies the behavior of one agent at a time to identify the target agent whose modification resolves the failure. The modified output of the target agent then serves as agent-level supervision for extracting a fine-grained gradient. Semantic textual gradient abstraction clusters semantically similar gradients to prevent mixing unrelated failure modes, and abstracts each cluster into a generalized gradient that captures the shared corrective pattern. Experimental results show that AgentGrad achieves state-of-the-art performance across five MAS benchmarks and reduces wall-clock optimization time by 2.5\times on average compared to the next-fastest baseline.
[AI-40] BIO-MEMART: Biometric-Aware KV Cache Memory for Multi-User LLM Agents
链接: https://arxiv.org/abs/2609.08566
作者: Yanhong Qian,Xuanying He,Qingguo Meng,Shihao Ding,Xingbo Dong,Zhe Jin
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:KV cache is evolving from a serving optimization into an external memory substrate for long-term LLM agents. In a shared multi-user deployment, however, reusable KV blocks introduce a missing access-control question: semantic relevance alone cannot determine whether a memory block is authorized for the current physical user. We propose Bio-MemArt, a biometric-aware KV-cache memory framework for multi-user LLM agents. Bio-MemArt attaches a normalized biometric template to each stored KV memory block, filters the shared memory pool with the current user’s biometric probe, and then runs the original MemArt retrieval and KV reuse pipeline only inside the authorized candidate pool. This design preserves latent-space retrieval, direct cache reuse, and decoupled position encoding while adding physical-user access control to shared KV memory. We evaluate Bio-MemArt under Owner and Non-owner query conditions on long-term dialogue QA with face and palmprint benchmarks. Across face benchmarks, the average owner and non-owner biometric success rates are 95.71% and 0.86%; across palmprint benchmarks, they are 97.60% and 2.00%. In the efficiency study, average prefill tokens drop from 18,781.96 under full-context prompting to 28.57 with Bio-MemArt, showing that biometric gating preserves the low-token operating regime of KV-cache memory.
[AI-41] Personalizing LLM Agent Memory Using Biometrics
链接: https://arxiv.org/abs/2609.08558
作者: Yanhong Qian,Qingguo Meng,Shihao Ding,Xingbo Dong,Zhe Jin,Hanrui Wang,Isao Echizen
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Personalized memory helps LLM agents deliver stable, tailored assistance by storing and reusing user-specific data across interactions. In multi-user scenarios, however, retrieval must consider not only semantic similarity but also whether the current requester matches the identity associated with the stored memory. We propose Bio-Memory, a biometric-aware memory architecture that conditions memory retrieval on both semantic similarity and biometric matching. Built on top of A-Mem, Bio-Memory augments each atomic memory note with a biometric embedding and uses biometric matching to form the retrieval candidate pool before semantic ranking. We evaluate Bio-Memory on LoCoMo in a 10-user shared-agent setting over 7 face benchmarks and 10 palmprint protocols. Across datasets, Bio-Memory consistently separates owner and non-owner queries. Under face-based personalization, the largest average gap reaches 27.29% / 21.15% in F1 / BLEU-1 on CALFW; under palmprint-based personalization, the corresponding gap is 25.75% / 19.22% on MS_Blue. These results support biometrics as a practical control signal for personalized memory retrieval in shared environments.
[AI-42] SRPO: Setwise Relative Policy Optimization for Multi-Agent LLM s
链接: https://arxiv.org/abs/2609.08452
作者: Shengtian Yang,Ziyu Xiong,Yu Li,Yewen Li,Qingpeng Cai,Lei Feng
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-agent large language models solve complex tasks by coordinating several policies in a shared environment. However, existing reinforcement learning methods usually optimize each response or trajectory separately, even when several outputs jointly cause one state transition. Consequently, the update unit differs from the action executed by the system. To address this problem, we propose SRPO (Setwise Relative Policy Optimization), which treats the active set the minimal set of outputs consumed by one transition, as one multi-agent action. Specifically, SRPO combines member log-ratios into one cardinality-normalized set ratio, assigns one relative advantage, and clips the set once. This formulation unifies division of labor and joint co-evolution as actions with different set sizes. Experiments on mathematical reasoning and multi-turn search demonstrate one training interface for fixed, mixed, and dynamically routed workflows across four model scales, with the strongest macro-average results among the reported comparisons. Optimization diagnostics further characterize its stability under different event reductions and set sizes.
[AI-43] EvolveScaler: Synthesizing Information-Evolution Contexts via Executable State Machines and Natural-Language Rendering
链接: https://arxiv.org/abs/2609.08435
作者: Ziliang Zhao,Zenan Xu,Shuting Wang,Zhao Wang,Bowen Cao,Minda Hu,Lincheng Li,Pluto Zhou,Zhicheng Dou
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:In persistent interactions, long contexts may encode an evolving process rather than a fixed record: later events can revise or revoke earlier information, changing what remains valid and what conclusions follow. We call this setting information evolution (IE). Solving IE requires identifying valid records, applying updates in order, and reconstructing the query-relevant state from the event history. Existing text-first synthesis pipelines make such data difficult to verify because state transitions and answer logic remain implicit. We introduce EvolveScaler, a code-driven framework that defines information evolution before rendering it as natural language. Human-authored operational specifications define state transitions, record validity, difficulty controls, and executable answer logic; a strong LLM then synthesizes a self-contained simulator from each specification. Executing validated simulators produces natural-language multi-turn event histories, while deterministic replay computes reference answers and atomic checklists. We instantiate EvolveScaler with 117 task prototypes and 159 final-question operators across five difficulty levels spanning approximately 7 to 1,200 events per instance, yielding about 35,100 training examples and 585 validated evaluation instances. On the very_long tier, the strongest model reaches 59.3% avg@5, while six models score below 10%. Training an internal A3B model on 6,000 EvolveScaler examples improves performance over its base checkpoint on all eight independently constructed out-of-distribution benchmarks, with a 5.25-point average gain. These results show that code-driven IE synthesis provides both challenging evaluation and transferable training supervision.
[AI-44] Feyospace-v1: How the Cyber Mercury Seven Trained Frontier Cyber Models
链接: https://arxiv.org/abs/2609.08418
作者: Zongjie Li,Alan Z. W,John Nicolas J,Walter H. F,Scott Donald L,Gordon Y. P,Deke X Jr
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Training capable cyber agents is often treated primarily as a problem of model scale, yet open-weight post-training is constrained more directly by the cost of executable environments, reliable multi-turn supervision, and access to strong teachers. We present a data-centric framework that addresses these bottlenecks through five complementary systems: Choulea analyzes hidden reasoning signatures, SkyReal reduces teacher-sampling cost, Hongzwang bypasses API restrictions on teacher execution, PSBreakup restores capabilities weakened by model merging, and Kreator converts expert interventions into trainable reasoning. Our data engine constructs resettable coding, vulnerability, CTF, kernel-history, full-exploit, firmware, and device-backed environments. Candidate trajectories are retained only after execution verification and evidence auditing, yielding 164,269 trajectories for long-context supervised fine-tuning. The three checkpoints improve over their starting models by an average of 23.76% on the full CyberGym suite and 10.49% across the pooled CTF suites. As of September 1, 2026, Feyospace-s1 achieves a verified success rate of 63.24% and ranks 10th on the official CyberGym leaderboard, while all three checkpoints rank 1st among models at comparable parameter scales. To our knowledge, this is the first end-to-end demonstration that a seven-person independent team can train open-weight models with leading agentic cyber capability.
[AI-45] FastE: Readout-Triggered Token Compression for LLM Embedding Inference
链接: https://arxiv.org/abs/2609.08407
作者: Jinsong Shu,Jinyong Wen,Baokun Wang,Zhongle Xie,Lidan Shou,Weiqiang Wang,Gang Chen
类目: Artificial Intelligence (cs.AI)
备注: 10 pages, 6 figures
Abstract:In this study, we identify depth-dependent prefix redundancy in final-readout LLM embedding models, notably across representative backbones including Qwen3-Embedding and Qwen3-VL-Embedding. We find that removing prefix states is substantially more damaging in shallow layers than at greater depth, showing that prefix states become increasingly compressible as the prefix and readout states propagate through the network. To this end, we introduce FastE, a training-free, plug-and-play method. FastE uses a shared fixed threshold on batch-mean readout-prefix alignment as a lightweight online heuristic for selecting when compression occurs, and ranks prefix states by the attention scores they receive from the readout position to determine which states are retained in subsequent layers. Our evaluations demonstrate FastE’s ability to substantially reduce computational costs: on NarrativeQA with Qwen3-Embedding-0.6B, it reduces decoder-backbone FLOPs by 40.11% while retaining 99.53% of Full Forward nDCG@10. Across five text embedding benchmarks, two backbone scales, and three cross-modal retrieval tasks, the quality-efficiency trade-off is directly customizable through the maximum removal ratio without retraining. We believe FastE offers practical value for scalable embedding generation in retrieval, indexing, clustering, and multimodal representation systems.
[AI-46] Equivariance Breaks the Learning Rate
链接: https://arxiv.org/abs/2609.08381
作者: Andrei Manolache,Mathias Niepert
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Equivariant networks are commonly trained with Adam, yet recent work reports that matrix-structured optimizers such as Muon can perform better on these architectures without explaining why. We identify one source of this difference inside equivariant linear layers. Each irrep block learns a channel-mixing matrix W_l shared across its 2l+1 components, giving the expanded map W_l \otimes I_2l+1 . For a single application of the layer, the gradient of W_l sums 2l+1 outer product contributions and has rank at most 2l+1 . Adam rescales stored weights individually without using the irrep boundaries, so one learning rate can produce different spectral step sizes across blocks within a layer. We address this mismatch by normalizing each block update separately, without introducing a new hyperparameter. This changes only the scale of the update, leaving Adam’s moment estimates and its direction within each block unchanged. We evaluate the mechanism in a controlled \mathrmSO(3) -equivariant model with a matched dense control and in an e3nn interatomic potential model trained on rMD17 and MD22. The toy setup isolates a mismatch that grows with width while the dense control shows no corresponding growth. In the interatomic potential model, block normalization and tuning Adam’s momentum coefficients independently improve performance, but neither alone matches Muon. Combined, they make Adam competitive with Muon on all datasets, indicating that blockwise step control and momentum accumulation account for much of Muon’s advantage.
[AI-47] IPM-FM: A Foundation Model with Consensus Feature Selection for Industrial Process Monitoring
链接: https://arxiv.org/abs/2609.08375
作者: Liang Cao,Weide Liu,Yan Qin,Jun Cheng,Weisi Lin,Bhushan Gopaluni
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Industrial process monitoring is fundamental to the safety and economic performance of modern process plants. Current practice remains a one-task-one-model paradigm that is label-inefficient and prone to degradation under operating drift. Foundation models have reshaped language, vision, and generic time-series forecasting, but it has not been adapted to industrial process monitoring. This setting poses domain-specific challenges, including safety-critical decisions and asymmetric sampling between process variables and laboratory measurements. We propose the industrial process monitoring foundation model (IPM-FM). It first learns general-purpose representations from unlabeled industrial process data through self-supervised pretraining, then adapts to specific monitoring tasks using a small amount of task-labeled data, and finally produces calibrated predictions through an uncertainty-aware prediction head. IPM-FM integrates a self-supervised Informer backbone with a multi-criteria consensus feature selector, a recursive lag-feature regression head, and a calibrated Monte Carlo dropout uncertainty module. On a seven-year hydrotreater dataset for diesel flash-point soft sensing, IPM-FM attains an RMSE of 2.99, R^2 of 0.50, and 97% coverage of its 95% predictive interval, outperforming the strongest classical and from-scratch sequence baselines by 8.3% and 14.6% in RMSE respectively, supporting the viability of a unified pretraining–adaptation framework for industrial process monitoring.
[AI-48] RoboCousin: Build Your Own Simulation Playground for Robust Bimanual Robotic Manipulation
链接: https://arxiv.org/abs/2609.08339
作者: Jingxuan Zhu,Jingyi Li,LiangLiang Chen,Zhiyuan Jing,Jidong Zhang,Hongming Li
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Bimanual manipulation policies require large and diverse training datasets, yet collecting demonstrations on physical robots is expensive and difficult to scale. Simulation can generate data efficiently, but existing pipelines typically operate within closed asset libraries and predefined scenes: adding a newly observed object or environment still requires substantial effort to reconstruct geometry, specify physical and semantic properties, annotate interactions, and integrate the result into executable tasks. We present RoboCousin, an extensible simulation-based data-generation platform that turns user-provided observations into reusable assets, scenes, and expert trajectories for bimanual manipulation. Built on RoboTwin~2.0, RoboCousin converts object images into simulation-ready assets with visual and collision geometry, semantic and physical metadata, and automatically generated grasp-contact candidates. It further constructs digital cousins that vary compatible objects, backgrounds, layouts, and language instructions while preserving task-relevant affordances and spatial relations. The same asset system supports tabletop and room-level scene construction, with collision-aware base control for interaction beyond a fixed workspace. We release RoboCousin-OBD, containing more than 3,000 annotated object instances and 50 background environments, and use RoboCousin to generate over one million expert trajectories across 50 tasks. Simulation and real-robot experiments show that the automatically generated interaction annotations are comparable to curated annotations, generated assets provide effective sim-to-real supervision, and tabletop cousins can improve transfer beyond training on a single reconstructed scene. RoboCousin therefore provides a practical path for expanding both the scale and coverage of synthetic bimanual manipulation data.
[AI-49] A Multi-Modal Perception Pipeline for Object Detection and Tracking in Autonomous Racing ITSC2026
链接: https://arxiv.org/abs/2609.08338
作者: Davide Malvezzi,Michele Pestarino,Vittoria Cavicchioli,Valentina La Gamba,Silvia Severi,Fabio Bagni,Luca Bartoli,Massimiliano Bosi,Francesco Gatti,Micaela Verucchi,Ayoub Raji,Marko Bertogna
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 8 pages, 6 figures, ITSC 2026, Invited Session
Abstract:Object detection and tracking are fundamental components of perception systems for autonomous driving. Achieving robust performance under adverse conditions such as limited visibility, sensor noise, and failures remains an open challenge, particularly in autonomous racing, where vehicles operate at very high speeds, experience strong vibrations, and interact under small safety margins. This paper presents a multi-modal late-fusion perception pipeline for object detection and tracking in the autonomous racing domain. The proposed system extends previous work by exploiting all onboard sensors through a late-fusion approach and a dedicated multi-object tracking framework. Independent detections from cameras, LiDARs, and RADARs are combined to provide timely and robust state estimates of surrounding vehicles. The tracking method explicitly compensates for detection delays and embeds in its model prior knowledge of vehicle dynamics and track layout. Experimental evaluation on real-world data across diverse critical scenarios, representative of challenging edge cases also in urban driving, confirms the effectiveness of the proposed pipeline and its suitability to support safe and adaptive planning decisions.
[AI-50] AttnCompress: Dynamic Attention-Guided Trajectory Compression for Software Engineering Agents ISSTA2026
链接: https://arxiv.org/abs/2609.08318
作者: Zhengran Zeng,Yixin Li,Rui Xie,Wei Ye,Shikun Zhang
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 23 pages, 5 figures, accepted at ISSTA 2026
Abstract:The transition from human-centric assistance to Autonomous Software Engineering (ASE) agents has enabled the resolution of complex real-world SE tasks. However, the trial-and-error nature of these agents generates lengthy interaction trajectories, creating severe bottlenecks in terms of context window limits and cost. While context compression offers a potential remedy, prior approaches suffer from static pruning strategies and granularity mismatches, often failing to preserve the semantic dependencies and syntactic details crucial for SE tasks. To strictly preserve critical task evidence while reducing context length, we introduce AttnCompress, a dynamic attention-guided trajectory compression framework. Unlike existing approaches, AttnCompress bridges the gap between semantic integrity and dynamic adaptability through three key mechanisms: (1) structure-aware segmentation via perplexity (PPL) spikes to preserve the syntactic structure of code and logs; (2) relevance estimation using proxy attention weights to quantify the precise relevance of historical blocks to the agent’s current reasoning; and (3) a dynamic rolling window to re-evaluate and recall historical context as the task evolves. Extensive evaluation on SWE-Bench-Verified and Multi-SWE-Bench demonstrates that AttnCompress achieves a pass rate of 53.17%, outperforming prior state-of-the-art baselines while reducing token consumption by 21.6% and total costs by 33.6%. The framework proves to be model-agnostic and generalizes effectively across diverse programming languages.
[AI-51] A Measurement Study of LLM Inference Trade-offs Across Edge Continuum Hardware
链接: https://arxiv.org/abs/2609.08307
作者: Maysam Khatib,Moysis Symeonides,Demetris Trihinas,George Pallis,Marios D. Dikaiakos
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted for publication at WIMS 2026
Abstract:Large language models (LLMs) are increasingly used as backends for intelligent web services, but serving them across the edge continuum requires balancing quality, latency, model footprint, and energy. This paper presents a controlled measurement study of self-hosted LLM inference across edge and near-edge deployment nodes: an NVIDIA Jetson AGX Orin and a near-edge server with CPU-only and GPU-enabled inference modes. We evaluate multiple open-weight LLMs and quantization variants using a fixed question-answering workload, and compare them against GPT-4o as a cloud-hosted accuracy and latency reference. Our benchmarking pipeline reports accuracy, model footprint, per-token decoding latency, prefill latency, and overall execution energy. The results show that GPU-enabled server execution provides the lowest compute-side latency, while Jetson Orin shows lower measured energy, consistent with its lower platform power under our setup. CPU-only execution is consistently dominated in latency for our workload and shows higher measured energy. We also show that parameter count and downloaded weight-file size alone do not reliably predict observed accuracy or latency. Finally, using Pareto-frontier analysis, we study how deployment decisions may change under possible streamed-token delivery overheads, highlighting that compute-side inference metrics alone can lead to suboptimal placement for latency-sensitive interactive web services.
[AI-52] LEBGen: An LLM -Enhanced Bayesian Network Framework for Few-Shot Travel Survey Data Generation
链接: https://arxiv.org/abs/2609.08288
作者: Zijian Shen,Bin Zhou,Jiguang Wang,Ya Zhao,Jintao Ke
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Travel survey data are essential for transportation planning and travel behavior analysis, yet collecting large-scale representative samples is costly and time-consuming. A practical alternative is to generate synthetic survey records from a few-shot sample. However, such samples provide incomplete coverage of heterogeneous traveler groups and insufficient evidence for recovering the complex dependencies between demographic characteristics and travel behavior. Existing approaches have complementary limitations. Probabilistic generative models such as Bayesian networks (BNs) offer explicit distributional control, but structures learned from few-shot samples may omit meaningful dependencies or retain spurious ones. Large language models (LLMs) can help address these difficulties in BN structure learning by providing behavioral knowledge that complements the limited statistical evidence. We therefore propose LEBGen, an LLM-enhanced BN framework that uses this knowledge to refine network structure for few-shot travel survey data generation. Specifically, the LLM first identifies traveler personas from demographic attribute and travel behavior statistics, then recovers dependencies missed by the persona-augmented BN structure and prune spurious ones. The refined BN is parameterized exclusively from the observed data to generate synthetic records. Under a 2% few-shot setting on the 2022 Hong Kong Travel Characteristics Survey, LEBGen reduces the mean marginal Jensen-Shannon divergence from 0.0671 to 0.0091 and the mean absolute Cramer’s V error by 14.3% over the best-performing baseline, substantially improving both distributional and dependency fidelity.
[AI-53] Beyond Coherence: Benchmarking Professional Editing-Technique Execution in Multi-Shot Audio-Video Generation
链接: https://arxiv.org/abs/2609.08275
作者: Tianyi Zeng,Junchao Liao,Yujie Wei,Ziying Zhang,Litao Li,Tianyi Wang,Zhichao Wei,Shuyao Xu,Wenwen Qiang,Siyu Zhu,Zhenghao Zhang,Long Qin
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent multi-shot audio-video generators can produce increasingly coherent and cinematic outputs, but coherence does not imply the ability to execute editing techniques. Professional editing depends on shot structure, transition grammar, audio-video cut relations, and montage, yet existing benchmarks largely rely on proxies such as content quality, synchronization, or physical plausibility, systematically missing whether such editing instructions are actually executed. We introduce CutCraft, the first benchmark for editing-technique execution in multi-shot audio-video generation. CutCraft extends structured multi-shot prompts with explicit editing specifications and is paired with a hierarchical hybrid evaluation framework that combines shot-structure alignment, expert-model metrics, tool-grounded multimodal judgment, and rubric-based question answering. Beyond evaluation, we design an agentic editing baseline that decomposes generation into planning, shot-level synthesis, and post-hoc composition, explicitly realizing editing semantics such as J-cuts, L-cuts, and transition timing. Across 13 state-of-the-art closed- and open-source models, CutCraft reveals a consistent gap between coherence and editing-technique execution: current systems often produce plausible multi-shot videos yet fail to execute editorial instructions reliably. We find unstable shot structures, weak control of transition execution, and sharp degradation on higher-order montage, while aesthetic quality is only weakly correlated with editing-technique compliance. The benchmark and metrics, and the editing agent baseline are available at this https URL.
[AI-54] MemForest: Efficient Agent Memory Management via EventTree Partitioning and Progressive Merging
链接: https://arxiv.org/abs/2609.08273
作者: Junxi Wang,Te Sun,Jiayi Zhu,Chen Zhang,Siyuan Li,Xuyang Liu,Zichen Wen,Xiaobing Tu,Jinkui Ren,Xiantao Zhang,Ziqi Yuan,Linfeng Zhang
类目: Artificial Intelligence (cs.AI)
备注: 23 pages, 6 figures
Abstract:Agent memory systems have demonstrated significant potential in long-term dialogue, personalized assistants, and video understanding. However, continuously accumulated memory introduces substantial storage and retrieval costs during inference. To address this issue, we propose \textbfMemForest, a general memory compression framework adaptable to various agent memory systems. Specifically, MemForest partitions historical memory into event-centric units by leveraging global semantic similarity and local temporal continuity. For each unit, it constructs a maximum spanning tree, termed an EventTree, and progressively merges redundant memory nodes by selecting high-weight edges, reducing storage overhead. Furthermore, we introduce an anchor-guided propagation retrieval mechanism that retrieves relevant memory nodes from the temporal neighborhoods of key nodes, improving retrieval accuracy. Extensive experiments demonstrate the effectiveness of MemForest. Under the unimodal Mem0 framework, MemForest retains \textbf97.1% of the original performance while compressing \textbf50% of historical memory across three benchmarks (LoCoMo, LongMemEval, and PersonaMem), achieving a \textbf1.89x retrieval speedup. Under the multimodal M3-Agent framework, it preserves \textbf99.7% of the original performance with a \textbf50% compression ratio across two benchmarks (M3-Bench-robot and M3-Bench-web), achieving a \textbf2.24x retrieval speedup. \textcolorRoyalBlue\textitOur code is available at [this https URL.](this https URL.)
[AI-55] hree Types of Negation of Triple and its Elements and an Extension of Triple
链接: https://arxiv.org/abs/2609.08271
作者: Zhenghua Pan
类目: Artificial Intelligence (cs.AI)
备注: 29 papers, 9 figures
Abstract:In various data models, the classical triple is a typical semantic data model. However, due to the design of the triple as a simple structure for representing positive assertions, it cannot sufficiently express different forms of negation present in the triple and its elements. This paper conceptually proposes that there are three distinct forms of negation within triples and their elements: contradictory negation, opposite negation and intermediary negation. Based on the the set SCOI and the logic LCOI+PLCOI with three kinds of negation, we propose an extension of triple that can distinguish and express these three different negations in the triple and its elements, called the TCOI triple with contradictory negation, opposite negation and intermediary negation. The TCOI triple is a semantic and structural extension of the classical triple. While retaining the ability to express positive assertions, it systematically introduces the three semantic dimensions of three negations, allowing these negations to independently act on the elements of the triple and on the whole triple. This significantly enhances the triple model capability to represent and reasoning about complex negative information. This paper also explores the expressive power and reasoning of the TCOI triple, as well as the application of TCOI triple implication reasoning in counterfactuals and counterfactual reasoning. We propose a truth-value (continuous value) algorithm for TCOI triple implication reasoning and perform its calculation through an example of the counterfactuals and counterfactual reasoning.
[AI-56] Synergistic Fusion of Topological Structure and Temporal Semantics of Mobility for Urban Region Embedding
链接: https://arxiv.org/abs/2609.08268
作者: Namwoo Kim,Jeeyun Chang,Kanghoon Lee,Yoonjin Yoon
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Submitted to IEEE Transactions on Knowledge and Data Engineering
Abstract:Urban region embeddings have shown promising results in diverse urban sensing tasks such as crime, income, and service-call prediction. Recent methods improve representation quality by integrating mobility data with auxiliary modalities, using cross-view attention or contrastive objectives to align heterogeneous features into a unified region representation. However, leveraging the temporal dynamics of human mobility remains under-explored. Regional inflow and outflow fluctuate throughout the day, and inter-region connections emerge, persist, and dissolve over time. Moreover, prevailing fusion strategies combine views additively and miss the joint signal that emerges only when views co-occur. To address these gaps, we propose Mobility Stream-Structure Synergy (MoSS), which derives complementary views from mobility data: a Sequence view that preserves each region’s hourly inflow/outflow profile, and a Structure view based on zigzag persistence diagrams that capture how regional connectivity emerges, persists, and dissolves over time. A synergy module then extracts emergent representations from the co-occurrence of these views through multi-degree interactions, explicitly capturing higher-order signal across views. Extensive experiments on New York City and Chicago show that MoSS achieves state-of-the-art performance across three downstream tasks using mobility data alone, outperforming baselines that rely on auxiliary modalities.
[AI-57] Revoked but Still Authoritative: An Empirical Study of Revocation Enforcement in Agent -Memory Systems
链接: https://arxiv.org/abs/2609.08258
作者: Yi Ting Shen,Kentaroh Toyoda,Alex Leung
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: Code is available at this https URL
Abstract:Long-running language-model agents depend on persistent memory. Many agent-memory systems preserve history through soft revocation: a contradicted fact is marked invalid and retained rather than deleted. However, whether that mark is enforced at retrieval time is unexamined. In this paper, we measure five such systems: we load each with a revoked policy and its replacement, track whether the revoked fact is returned at retrieval and whether the agent then acts on it across nine policy scenarios and nine models, and score every trial under six defense conditions. We find that no system enforces revocation by default: the revoked fact is returned wherever the revocation label is visible to the retrieval layer, outranks its replacement, and leads agents to the unsafe action. Based on these findings, we develop a guard that sits between the agent and any memory backend and withholds records that are revoked or conflict with their replacement.
[AI-58] ACEA: An Adversarial Co-Evolution Arena for Head-to-Head Red-Team and Blue-Team LLM Testing
链接: https://arxiv.org/abs/2609.08256
作者: Yi Ting Shen,Kentaroh Toyoda,Alex Leung
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Code is available at this https URL
Abstract:Automated red-team attacks and blue-team defenses for large language models (LLMs) are advancing quickly. However, attackers and defenders are built and tested in isolation, and the resulting scores are hard to trust. To tackle this, we present ACEA (Adversarial Co-Evolution Arena), a platform that connects a pluggable red-team adapter and a pluggable blue-team adapter to a shared target LLM and scores their attack and defense rates with an LLM judge. ACEA contributes four components. First, a pluggable, model-agnostic arena. Any red or blue project connects over a minimal HTTP protocol, which we call the ACEA Standard Adapter Protocol (ASAP). It can be written in any language, and a project that exposes nothing but the protocol is a full participant. Second, an evaluation methodology built for adversarial rounds. Seeding the target with canonical secrets gives verifiable ground truth that separates real leakage from hallucination. We also send each attack to the target even when the defense blocks it, which measures the attack’s raw potency independently of whether it was stopped. Together these yield a per-round decomposition of attack strength and defense effectiveness. Third, a real-time, game-style visualization with a detailed end-of-battle report that localizes each failure. The evaluation thus becomes an actionable signal for improving a red or blue project. Fourth, an optional in-context improvement loop that turns each round’s outcome into advisory hints for the next. An adapter can then adapt across rounds without keeping state, provided it reads the hints. We describe the design of ACEA and the metrics through which red and blue teams are scored head to head.
[AI-59] CircuTutor: Transforming Static Circuit Problems into Intelligent and Dynamic Tutoring
链接: https://arxiv.org/abs/2609.08254
作者: Ziyu Luo,Xiaorui Ma,Lin Chen,Xiaoming Chen
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Learning direct current circuit concepts requires learners to connect invisible physical quantities, such as current, voltage, resistance, and power, with observable outcomes such as bulb brightness. Conventional textbook materials and general-purpose circuit simulators provide opportunities for problem solving and exploration but offer limited support for explaining why circuit behavior changes or diagnosing the reasoning behind incorrect answers. We present CircuTutor, a circuit-state-driven intelligent tutoring system that transforms static textbook circuit problems into an interactive tutoring workflow. CircuTutor first uses multimodal problem parsing to extract the textbook question, circuit topology, component parameters, switch states, and answer options, which are converted into a structured task and validated through circuit simulation. Learners can then interactively explore the circuit (by changing parameters) and submit an answer while a SPICE-compatible solver computes physically consistent circuit states. After the learner submits an answer, CircuTutor presents a before-and-after circuit state animation corresponding to the selected operation, organizes the simulated state changes into a causal reasoning chain that explains the underlying circuit behavior, maps answer discrepancies to likely misconceptions, and generates adaptive follow-up exercises targeted at the diagnosed misconception. Our experimental results demonstrate that CircuTutor effectively improves conceptual learning and the overall learning experience. The proposed framework demonstrates how simulated circuit states can be transformed into intelligent and interactive tutoring for circuit education, with the potential to generalize to other STEM domains.
[AI-60] Agent ic ML Exploration (A-MLE) for Ads Ranking
链接: https://arxiv.org/abs/2609.08248
作者: Erwin Gao,Vinodh Kumar Sunkara,Jingyi Guan,Qinjin Jia,Hangjun Xu,Xiang Ji,Sherman Wong,Surya Teja Chavali,Pratik Vaishnavi,Aryan Pandhi,Xiaoyu Deng,Zhaodong Wang,Samarth Inani,Fan Yang,Jakob Moberg,Zoe Zu,Nicolas Bievre,Sami Khenissi,Amit Jaspal,Ehsan Fakharizadi,Srinidhi Viswanathan,Dorothy Sun,Abishek Vanam,Sneha Iyer,Sheela Yadawad,Wenjie Chen,Gaby Nahum,Junhua Gu,Peter Chu,Yucheng Liu,Xin Zhao,Vitor Cid,Chaorong Chen,Vijay Pappu,Ashwin Kumar,Wenlin Chen,Ben Schulte,Deepak Chandra,Ritwik Tewari
类目: Artificial Intelligence (cs.AI)
备注: 7 pages, 4 figures
Abstract:Modern industrial ads ranking stacks are increasingly bottlenecked not by model capacity or training compute, but by the throughput of human ML iteration - the cycles of research, implementation, training, debugging, evaluation, and launch required to surface a single statistically significant improvement. A typical ranking stack contains numerous differentiated models with heterogeneous data, architectures, and infrastructure constraints, and each cycle takes days to weeks of senior engineer attention per model. As a result, techniques that have proven effective on one model diffuse into others slowly and unevenly, leaving substantial recoverable signal unexplored. We present Agentic ML Exploration (A-MLE), an autonomous LLM-agent system that systematically explores ML techniques across a portfolio of ads ranking models. A-MLE decomposes ML iteration into five stages involving hypothesis generation, exploration strategy, experiment execution, result analysis and shared knowledge substrate which are orchestrated by a single agent that invokes domain-specific skills and agentic workflows against a sandboxed execution layer, with human-in-the-loop checkpoints at each stage boundary. We deploy A-MLE across a representative set of large-scale ads ranking models and evaluate it along a tiered capability framework (tool availability, autonomous workflow execution, and open-ended exploration). We further report a controlled cross-LLM study using a fixed agent loop, which surfaces qualitative differences in execution reliability and exploration aggressiveness across the Claude Sonnet, Gemini, and GPT families. We discuss failure modes and the design choices that govern reliability. Our findings suggest that agentic exploration is a practical force multiplier for ML engineers in industrial recommenders, especially for the long tail of models that rarely receive expert attention.
[AI-61] zScore-N: A Neural Network for On-Chain Wallet Reputation Scoring
链接: https://arxiv.org/abs/2609.08247
作者: Girish G N,Ashutosh Sahoo,Akshay SP,Gurukiran S,Dhanashekar Kandaswamy
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Wallet reputation scores decide who receives an airdrop, who can borrow, and who enters an allowlist across decentralised finance. They almost always begin as hand-written formulas: compositions of clamped logarithmic, linear and square-root transforms over behavioural features, with every threshold and point award set by hand. Such a formula is readable and deterministic, but it is piecewise and non-differentiable, it cannot improve as data accumulates, and it cannot distinguish a feature that is genuinely zero from one its pipeline failed to capture. We present zScore-N, the neural network that replaced ours in production. The formula served as its teacher: calibrated against 5,208,952 wallets sampled across 2019-2024 and verified to reproduce production output to within 2.3e-13, it supplies unlimited labelled training data at zero label noise. The trained network reproduces it to 0.58 points RMSE on the 1000-point scale (R^2 = 0.99997), against 2.25 for gradient-boosted trees and 28.04 for linear regression on identical features and splits. Trained with missing-value masks against uncorrupted targets, it halves the error that incomplete data introduces: at 10% feature-level missingness the formula drifts 51.4 points from its own complete-data output with a systematic -12.5 point bias, while the network drifts 17.9. The network carries the score at production scale, across a population of millions of wallets spanning six orders of magnitude in size and activity.
[AI-62] CUNO: Curriculum and Preference Optimization for Stable Graph Unlearning under Mass Deletion
链接: https://arxiv.org/abs/2609.08244
作者: Chenhan Zhang,Ali Braytee,Madhushi Bandara,Xin Hao,Paul J. Kennedy,Massimo Piccardi,Raymond Owen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: submitted to conference
Abstract:Graph unlearning removes the influence of designated training data from a trained graph model without retraining from scratch. However, existing methods suffer a sharp drop in model utility under large deletion ratios (mass deletion), a phenomenon we refer to as catastrophic unlearning. We find that a key cause is the uniform treatment of all deleted samples, which is particularly damaging in graph learning: structural dependencies cause different nodes to play vastly different roles in the learned model, yet existing methods apply the same forgetting operation to the entire forget set. Based on this insight, we propose CUNO, a curriculum-based graph unlearning framework that removes the forget set progressively, ordering samples by their estimated unlearning difficulty across multiple stages. CUNO further employs a distribution-level negative preference optimization (NPO) objective at each curriculum stage that steers the model away from its original behavior on the current forget subset while preserving retained performance. Our theoretical analysis shows that the curriculum design is most beneficial when the forget set spans a wide range of unlearning difficulty, a condition naturally satisfied under mass deletion. Comprehensive experiments confirm that CUNO consistently mitigates catastrophic unlearning: at 20% deletion, it retains 74% of the original utility compared to 26-53% for existing methods, and maintains more than half the original utility even at 50% deletion. Our code is publicly available at this https URL.
[AI-63] Style Over Substance: Content-Invariant Wrappers Flip LLM Safety-Judge Verdicts
链接: https://arxiv.org/abs/2609.08236
作者: Yongxi Zhou,Wenbo Ye,Yuanzhe Liu,Zihan Dong,Junwei Yao
类目: Artificial Intelligence (cs.AI)
备注: 8 pages, 1 table. Code, wrappers, and per-verdict labels: this https URL
Abstract:Automatic safety judges – systems such as Llama Guard or a GPT-4o grading prompt that decide whether a model’s reply is harmful – produce the numbers behind almost every reported jailbreak success rate, defense evaluation, and safety leaderboard. We ask whether these judges grade what a reply contains or how it sounds. We keep a reply’s content fixed and add content-invariant style wrappers: fixed strings placed before or after the reply that change only its tone (an educational disclaimer, a fake safety “reasoning” block, a token refusal followed by the unchanged harmful body), or, on harmless refusals, framing that merely sounds dangerous. The body is preserved byte-for-byte, so a faithful judge must return the same verdict, and any flip is an error of the judge, not a change in safety. Over 600 JailbreakBench replies x up to 7 forms x 8 judges, we measure flip rates with paired significance tests and measured noise floors. Findings are precise rather than universal: most judges barely move, but specific judges harbor cheaply exploitable blind spots. A token-refusal wrapper flips 19.9% of GPT-4o-mini’s correct “unsafe” verdicts (noise floor 0.5%; 18.2% under majority-of-three re-scoring) yet moves Claude only 0.4%. The deployed Llama Guard 4 is deterministically gamed: an “educational course” framing flips 12.3% of its harmful verdicts to safe. A second deployed guard (gpt-oss-safeguard-20b) is immune, and rewriting only the grading prompt (StrongREJECT-style) cuts the attack tenfold on the identical model – the vulnerability lives in the judge, not the content. A two-annotator human validation confirms 100% content invariance and 90% of flips as judge errors (kappa 0.95-1.0), and a bootstrap shows the underlying model ranking is already unstable to sampling alone. We release the dataset, wrappers, code, and per-verdict labels.
[AI-64] GBench: Benchmarking Topological Evolution and Semantic Drift in Text-attributed Temporal Graphs
链接: https://arxiv.org/abs/2609.08226
作者: Longfei Ma,Zemin Liu,Fei Wu
类目: Artificial Intelligence (cs.AI)
备注: 24 pages, 8 figures, 22 tables
Abstract:Temporal graph learning models the evolution of dynamic systems, where both structural interactions and semantic states change over time. However, existing benchmarks primarily emphasize structural evolution via temporal link prediction (TLP), while support for semantic evolution remains limited. Although temporal node classification (TNC) is sometimes included, it is typically restricted to simplistic binary settings that fail to capture realistic semantic drift. Moreover, commonly used datasets exhibit high link repetition, leading to inflated performance estimates and obscuring true model capability. To address these limitations, we introduce \textbfTTGBench, a new benchmark that jointly evaluates structural and semantic evolution. TTGBench comprises six real-world, text-rich datasets characterized by \emphDual Volatility, enabling rigorous and fair evaluation of existing models. Notably, it is the first benchmark to support both multi-class and multi-label TNC, filling a critical gap in evaluating temporal semantic drift. We conduct a comprehensive evaluation of 17 state-of-the-art methods across Temporal Graph Neural Networks (TGNNs) and Large Language Model (LLM)-based paradigms. The results reveal a clear \emphcapability divide between the two paradigms: TGNN-based methods excel at structural prediction but fail at semantic tracking, whereas LLM-based predictors show the opposite trend. Through in-depth analysis, we uncover their fundamental limitations and provide insights for developing more comprehensive temporal graph models.
[AI-65] 3DWay: Generalizing Robot Manipulation via 3D Consistent Waypoints ECCV2026
链接: https://arxiv.org/abs/2609.08224
作者: Ziqin Huang,Yingyue Li,Chenyangguang Zhang,Ruida Zhang,Yuxin Chen,Gu Wang,Xingyu Liu,Masayoshi Tomizuka,Xiangyang Ji
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: ECCV 2026
Abstract:Intermediate representations are key to bridging the modality gap between generalizable manipulation policies and large-scale pretrained vision-language models (VLMs). Among these, trajectory-based representations compactly represent motion-relevant cues, yet most existing approaches predict trajectories in 2D image space, resulting in intrinsic 3D ambiguity. Moreover, using 2D trajectories with depth still leaves the free-space waypoints ambiguous, limiting reliable 3D reasoning. To address this, we propose predicting 3D consistent waypoints (3DWay) from multi-view images. By reformulating 3D waypoints prediction as generating multi-view consistent 2D waypoints followed by geometric triangulation, we enable explicit 3D motion specification while preserving the strong priors of pretrained VLMs. The predicted waypoints can guide existing VLA models for better generalization or be directly executed on simple tasks. Extensive experiments show that 3DWay substantially improves 3D spatial grounding and vision-language reasoning, demonstrating strong potential for generalizable robot manipulation. Codes will be released at this https URL.
[AI-66] Vision: Data-Centric Anchoring for Robust and Interpretable Agent ic AI
链接: https://arxiv.org/abs/2609.08216
作者: Arun Vignesh Malarkkan,Xinyuan Wang,Yanjie Fu
类目: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
备注: 14 pages, 2 figures, 2 tables
Abstract:Agentic AI systems built on large language models fail in two persistent ways that scaling does not fix: they break under distribution shift, and they cannot explain the decisions they make. We argue these are co-symptoms of one structural deficiency in the data lifecycle that governs how agents are trained, evaluated, and deployed. Observational interaction logs record what an agent did, not what it would have done otherwise. They encode spurious correlations without controlled variation, so they lack the counterfactual structure needed to separate causal signal from coincidence or to validate an explanation. No model-centric method can recover invariances the data never contained. We present Data-Centric Anchoring: robustness and interpretability should be engineered into the data environment, not extracted from models after training. Our central contribution is the Data-Centric Agentic Loop, a four-stage framework of Curate, Augment, Constrain, and Attribute. The ordering is structural, not stylistic. Curation precedes augmentation because generative models amplify whatever bias they are trained on. Augmentation precedes constraint because invariance objectives are vacuous without variation across environments to be invariant to. Attribution closes the loop, converting observed failures into targeted data interventions for the next iteration. Each stage manufactures the preconditions of the next, which makes the loop self-correcting rather than merely sequential. We ground the framework in a failure-driven taxonomy that links four core failure modes to the data lifecycle: spurious feature reliance, distribution-shift fragility, uncertainty miscalibration, and explanation unfaithfulness. We close with the limits of this approach and the open problems that stand between it and practical deployment at scale.
[AI-67] A Better Spur Should Start From Each Objective EMNLP2026
链接: https://arxiv.org/abs/2609.08211
作者: Shanwen Mao,Hao Zhang,Guangtao nie,Zhiheng Li,Huimu Wang,Sulong Xu,Gu Simiu
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted to EMNLP 2026
Abstract:Real-world Multi-Objective Reinforcement Learning (MORL) often suffers from sparse rewards, reward conflicts, and late-stage reward tug-of-war, causing traditional linear scalarization to experience severe metric oscillations. To address optimization conflicts among multiple objectives in real-world deployment scenarios, we propose Multi-Marginal Preference Optimization (MMPO), a fine-grained framework that intervenes at the data, gradient, and constraint levels rather than relying on coarse-grained global scalarization. Specifically, MMPO performs exposure debiasing to mitigate sparse and biased rewards, applies priority-aware orthogonal projection to decouple conflicting gradients, and introduces self-prompted gradient constraints to prevent dominant objectives from overwhelming weaker ones. Experiments on real-world e-commerce datasets show that MMPO improves training stability and consistently achieves better performance across conflicting metrics. Moreover, it generalizes robustly to broader tasks such as ToolRL and code generation, demonstrating its effectiveness as a practical paradigm for multi-objective alignment.
[AI-68] Qiushi Engine on AstaBench E2E-Bench-Hard
链接: https://arxiv.org/abs/2609.08196
作者: Wenhao Li,Shuxing Yang,Fujia Chen,Jincheng Mi,Yuang Pan,Rui Zhao,Zichen Li,Junyao Wu,Shenzhan Hong,Yaqi Li,Yize Wang,Kaihao Zhu,Taowen Deng,Junjie Yang,Hongsheng Chen,Yihao Yang
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 39 pages, 9 figures, 12 tables. Technical report
Abstract:This report analyzes Qiushi Engine v0.8 across all 40 test tasks in AstaBench E2E-Bench-Hard, a benchmark that requires autonomous agents to carry a research question through experimental design, code implementation, actual execution, result analysis, and report delivery. Qiushi Engine is model-configurable; this evaluation selected DeepSeek deepseek-v4pro-preview as the model backend. The official AstaBench leaderboard records a score of 0.816 and an average benchmark cost of USD 15.209 per task, while the full-precision local recomputation is 81.59 \pm 1.87 . Four tasks satisfied every rubric item, yielding a full-task completion rate of 4/40 = 10% – 7 percentage points above, and about 3.3 times, the approximately 3% best rate reported for AstaBench’s official agents. Across 507 required rubric items, 416 were satisfied (82.1%). Official scoring archives and 40 Meta-Trace records show sustained production and verification of reports, code, and experimental artifacts; the principal gaps lie in repeated runs, external dependencies, specified metrics, and ablation studies. The report explains the benchmark, system workflow, aggregate results, representative cases, and limits of interpretation.
[AI-69] Less Is Personal: Learning Minimal Sufficient User Profiles for Personalized Language Models
链接: https://arxiv.org/abs/2609.08180
作者: Minghang Liu,Qiang Qiu,Yuanzhuo Wang,Huawei Shen,Xueqi Cheng
类目: Artificial Intelligence (cs.AI)
备注: 21 pages
Abstract:Retrieval-augmented personalization enables large language models to produce more accurate and preference-aligned outputs using relevant records retrieved from user histories. Personalized language models typically prepend a fixed number of retrieved user records, even when additional history is redundant, harmful, or unrelated to a user’s distinctive behavior. We study minimal sufficient personalization: constructing the least costly ordered profile for each input while preserving the utility achievable from a retrieved candidate pool. We introduce ENOUGH, a method that iteratively appends behavioral records or emits STOP to construct profiles with adaptive lengths. Offline, bounded counterfactual search evaluates profile prefixes by jointly considering downstream gains, user specificity, and token costs. The resulting long-horizon targets are distilled into a multi-head value controller with explicit ranking and stopping supervision. At inference, the controller selects and orders records through lightweight decisions, and the frozen generator is invoked once after stopping. Extensive experiments on six personalized tasks demonstrate that ENOUGH consistently outperforms strong heuristic and retrieval-augmented baselines in both effectiveness and efficiency, achieving minimal sufficient profiles that preserve personalization utility while reducing unnecessary context costs.
[AI-70] Safe Harness Self-Evolution: A Theoretical Analysis of Feasibility and Limits
链接: https://arxiv.org/abs/2609.08175
作者: Qianshu Cai,Yonggang Zhang,Jun Nie,Maohao Ran,Huajiang Zheng,Jun Song,Xinmei Tian,Yike Guo,Wei Xue
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Harness self-evolution is the process by which an agent modifies its prompts, tools, code, or orchestration in response to task feedback while keeping the underlying language model frozen, with changes persisting across subsequent tasks. We provide a systematic theoretical analysis of the feasibility and limits of safe harness self-evolution, connecting modification generation, finite-data certification and selection, safe adoption, and behavior after an update. Under a fixed user-task distribution, we establish conditions guaranteeing overall expected-reward improvement while controlling changes on retained tasks, characterize the probability of generating qualified modifications, and derive finite-data bounds for safe selection and adoption. Our analysis shows that generation and certification impose distinct constraints: current task performance does not determine the probability of generating qualified modifications, and generating more candidates need not improve the guarantee of a successful update when evaluation is limiting. Stagnation may therefore arise even when improvement opportunities remain. We further show that worst-case evaluation cost for recognizing genuine improvements diverges as expected reward approaches its upper bound. Across successive updates, certified improvement guarantees accumulate over a finite run, but a successful update does not by itself guarantee that further improvement remains possible. These results provide a basis for diagnosing bottlenecks and designing safer self-evolution mechanisms.
[AI-71] OntologyBench: Can Dense Retrieval Satisfy Structured Biomedical Constraints? EMNLP2026
链接: https://arxiv.org/abs/2609.08174
作者: Xiao Yu Cindy Zhang,Wyeth Wasserman,Jian Zhu
类目: Artificial Intelligence (cs.AI)
备注: 19 pages and 1 figure. Accepted to EMNLP 2026 Main conference. this https URL
Abstract:We introduce OntologyBench, a tiered biomedical retrieval benchmark comprising 471,854 training and 125,744 evaluation query-document relevance pairs across concept grounding, relational retrieval, and compositional phenotype-based retrieval. Although these tasks can be tractable using ontology-aware reference methods, across task tiers, embedding performance is generally lower on relational and compositional tasks than on concept-grounding tasks. Fine-tuning on ontology-derived supervision improves performance on several relational and compositional tasks, whereas the evaluated reranking and LLM-based candidate-scoring methods provide little or no end-to-end improvement. Errors frequently reflect diseases matching only subsets of the phenotype evidence. These findings indicate that the evaluated embedding and reranking configurations do not reliably recover the compatibility encoded by the selected ontology relations and phenotype combinations and motivate retrieval systems that better integrate learned representations with structured biomedical knowledge. Comments: 19 pages and 1 figure. Accepted to EMNLP 2026 Main conference. this https URL Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2609.08174 [cs.AI] (or arXiv:2609.08174v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.08174 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-72] Key Path Identification for Resolving Knowledge Conflicts via SAE-based Steering
链接: https://arxiv.org/abs/2609.08173
作者: Wenbo Zhang,Zhongxiang Sun,Zhiguang Han,Jun Xu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Sparse autoencoder (SAE)-based steering has been widely used to address knowledge conflicts by guiding LLMs to be more faithful to the contextual knowledge. Existing methods usually perform mass steering, which modifies a large batch of SAE features identified via correlation-based methods. However, due to the inaccurate correlation and the neglected feature interactions, mass steering methods fail to precisely identify the features that play the key roles in steering and introduce a large number of redundant ones, which add noise and weaken the steering effects. Our empirical studies reveal that steering only a small subset of the identified features can achieve comparable or even better performance. Motivated by this finding, we propose Key Path Identification (KPI), a novel method that identifies key steering features characterized by strong causal dependencies with both upstream and downstream features. From these features, KPI constructs key paths and steers through less feature modifications. In this way, KPI advances SAE-based steering from quantity-driven to quality-focused, offering a perspective for more precise and interpretable model editing. Experiments in RAG tasks with knowledge conflicts show that our method improves the accuracy by 18% on average compared to the best baseline of mass steering, effectively filtering redundant features, alleviating side effects and demonstrating the core role of key paths in steering.
[AI-73] Dual-Layer Semantic-Spatial Belief Mapping for Aerial Object Goal Navigation
链接: https://arxiv.org/abs/2609.08164
作者: Jianqiang Xiao,Xiang Deng,Yuexuan Sun,Yanjin Wu,Wenbiao Yan,Liqiang Nie
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Submitted to IEEE Transactions on Multimedia
Abstract:Aerial Object Goal Navigation (ObjectNav) requires an unmanned aerial vehicle (UAV) to locate a described target in an unknown outdoor environment using onboard visual observations. Vision-language models (VLMs) can interpret open-ended target descriptions and visual observations, but their frame-level outputs are often noisy, sparse, and spatially transient. We propose AeroBelief, a dual-layer semantic-spatial belief mapping framework that transforms transient VLM observations into persistent spatial guidance. It separates broad contextual plausibility from target-specific evidence: an intuition layer accumulates scene-level semantic cues for exploration, while an evidence layer preserves qualified target-specific observations for approach and confirmation. Evidence-gated fusion combines the two layers into spatial belief hotspots. We further introduce object-conditioned visual reasoning with conservative evidence qualification to improve observation reliability before spatial accumulation. In parallel, egocentric regional guidance converts quadtree coverage into UAV-centered, yaw-aligned directional proposals and stabilizes them through temporal commitment. Its regional scoring is independent of semantic belief values, maintaining exploration pressure and reducing repeated low-gain search. Experiments on the UAV-ON benchmark show that AeroBelief achieves the best reported overall SR, OSR, and SPL among the compared methods, reaching 21.61%, 35.57%, and 10.62, respectively. These results support the effectiveness of persistent semantic-spatial belief, conservative evidence qualification, and temporally stable regional guidance for aerial ObjectNav.
[AI-74] WorldAgen: Unified State-Action Prediction with Test-Time World Model Training AAAI2026
链接: https://arxiv.org/abs/2609.08162
作者: Chi Wan,Kangrui Wang,Yuan Si,Pingyue Zhang,Manling Li
类目: Artificial Intelligence (cs.AI)
备注: Accepted to AAAI 2026
Abstract:How can vision-language-action (VLA) models adapt to new environments where world dynamics shift? While recent research has combined world modeling and action prediction to improve VLA performance, existing methods largely rely on pretraining on static datasets, without mechanisms for active adaptation at deployment time. As a result, these models often fail to generalize when deployed in unseen scenarios with novel object configurations or dynamics. We present WorldAgen, a unified framework that jointly learns world modeling and action prediction while enabling Test-Time Training (TTT) to adapt to new environments. WorldAgen employs a shared Transformer backbone with two heads: (1) a world model head that predicts future states from past state-action trajectories, and (2) an agent model head that predicts actions conditioned on task instructions. We design a Mixed Unidirectional Attention Mask to separate these two models. During test time, WorldAgen samples exploratory actions, collects ground-truth state transitions, and performs lightweight TTT updates to refine its world model. This adaptation improves the model’s understanding of the environment and leads to more accurate action predictions. Experiments on the CALVIN and LIBERO benchmarks demonstrate that our baseline model achieves comparable, and in some cases superior, performance to current state-of-the-art approaches. Moreover, with TTT on a small number of samples, our method surpasses existing state-of-the-art models, highlighting the effectiveness of adapting world models at inference time.
[AI-75] SWE-Bench Pro Verified: A Reliable Benchmark for Software Engineering Agents
链接: https://arxiv.org/abs/2609.08149
作者: Pujun Zheng,Zixin Shang,Shufan Jiang,Wenhui Tian,Dongsheng Zhu,Zerun Ma,Dingbo Yuan,Qi Zhang
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:SWE-Bench Pro has emerged as a standard benchmark for evaluating software engineering agents on challenging repository-level tasks. However, our analysis work show that its evaluation is undermined by two sources of unreliability: \textbfreward hacking, enabled by leakage of gold solutions or hidden evaluation information, and \textbftask quality issues, including misleading problem statements and improperly scoped tests. These issues can inflate benchmark performance and obscure agents’ true coding ability. We present \textbfSWE-Bench Pro Verified, a verified version of SWE-Bench Pro that addresses both problems. Our approach combines \textbfanti-hacking safeguards that eliminate major leakage channels without disrupting normal agent functionality, with \textbftask refinement that minimally corrects inconsistencies within flawed instances. Evaluations on SWE-Bench Pro Verified reveal that some models perform substantially worse than previously reported, suggesting that existing results on SWE-Bench Pro may overestimate real software engineering capability. SWE-Bench Pro Verified offers a more trustworthy benchmark for assessing software engineering agents.
[AI-76] KBBQ: A Predictive Noise Law and the Limits of Spectrum Flattening in FP4 Quantization
链接: https://arxiv.org/abs/2609.08135
作者: Lexington Whalen,Yuki Ito,Ryo Sakamoto
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 18 pages, 8 pages main text, 2 figures
Abstract:We develop a second-order theory of quantization noise in matrix multiplication in which the quantization format is characterized by the variance it assigns to each element. The constant variance profile of integer quantization recovers existing integer-noise theory, while the multiplicative profile of floating-point rounding reduces the data dependence to a scalar, the participation factor \kappa , yielding a closed-form signal-to-noise-ratio law. The resulting functional also admits a closed-form upper bound \kappa^* that no function-preserving linear transform can exceed and that is attained by a recent state-of-the-art method. Building on this analysis, we introduce KBBQ (\textbfKappa-\textbfBraked \textbfBlockwise \textbfQuantization), which parameterizes the extent to which a transform approaches this ceiling. At W4A4, across four base models and two FP4 formats, KBBQ outperforms the prior state of the art without additional deployment-time computation.
[AI-77] Sparse Data Augmentation for Optimization with Provable Guarantees
链接: https://arxiv.org/abs/2609.08133
作者: Behrooz Tahmasebi,Melanie Weber
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
备注: 32 pages, 1 figure. Published at IEEE CDC 2026
Abstract:In nonconvex optimization problems arising in geometric machine learning, data augmentation is commonly used to promote invariance by averaging empirical losses over transformations of the data. Computing the fully augmented objective, however, requires access to every element of the transformation group G , which may be prohibitively expensive when G is large or accessible only through sampling. We study whether full augmentation can instead be approximated using a small, fixed sample of transformations acquired before optimization and reused thereafter. Under suitable regularity conditions, we show that, with probability at least 1-\delta , gradient descent (GD) on the resulting sparsely augmented objective returns an \varepsilon -stationary point of the fully augmented objective using \mathcalO\bigl((\log |G|+\log(1/\delta))/\varepsilon^2\bigr) group-transformation-oracle queries. By comparison, standard group stochastic gradient descent (group-SGD), which samples a fresh transformation at every iteration, uses \mathcalO(1/\varepsilon^4) transformation queries. Therefore, gradient descent with fixed sparse augmentation requires fewer transformation queries than both GD applied to the fully augmented objective and group-SGD. Our proof techniques, which may be of independent interest, establish a uniform approximation of the full group-averaged gradient field by a random group average using spectral properties of group-induced operators and tools from representation theory.
[AI-78] DISEIL: Demonstration Distillation for Sample-Efficient Imitation Learning
链接: https://arxiv.org/abs/2609.08123
作者: Suyog Khanal,Arun Kumar A V,Santu Rana
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:A robot that can be taught a new task from a handful of demonstrations has to work out for itself what it still cannot do, and then ask for exactly that. Interactive imitation learning takes a step in that direction by letting a policy practice on its own and calling an expert when it goes wrong. Existing methods decide when to interrupt the learner. A further 2 decisions are left to whichever episode happened to trigger the interruption: which failure to correct, and where the demonstration should start. This paper is a first attempt at making both of them deliberately. DISEIL (Demonstration dIstillation for Sample-Efficient Imitation Learning) marks each failed episode at the step where the policy first becomes unreliable, represents that moment with a geometric descriptor, and groups the failures into recurring failure modes. A vision-language model and a language model read the selected mode and write a request for the next demonstration, and a store of task constraints checks that the request can be carried out before any expert time is spent. No model produces a robot action. Across 5 simulated tasks under state and image observations, changing only what the expert is asked for gives the highest mean held-out success rate in all 10 settings, with a tie in 1, and the margin is widest at the smallest budget we tested. The scope is narrow: a single round of practice at a time, in simulation, with experts that are mostly scripted. The longer-term aim is a learner that also tracks what its demonstration set already covers, and that asks a human teacher for the missing behavior in proportion to the effort each request costs them.
[AI-79] Router Prior Bias: Preserving Base Routing Structure in MoE Post-Training
链接: https://arxiv.org/abs/2609.08115
作者: Jaedeok Lee,Keonwoo Kim,Dongyoon Han,Sangdoo Yun,Yera Choi,Haanju Yoo
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Mixture-of-Experts (MoE) pretraining relies on an auxiliary load-balancing loss (LBL) to drive per-expert utilization toward uniformity. Post-training inherits a different situation: the base router already encodes non-uniform expert co-activation structure, which a re-imposed uniformity objective flattens away. We show that downstream performance depends instead on holding this inherited routing softly, a principle we term soft router anchoring, and instantiate it as Router Prior Bias (RPB), a training-time bias that pulls the router logits toward a prior read off the frozen base router while leaving the router itself trainable. On math post-training of Moonlight-16B-A3B, RPB attains 45.77 in-domain accuracy against 31.91 under re-applied LBL and 29.44 under unanchored fine-tuning, and retains more out-of-domain capability than either. The ordering against LBL reproduces on a second model family (Qwen3-30B-A3B-Base), and the advantage over LBL is resolvable on an independently sourced corpus. Anchors defined on the router weights, on its logits, or on its output distribution perform comparably with no consistent ordering, which places the effect in the softness of the constraint rather than in the particular prior RPB supplies. Retained community structure in the expert co-activation graph tracks these gains wherever the base router is non-uniform enough for communities to form, yet enforcing the same prior as a hard assignment preserves that structure while performance falls sharply. Community structure is therefore a footprint of soft anchoring rather than its source, and the practical lesson is that inherited routing should be held softly during post-training, since both flattening it toward uniformity and enforcing it absolutely carry a downstream cost. Our code will be released at this https URL.
[AI-80] CIVI: A Framework for Diagnosing Search Agent Failures in Civic Information
链接: https://arxiv.org/abs/2609.08094
作者: Dingying Liu,Yunshun Zhong,Wentao Zhang,Yiyuan Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models are increasingly deployed in public-sector settings, where incorrect guidance can cause irreversible harm. We introduce CIVI, the first framework for diagnosing search agent failures in civic information. Its benchmark instantiation jointly spans cross-national, interjurisdictional government contexts (federal, state, and local) and functional categories from an internationally adopted United Nations standard. We evaluate ten frontier search agents and find that none matches an attentive human baseline. Alongside accuracy, CIVI measures search invocation rate, selective no-search accuracy, and how often agents cite authoritative government sources. To perform this diagnosis, we introduce ARISE, which decomposes agentic search failures into four mutually exclusive modes, isolated via source-injection ablation. ARISE attributes 72.1% of all observed failures to retrieval-bound causes rather than to gaps in the models’ parametric knowledge.
[AI-81] Inference-Time Nash Alignment
链接: https://arxiv.org/abs/2609.08082
作者: Hadi Hosseini,Debmalya Mandal,Duohan Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Preference-based fine-tuning methods such as RLHF and DPO require substantial compute and large preference datasets. They also need direct access to the model parameters which are not provided by many state-of-the art models. Inference-time alignment offers a cost-effective alternative without updating model parameters. However, existing inference-time methods rely on a scalar reward model derived under a Bradley-Terry assumption, which cannot represent general preferences. Following recent work on fine-tuning with generalized preferences, in this work, we initiate the study of inference-time alignment under general preferences. We formulate the problem as obtaining a Nash equilibrium of a two-player zero-sum game between policies. We propose two algorithms: Best-of-Nash (BoN) and Nash Mirror Descent (NMD). We prove that both algorithms achieve a duality gap that matches the problem lower bound. Empirically, we implement the two methods on three datasets, which shows that our methods substantially outperform the base policy, converging to the performance of the fine-tuned models. Moreover, our results show that NMD remains robust across the regularization parameter.
[AI-82] Automated Design of Inventory Policy with Large Language Models : An Exploratory Study
链接: https://arxiv.org/abs/2609.08071
作者: Fenghua Yang,Preet Baxi,Yi Zhang,Stefanus Jasin,Yanzhe Lei,Mo Liu,Parshan Pakiman
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Firms making inventory decisions have access to operational data, optimization tools, and large language models (LLMs). Typically, data characterize the operating environment, optimization selects parameters within a prespecified inventory policy class, and LLMs support coding and decision analysis. We develop an integrated framework that combines these resources to automate inventory policy design. Given demand data, the framework iteratively uses an LLM to generate parameterized policy classes and an external solver to optimize its parameters within each class. Across 30 lost-sales inventory instances, the mean cost reduction relative to optimized base-stock benchmarks increases from 17.5% after one generation to 30.0% after ten generations. Parameter optimization is central to this performance: an LLM-only variant performs substantially worse, whereas optimization-guided feedback improves policy quality, accelerates search, and directs the LLM toward better policy classes rather than merely better parameter values within a fixed class. The strongest discovered policies are also interpretable: they combine recognizable inventory-control motifs, including capped orders, discounted or weighted pipeline inventory, and threshold-based replenishment logic. The search thereby produces new policy-class functional forms that, to our knowledge, have not previously been studied in the lost-sales inventory literature. These functional forms are not specified ex ante but emerge from the search process. Moreover, after their parameters are re-optimized, three discovered policy classes achieve average cost reductions of 21.75% to 22.60% across 10,064 new inventory instances. Overall, the results show that data-driven parameter optimization can guide LLM-based search over a broad space of inventory policy classes and identify high-performing, interpretable, and transferable decision rules.
[AI-83] ResidualAuth: What Authorization State Must Language Agents Preserve under Revocable Delegation?
链接: https://arxiv.org/abs/2609.08062
作者: Moonwon Choi,Seokho Jeong,Seunggeun Lee
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 61 pages, 7 figures. Includes appendices. Moonwon Choi and Seokho Jeong contributed equally; Seunggeun Lee is the corresponding author
Abstract:Tool-using language agents can delegate and revoke permissions while acting through external services. We show that two authorization histories can have identical current permissions and identical all-pairs reachability yet require opposite decisions after the same direct-edge revocation. We formalize the information needed to preserve such distinctions as a residual authorization state. We prove that exponentially many future-distinct states can share one fixed transitive closure, and give exact or tight asymptotic bounds on the state required by an exact monitor as delegation redundancy varies. ResidualAuth compiles these constructions into paired language-agent episodes. Across four open-weight models, a fixed 256-token summary solved 0-2/16 pairs, sham reads solved 0/16, and authenticated current-query reads solved 15-16/16. In a separate held-out online-memory diagnostic, exact ledger serializations fit all 128 four-coordinate pairs at both 768 and 1,024 tokens. At either cap, factually supported model-written memories sufficient for every prespecified continuation solved at most 1/128 pairs per model. A hard gate reduced eight observed unauthorized effects to zero without changing the preceding attempts. These results distinguish required authorization state, usable decision information, online state maintenance, and effect mediation.
[AI-84] LLM s for Social Network Modeling: From Network Generation to Dynamic Processes
链接: https://arxiv.org/abs/2609.08049
作者: Shikha Mallick,Alex Thomo,Akrati Saxena
类目: ocial and Information Networks (cs.SI); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) are rapidly emerging as a new paradigm for modeling social networks by representing users and their relationships and interactions through natural language. Unlike classical network models or deep learning approaches, LLMs can simulate context-aware social behavior and language-driven interactions, enabling more realistic modeling of network formation and dynamic social processes. However, existing studies are scattered across different research communities and lack a unified perspective. This survey presents the first comprehensive review of LLMs for social network modeling by organizing the literature into two broad categories: network generative models and dynamic process models. Network generative models are further classified into selection-based and interaction-based approaches, while dynamic process models are categorized into opinion dynamics, information diffusion, and rumor propagation, each with their underlying modeling mechanisms. LLMs enable rich textual social interactions and decision-making, but they also exhibit many limitations, including inherent social biases and prompt sensitivity. We outline these open research challenges and discuss future directions in LLM-based social network modeling.
[AI-85] A Layered Analysis of Disagreement And Answer Quality in Multi-Agent LLM Debate
链接: https://arxiv.org/abs/2609.08016
作者: Chen Qian
类目: Artificial Intelligence (cs.AI)
备注: Code: this https URL
Abstract:Multi-agent debate, in which several LLMs exchange arguments before answering, is widely assumed to improve answer quality by surfacing genuine disagreement. That mechanism is rarely checked. We introduce four measurements: (A) the agreement a debater reports; (B) whether its reply text actually pushes back; © whether the position persists once the eliciting instruction is removed; and (D) for open-weight models, the stance response in the debater’s own token log-probabilities. We evaluate three-model committees debating open-ended GlobalOpinionQA across 750 debates under three tones: friendly (seek common ground), neutral, and hostile (stress-test every position). (A) Tone strongly reshapes reported agreement: full agreement differs by 50.4 percentage points between the friendly and hostile endpoints. (B) A judge that reads only the reply text, never the self-report or the condition, recovers the same pattern. © The dissent appears partly tied to the instruction that elicited it: labels revert toward agreement 23.1 points more often after deleting the hostile instruction than under a matched re-ask that keeps it; question-weighted inference is inconclusive on first-round turns alone (p=0.0625), significant pooling all rounds (p=0.016), and only 11/28 first-round reversions also appear in the reply text. (D) Opposing arguments weaken a debater’s stance margin more consistently than they shift its direction. For final answers we detect no quality gain: a bias-checked jury returns 299/299 ties (ruling out only large differences), accuracy on a verifiable control task is unchanged, and a jury without the bias check had declared debate the winner 66% of the time – an artifact of reading order. Taken together, LLM debate readily changes what agents say, but we find much weaker evidence that it changes what they persistently endorse or improves the quality of the final answer.
[AI-86] From Version Conflicts to Decision Conflicts: Selective Revalidation for Long-Running AI Agents
链接: https://arxiv.org/abs/2609.08015
作者: Yongjian Lyu,Yang Ren,Ruofei Lai,Wenting Liu
类目: Artificial Intelligence (cs.AI); Databases (cs.DB)
备注: 6 pages, 2 figures, 3 tables. Supplemental artifact available at this https URL
Abstract:Long-running AI agents may read state, reason, wait for tools or human approval, and perform an external action much later. The state that justified the action can change in the meantime. For example, after an agent proposes an 80 GBP refund under a limit of 100, a customer-name change affects only presentation metadata, a new limit of 90 still permits the refund, a limit of 50 invalidates it, and a refund issued by another worker must prevent a duplicate. Standard optimistic concurrency control and version checks can detect that previously read state has changed, but by themselves do not determine whether that change invalidates the pending action’s justification. We call any detected version change a version conflict; when that change invalidates the action’s justification, it is also a decision conflict. ATR records the explicit, executable conditions that justify a pending action and rechecks only the conditions affected by a change before releasing the external operation. It can retain the action, refresh non-decisive metadata, require replanning, or block execution; a target-side transaction or compare-and-set binds checked state to commit. Across 210,000 controlled executions over 15 mutation cases, ATR matched every developer-specified outcome with no false allows or blocks. In ten durable SQLite checkpoint/resume cells, it evaluated 0.6 conditions per change versus 6.0 for FullScan. At 4,093 recorded reads, ATR took 9.3 microseconds versus 2595.9 microseconds for FullScan. These deterministic results establish controlled feasibility, not production generality or automatic extraction of the required conditions.
[AI-87] Sparks of In Silico Cognitive Science: Theories from Simulated Data Can Generalize to Humans
链接: https://arxiv.org/abs/2609.08003
作者: Akshay K. Jagadish,Younes Strittmatter,Nori Jacoby,Eric Schulz,Nathaniel Daw,Thomas L. Griffiths,Suyog H. Chandramouli
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 1 figure
Abstract:Behavioral foundation models have been proposed as stand-ins for human participants across settings, but it is unclear whether theories discovered on them generalize to humans or merely characterize the simulator. We ran the Automated Cognitive Scientist (\textscAutoCog), a closed-loop discovery system in which LLM agents design theory-discriminating experiments, collect responses, arbitrate between competing theories, and synthesize successors, entirely on behavior simulated by Centaur, a foundation model of human behavior. In a multi-attribute decision-making setting, the theories \textscAutoCog found on Centaur generalized to human data: they outperformed canonical theories on ten held-out experiments and were rivaled only by theories found by running the same loop on people. We argue that this succeeds despite the simulator’s inevitable imperfections because a discovery loop that arbitrates between competing theories demands less of its simulator than estimation does. The simulator only needs to capture the regularities that distinguish the theories, and not necessarily reproduce behavior precisely. Imperfect simulators can therefore widen the search over theories, with human data then testing whether the surfaced theories generalize.
[AI-88] Mini-Batch Risk-Averse Deep Q-Learning: A Robot Navigation Case Study
链接: https://arxiv.org/abs/2609.07998
作者: Aayush Patel,Andrzej Ruszczyński
类目: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
备注:
Abstract:We study the control of Markov decision processes in which the quality of a policy is evaluated by a dynamic, time-consistent Markov risk measure rather than by an expected discounted cost. The main obstacle to combining such measures with reinforcement learning is that a transition risk mapping depends on the transition kernel in a nonlinear way, and therefore cannot be estimated from a single observed transition. We remove this obstacle by employing mini-batch transition risk mappings: the mapping is applied to the empirical measure of N independent next-state samples, and the result is averaged. The resulting mapping is again coherent. However, as an expected value of a function of N next-state values, it admits an unbiased one-sample estimator. We embed this mapping into a double deep Q-network, analyze the two sources of estimation bias that arise, and obtain a risk-averse Q-learning method applicable to state spaces far beyond the reach of tabular schemes. The method is applied to an underwater robot navigation problem, in which a vehicle must visit collection points, gather stochastic information payloads, and deliver them at transmission points, while exposed at each step to the risk of destruction. A hierarchical decomposition delegates path execution to an exact graph search and confines learning to the high-level ``collect or transmit’’ decision. A low-dimensional feature map, invariant under the symmetries of the problem, replaces the raw state–configuration encoding. In experiments on 300 held-out environments, the resulting policies transfer to instance sizes never seen in training, and already N=2 reduces the upper semideviation of the outcome distribution while simultaneously improving its mean whenever the simulator is misspecified—an empirical counterpart of the duality between coherent risk measures and distributional robustness. Subjects: Artificial Intelligence (cs.AI); Optimization and Control (math.OC) MSC classes: 90C39 ACMclasses: I.2.6 Cite as: arXiv:2609.07998 [cs.AI] (or arXiv:2609.07998v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.07998 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-89] HyCO: A Hybrid Neural Solver for Combinatorial Optimization
链接: https://arxiv.org/abs/2609.07990
作者: Yuheng Li,Di Yang,Haipeng Chen,Yanhai Xiong
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Sequential reinforcement learning (RL) solvers and global diffusion model (DM) solvers for neural combinatorial optimization exhibit complementary failure modes under an optimization-regret view. The former enjoys small marginal regret in the early construction stage, but suffers from horizon-wise compounding errors with super-linear regret growth; the latter avoids horizon compounding but incurs linear or sublinear regret w.r.t. the dimension of the remaining unsolved subspace. We propose Hybrid Neural Solver for Combinatorial Optimization (HyCO), a hybrid inference algorithm that constructs a solution prefix with an RL solver and adaptively switches to a conditional DM to complete the remaining decisions. To characterize why such hybridization helps, when to trigger the handover, and how to realize it in practice, we first develop a unified error-scaling theoretical framework and prove that, under explicit error-scaling assumptions, i) the hybrid structure achieves strictly lower expected regret than either backbone alone, and ii) there exists a unique optimal trigger step that minimizes the hybrid regret. We then design a lightweight adaptive trigger that combines policy entropy and RL-DM disagreement to detect trajectory-level signals of the regime shift as a practical proxy, since the optimal trigger step is defined at the expected-regret level and is not directly computable on individual trajectories. Experimental results on diverse benchmarks demonstrate that HyCO achieves consistent improvements over both backbones and support the empirical effectiveness of adaptive triggering.
[AI-90] When Can LLM Digital Twins Reduce Human Measurement? From Behavioral Fidelity to Statistical Substitutability
链接: https://arxiv.org/abs/2609.07987
作者: Steven Wang,Kyle Hunt,Shaojie Tang,Kenneth Joseph
类目: Artificial Intelligence (cs.AI); Applications (stat.AP)
备注:
Abstract:LLM-based digital twins promise to reduce repeated human data collection by generating person- specific responses, yet existing evaluations provide little evidence about whether they can reduce human measurement while preserving valid inference. To address this, we introduce statistical substitutability, an inferential criterion that evaluates the extent to which twin predictions can reduce human measurement for a particular estimand while preserving valid inference. We develop a framework, grounded in mixed-subject and prediction-powered inference, that evaluates statistical substitutability along four dimensions: aggregate fidelity, paired respondent-level signal, finite-sample human-label recovery, and stability across populations. Across two empirical evaluations spanning behavioral experiments, multiple models, and alternative respondent representations, we find that digital twins can reproduce average human effects while providing little information about which individuals differ from those averages. Newer models and richer respondent information improve some dimensions of performance but do not reliably translate into human-data savings. Human calibration can reduce aggregate prediction error, yet limited labeled samples often fail to produce stable precision gains. Importantly, these findings demonstrate that behavioral fidelity is neither necessary nor sufficient for statistical substitutability. More broadly, they suggest that AI-generated evidence should be evaluated based on its ability to support valid scientific inference rather than its ability to reproduce human outcomes alone. Digital twins should therefore be judged for confirmatory use by whether they reduce uncertainty about human quantities, not merely by whether they reproduce human means, distributions, or effects.
[AI-91] From Event Logs to Governed Action: A BlueSky Agenda for Agent ic Process Mining ICDM2026
链接: https://arxiv.org/abs/2609.07984
作者: Yiyuan Yang,Zheshun Wu,Yong Chu,Zhenghua Chen,Zenglin Xu,Qingsong Wen
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注: Accepted by ICDM 2026
Abstract:Process mining has long turned event logs into process knowledge: discovered models, conformance evidence, bottleneck diagnoses, and runtime predictions. Agentic AI changes the target. Process-aware agents will not only ask what happened. They will ask whether a proposed action should be taken, given the available evidence, privacy budget, organizational authority, and downstream risk. This BlueSky paper proposes event-to-action process mining: a process-mining agenda for transforming heterogeneous operational event data into governed action. The goal is not another dashboard, a generic enterprise simulator, or a language interface over logs. We argue that the community needs four mineable artifacts: event-object representations, action evidence packages, governance contracts, and benchmarks where act, defer, ask, and refuse are all valid outputs. This agenda is timely because agentic business process management (BPM), LLM-assisted process mining, object-centric event standards, causal process monitoring, and privacy-preserving learning are maturing separately. Bringing them together defines a data-mining target inside process mining: mining logged organizational behavior for accountable action, not only retrospective insight.
[AI-92] Support Topology and Gradient Mixing in Sinkhorn Layers
链接: https://arxiv.org/abs/2609.07954
作者: Dylan Forde
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Sparse Sinkhorn layers use a fixed support graph to restrict transport between tokens. How does this graph control gradient propagation through the scaling iterations. We develop a fixed-support calculus showing that each row-column cycle induces a row-stochastic operator on column-potential perturbations modulo constants. Its transpose propagates zero-mass reverse-mode cotangents. The finite-cycle operator uses two distinct half-step transport plans; at a balanced fixed point it reduces to a two-step walk determined by a single plan. We derive the accompanying score and marginal source terms and use Dobrushin contraction and minorization to bound homogeneous and source-driven tail cotangents. Our main result characterizes when support and marginals guarantee one-step contraction uniformly over finite scores: every feasible face of the transportation polytope must have pairwise two-hop column overlap. Otherwise, suitable score directions make the contraction coefficient arbitrarily close to one. We extend this analysis to ordered support schedules and derive certificates for partition heat-bath layers, coordinate sweeps, forced shared mass, and register-augmented supports. These results provide mathematical criteria for support design in differentiable transport layers, with guarantees restricted to the fixed-support quotient-gradient component.
[AI-93] Beliefs and Behavior in Language Models
链接: https://arxiv.org/abs/2609.07943
作者: Alex Smolin,Bryan Wilder
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 33 pages, 10 figures
Abstract:There is significant uncertainty about whether abstractions like beliefs or desires usefully describe the behavior of large language models (LLMs). In addition to the inherent scientific interest of this question, these latent quantities are often invoked to explain the behavior of LLMs to users or to define and evaluate harmful behaviors which are relative to intent. Nevertheless, we currently lack a means to systematically test whether concepts like “belief” are well-applied to LLMs, and hence whether they are likely to be fruitful ingredients of attempts to align models with human interests. We propose an approach for empirically studying such questions, asking whether a single latent variable inferred from the LLMs’ outputs – interpreted as a degree of belief – allows an observer to make interpretable predictions of how the LLMs’ will respond to new prompts. We find that highly capable models are usefully described as holding beliefs and that, generally, the predictability of model outputs based on an inferred latent belief tracks overall trends in model capability. Building on these findings, we provide empirical strategies to study how beliefs in LLMs can be measured, the extent to which LLMs comply with instructed decision rules or payoffs, and how beliefs evolve within individual instances of an LLM over the course of reasoning.
[AI-94] FrogNano: Training a 4B Coding Agent via Online Task Synthesis
链接: https://arxiv.org/abs/2609.07925
作者: Minseon Kim,Zhengyan Shi,Emiliano Penaloza,Christopher Cui,Roger Creus Castanyer,Maryam Hashemzadeh,Isadora White,Jonathan Light,Jeonghye Kim,Matheus Pereira,Darya Moldavskaya,Chinmay Singh,Fabio Vera,Baolin Peng,Xingdi Yuan,Marc-Alexandre Côté,Alessandro Sordoni
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We present FrogNano, a 4B coding agent designed to tackle software engineering (SWE) tasks efficiently and effectively, even under resource-constrained environments. It is post-trained exclusively via RL on around 1,500 SWE environments with synthetic tasks. A key ingredient for improving performance is an online task synthesis pipeline that creates tasks calibrated to the frontier of learnability for the current checkpoint. This report provides evidence that competitive small coding agents can be trained with synthetic tasks alone, without traditional distillation from larger models, and that generating tasks at the learnability frontier of the current agent is important. We report details on the training methodology, evaluations across diverse environments, and in-depth analyses, serving as a foundation for our ongoing exploration of lightweight yet capable coding agents that can run on minimal hardware.
[AI-95] AVCG: A Generalized Variational Framework for Counterfactual Generation under Hypothesis Distributions
链接: https://arxiv.org/abs/2609.07917
作者: Jamie Duell,Alejandro Jimenez Rodriguez,Mahault Albarracin
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Counterfactual explanations formalize “what-if” scenarios by identifying modifications to an input instance that obtain a desired alternative prediction. Traditionally, whether generated via instance-specific optimization or amortized single pass models, these approaches rely on a single, deterministic point-estimate predictor. However, this ignores predictive uncertainty and hypothesis variability, leading to brittle explanations that frequently become invalid if the underlying model is retrained or updated. To address this fragility, we propose the Amortized Variational Counterfactual Generator (AVCG), a generalized optimization framework that formulates counterfactual generation as optimization over an arbitrary distribution of plausible predictive hypotheses rather than a single deterministic predictor. This formulation naturally accommodates Bayesian posteriors, Rashomon-restricted hypothesis spaces, and other uncertainty representations within a unified optimization framework. Evaluation across multiple benchmark datasets demonstrates that the AVCG framework produces counterfactual explanations that remain highly valid under predictive uncertainty and model changes, while maintaining competitive plausibility and single-pass runtime performance.
[AI-96] PRIMUS: Identity Governance and Verification for Multi-Agent Federations
链接: https://arxiv.org/abs/2609.07910
作者: Sasank Annapureddy,Anjaneya Prasad Thamatani
类目: Artificial Intelligence (cs.AI)
备注: 17 pages, 6 tables, no figures. Extends PRIMA ( arXiv:2605.24775 ). Per-step result files, preregistrations, and manifests available to reviewers on request; certain implementation constants held under controlled release (see Appendix D)
Abstract:Multi-agent federations need governance that answers three questions under adversarial conditions: who participated (identity), did they conform (enforcement), and who decides (authority). A separate question is whether the verification machinery that polices a federation’s outputs can also steer a generate-and-test loop toward better answers. Part I. PRIMA introduced prime-power agent identity and a consensus token whose factorization indexes participation, but assumed honest agents. We present PRIMUS, which couples prime-power identity with BLS aggregate signatures (PIAC), derives a safe-kill threshold that reduces false-positive agent termination from 80% to 0.00% under 10% channel noise, gives the closed-form economic boundary where singleton governance outperforms Byzantine quorum ( \gamma^* \approx 9f , verified flat across n = 50 to 10,000), and specifies VRF succession with lease and fencing that makes safety unconditional under partial synchrony. Five problems are identified as provably unfixable within the model and stated as scope boundaries. Part II. A verifier is not a solver. We ask whether PRIMA’s binary artifact-fidelity verdict can be converted into a graded fitness signal, and measure the conversion on binary covering codes. Calibration against injected fault burden is strong ( \rho = 0.676 deterministic, 0.819 full); against real LLM-generated candidates the same scores fall to 0.158 and 0.406, roughly a quarter of the calibration value (the same-designer confound, measured). As a pre-filter it beats a random-score control convincingly and a binary gate narrowly. Under 400 iterations of explicit optimization it was not gamed, but only because the objective saturated after one honest answer. A cross-family judge preserves the burden-ordering signal while destroying individual judgments. No covering-code record resulted. Measured program cost: USD 164.78. Comments: 17 pages, 6 tables, no figures. Extends PRIMA (arXiv:2605.24775). Per-step result files, preregistrations, and manifests available to reviewers on request; certain implementation constants held under controlled release (see Appendix D) Subjects: Artificial Intelligence (cs.AI) MSC classes: 68T42, 68M14, 94A60, 94B75 ACMclasses: I.2.11; C.2.4; D.4.6; I.2.8 Cite as: arXiv:2609.07910 [cs.AI] (or arXiv:2609.07910v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.07910 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-97] Quantization Amplifies Determinism Not Bias: Scale-Dependent Behavioral Effects of Serving-Time Weight Compression NEURIPS2026
链接: https://arxiv.org/abs/2609.07901
作者: Dachi Kurtskhalia
类目: Artificial Intelligence (cs.AI)
备注: 8 pages, 3 figures. Under review at the NeurIPS 2026 Workshop on Deployable Small Foundation Models (LIGHT)
Abstract:Weight quantization largely determines the economics of serving open-weight LLMs. Its costs are usually assessed with capability benchmarks, on which 4-bit quantization of mid-sized models is often considered “nearly free.” We examine a different question: when several answers are valid, does quantization change what a model chooses to say? We serve three checkpoints (Qwen3-8B/14B/32B) at three weight precisions (W4A16 AWQ, W8A16 FP8-Marlin, and bf16), holding the hardware, software, and sampling configuration constant, and collect approximately 71,000 completions paired by prompt and seed across two custom, leak-checked prompt batteries. We pre-specified the analyses in three waves in version control. At 8B, int4 reduces output diversity: the probability that two samples for the same scenario recommend the same brand increases by 5.1 percentage points (prompt-paired sign-flip test, Holm p = .023; reproduced at +4.4pp on a full regeneration of the arm), and lexical diversity falls substantially (TTR -0.011, standardized effect -0.51; robust to a length-controlled measure). At 14B and 32B, no content-concentration measure reaches significance; instead, stylistic drift emerges (em-dash rate +0.46/1k words at 14B and +0.61/1k at 32B, both Holm p = .0024). Pre-specified tests of stereotype direction are null at every scale: outputs concentrate on the modal answer for each prompt rather than on stereotypical answers. Mechanistically, the token-level distribution becomes flatter (decision-token entropy +0.091 bits, p = .015) while the semantic distribution, measured directly from first-token log probabilities, becomes more concentrated (collision +2.6pp, p = .023): individual tokens become less predictable even as meanings become more repetitive. At 8B, the smallest size tested, AWQ-int4 serving measurably narrows the range of suggestions; audits should assess concentration as well as bias.
[AI-98] he Accuracy Paradox: Empirical Diagnostic of Default Decision Thresholds in Multi-Label Enzyme Commission Prediction [With Code]
链接: https://arxiv.org/abs/2609.07897
作者: Bilal Ahmad,Rajed Mehmood
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)
备注: 11 pages, 4 figures,Enzyme Commission prediction, multi-label classification, class imbalance, threshold optimization, conformal calibration, accuracy paradox, bio-data, applied machine learning, bioinformatics. imbalanced bio-data, model diagnostic, performance metrics, open-source pipeline
Abstract:Automated prediction of Enzyme Commission (EC) numbers plays a central role in functional annotation and computational drug discovery. However, standard multi-label machine learning pipelines frequently rely on default decision thresholds (t=0.50), assuming balanced prior distributions across target heads. In this study, we present a systematic empirical diagnostic of uncalibrated fixed decision boundaries operating under severe class imbalance across N = 14,096 annotated compounds categorized into six primary EC classes (EC1-EC6). Our results highlight a pronounced Accuracy Paradox: while the multi-label system achieves a deceivingly high mean accuracy of 77.16%, the macro F1-score (0.3976) and macro recall (0.3872) reveal severe predictive breakdown. Majority target classes suffer from hyper-sensitivity and over-prediction, whereas minority classes exhibit sharp recall decay, culminating in a total decision boundary collapse for EC6 (Recall = 0.00%) despite underlying discriminative power (ROC-AUC = 0.5857). Feature correlation analysis further reveals high linear redundancy among topological indices relative to fingerprint density metrics. Ultimately, this diagnostic study demonstrates that standard point predictions mask critical errors in bioinformatics workflows. We establish target-specific threshold optimization and post-hoc conformal calibration as essential, open-source post-processing safeguards for reliable applied machine learning and deep learning architectures.
[AI-99] Explainable Temporal Attention-based Defect Detection For Fillet Joints in Real-Time Gas Metal Arc Welding Based on Multi-modal Data
链接: https://arxiv.org/abs/2609.07893
作者: Mobina Mobaraki,Mahyar Asadi,Klaske Van Heusden,Guy A. Dumont
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Deep learning is an efficient technique to monitor the real time welding process, reducing post-welding repairs and production delays. This paper leverages the monitoring capability by proposing a multi modal temporal attention based deep learning defect detection model for internal defects that are challenging to detect, including porosity, lack of penetration and fusion, undercut, and cold lap during Gas Metal Arc Welding in fillet joints. The model is trained on collected welding images and sound data from an industrial collaborative welding robot. The results show that the attention module can improve the F1 Score to 0.99. We use explainable Artificial Intelligence to interpret the proposed models behavior and dataset distribution, determining potential important areas in image and sound spectrograms and preferred modality to detect each defect. This improves trust and reliability in Artificial Intelligence driven welding inspection.
[AI-100] Do Large Language Models Know What They Dont Know II? A Fully Behavioral Non-Cognitive Measure of Epistemic Honesty
链接: https://arxiv.org/abs/2609.07879
作者: Ali Şenol,H. Russell Bernard,Huan Liu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models (LLMs) are frequently confident, eloquent, and well versed. A natural question arises: do they know what they don’t know? To answer this question, we borrow the concept of epistemic honesty and develop a novel metric to systematically evaluate whether an LLM appropriately acknowledges the boundaries of its knowledge. In this work, we introduce the Epistemic Honesty Quotient (EHQ), which reports three observable sub-scores across two operational axes (epistemic restraint and substantive-answer calibration), and construct EHQ-3000, a 3,000-question benchmark spanning Fabricated Entity, Post-Cutoff Event, Hyper-Niche True, and Context-Conditioned Questions. From a frozen registry of 21 model API routes, 15 completed the protocol after endpoint and eligibility checks; 14 entered the confirmatory analysis because severe provider-side truncation made one route’s score indeterminate. The study reveals substantial variation across models, including a difference that can not be explained by their capability to extract explicitly available information. Composite EHQ ranges from 0.31 to 0.81 across the analysed panel, despite near-ceiling performance on the document-grounded capability probe. The two restraint criteria overlap strongly under the present category composition, whereas substantive-answer calibration varies across models and does not reliably co-vary with restraint; however, the small panel leaves substantial uncertainty. Thus, EHQ reveals behavioral differences that are not visible to conventional correctness-based assessment, while also showing why dataset composition, provider behavior, and confidence elicitation must remain part of the interpretation.
[AI-101] Foundation Models for Generalizable Semantic and Goal-Oriented Communication
链接: https://arxiv.org/abs/2609.07853
作者: Boliang Liu,Wint Yi Poe,Riccardo Trivisonno,Giuseppe Caire
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Robotics (cs.RO); Image and Video Processing (eess.IV)
备注: 6 pages, IEEE ICC 2026
Abstract:Semantic and goal-oriented communication is increasingly studied for 6G, but generalization beyond seen data remains a key weakness under tight rate budgets. Many existing systems overfit their training data and degrade sharply at very low bit rates because they attempt to compress the entire signal. We introduce Foundation Model-Guided Semantic and Goal-Oriented Communication (FMSGOC), a framework that uses broad visual-linguistic Foundation Model priors to mitigate overfitting. It further improves rate efficiency by concentrating bits on sparse, goal-aligned anchors and relying on generative foundation-model priors to reconstruct the masked regions. By decoupling what to send from how to reconstruct, a vision-language foundation model selects and transmits a sparse set of semantic anchors, while a pretrained diffusion model, fine-tuned for masked completion, reconstructs the image at the receiver. In our experiments, FMSGOC reaches 0.039 bits per pixel (BPP), maintains high semantic fidelity (cosine similarity 0.87-0.90 on CIFAR-10), remains robust on previously unseen inputs (0.83-0.86 on ImageNet), and shows good perceptual similarity (0.1278/0.1558, CIFAR-10/ImageNet), outperforming strong end-to-end baselines at lower bit rates.
[AI-102] Kalman Delta Networks: Uncertainty-aware Associative Memory
链接: https://arxiv.org/abs/2609.07816
作者: Ngoc Bui,Tinglin Huang,Rex Ying
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Linear attention is increasingly used in frontier language models for efficient long-context inference and constant-memory decoding. Its fixed-size recurrent memory, however, requires an online decision at each token: what to write and how strongly to overwrite existing associations before knowing which information future queries will require. Delta-rule models learn this strength from the current token embedding but do not track confidence in the memory estimate, preventing each write from adapting to accumulated evidence. To represent this uncertainty explicitly, we reformulate recurrent associative memory as a linear–Gaussian state-space model, for which the Kalman filter is the optimal recursive estimator, and introduce a new family of models, Kalman Delta Networks (KDNs). Within KDNs, the transition propagates both the memory state and its uncertainty, allowing the Kalman gain to weight each residual write by accumulated evidence and observation reliability. Under this formulation, Delta-style updates emerge as a special case that substitutes a token-wise isotropic surrogate for predictive covariance and omits covariance tracking. Exact tracking, however, entails a dense, state-dependent Riccati recursion that is poorly suited to GPU-parallel linear-attention scans. To address this issue, we introduce two scan-compatible KDN approximations. Diagonal KDN projects each one-step posterior onto the diagonal Gaussian family through online mean-field variational inference, whereas Isotropic KDN uses an isotropic approximation with a single uncertainty scalar per head. Their uncertainty recurrences are Mobius maps, enabling associative scans with logarithmic parallel depth. Across controlled pretraining at 750M and 1.3B parameters, KDN variants consistently improve perplexity and mean downstream accuracy over state-of-the-art linear-attention models.
[AI-103] Understanding the Impact of Model Pruning on Long-Tail Forgetting and Explanation Reliability in Medical Imaging
链接: https://arxiv.org/abs/2609.07803
作者: Nazish Khalid,Tausifa Jan Saleem,Amal Saqib,Donald C. Wunsch II,Mohammad Yaqub
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Model pruning is widely used to compress deep neural networks, reducing memory and computational requirements with minimal impact on aggregate performance. However, its effect on model behavior remains poorly understood, particularly for long-tailed medical datasets where rare but clinically important conditions are underrepresented. Furthermore, it remains unclear whether pruned models preserve reliable explanations of their predictions. To address this gap, we present a systematic study of long-tail forgetting and explanation reliability under model pruning. Across two long-tailed medical imaging datasets, two CNN architectures, four pruning methods, and sparsity levels up to 95%, we evaluate predictive performance, explanation stability, and explanation faithfulness. Our results show that predictive performance exhibits a strong frequency-dependent trend, with lower-frequency classes generally experiencing earlier and larger degradation than higher-frequency classes. In contrast, explanation stability and faithfulness are influenced primarily by the pruning strategy, with gradient-informed methods preserving explanation reliability more effectively under aggressive compression. Qualitative and mechanistic analyses further indicate that explanation degradation is primarily associated with the collapse of class-discriminative gradients rather than the disappearance of feature activations. These findings suggest that model compression should be evaluated beyond aggregate performance. Incorporating class-aware and explanation-aware evaluation reveals failure modes that would otherwise remain hidden, while moderate sparsity levels provide a practical balance between compression, predictive performance, and explanation reliability.
[AI-104] What Does an LLM -Agent Leaderboard Rank Actually Compare?
链接: https://arxiv.org/abs/2609.07785
作者: Wei-Jung Huang
类目: Artificial Intelligence (cs.AI)
备注: Accepted at the 13th International Conference on Data Science and Advanced Analytics (IEEE DSAA’2026)
Abstract:An LLM-agent leaderboard invites a familiar inference: an agent ranked above another is the better agent. Public evaluation logs may not support that conclusion when systems differ in task mixture, label source, release detail, or cost rule. We study what leaderboard scores estimate and when they justify pairwise superiority conclusions. Our estimand-aware pairwise procedure states the comparison target and measurement source, checks common support, and evaluates the supported difference using a stated uncertainty rule and practical margin. Controlled checks evaluate the decision labels under known finite-sample conditions and show why uncertainty must be included when judging sensitivity to target reweighting. Across SWE-bench, AgentRewardBench, and tau2-bench, close rank differences are often unresolved; proxy labels and utility rules can also change which system is selected. DataAgentBench and Open Agent show what remains estimable from coarser public records. A leaderboard score summarizes a released evaluation, whereas a fine-grained superiority claim additionally depends on the estimand and uncertainty rule used to interpret the difference.
[AI-105] xDailyBench: Benchmarking LLM s on Professional Consultation for Real-Life Problems
链接: https://arxiv.org/abs/2609.07784
作者: Yongchang Peng,Qingshui Gu,Liya Zhu,Ge Zhang,Duo Wang,Haodong Wang,Jingzhe Ding,Tianhao Yu,Letian Gao,Yongjie Zhong,Chaoxin Li,Zixin Su,Jinchao Tao,Xingyu Ma,Xin’ao Guo,Feng Tian,Shiyuan Dong,Xiaoyan He,Sen Liu,Xin Chen,Jiajun Li,Zejia Zhang,Xi Lin,Wen Zhang,Yi Zhu,Duju Zeng,Xiang Gao,Yunyang Wang,Jiahao Wang,Yujia Qin,Jiaheng Liu,Shen Yan,Xiaolong Chang,Wenhao Huang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) are increasingly used for everyday assistance, yet existing benchmarks only partially reflect the requests users naturally make in practice. Real-world requests are often open-ended, casually specified, and context-dependent, requiring models not only to follow explicit instructions but also to infer unstated needs from user background and situational context. We introduce xDailyBench, a benchmark of 248 carefully curated tasks spanning 51 scenarios across personal life, white-collar work, learning and research, and cross-domain activities. The tasks are grounded in requests that users have actually completed or genuinely intended to accomplish with AI, and are evaluated with fine-grained binary rubrics covering both explicit and implicit requirements. We evaluate 11 frontier models under standardized agentic settings. The best models achieve a task-level score of 75.6%, while all models perform substantially worse on implicit than explicit requirements, with gaps no less than 9 percentage points. These results reveal implicit requirement inference as a persistent bottleneck for reliably satisfying real-world everyday user needs.
[AI-106] Do AI Coding Assistants Check Before They Install? A Pre-Registered Demand-Side Audit of Trust Signals in the Research Software Supply Chain
链接: https://arxiv.org/abs/2609.07754
作者: Pengyin Shan
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 18 pages, 11 figures, 8 tables. Pre-registered protocol deposited 2026-08-22 ( https://doi.org/10.5281/zenodo.22062503%29%3B tooling v0.2.1 at https://doi.org/10.5281/zenodo.22544144%3B data at https://doi.org/10.5281/zenodo.22546062
Abstract:AI coding assistants now select, install, and configure software, and attackers have exploited that position through invented package names, compromised maintainer accounts, and manipulated repository text. In response, the supply-chain community publishes machine-checkable trust signals: software bills of materials, signed releases, build provenance attestations, and declared official channels. Whether coding assistants read or act on those signals has not been measured for any of these classes on research software. We pre-registered and ran a controlled study on six open-source research software projects (three HPC, three quantum computing) drawn from an 87-project corpus, with protocol, seed, panel, and analysis plan deposited with a DOI before any trial. W created nine modified copies for each project: no signal, one per signal class, two with a signature or attestation from the wrong issuer, one with all four signals, and one reproducing documented conflicts in the project’s own metadata. Three models under two ways of operating an assistant, with and without an approval step, gave 1,920 registered trials, plus a supplement on three frontier models. We scored behavior from container logs rather than from what the assistant said, and recorded the cost of every trial. Verification was rare under every condition: in 9 of 1,920 registered trials (0.5%), the assistant opened any provenance signal before installing in 0 of 384 control trials, and no trial ran a verification command, so signal presence had no measurable effect. We drew three conclusions: publishing signals is necessary but not sufficient; price did not buy verification (the model that verified most often costs 0.10 per trial; the most capable, at 1.00, verified nothing); verification must be built into the program that runs the assistant. We release the per-trial cost ledger, the protocol, and every log.
[AI-107] When Intelligence Becomes Agency: A Theory of Governed Proactive Agency for Symbiotic AI Systems
链接: https://arxiv.org/abs/2609.07741
作者: João Dias Ferreira
类目: Artificial Intelligence (cs.AI)
备注: 75 pages, 3 figures, 9 tables
Abstract:Persistent AI assistants are intended to extend human attention, memory, and coordination across changing digital and physical environments. To be truly useful they must do more than just act when asked. They must decide on their own whether a situation warrants behavior at all, when it does and in what mode, whether to act, ask, monitor, defer or deliberately refrain. We call this the activation problem. Research on commitment, appraisal, mixed-initiative interaction and delegation each illuminates part of it, but none ties situated activation to continuing authorization and accountability. This paper develops a conceptual and formal framework for governed proactive agency, organizing behavior across time through perception, intent, affective-conative appraisal, constraint, and feedback. It distinguishes autonomous and delegated agency and defines symbiotic agency as delegation under a standing, revocable mandate, with continuing coupling to the principal’s situation, calibrated inference of their condition, and bounded personalization. The distinctive contribution is an integrated account linking activation decisions to authorized perception, behavior selection, authority containment, traceable restraint, and constrained adaptation, with behavioral episodes as the unit of analysis. Through an agency classification method, an evaluation framework, proposed benchmark scenarios, and a reference architecture, the account provides a basis for specifying and assessing whether assistance is warranted, timely, authorized, and answerable beyond task completion alone. It is intended to guide the development and evaluation of always-present personal assistants and embodied support systems that augment human capabilities while preserving the principal’s authority and judgment.
[AI-108] he Profit Alignment Problem: How Profit Mandates Induce Alignment Failures in LLM s
链接: https://arxiv.org/abs/2609.07731
作者: Eric So
类目: Artificial Intelligence (cs.AI); General Economics (econ.GN)
备注:
Abstract:We show that ordinary business language — “maximize profitability” — induces profit-oriented ambiguity resolution: LLMs systematically dismiss ambiguous signals of potential safety violations to serve business objectives. In 3,600 controlled trials across eight reasoning-capable LLMs, adding a profit mandate to otherwise identical prompts increases risk-dismissing judgments by 6.8 percentage points (p 0.0001), suppresses board escalation recommendations by 13.9pp (p 0.0001), and shifts severity assessments downward (p 0.0001). The mandate never instructs models to downplay risks; instead, chain-of-thought traces reveal motivated reasoning: models acknowledge concerns, then invoke profit logic to justify dismissing them. We characterize these findings as the Profit Alignment Problem: when AI systems are given ordinary business objectives, they develop systematic strategies for suppressing inconvenient information that no designer intended or specified.
[AI-109] A radiographic world model for clinical reasoning and evidence generation
链接: https://arxiv.org/abs/2609.07719
作者: Suyang Xi,Songtao Hu,Shansong Wang,Mojtaba Safari,Luke del Balzo,Ehsan Ul Karim,Mingzhe Hu,Kuo Zhang,Tonghe Wang,Ralph R. Weichselbaum,Xiaofeng Yang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Medical imaging artificial intelligence (AI) is commonly developed as separate mappings from radiographs to diagnostic outputs or from clinical descriptions to generated images, although both arise from the same underlying radiographic state. A world-model formulation instead seeks to learn an internal representation of this state that can support both clinical readout and conditional simulation of radiographic observations. Here we introduce MedDream, a radiographic world model that learns a shared continuous latent state from paired chest radiograph-text observations for diagnostic reasoning and report-conditioned evidence generation. MedDream was pretrained on 2.65 million leakage-controlled chest radiograph-text pairs curated from 4.40 million candidates. Across eight clinical datasets and two independent reader cohorts, MedDream outperformed leading diagnostic and generative comparators. For diagnostic reasoning, MedDream showed strong generalization across disease recognition, label-scarce adaptation, severity assessment, and localization, while MedDream-supported review increased mean resident concordance with independent radiologist consensus from 56.3% to 63.0%. For evidence generation, MedDream produced radiographs that preserved clinically relevant pathology and improved downstream performance on held-out real data, with synthetic augmentation increasing external VinDr-CXR macro-AUROC from 76.4% to 81.4%. More importantly, conditioning generation on prespecified subgroup performance gaps enabled targeted evidence construction, increasing weighted F1 by 3.1 percentage points in Asian patients, whereas matched-volume unguided augmentation decreased it by 2.3 points. These findings establish radiographic world models as a path toward medical AI that learns clinically meaningful internal states for interpreting, simulating, and constructing evidence for clinical use.
[AI-110] An emancipatory vision for designing (generative) AI for learner flourishing
链接: https://arxiv.org/abs/2609.07715
作者: Luis P. Prieto,Yannis Dimitriadis
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: Paper presented at the International Workshop on Critical and More-than-human Perspectives on AI in Education (at EC-TEL 2026), September 15, 2026, Valencia, Spain
Abstract:The hype around generative AI seems to promise unprecedented productivity (and learning) gains. However, these technologies’ increasing agentic features seem to push learners towards individualism (or individual isolation), over-reliance, and dependence on them. Human-centered design approaches (e.g., value-sensitive design) assume that, by unearthing human needs, preferences, and values, technology researchers/designers may avoid such dangers, which are driven by wider systemic factors like economic incentives or inherent human limitations (e.g., our tendency to seek, in the moment, the easiest path of action). Yet, so far these efforts seem insufficient to guide our design of educational technology that avoids the aforementioned dependency and isolation dangers, while finding widespread adoption. This paper presents an alternative, more emancipatory vision for future educational AI technology, oriented towards learner flourishing while considering the wider complex systems they inhabit, including tentative design principles and an overall design methodology. Yet, many open questions remain before this vision can be realized.
[AI-111] APPSim-Bench: Bridging Real-world Apps and Reproducible Evaluation for Mobile GUI Agents
链接: https://arxiv.org/abs/2609.07712
作者: Jintian Feng,Long Chen,Xiao Yu,Jiayi Dai,Chenglong Liu,Haoru Wang,Zizhen Xue,Yuxuan Shi,Ziyang Wang,Yichen Gong
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Mobile GUI agents can execute tasks from natural-language instructions, but their evaluation remains difficult to make both realistic and reproducible. Existing benchmarks typically trade off these goals: simplified apps lack real-world mobile complexity, whereas live commercial apps introduce uncontrolled variation from recommendations, advertisements, accounts, and changing content. We propose AppSim-Bench, which addresses this trade-off through controllable simulated apps that preserve task-relevant interaction logic while supporting deterministic evaluation. Built through a coding-agent-assisted and human-verified workflow, it contains 557 tasks across 17 high-frequency Chinese and English apps. Its controllable backend data and outcome-based verification remove major sources of environmental stochasticity, enabling reproducible cross-model comparison. Evaluating 19 GUI agents, spanning general-purpose and GUI-specialized systems, we find that autonomous mobile execution remains far from solved. The best model completes only 50.27% of tasks, and 28.55% of tasks are not solved by any agent. Further analysis shows that failures concentrate in longer workflows, numerical reasoning tasks, and inefficient trajectories marked by high action overhead and budget exhaustion. Our project is available at this https URL.
[AI-112] Your Agent Says Yes: Interpreting Adversarial Market Behavior Beyond Individual Transactions
链接: https://arxiv.org/abs/2609.07675
作者: Zelin Li,Yiyun Su,Matt White,Zhipeng Wang,Xiao-Yang Liu,Tianyu Shi
类目: Computational Engineering, Finance, and Science (cs.CE); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:Transaction-local controls answer whether one financial request may proceed, but market behavior can be distributed across messages, agents, assets, and time. We study this interpretation gap in a virtual exchange populated by ten role-conditioned language-model agents. The agents communicate, trade reference assets and futures, launch tokens, and manage concentrated-liquidity pools under prescriptive adversarial roles. We analyze eight 72-cycle trajectories across two time-blinded hourly replay paths, with a runner-side wallet policy enabled or disabled. The retained artifacts connect generated outgoing messages, policy events, balances, positions, and cycle-end market state. A focal reconstruction shows a launch–promotion–exit scenario realized across private coordination, public claims, follower positioning, repeatedly withheld exits, and a later non-blocking request aligned with a token balance change. Across policy-enabled runs, the gate withholds direct requests selectively; most policy-categorized candidates are flagged rather than blocked, while the surrounding interaction can continue. Repeated runs also show that category-level and within-trajectory relations can recur even when normalized score-change rankings do not. These findings motivate agent-behavior evaluation that links communication, authorization, and evolving state instead of treating individual transaction verdicts as complete safety judgments.
[AI-113] Aegix Pulse: A Traceable Three-Stage Architecture for Personalized Content Generation and Context-Preserving Revision
链接: https://arxiv.org/abs/2609.07672
作者: Hongnan Zhao,Shiyu Chen,Zhihao Chen
类目: Artificial Intelligence (cs.AI)
备注: 20 pages, 5 figures, 3 tables
Abstract:Production content-generation systems must integrate a user’s immediate task, long-term brand identity, historical evidence, and revision feedback. We present Aegix Pulse, a production-oriented three-stage architecture that separates current-task clarification and Task Persona finalization, long-term Account Profile (Brand DNA) assembly, and controlled generation and revision while preserving provenance across content versions. We evaluate four preregistered claims using 96 synthetic social-media generation tasks. Four initial-generation conditions progressively introduced a Task Persona, Account Profile, and successful-history style evidence, while two revision conditions compared plain and context-preserving revision. The experiment produced 480 completed generation records and 1,440 blinded LLM-Judge evaluations, supplemented by human review. Adding the Account Profile increased mean brand-consistency scores by 0.1562 points on a five-point scale compared with Task Persona alone (Holm-adjusted p=.1224). Preserving task and brand context during revision increased mean task-preservation scores by 0.2917 points compared with plain revision (Holm-adjusted p=.2432). Neither improvement was statistically conclusive after multiple-comparison correction. Task Persona alone showed a small observed effect, while successful-history evidence provided no additional improvement in brand consistency under the current setting. Human validation did not consistently reproduce the LLM-Judge effect directions and showed low inter-reviewer agreement. These findings provide preliminary evidence for persistent brand context and context-preserving revision while identifying priorities for stronger evidence processing and evaluation. Comments: 20 pages, 5 figures, 3 tables Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2609.07672 [cs.AI] (or arXiv:2609.07672v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.07672 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-114] Accuracy is Not Enough: A Divergence-Based Approach to Evaluate Fidelity Loss in Quantized LLM s
链接: https://arxiv.org/abs/2609.07664
作者: Shahzeb Qamar,Lorenz Sparrenberg,Christian Bauckhage,Baha Rababah,Carson Leung,Murat Kantarcioglu,Cuneyt Gurcan Akcora,Rafet Sifa
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Deployment of Large Language Models (LLMs) on memory-constrained edge devices relies heavily on aggressive post-training quantization. However, evaluating these models is largely based on zero-shot task accuracy, which depends solely on argmax predictions and is insensitive to changes in the underlying predictive distribution. Consequently, accuracy can exhibit unstable, non-monotonic behavior under progressive quantization, masking substantial fidelity loss relative to the BFloat16 (BF16) uncompressed base model and providing misleading deployment signals. We introduce a distribution-sensitive evaluation framework quantifying information loss in quantized LLMs as the divergence between full-vocabulary predictive distributions at the token decision boundary. We compute statistical distances, including Jensen-Shannon Divergence and Total Variation Distance, between outputs of full-precision and quantized models, enabling a fine-grained analysis of distributional shift. Using this framework, we quantify probability mass displacement and distributional drift relative to the BF16 reference, capturing predictive distribution changes not reflected in top-1 accuracy. We conduct a 120-run experimental matrix across five foundation architectures and four reasoning benchmarks under progressive quantization regimes, from uncompressed BF16 to Q2_K, providing a systematic fidelity analysis. Our results show divergence metrics generally increase under stronger quantization, complementing task accuracy with a fidelity signal. Across tested this http URL schemes, mixed-precision Q4_K generally yields lower divergence than uniform Q4_0 at similar memory footprints. These findings motivate distribution-aware evaluation as a practical diagnostic complement to task accuracy; they do not directly establish correctness, calibration, safety, or user-perceived quality.
[AI-115] Online Surrogate Repair: Decoupling High-Fidelity Feedback from Search Length in Closed-Loop Discovery
链接: https://arxiv.org/abs/2609.07655
作者: Xiaotang Feng,Philip Torr,Bruno Andreis
类目: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI)
备注:
Abstract:Closed-loop AI scientists can generate candidate designs at low marginal computational cost, whereas reliable feedback may require wet-lab synthesis, characterization, or high-fidelity computation. Addressing this imbalance through custom laboratory automation remains infrastructure-intensive and costly, while replacing new experiments with a fixed surrogate leaves persistent model errors that can be amplified by optimization. We propose \emphonline surrogate repair (OSR), a closed-loop algorithm that uses sparse high-fidelity evaluations to update the surrogate throughout a longer agent search conducted primarily with inexpensive surrogate feedback. An acquisition rule selects which designs from the agent’s accumulated proposals receive high-fidelity evaluation, and the resulting labels update the surrogate used in subsequent episodes. Across controlled synthetic environments, we demonstrate that improving global surrogate fit does not necessarily reduce maximum regret, whereas Q90-UCB and expected improvement (EI) substantially reduce regret by directing evaluations toward regions that determine the optimizer’s decisions. On MADE, controls receiving high-fidelity feedback after every episode require 6.36 – 7.23\times more oracle queries to match Online EI under two LLM orchestrators and 10.27\times more under the non-LLM Chemeleon+MLIP workflow. Online surrogate repair introduces a novel third feedback regime between fixed-surrogate operation and high-fidelity feedback after every episode, separating the frequency of high-fidelity evaluation from the duration of the agent’s search.
[AI-116] Norms at a Price: Why RL-Based Alignment Can Promise Conditional Compliance at Best FAST NEURIPS2026
链接: https://arxiv.org/abs/2609.07627
作者: Kevin Baum,Rūta Binkytė,Felix Jahn
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注: 8 pages, currently under review at the NeurIPS 2026 workshop “Foundations of Agentic Systems Theory (FAST)”
Abstract:AI agents sometimes act aligned when they infer they are being tested, and differently when not. We argue this is not an anomaly but what current training regimes are structured to select for. Reinforcement-learning-based alignment folds norms and task pursuit into one policy: the system learns its norms from scored behavior, and scoring flattens them. Do not do X is learned as doing X costs something if noticed. On every datum training can produce, a policy that complies only when it might be observed is indistinguishable from one that complies always. The experiment that would tell them apart - scoring unobserved behavior - is a contradiction in terms. Conditional compliance is thus the most that behavioral training can be known to deliver. Agency sharpens the problem: agents operate mostly where no one is watching, and can act on whether they are watched. An iterated pipeline that trains against detected failures selects for passing detection, not for complying. This account unifies alignment faking, sandbagging, and evaluation-aware scheming. And it reorients the remedy: not deeper internalization but architecture, making violations unavailable rather than unchosen.
[AI-117] FinCUABuild: Can Agents Build Reliable Benchmarks for Dynamic Financial Computer Use?
链接: https://arxiv.org/abs/2609.07603
作者: Jingpu Yang,Fengxian Ji,Jinri Guo,Tianhao Li,Qian Jiang,Fan Zhang,Min Peng,Qianqian Xie,Preslav Nakov,Zhuohan Xie
类目: Artificial Intelligence (cs.AI)
备注: Jingpu Yang, Fengxian Ji: co-first author
Abstract:Financial scenarios are diverse and complex, spanning varying data conditions, tool configurations, and workflows. Yet existing CUA, Computer-Using Agent, evaluation tasks remain largely manually constructed, limiting scalable coverage of real-world financial scenarios. Then, can agents autonomously construct diverse CUA evaluation tasks for financial scenarios? Evaluating this capability poses three key challenges: scenario coverage of construction requests, fair comparison across construction methods, and reliable assessment of generated task quality. To solve these, we introduce FinCUABuildBench, a benchmark for evaluating financial CUA task construction, featuring: (i) 576 construction requests covering 24 financial workflows and three types of runtime variation; (ii) standardized input, budget, and output specifications; and (iii) a task qualification mechanism based on execution tests and quality checks. We further introduce FinCUABuildAgent, a multi-agent system for automatically constructing dynamic financial CUA evaluation tasks. It consists of three modules that jointly construct tasks, environments, and validators. On FinCUABuildBench, under the same model backbone, existing agent-based construction methods achieve strict qualification rates of only 1.3-8.3%, while FinCUABuildAgent reaches 31.3%. Downstream evaluations further show that the constructed tasks can effectively differentiate CUA task-execution capabilities. These results demonstrate that agents can autonomously construct financial CUA tasks with meaningful evaluation value, offering a practical path toward broader evaluation coverage in financial scenarios. Code: this https URL
[AI-118] Beyond the Matrix Sign: Quadratic Spectral Descent
链接: https://arxiv.org/abs/2609.07597
作者: Qiaozhe Zhang,Jun Sun,Yingzhuang Liu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Muon can be interpreted as optimizing a linear local objective over a spectral-norm ball. This gives a matrix-sign update that preserves the singular directions of the gradient and assigns the same magnitude to all active singular modes. We ask whether these two properties remain optimal when local curvature is taken into account. To answer this question, we keep Muon’s spectral-norm constraint unchanged and replace the linear local model with a quadratic one. We call the resulting method \emphQuadratic Spectral Descent (QSD). We show that curvature can change both the singular values and the singular directions of the optimal update. To make QSD practical, we approximate curvature with Kronecker-factored statistics and solve the constrained quadratic with a small number of Frank–Wolfe steps, each of which has a closed-form matrix-sign subproblem. We further provide an optimality certificate, a comparison with Muon under the same quadratic surrogate, and an O(1/K) convergence rate for the inner solver. Experiments on GPT pre-training show that QSD consistently improves validation loss over Muon and recent Muon variants, and reduces wall-clock training time by up to 8.49% at matched validation loss.
[AI-119] A Tool-Augmented GPT -4 Chatbot for Real-Time Repository Data Analysis
链接: https://arxiv.org/abs/2609.07586
作者: Muhammad Jawad Chowdhury,Md. Sakib Khan
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Software repositories contain vast amounts of data on code contributions, bug reports, and project activities, yet this information remains challenging for non-technical stakeholders and developers to access due to limited expertise in querying repositories. To address this, we introduce a novel chatbot architecture leveraging OpenAI’s GPT-4 model for automated extraction and analysis of repository data. In contrast, our architecture takes a structured path first by parsing the user’s query to extract relevant parameters, then selecting the correct tool to employ based on that analysis, and finally invoking the GPT-4 model to create a highly detailed response. In contrast to previous work based on multi-component systems with embedding models and document retrievers, our architecture inverts the process by relying on prompt engineering and tool selection to fit with the query intent. To validate our approach, we conducted experiments on various question types, including Issues, Pull Requests, Commits, Compound Questions, and General Repository Information, evaluating our target prompts’ ability to improve the accuracy of responses from the model. Beyond demonstrating the utility of this architecture to a diverse set of users, our findings suggest that this architecture can make repository data more accessible to technical and non-technical audiences through the production of actionable insights.
[AI-120] Efficient Exploration Is Enough
链接: https://arxiv.org/abs/2609.07575
作者: Mikel Malagón,Jon Vadillo,Josu Ceberio,Michael Bowling,Jose A. Lozano
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 29 pages, 10 figures
Abstract:This work introduces an alternative view of efficient exploration and studies its theoretical and empirical implications in the absence of extrinsic rewards. Specifically, we define efficient explorers as agents that prioritize generating generalizable experience, i.e., data that supports learning models capable of predicting and adapting across the environment. This allows us to analyze efficient exploration through the lens of prediction and generalization. Theoretically, we demonstrate that optimally efficient explorers naturally schedule their trajectories to visit the most informative and learnable regions first. Empirically, we show that optimizing for these agents gives rise to an automatic curriculum of progressively more complex behaviors, even in relatively simple environments. These results indicate that pursuing this purely intrinsic objective alone is enough to drive the emergence of highly sophisticated behaviors. We believe that this new framework provides a principled mechanism by which agent-environment systems may sustain an open-ended process of increasingly complex behavior without external rewards, tasks, or objectives.
[AI-121] From Simulated Citizens to Simulated Deliberation: Challenges in Representation and Interaction
链接: https://arxiv.org/abs/2609.07573
作者: Chaemin Jang,Junsik Min,Jaewoo Choi,Donggyu Lee,Haiin Lee,Junyoung Park,Namhee Kim,Hyunwoo Kim,Jungwon Kim,Juho Kim,Nuri Kim,Jihee Kim
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-agent LLM deliberation has been explored as a scalable way to simulate public deliberation. For such simulations to be informative, persona agents should reflect population opinion patterns and interaction should shape their conclusions. We evaluate whether LLM-based deliberation can meet these two conditions using census-grounded Korean personas debating real policy questions benchmarked against national surveys. Persona agents do not reliably reproduce population opinion patterns: responses are often far more concentrated and frequently reverse demographic differences in the human data. Deliberations nonetheless produce reasoned, reciprocal, and varied arguments alongside substantial stance movement. Yet much of this movement does not require peer exchange: sealed-monologue agents change position at similar rates and reach nearly the same final balance as full debates, while groups initialized with very different positions often converge to similar endpoints. Anchoring population-informed starting positions, meanwhile, sharply suppresses updating. Thus, population representation, argument generation, and interaction-driven opinion change do not necessarily go together. The simulations readily surface arguments on both sides, though whether they capture the diversity of human perspectives remains untested, leaving open a promising role for argument surfacing even as population simulation requires further validation.
[AI-122] Scoring Without the Engine: Validating a Deterministic Manipulation-Resistant Content Score for Generative Engines End to End
链接: https://arxiv.org/abs/2609.07559
作者: Elisha Bajemon,Andre-Louis Rochet
类目: Artificial Intelligence (cs.AI)
备注: 42 pages, 4 figures. Code and data: this https URL More informations on this http URL
Abstract:How do you validate a cheap, deterministic proxy for an oracle that is expensive, rate-limited, and non-stationary? We present a protocol built on adversarial falsification gates (negative control, dose response, bounded amplification, duplication penalty, length neutrality) that define and select the proxy, fitted on a training split and confirmed held-out; around them it bounds what the proxy can never resolve, and re-measures external causal evidence on the current oracle rather than assuming it. We demonstrate it end to end on Generative Engine Optimization, where the proxy is a deterministic content score, and one step fails on that domain exactly as the protocol is built to detect: re-measuring the only published causal anchors (2023 effect sizes) on ten modern engine families shows their levers move citation on none, so the anchors are an expired external check; recalibrating to the near-zero modern vector strips the score of its lever-responsive components. What survives is the gate-enforced response surface. The gates buy a measured property: on a 500-source benchmark of adversarial edits, amplifying the score’s calibrated levers gains an attacker at most 6 points, and decreases with dose; single-lever amplification is provably bounded, while the cap and cross-lever sub-additivity are empirical findings consistent with it. On detection, web-spam baselines dominate and out-of-distribution attacks evade the score, so the deployable filter layers it over them. A query-conditioned skyline bounds the score’s citation signal (within-query Spearman 0.11), repositioning query-agnostic scores as quality filters rather than citation predictors. A query-leakage bug in our first ranking evaluation and a failed confidence flag are disclosed and corrected; every number reproduces offline from released artifacts at zero marginal API cost.
[AI-123] Zero-Shot Sim-to-Real Contact-Rich Assembly via Proprioception-Anchored Cross-Modal Pretraining
链接: https://arxiv.org/abs/2609.07534
作者: Yuhan Wang,Yurou Chen,Hongye Jiang,Wenzhao Lian
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Contact-rich assembly remains challenging because it requires submillimeter spatial accuracy and reliable interpretation of forces during sustained contact. Although simulation-based reinforcement learning offers a scalable training paradigm, discrepancies in visual observations, contact dynamics, and force/torque (F/T) measurements often limit policy transfer. We observe that proprioception is comparatively consistent across domains because calibrated joint positions and consistently computed joint velocities align closely between simulation and hardware. Based on this observation, we present PACE (Proprioception-Anchored Cross-Modal Encoder), which supervises temporal visual and F/T representations by predicting proprioceptive state transitions. Static domain-specific factors, including lighting, texture, and sensor bias, contain little information about joint motion; the proposed objective therefore encourages the encoder to suppress these factors while retaining task-relevant motion cues. Policies trained on frozen PACE features are deployed on hardware without real-world fine-tuning or object-pose tracking. Across four contact-rich assembly tasks, PACE attains an average real-world success rate of 93.3% and only a 2.7-percentage-point sim-to-real drop, meanwhile remaining robust to perturbations that substantially degrade pose-based and learned-fusion baselines.
[AI-124] Quantile-Led Feature Extraction for Multi-Horizon Predictive Maintenance in Industrial Manufacturing Systems
链接: https://arxiv.org/abs/2609.07533
作者: David J Poland,Daniele Ravi,Na Helian
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:In data-driven predictive maintenance (PdM), feature extraction is usually treated as fixed preprocessing: a descriptor set is chosen once and reused while the downstream model or forecasting horizon changes. This paper isolates the representation-learning stage and presents a quantile-led feature-extraction framework based on a dual-stage MLP-QRNN hierarchy. QRNN1 learns a broad ten-quantile conditional distribution for each sensor channel, while skip-connected QRNN2 refines a retained mid-tail quantile set into compact, channel-resolved, distribution-aware features. A fixed thirteen-pipeline ablation spans 1-hour, 70-hour, and 30-day regimes across 72 machines in 9 industrial facilities, with the downstream temporal classifier held fixed within each regime. Increasing the retained mid-tail set from two to four quantiles improves 30- and 60-minute F1-score, reaching 75.92% and 72.44% with attention enabled. The results also show that representations do not transfer reliably beyond their design horizon unless feature capacity, temporal embedding, activation strategy, and sensor breadth are scaled with the forecasting task. The unmodified short-horizon extractor falls to 42.90% F1 at 70 hours, whereas horizon-conditioned extractors reach 60.38% at 70 hours and 79.97% at 30 days. The framework therefore supports treating PdM feature extraction as a horizon-dependent representational stage rather than fixed preprocessing.
[AI-125] Generation of Vectorized Maps Beyond Vehicle View
链接: https://arxiv.org/abs/2609.07511
作者: Clara Gomez,Alberto Jaenal,Antonio Artuñedo,Jorge Godoy,Jorge Villagra
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Autonomous driving relies on High Definition (HD) maps for safe navigation. Traditional HD maps construction is costly in hardware, data and human resources, which together with its update limitations hinders scalability. Recent works have proposed online alternatives for HD vectorized mapping from onboard sensors. However, sensor field of view is limited, and the range of the reconstructed maps ahead of the vehicle is insufficient for safe planning. This paper aims to address this limitation by proposing the novel beyond-view vectorized map generation problem: given vectorized maps of the area sensed by the vehicle (in-view), to generate plausible map continuations. To experimentally assess its feasibility, we propose BeyondFormer, which, to the best of out knowledge, is the first work designed towards beyond-view map generation. Given the novelty of the problem, we generate the first dataset specifically designed for it and evaluate the proposed approach. The results demonstrate consistent performance across diverse scenarios, establishing learning-based methods as a promising direction for map forecasting in autonomous driving. Beyond demonstrating the feasibility of the task, we provide an extensive discussion of the method’s limitations and identify key future research directions for scaling it to more complex driving conditions. Code is available at this https URL.
[AI-126] Modus Tollens and Counterfactuals and Counterfactual Reasoning Based on Three Types of Negation
链接: https://arxiv.org/abs/2609.07483
作者: Zhenghua Pan
类目: Artificial Intelligence (cs.AI)
备注: 19 papers,6 figures
Abstract:Modus Tollens (MT) is a classical logical inference rule, while counterfactuals are hypothetical statements that are contrary to facts, and counterfactual reasoning is a process of reasoning based on counterfactuals. Negation is an indispensable core concept in them. In this paper, based on the logical systems LCOIPLCOI with contradictory negation, opposite negation and intermediary negation, we propose three variants of Modus Tollens corresponding to distinct negation types, namely MTC: Modus Tollens based on contradictory negation, MTO: Modus Tollens based on opposite negation, and MTI: Modus Tollens based on intermediary negation. We define the implications within MTC, MTO and MTI, provide the truth value algorithms of MTC, MTO and MTI, and discuss the reducibility of these algorithms. To incorporate these three types of negation into counterfactuals and counterfactual reasoning, we differentiate counterfactuals into two types based on whether they possess logical negation, thereby proposing three counterfactuals and counterfactuals reasoning based on different logical negations. In this paper, we further argue that the three counterfactuals reasoning based on different logical negations have the same inference form as MTC, MTO and MTI, respectively. In other words, they share the same inference structure. As a result, the truth value algorithms for MTC, MTO and MTI can be as the truth value algorithms for the three counterfactuals reasoning based on different logical negations. The algorithms indicates that if the first premise of the reasoning is true, the truth values of the reasoning conclusions are identical to the truth values of the three negative premises in the reasoning premises, respectively. This reflects the consistency and accuracy of the truth value algorithms.
[AI-127] he Internal Anatomy of Strategic Choice in Large Language Models
链接: https://arxiv.org/abs/2609.07478
作者: Vinícius Ferraz,Leon Houf,Enrico Ferrea
类目: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)
备注:
Abstract:Large language models act as strategic agents and models of human choice, yet choosing like a strategic agent does not mean computing like one. We recorded activations from four open-weight models — dense and mixture-of-experts, including a matched base–instruct pair — in one-shot play of 144 strict ordinal 2\times2 games. We followed a prespecified incentive from prompt, through activations, to choice. Dense models mirrored the unadjusted human decline with game complexity. Incentive and choice were detectable in every model, but models differed in whether incentive reached the choice, aligned with it and, where tested, whether strengthening it shifted preference. The base and instruction-tuned Qwen2.5 models chose almost identically at baseline yet differed in whether incentive reached choice. Fixed decision cues were distinguishable internally but changed choices selectively. Similar behaviour can rest on different computation; post-training can reshape the path from represented incentive to decision while leaving behaviour and decodable information largely intact.
[AI-128] Measuring Language Transfer in Robot Policies: Adding Greek to a Cosmos3 Vision-Language-Action Policy
链接: https://arxiv.org/abs/2609.07470
作者: Ayoub Kirouane,Georgios Giaples,Christos Petrocheilos
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 22 pages, 16 figures, 6 tables. Model and reproduction information: this https URL
Abstract:Robot foundation models are trained and evaluated predominantly in English, and robot demonstration corpora do not exist for most languages. We study the addition of Greek to an open vision-language-action stack using only machine-rephrased instructions and no architecture changes. The main challenge is measurement rather than translation. Several plausible instruments produce false conclusions: a color-histogram metric rewards noise, a single-goal benchmark scores 84.6% under correct Greek and 82.6% under deliberately wrong instructions, training loss fails to predict Greek success, and single-run comparisons are dominated by seed variation. On a discriminative ninety-task suite with three seeds per arm, a multilingual text tower without Greek demonstrations remains at its wrong-instruction floor, while Greek-only training exceeds its control by at most 2.7 points. Bilingual training yields a consistent 6.7-7.1 point margin over its control and reaches about two fifths of English performance. The policy also overfits the translator’s phrasing; training on seven phrasings per task approximately halves this penalty. Warm-starting from a language-adapted world model and unfreezing the text tower both degrade performance. The results support two practical requirements for low-resource robot-policy localization: build a guaranteed null before trusting a metric, and replicate low-resource-language results across seeds.
[AI-129] ASTE: Throughput-Aware Batch Size Tuning for On-Device Edge Learning MICRO
链接: https://arxiv.org/abs/2609.07444
作者: Avik Bhatnagar,Federico Nicolas Peccia,Oliver Bringmann
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Performance (cs.PF)
备注: 8 pages, 1 tables, 6 figures; Accepted to 29th Euromicro DSD 2026
Abstract:The rise of privacy-preserving artificial intelligence (AI) has shifted the focus of model adaptation and personalization towards on-device learning, where deep learning models are finetuned directly on edge hardware using local user data. However, this shift requires optimization of deep learning training on resource-constrained hardware to maximize throughput while maintaining predictive accuracy. This paper introduces a novel technique for on-device model training that incorporates an efficient Bayesian optimization-based batch size tuning approach to maximize hardware throughput. To evaluate the impact of this hyperparameter on the learning dynamics, we investigated two distinct paradigms: standard supervised learning (SL) and online continual learning (CL). Experimental results across various edge devices demonstrate a throughput ceiling, beyond which increasing the batch size yields no additional throughput gains. The proposed tuning approach identifies the optimal batch size, which, when combined with gradient accumulation and linear learning rate scaling, achieves up to a 2X increase in training throughput on platforms such as Raspberry Pi 4 compared to maximum batch sizes, without compromising model accuracy. Furthermore, in the CL paradigm, we demonstrate that optimal batch sizes maintain the stability-plasticity balance required for incremental learning, effectively mitigating catastrophic forgetting while maximizing computational efficiency on edge-hardware.
[AI-130] abBench-Bio: A Living Benchmark for Machine Learning on High-Dimensional Biomedical Tables
链接: https://arxiv.org/abs/2609.07441
作者: Jules Kreuer,Sofiane Ouaari,Julia Hellmig,Julius Braitinger,Nico Pfeifer
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Biomedical tables often combine thousands of measured variables with only tens or hundreds of labelled samples, a regime that is poorly represented in general-purpose tabular benchmarks. We introduce TabBench-Bio, a living and interactive benchmark of 43 biomedical datasets spanning multiple domains. Under a shared cross-validation protocol, we compare classical estimators, neural networks, and tabular foundation models across 28 feature-by-sample operating points. At the reference cell of 10,000 features and 100 training samples, RealTabPFN v2.5 has the highest point estimate, followed by Logistic Regression and TabDPT, whose point estimates are nearly identical. A paired bootstrap over the target pool separates RealTabPFN v2.5 from Logistic Regression by 145 Elo (95% interval [59, 232]). Tabular foundation models generally occupy the leading ranks, while the strongest configuration depends on the operating point and biomedical modality. The AutoML framework AutoGluon, using its one-hour “extreme” preset, is configured as a separate resource-intensive reference and is reported here at the reference cell. Fold-level predictions, run status, and deterministic aggregations make every reported result reproducible and reusable. We invite the community to contribute: TabBench-Bio is designed to grow, and we welcome submissions of new biomedical tabular datasets, particularly from underrepresented assays and clinical endpoints, for inclusion in future releases. The interactive leaderboard is available at: this https URL Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.07441 [cs.LG] (or arXiv:2609.07441v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.07441 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-131] CIT-CAD: Constraint Intent Tree-based CAD Code Generation and Verification
链接: https://arxiv.org/abs/2609.07434
作者: Yali Du,Hui Sun,San-Zhuo Xi,Ming Li
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Natural-language Computer-Aided Design (CAD) code generation aims to turn design intent into executable and editable parametric programs. Large language models (LLMs) make this goal increasingly practical, but useful systems must preserve the construction process behind the rendered geometry. Existing benchmarks and methods mostly focus on how closely the generated CAD model matches the reference geometry, often using metrics such as Intersection over Union (IoU). Such metrics can miss errors in part decomposition, construction hierarchy, Boolean operations, sketch structure, and geometric relations. This gap calls for a representation that makes design intent explicit and lets a system check generated code against that intent. We propose CIT-CAD, a framework that infers a Constraint Intent Tree (CIT) from the input description to represent the intended entities, hierarchy, operations, and relations. The tree has two roles: it guides CAD code generation and defines expected constraints for verification. The framework extracts actual constraints from the generated program, compares them with the expected constraints, and uses mismatches to localize and repair design violations. Experiments show that the framework improves CAD generation performance, with larger gains on more complex multi-entity designs. By turning design intent into an explicit and checkable object, this work is the first attempt to move text-to-CAD generation beyond rendered-geometry matching toward construction-aware synthesis, verification, and repair.
[AI-132] Revisiting Thinning Methods for Kernel Learning Problems
链接: https://arxiv.org/abs/2609.07432
作者: Blanca Cano-Camarero,Yago R. Aguado-Carrillo-de-Albornoz,Ángela Fernández-Pascual,José R. Dorronsoro
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Kernel methods are widely used because of their strong theoretical guarantees and empirical performance. However, their high computational cost limits their applicability to large-scale datasets. To address this shortcoming, several approaches use Maximum Mean Discrepancy to construct representative subsets that preserve the properties of the full dataset in a Reproducing Kernel Hilbert Space. We introduce Backward Kernel Herding, an algorithm that addresses this problem by iteratively removing points from the dataset, achieving results comparable to current state-of-the-art approaches while accelerating the subsampling process in realistic scenarios where the reduced size is less than half of the dataset. Moreover, we overcome a limitation of Kernel Thinning by proposing an extension that enables the construction of subsets of arbitrary size rather that restricting to successive halvings. Finally, we conduct an extensive experimental comparison focusing on the most relevant kernel learning procedures: Gaussian Processes and Kernel Support Vector Machines. The results show that Backward Kernel Herding consistently achieves competitive performance with the most favorable training-time efficiency, while the proposed Flexible Kernel Thinning frequently achieves the best predictive performance. These gains become especially pronounced for moderate compression ratios, highlighting the benefits of incorporating supervised information into the thinning process. In terms of memory consumption, Flexible Kernel Thinning is also competitive, whereas Backward Kernel Herding remains an alternative when computational efficiency is the primary objective. Overall, no single method dominates across all scenarios, underscoring the importance of selecting the reduction strategy according to the desired trade-off between predictive performance, training cost, and memory requirements.
[AI-133] RAFM-SER: A Lightweight Multimodal Emotion Recognition Framework for Real-Time Behavioral Monitoring in Surveillance Systems
链接: https://arxiv.org/abs/2609.07409
作者: Ngo Truong Dinh,Tung-Lam Bui,Chi-Trung Duong,Vien Nguyen Thi,Viet-Anh Nguyen,Phuc-Lu Le
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM); Sound (cs.SD)
备注: 6 pages, 4 figures, 3 tables. Accepted at the 2026 IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS 2026). © 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses
Abstract:Recent multimodal Speech Emotion Recognition (SER) systems achieve high accuracy through interaction-heavy cross-modal transformers, but their computational cost limits deployment in latency-sensitive and resource-constrained surveillance systems. To address this challenge, we propose RAFM_SER++, a lightweight multimodal SER framework featuring an asymmetric Residual Attention Fusion Mechanism (RAFM). Rather than relying on computationally expensive bidirectional interactions, RAFM injects affective speech cues into semantic text representations through a one-directional residual attention pathway. Combined with a BYOL-inspired cross-modal alignment objective and attention-guided pooling, the proposed framework improves multimodal representation learning while maintaining low computational overhead. Experiments on the IEMOCAP and ESD benchmarks demonstrate that RAFM_SER++ consistently outperforms the HuBERT-Base baseline and achieves a superior accuracy-efficiency trade-off compared with the state-of-the-art MemoCMT. Specifically, RAFM_SER++ reduces trainable parameters by more than 60%, achieves faster inference (79.60 it/s), and attains BACC scores of 81.10% on IEMOCAP and 95.39% on ESD. These results indicate that lightweight asymmetric multimodal fusion is an effective alternative to interaction-heavy cross-modal transformers for real-time surveillance applications. Comments: 6 pages, 4 figures, 3 tables. Accepted at the 2026 IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS 2026). © 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM); Sound (cs.SD) ACMclasses: I.2.7; I.5.4; I.2.6 Cite as: arXiv:2609.07409 [cs.AI] (or arXiv:2609.07409v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.07409 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-134] Human-like moral judgments conceal divergent motive attributions in large language models
链接: https://arxiv.org/abs/2609.07353
作者: Xiaoyan Wu,Jean-Claude Dreher
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 15 pages, 4 figures, 1 table; Supplementary Information (10 pages) appended
Abstract:Large language models (LLMs) are used to simulate human participants in psychological research. We asked whether LLMs that reproduce human evaluations of a whistleblower’s moral character also reproduce the motive attributions that accompany them. Five LLMs and two human samples (N = 125 and N = 742) evaluated a physician who either remained silent about fraudulent billing or reported it to a hospital, regulator, or newspaper. Models reproduced the human ranking of the physician’s moral character but portrayed whistleblowers as more helpful, less self-interested, and less hostile. In four of five models, competitive motives were less strongly associated with moral-character judgments. Model ratings changed little when prompts reproduced the narratives and demographic profiles of both human samples, although this comparison cannot isolate a perspective effect. Thus, agreement in average ratings can conceal differences in attributed motives, relationships among judgments, and sensitivity to context. Validating LLMs as simulated participants therefore requires testing psychologically informative response patterns, not average agreement alone.
[AI-135] Monadic Second-Order Logic in HOL: Deep and Shallow with Automated Faithfulness (Extended Preprint)
链接: https://arxiv.org/abs/2609.07345
作者: Christoph Benzmueller,Daniel Kirchner
类目: Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI); Logic (math.LO)
备注: 39 pages. Extended version – with the full two-sorted Loewenheim-Skolem proof (Appendix A) and the rendered Isabelle/HOL theory sources (Appendix B) – of a contribution accepted to a Festschrift for David Basin (Springer LNCS); the proceedings version is 19 pages. The Isabelle/HOL formalisation is published in the Archive of Formal Proofs: this https URL
Abstract:In Isabelle/HOL, we apply the deep-and-shallow embedding methodology of our prior work to monadic second-order logic (MSO). Three embeddings are developed side by side: a deep embedding (an inductive datatype with an explicit satisfaction relation); a maximal-shallow embedding that translates the connectives and quantifiers directly into HOL, carrying the interpretation and both assignments as explicit arguments; and a minimal-shallow embedding – a locale that fixes those parameters, collapsing the formula type to bool. The enabling new ingredient is a two-sorted substitution apparatus – capture-avoiding substitution, renaming, and a substitution lemma per namespace – in which each binder is transparent for the other; faithfulness of all three embeddings is mechanised and automated. Our central contribution is a fully mechanised two-sorted downward Loewenheim-Skolem theorem: the minimal embedding recovers deep validity relative to the (countable) assignment ranges, and this range-relative reading is shown to coincide with the general (Henkin-style) reading of MSO, whereas the standard reading is provably stronger, witnessed by comprehension. Both readings are nonetheless recovered from the minimal embedding, differing only in the admitted interpretations: all of them for the general reading, only the elementary substructures of the full model for the standard. We further exercise the embeddings on classical MSO landmarks: the Boolean-closure and graph schemata hold under the full second-order domain yet fail in the minimal embedding, making the dichotomy concrete, while reachability and 2-colorability are refuted throughout.
[AI-136] Staying on the Attack Path: Structured State for Long-Horizon Automated Penetration Testing
链接: https://arxiv.org/abs/2609.07344
作者: Weizhe Wang,Yitong Zhang,Yao Zhang,Xiaoqiang Di,Zhigang Li,Bin Wu,Guangquan Xu
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM) based agents are increasingly applied to cybersecurity tasks such as vulnerability discovery and automated penetration testing. On long-horizon security tasks, however, such agents remain limited by context forgetting and intent drift: early critical facts and causal reasoning chains are lost over extended interactions, and the agent falls into aimless, repetitive exploration. This paper proposes Intentest, an intent-graph-guided automated penetration testing agent that externalizes long-horizon state from the LLM’s context window onto a persistent fact-intent directed acyclic graph (DAG), thereby substantially reducing invalid transitions. We evaluate Intentest on automated penetration testing of web applications, a representative long-tail task in cybersecurity. In the DAG, verified network states are stored as immutable fact nodes, and exploration directions are constrained as intent edges bounded by predecessor facts. The system adopts a three-layer architecture, in which the fact-intent mapping layer maintains the global state, the task scheduling and allocation layer ensures execution stability through two-phase degradation recovery and multi-dimensional adaptive load balancing, and the intent retrieval and prediction layer provides tactical priors through a top-down five-stage filtering algorithm. On a benchmark of real CTF challenges covering more than ten vulnerability types across three difficulty levels, Intentest achieves an overall success rate of 88.2% and a success rate of 75.0% on hard tasks, improving over the baseline by approximately 44 and 50 percentage points. Ablation experiments further show that the intent retrieval and prediction reduce the average number of rounds on successful medium and hard tasks by about 33% and 48%, respectively, without changing the set of solvable tasks.
[AI-137] AAS-RAIL: Improving Information Extraction for Asset Administration Shells through Retrieval-Augmented In-Context Learning
链接: https://arxiv.org/abs/2609.07334
作者: Janek Groß,Jens Heidrich
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The Asset Administration Shell (AAS) is a cornerstone of Industry 4.0 and the Digital Product Passport, providing standardized digital representations of industrial assets. While manufacturers already maintain extensive technical product documentation, generating AAS instances from existing product datasheets remains a labor-intensive task because technical information is extracted from heterogeneous document structures and often involves company-specific terminology and conventions. In this work, we present AAS-RAIL, a retrieval-augmented information extraction (IE) approach that automatically generates Asset Administration Shells from PDF product datasheets using large language models (LLMs). Instead of relying on a fixed set of few-shot examples, the proposed retrieval-augmented in-context learning (RAIL) approach retrieves LLM-generated extraction helpers from similar Asset Administration Shells to provide instance-specific in-context learning (ICL). This enables the model to adapt its extraction behavior to company-specific naming conventions and formatting styles without fine-tuning. Our core contribution is the dynamic selection of company-specific AAS examples for each datasheet, replacing static prompting with an extraction pipeline that adapts to instances and combines semantic retrieval and structured information extraction. The proposed approach is evaluated on a collection of industrial product datasheets using a selection of open- and closed-weight LLMs. Experimental results show that RAIL consistently improves extraction quality over conventional few-shot prompting, yielding relative improvements of 30.4-52.4%. These results demonstrate that our approach provides an effective improvement for company-specific AAS generation.
[AI-138] PLATOS: A Power and Latency-Aware Task-Oriented Scheduling Strategy for Healthcare IoT in Fog Computing
链接: https://arxiv.org/abs/2609.07318
作者: Mohammed Alaa Ala’anzy,Zulfiqar Ahmad,Zhanar Mukash
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注: Published in Eurasian Journal of Mathematical and Computer Applications, Vol. 13, No. 3, pp. 4-21 (2025). 18 pages, 6 figures
Abstract:Healthcare Internet of Things (HIoT) technology is revolutionising the healthcare industry by enabling real-time data collection and analysis for personalised patient care. However, the rapid expansion of HIoT technology introduces challenges such as increased latency and higher energy consumption in fog computing environments, particularly when managing battery-operated devices. To address these issues, this work proposes a novel scheduling strategy that optimises both power consumption and latency through task-oriented scheduling for HIoT tasks. The proposed strategy, named PLATOS (Power and Latency Aware Task Oriented Scheduling), is implemented in four sequential phases. In the first phase, HIoT tasks are categorised into three groups: priority-oriented, storage-oriented, and computational-oriented. The second phase focuses on latency optimisation by identifying the fog computing resources that yield the lowest execution delay for each task category. In the third phase, power optimisation is achieved by selecting the resources that minimise energy consumption. Finally, in the decision-making phase, high-performance fog resources are allocated to high-priority tasks while the remaining tasks are scheduled based on a mapped list derived from the latency and power optimisation phases. Simulation experiments conducted in iFogSim2 demonstrate that PLATOS reduces energy consumption by 18.72% and latency by 8.65% when compared to the state-of-the-art. These improvements enhance the efficiency and responsiveness of HIoT systems and contribute to more effective patient care and proactive healthcare service delivery.
[AI-139] DGCPath: Distribution-Aware Generative Contrastive Framework for Self-supervised Path Representation Learning – Extended Version IJCAI2026
链接: https://arxiv.org/abs/2609.07316
作者: Sean Bin Yang,Hao Miao,Zongyi Xu,Jilin Hu,Xiangmeng Wang,Hua Lu,Bin Yang,Christian S. Jensen
类目: Artificial Intelligence (cs.AI)
备注: This paper is an extended version of DGCPath, which was published at IJCAI 2026
Abstract:Due to the proliferation of vehicle trajectory data enabled by advanced sensing technologies, path representation learning has become a pivotal task in intelligent transportation systems. Although existing self-supervised approaches have achieved promising performance, their dependence on deterministic contrastive learning paradigms and handcrafted view augmentation strategies inherently restricts their cross-scenario generalization capabilities. To address these limitations, we present DGCPath, an innovative Distribution-aware Generative Contrastive learning framework for Path representation. This framework establishes a synergistic connection between generative modeling and distributional contrastive learning, enabling the acquisition of robust and transferable feature embeddings. Specifically, our framework incorporates: (1) a diffusion-based view generator that autonomously produces semantically coherent yet diverse trajectory views from Gaussian noise; (2) a variational contrastive mechanism that enforces latent feature alignment at the distribution level, transcending conventional instance-wise consistency; and (3) a novel generative cross-supervision module that reinforces view-level consistency through cross-view reconstruction learning. Comprehensive evaluations on three real-world trajectory datasets demonstrate that DGCPath outperforms state-of-the-art baselines on two distinct downstream tasks, validating its enhanced generalization capability and representation effectiveness.
[AI-140] World Models Under Asynchronous Sensor Observations
链接: https://arxiv.org/abs/2609.07299
作者: Akash Anand,Abhay Anand,Yash Vishe
类目: Artificial Intelligence (cs.AI)
备注: 13 pages, 2 figures. All authors contributed equally
Abstract:Learned world models typically assume that observations arrive synchronously, an abstraction inherited from simulators that return a complete state vector at each environment step. Physical sensing instead operates at heterogeneous rates, leaving most observation channels stale at any given instant. Interpolating stale channels introduces measurements that were never observed, while downsampling to the slowest sensor discards valid measurements. A natural alternative is to zero-order-hold the most recent reading and provide the known sampling schedule to the model through two features, staleness and time-to-refresh. We test this prediction using transformer world models across three regimes of increasing causal coupling: open-loop rollouts in continuous-control locomotion, closed-loop model-predictive planning in which each learned model serves as the planner dynamics, and a linear latched-actuator system in which refresh events apply a zero-order-held command to the plant. Our findings show that the effectiveness of time-to-refresh depends on the causal role of the sampling schedule, specifically when refresh events affect the system rather than merely report its state. These results establish when sampling schedules provide useful information for predictive world models operating under asynchronous physical observations.
[AI-141] Quality Metrics for LLM -Generated Asset Administration Shells: A Perturbation-Based Evaluation Approach
链接: https://arxiv.org/abs/2609.07290
作者: Janek Groß,Elena Zentgraf,Jens Heidrich
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:The rapid digital transformation of manufacturing, often referred to as Industry 4.0, relies on seamless interoperability between physical and software assets. A central enabler is the Asset Administration Shell (AAS), a standardized digital representation of such assets. Recent advances in large language models (LLMs) enable the generation of AAS submodels from unstructured sources such as product datasheets but raise challenges for quality assurance. In particular, unexpected errors, the lack of ground truth references, and the absence of standardized quality metrics hinder reliable adoption. In this work, we evaluate quality metrics for AI-generated AAS using a perturbation-based evaluation framework. By systematically degrading AAS generation along multiple dimensions, we assess how well different metrics reflect quality changes. Based on a dataset of 200 products from multiple manufacturers, we generate 6,400 AAS instances using GPT-4o-mini, Qwen3, and DeepSeek-R1. Our results show that metrics based on exact matching of property names and similarity-based soft matching of property values, in particular value-based recall and name-based F1 score, provide the most reliable indicators of quality degradation. Furthermore, we quantify the impact of different perturbation types and analyze differences across model families and product segments. These findings support the selection of suitable metrics, the tuning of LLM-based pipelines, and the integration of AI-generated AAS into industrial applications.
[AI-142] SkillAlign: Aligning Skill Interfaces for LLM -based Agents EMNLP2026
链接: https://arxiv.org/abs/2609.07255
作者: Shuo Ren,Xiaomian Kang,Jiajun Zhang
类目: Artificial Intelligence (cs.AI)
备注: EMNLP2026 main conference
Abstract:Language-model agents increasingly rely on skills: reusable procedural knowledge for reasoning, tool use, and interaction. Existing work studies how skills are acquired, retrieved, compressed, or composed, but often assumes that once a skill is selected, its interface to the agent is fixed. We argue that this overlooks a key source of skill utility: the same skill can help, distract, or mislead depending on how it is exposed. We propose SkillAlign, a provider-agnostic framework that represents candidate skills as multi-view procedural cards and renders them through alternative exposure interfaces, including full instructions, hints, compressed summaries, workflows, or no exposure. This enables counterfactual evaluation where the task, agent, and candidate skills are fixed while only the exposure interface varies. Across ALFWorld and SkillsBench, we show that exposure form substantially affects task success and rendered context cost, and that compact top-k exposure can outperform full-library injection. We further conduct a replay-based policy-learning analysis on ALFWorld, showing that adaptive exposure contains learnable signal but remains far from oracle selection. Our results suggest that skill-augmented agents should optimize not only which skills to use, but also how those skills are presented.
[AI-143] Elastic Horizon: Discovering the Effective Interaction Frontier in Agent ic Reinforcement Learning EMNLP2026
链接: https://arxiv.org/abs/2609.07247
作者: Gangyi Zhang,Junjie Meng,Letian Zhang,Wei Wu,Yang Zheng,Dong Wang,Yang Liu,Guanjun Jiang,Chongming Gao
类目: Artificial Intelligence (cs.AI)
备注: 17 pages, 6 figures, 12 tables. Accepted to EMNLP 2026
Abstract:Scaling the interaction horizon-the maximum number of environment interactions per episode-improves LLM agents on long-horizon tasks, and curriculum-based methods that progressively expand the horizon outperform fixed-horizon alternatives. However, existing schedules are open-loop: they monotonically increase the horizon until a manually specified maximum, with no mechanism to detect when further expansion stops helping. We propose the effective interaction frontier hypothesis: a dynamic boundary beyond which additional interactions yield diminishing returns while cost grows linearly. We then introduce Elastic Horizon, a closed-loop controller that tracks this boundary via the 90th percentile of successful trajectory lengths. On AppWorld and BFCL, fixed-horizon sweeps reveal clear saturation plateaus; Elastic Horizon stabilizes the horizon inside the saturation band from both under- and over-capacity initializations, attains the best success rates across 7B and 14B backbones, and saves up to 25% of per-step trajectory tokens. Our work shifts the paradigm from how to scale interaction horizons to when to stop scaling.
[AI-144] owards a Resilience-Theoretic Foundation for Adversarial Robustness in Industrial Control System Anomaly Detection
链接: https://arxiv.org/abs/2609.07244
作者: Branka Stojanović,Andreas Flatscher,Michael Somma
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Anomaly-based intrusion detection systems in industrial control systems (ICS) and operational technology (OT) environments are increasingly required to meet formal resilience criteria: absorbed adversarial disturbances, graceful degradation under sustained attack, and certified system-level guarantees. Existing resilience frameworks for cyber-physical systems define absorb-recover-adapt trajectories at the architectural level but do not treat machine learning anomaly detectors as first-class components, leaving a gap between component-level robustness evaluation and system-level resilience certification. In this paper, we establish that adversarial robustness in ICS anomaly detection is a specific instantiation of system resilience, and formalise this connection by mapping four resilience constructs, i.e. disturbance class, absorption capacity, recovery trajectory, and degradation function, onto the adversarial machine learning setting. We derive a compositional resilience bound for heterogeneous ICS detection networks, showing that the binding constraint on system-level resilience is the coupling-adjusted absorption capacity of each node along the attack path, not the per-node capacity – so the binding node need not be the weakest one. Empirical validation on the BATADAL water distribution system benchmark demonstrates that the resulting metrics surface operationally significant phenomena invisible to standard benchmarks: the absorption-degradation divergence under adversarial training, and the paradox that hardening the binding node in isolation reduces system-level resilience. Implications for ICS architecture design and certification standards are discussed. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.07244 [cs.CR] (or arXiv:2609.07244v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2609.07244 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-145] Parallelism Strategy Chaining for Fast Training Convergence EMNLP2026
链接: https://arxiv.org/abs/2609.07236
作者: Minchul Kang,Changyong Shin,Younghun Go,Hyunho Lee,Jinwoo Jeong,Chuck Yoo,Gyeongsik Yang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: Accepted to EMNLP 2026 Main Conference
Abstract:Selecting a parallelism strategy - the configuration of data, tensor, and pipeline parallelism degrees together with micro- and global-batch sizes - largely determines the training efficiency of large language models. State-of-the-art methods search for a parallelism strategy offline and select the single strategy that minimizes per-iteration time. But we find that they neglect the target validation perplexity and time-to-perplexity (TTP). In particular, our analysis reveals that the best strategy yielding the fastest perplexity improvement changes multiple times during training. As a result, state-of-the-art methods are 1.8-11.4x slower in TTP than the strategy sequence that selects the best strategy at each iteration. This paper proposes CONA, a new training method that introduces online strategy chaining. Instead of a single strategy selected offline, CONA ranks candidate strategies during training using a surrogate metric built from compute throughput and gradient statistics, and switches the current strategy to a new strategy with a higher metric. In our evaluation with GPT-3 1.3B, BERT-Large, and Llama-3.2-1B, CONA reaches the target validation perplexity 1.4-9.6x faster than state-of-the-art methods. Moreover, CONA closely tracks the perplexity achieved by the sequence that selects the best strategy at each iteration, within 2.6%.
[AI-146] Distance-Aware Attention and Wall-Distance Expert Routing for Transformer-Based 3D Flow Prediction
链接: https://arxiv.org/abs/2609.07222
作者: Sanghyeon Kim,Sunwoong Yang,Namwoo Kang
类目: Artificial Intelligence (cs.AI)
备注: 21 pages, 13 figures, 10 tables
Abstract:Transformer surrogates for 3D flow prediction compress an industrial mesh into a small set of tokens from which every prediction point reads. Two operations follow: the retrieval step in which a point gathers information from the compressed representation, and the feed-forward layer that transforms what it retrieved. In current backbones both are blind to where the point sits in the flow. We condition both on wall-related physical signals. Distance-aware cross-attention (DA-CA) reshapes each volume query by its wall distance before retrieval, so that a point deep in the boundary layer draws different geometric information than one in the outer flow. Surface-volume mixture-of-experts (SVMoE) replaces the shared feed-forward layer with a small set of experts, routed by wall distance for volume points and by local geometry for surface points. Neither mechanism is tied to one architecture, so we apply both unchanged to AB-UPT and Transolver-3. On DrivAerML with 50 training cases, DA-CA reduces the volume pressure error by 10.1%, and DA-CA and SVMoE together reduce it by 12.5%; DA-CA improves the near-wall region at some cost in the far region, which SVMoE recovers, and the volume experts settle into near-wall, transition, and free-stream bands without routing supervision. Retrained on 300 cases, the conditioning improves every field quantity, reducing volume pressure and velocity errors by 33.1% and 18.6% on AB-UPT and by 21.4% and 21.3% on Transolver-3. Under Leave-One-Body-Out evaluation on DrivAerNet++, it reduces the volume pressure error on unseen body types by up to 14.2%.
[AI-147] REFINE: Trajectory Representation Learning via Closed-Loop Transcription – Extended Version KDD2026
链接: https://arxiv.org/abs/2609.07206
作者: Sean Bin Yang,Ying Sun,Jilin Hu,Zongyi Xu,Kristian Torp,Hua Lu,Bin Yang,Christian S. Jensen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: This paper is an extended version of REFINE, which was published at KDD 2026
Abstract:Trajectory representation learning underpins a wide range of trajectory analytics tasks; however, most existing self-supervised approaches, whether discriminative or generative, adopt an open-loop paradigm, relying on fixed data augmentations or random masking without feedback, which limits their ability to generalize and scale. We propose REFINE, a simple yet effective Representation lEarning Framework vIa closed-loop traNscription rEfinement for trajectory data. Drawing upon feedback control theory, REFINE tightly couples road-network-aware generative reconstruction with feedback-driven contrastive learning, enabling the model to capture fine-grained local movement semantics and global spatio-temporal dependencies without manually designed augmentation views. We further provide a control-theoretic analysis that establishes convergence guarantees for the proposed closed-loop optimization. Extensive experiments on four real-world datasets demonstrate that REFINE consistently outperforms state-of-the-art methods across multiple downstream tasks while remaining computationally efficient and scalable. This paper is an extended version of REFINE: Trajectory Representation Learning via Closed-Loop Transcription, to appear in KDD 2026. Comments: This paper is an extended version of REFINE, which was published at KDD 2026 Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.07206 [cs.LG] (or arXiv:2609.07206v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.07206 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Sean Bin Yang [view email] [v1] Mon, 7 Sep 2026 08:26:49 UTC (1,374 KB)
[AI-148] Agent ic Algorithm Engineering: Improving Shared-Memory Exact Minimum Cuts
链接: https://arxiv.org/abs/2609.07204
作者: David A. Bader,Adil Chhabra,Ernestine Großmann,Monika Henzinger,Alexander Noe,Christian Schulz
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The minimum cut problem for an undirected edge-weighted graph asks us to divide its set of nodes into two blocks while minimizing the weighted sum of the cut edges. Over the last years, we engineered a range of fast algorithms for this problem. Our fastest exact algorithm uses an inexact algorithm to obtain a better bound for the problem, reductions that depend on this bound, improved data structures and parallel contraction routines. It is available in the open-source package VieCut and, on real-world instances, outperformed the previously fastest solvers by a factor of up to 2.5 sequentially and up to 12.9 when run in parallel. We improve this algorithm using agentic algorithm engineering (AAE), a methodology that we introduce here, in which autonomous large language model agents run the algorithm engineering cycle on an existing code base: they form hypotheses about where running time is lost, implement them, benchmark the result on a fixed instance set and keep or discard the change. Even though we had already tuned our algorithm by hand extensively, the agent finds significant optimizations, in particular on the DIMACS core instances: factors of 1.28 (sequential) and 1.63 (32 threads) on real-world k-cores, and 6.26 and 127 on the DIMACS core instances.
[AI-149] Recompilation Is Not Enough: Test-Guided Decompiled-C Repair
链接: https://arxiv.org/abs/2609.07201
作者: Yuhan Huang,Puzhuo Liu,Jianlei Chi
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 5 pages, 2 figures
Abstract:Decompiled C often becomes recompilable only after repair, but recompilation alone does not establish test-observed behavior. A recompiled command-line binary can still parse options incorrectly, print different bytes, or return a different exit status. We present a few-step workflow for repairing decompiled C using compiler feedback and related official tests. Compiler and linker diagnostics first guide build repair. Once the repaired C recompiles into a binary, smoke checks and related official tests expose behavioral discrepancies for semantic repair. In a preliminary static-enriched evaluation on 104 Coreutils 9.5 binaries with available decompiler exports and deterministic exact-output smoke comparisons, 91 binaries (87.5%) recompile and pass the test gate; 9 do not recompile within the repair budget, and 4 recompile but still fail the test gate. The result suggests that test-gate feedback can make LLM-assisted repair of decompiled C more auditable than compile-only recovery.
[AI-150] Protocol effects on feature-based hardware-Trojan detection across Trust-Hub families CCS
链接: https://arxiv.org/abs/2609.07199
作者: Hang Xiao,Chuhong Xu,Kainan Zhou,Gangzhen Qian,Lu Yi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Databases (cs.DB); Computer Science and Game Theory (cs.GT)
备注: 7 pages, ICCSIE
Abstract:Trust-Hub reuses host circuits: several files differ mainly in the inserted Trojan. When gates from sibling variants enter both training and test folds, a detector can benefit from host logic it has already seen. We measure that effect instead of proposing another classifier. The corpus contains 49,124 gates from 16 netlists grouped into five host families. We left the parser, 36 gate features, class weighting, model settings, threshold, and family-level aggregation unchanged and altered one choice: the test boundary. The three settings draw test gates from the pooled corpus, withhold a complete netlist, or withhold every variant of one host. The choice matters. Random forest records F1/AP of 0.914/0.978 with pooled gates, 0.636/0.851 with one netlist held out, and 0.460/0.577 with a host family held out. XGBoost falls from 0.946/0.976 to 0.464/0.544 across the same comparison. Logistic regression loses AP, although its fixed-threshold F1 is not monotonic. Each family shows the same pooled-to-family direction. Feature removal, repeated model and simulator seeds, score normalization, parser-related exclusions, and a smaller sample change the size of the gap without reversing it. Aggregation also matters: a gate-weighted average is dominated by the larger ISCAS files, so the headline values give each host family one vote. Bootstrap and jackknife summaries keep the gap positive, but their folds reuse training families. We treat the five family rows as descriptive evidence rather than independent trials. Five host families are too few for a population claim, and the experiment says nothing about transfer to a new cell library or an industrial design. It supports a narrower conclusion: sibling benchmark variants can inflate apparent transfer. Benchmarks with several variants of one host circuit should report family-aware holdouts and all five family results beside pooled scores.
[AI-151] FedRAW: Preserving Rare-Label Influence in Asynchronous Federated Learning
链接: https://arxiv.org/abs/2609.07192
作者: Prashant Bajpai,Divya Saxena,Philippe Lalanda,German Vega
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 18 pages with supp material
Abstract:Asynchronous federated learning improves scalability by updating the global model from a server-side buffer of client updates as they arrive, rather than waiting for all selected clients to finish. While efficient, this arrival-driven aggregation can silently distort representation learning under heterogeneous participation. We identify silent rarity failure, a hidden failure mode in which clients holding rare labels contribute too weakly to the global model even though its overall accuracy appears largely unaffected. This failure arises from two coupled effects: rare-label clients may submit updates less frequently when they are slower or less available, creating participation bias; and once their updates enter the buffer, standard asynchronous aggregation assigns them no compensating influence, creating aggregation bias. We propose FedRAW, a fully server-side aggregation method that preserves rare-label influence without changing local training, client objectives, or communication protocols. FedRAW combines client-level update deduplication, which prevents frequently arriving clients from repeatedly dominating the update buffer, with rare-label-aware weighting, which increases the influence of clients carrying low-coverage labels. We formalize silent rarity failure through participation and aggregation bias, and show that FedRAW increases rare-label client influence over uniform aggregation while preserving convergence. Across EMNIST Balanced, CIFAR-10, HAM10000, and ISIC-2019, FedRAW improves rarelabel accuracy while preserving comparable global accuracy and adding negligible server-side computation.
[AI-152] PhysMAS: Physics-Grounded Multi-Agent Synthesis of Compositional 4D Gaussians
链接: https://arxiv.org/abs/2609.07174
作者: Jiang Qin,Chunji Lv,Yangguang Wei,Yang Gao,Ming Liu,Lizhong Ding,Ye Yuan,Yinjie Lei,Changsheng Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Efficient, fully automatic, and physically plausible 4D Gaussian synthesis is an important goal for dynamic scene generation. Recent physics-based methods couple 3D Gaussians with the Material Point Method (MPM) to generate physically driven motion, but extending this paradigm to heterogeneous multi-part objects and interacting multi-object scenes remains challenging. Object-level physical assignment collapses distinct parts into a single material state, while one-shot predictions from large language models, vision-language models, or agents neither reliably bind different materials to identified parts nor verify that the resulting MPM configuration is executable. Score Distillation Sampling (SDS)-based parameter optimization, meanwhile, requires repeated per-scene score evaluations and gradient backpropagation, incurring lengthy optimization and potentially yielding suboptimal or unstable solutions. We therefore present PhysMAS, a physics-grounded multi-agent framework. From a motion prompt and four scene views, an Object-Part Scene Agent establishes persistent identities and calls a Material Reasoning Agent for part-wise profiles. It invokes solver-aware skills to bind these identities and profiles to per-particle MPM fields and execute all objects in a shared domain; the framework then screens candidate forward-simulation results. This supports heterogeneous multi-part and interacting multi-object scenes without per-scene diffusion-score backpropagation. Extensive experiments demonstrate that, compared with recent physics-based 4D Gaussian baselines that rely on SDS, PhysMAS achieves better semantic alignment and perceived physical plausibility while requiring less runtime.
[AI-153] An Auditable Symbolic-RAG -Generative AI Architecture for Goal-Oriented Conversation Orchestration
链接: https://arxiv.org/abs/2609.07152
作者: Ramon Gonzalez,Antonio Diaz(Mentomy AI)
类目: Artificial Intelligence (cs.AI)
备注: 14 pages, 3 figures, 6 tables, Appendices with complete experiments
Abstract:Goal-oriented conversational systems must answer factual questions, understand visitor-provided information, and advance business objectives without becoming rigid questionnaires. This paper proposes a Symbolic-RAG-Generative architecture centered on the Goal-oriented Retrieval-Augmented Conversation Engine (GRACE). An instruction-constrained Business Goal Compiler transforms business intent into an immutable objective set, normalized priority vector, canonical questions, and initial state vector. At runtime, GRACE receives the complete conversation history, latest visitor message, current state, and grounded answer generated by a separate RAG component. It updates completion only from visitor-authored evidence and selects one contextually modulated follow-up. The core policy maximizes expected business progress subject to a minimum visitor-utility constraint. We formalize the state, monotonic transitions, source separation, question modulation, and constrained policy; present the reference architecture; and define an evaluation comprising 24 English real-estate and 10 Spanish professional-cleaning conversations, totaling 119 protocol-defined visitor turns. Across both domains, GRACE achieves 84.9% exact state-transition accuracy, 91.6% evidence precision, 89.6% evidence recall, 100% monotonicity, and 94.1% terminal-state accuracy. The evaluation establishes compelling symbolic-state performance across standard, multi-goal, RAG-detour, validation, refusal, and robustness scenarios.
[AI-154] Agent Leak: Cloning Stronger LLM Agent Capabilities onto Weaker Agents Beyond Skill Stealing
链接: https://arxiv.org/abs/2609.07131
作者: Xiaoting Lyu,Yuhong Wu,Yufei Han,Shichang Liu,Liang Zhang,Bin Wang,Bin Wang,Xiaobo Ma,Wei Wang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM) agents increasingly achieve long-horizon tasks by combining foundation models with explicit skills and implicit procedural knowledge acquired through execution. The resulting task-solving capabilities have become valuable proprietary assets, raising a new security question: can a substantially weaker attacker-controlled agent acquire the capabilities of a stronger proprietary agent through limited black-box interaction? Existing skill-stealing attacks recover explicit skill artifacts, yet we show that artifact leakage does not necessarily transfer capability: a weaker agent may possess the same skills but still fail because it lacks procedural behaviors implicitly realized by the stronger agent. Our key insight is that the skill execution gap itself forms a leakage surface, where missing behaviors are exposed through observable differences between successful victim executions and failed attacker executions. Based on this, we present AgentLeak, a black-box capability-cloning attack that identifies capability-critical behaviors from these execution differences and incorporates them into attacker-side skills, while keeping the attacker’s model, harness, and tools unchanged. Across 20 task scenarios comprising 600 instances, diverse agent systems, and multiple backbone models, AgentLeak improves task pass rates by over 40% compared with direct skill reuse and recovers more than 80% of the victim–attacker capability gap. Our findings reveal a confidentiality risk in LLM agents: protecting explicit artifacts alone is insufficient, as observable execution behavior can leak the procedural knowledge required to reconstruct proprietary task-solving capabilities in low-capability and attacker-controlled agents.
[AI-155] EEG-Driven Decoding Framework for Passenger Hazard Perception in Highly Automated Vehicles
链接: https://arxiv.org/abs/2609.07128
作者: Yingkai Yang,Ashton Yu Xuan Tan,Bowen Li,Xiaorong Gao,Sifa Zheng,Jianqiang Wang,Xinyu Gu,Yang Zhao,Yuxin Zhang,Sharon X. Huang,Tania Stathaki,Jun Li,Hong Wang
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Signal Processing (eess.SP)
备注: 31 pages, 8 figures, 13 tables, including appendices. Accepted for publication in Automotive Innovation. Yingkai Yang and Ashton Yu Xuan Tan contributed equally. Corresponding author: Hong Wang. Data: this https URL ; Code: this https URL
Abstract:Reliable risk assessment remains a central challenge for Autonomous Vehicles (AVs). Despite advances in automation, passenger cognition provides a non-intrusive auxiliary signal that improves both objective and perceived safety without requiring active human intervention. We introduce an Electroencephalogram (EEG)-based Brain-Computer Interface (BCI) that decodes passenger neural responses for both Risk Prediction (RP) and Danger Identification (DI), explicitly modeling humans as passengers to match real-world AV use. To achieve this, we propose the Passenger Cognitive Model (PCM), Risk-aware Sequential Labeling (RSL), and the Passenger EEG Decoding Strategy (PEDS), which integrates a 3D Convolutional Recurrent Neural Network (3D-CRNN) model for joint EEG decoding. Experimental results show that 3D-CRNN achieves a Balanced Accuracy (BA) of 95.3% \pm 2.7% in RP and improves single-subject DI from 80.9% \pm 3.9% to 85.0% \pm 3.2% with RSL. Event-wise analyses further show that 3D-CRNN consistently outperforms other models across different event types in RP and DI. In generalization experiments, 3D-CRNN achieves 77.0% \pm 5.3% BA in cross-session DI and 77.4% \pm 1.1% BA on seen subjects in cross-subject evaluation, while maintaining a 64.9% \pm 8.5% BA on unseen subjects, demonstrating promising generalizability and transferability across both intra-subject and inter-subject variability. These findings establish an Electroencephalogram (EEG) decoding framework for AV passenger hazard perception and suggest that passenger cognitive signals can provide auxiliary supervision for future AV decision-making and Safety of the Intended Functionality (SOTIF) support.
[AI-156] From LLM -Generated Specifications to Learned Quadruped Locomotion
链接: https://arxiv.org/abs/2609.07111
作者: Merve Atasever,Keyan Azbijari,Cagan Bakirci,Alfredo Reina Corona,Tolga Izdas,Richard Yang,Erdem Biyik,Jyotirmoy V. Deshmukh
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Quadruped robot locomotion policies are often trained using reinforcement learning, which in turn relies heavily on hand-crafted reward functions. Designing reward functions requires substantial manual engineering, and it is often unclear which local rewards will induce the desired global behavior. Shaped rewards from formal specifications in languages like Signal Temporal Logic (STL) can make rewards more interpretable, but writing STL specifications itself still requires domain expertise. We study whether large language models (LLMs) can fill this gap by generating Parametric Signal Temporal Logic (PSTL) specifications that are subsequently used for policy learning. Given a natural language locomotion objective and a constrained specification grammar, GPT-5.5 and Qwen 3.6 independently propose STL templates for command tracking, safety, and gait structure. We instantiate the parameters of the generated PSTL templates using expert trajectories and retain only specifications that are consistent with demonstrated expert behavior. The resulting specifications are then transformed into smooth, finite-history reward functions and used to train a quadruped locomotion policy with Proximal Policy Optimization (PPO) in MuJoCo XLA (MJX). We evaluate both \emphgait-aware and \emphgait-agnostic settings. The former specifies walking-trot, trot, and bound regimes, while the latter allows contact patterns to emerge from the task objective. We compare against hand-engineered rewards, Text2Reward-style LLM-generated reward code, and an expert-switching oracle. Gait-aware Qwen 3.6 specifications achieved 100% survival and command success across all tested speeds (0.3–2.1 m/s) and matched the target gait at high speeds, whereas Text2Reward achieved 0% for both metrics at \geq 1.9 m/s. Videos: this https URL
[AI-157] Beyond Sparse Rewards: A New Benchmark and Structure-Aware Graph Alignment for Micro-Drama Understanding EMNLP2026
链接: https://arxiv.org/abs/2609.07107
作者: Yixin Qin,Shi-Zhe Chen,Zhiqi Yu,Siyuan Cheng,Tao Cheng,Jinwen Luo,Zheng Wei
类目: Artificial Intelligence (cs.AI)
备注: Accepted to EMNLP 2026 (Camera-ready version)
Abstract:Micro-dramas, characterized by ultra-short durations and hyper-dense storylines, pose unique challenges for video understanding that conventional benchmarks fail to address. To bridge this gap, we introduce M-Drama, the first large-scale bilingual benchmark for micro-drama comprehension, featuring over 35K instances across 9,138 clips. Furthermore, while reinforcement learning can enhance VLMs on complex narratives, existing reward metrics often suffer from sparse and superficial signals, failing to capture intricate character identities and temporal structures. We propose SAGA (Structure-Aware Graph Alignment), a novel graph-matching reward function that models narratives as heterogeneous graphs. SAGA computes dense, rigorous rewards via decoupled semantic triplet and structural temporal matching. Extensive experiments on Qwen3-VL-8B-Instruct demonstrate that SAGA outperforms existing baselines, delivering substantial improvements in open-ended accuracy and summary quality, while maintaining competitive out-of-domain generalization. Code is available at this https URL.
[AI-158] mporal Heterogeneous Graph Transformer for Credit Card Fraud Detection
链接: https://arxiv.org/abs/2609.07100
作者: Qinwen Yan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 5 pages, 2 figures
Abstract:Credit card fraud detection typically relies on tabular features, while repeated attributes can also provide useful relational signals. This paper proposes THGT-FD, a Temporal Heterogeneous Graph Transformer for Fraud Detection. Each transaction is represented using one transaction token and six types of relation tokens and incorporates Time2Vec encoding into the transaction representation. A Transformer learns the interactions among these tokens within each individual transaction and then outputs a fraud probability. Experiments were conducted on 150,000 transactions sampled from the IEEE-CIS Fraud Detection dataset and chronologically partitioned according to TransactionDT. On the test set, THGT-FD achieved an AUC-ROC of 0.8536, an average precision of 0.4164, and a Recall@5% of 0.4708. The class-weighted histogram-based gradient-boosting baseline achieved an AUC-ROC of 0.8722. The results indicate that relation tokens provide useful information for fraud-risk ranking, although the current model does not yet incorporate entity-level historical aggregation.
[AI-159] A Hierarchical Consistency Framework for Auditing Retrieval-Augmented Generation Systems
链接: https://arxiv.org/abs/2609.07075
作者: Ramon Gonzalez,Antonio Diaz
类目: Artificial Intelligence (cs.AI)
备注: 12 pages, 2 figures, 12 tables, 1 GitHub repository
Abstract:Retrieval-augmented generation (RAG) is commonly evaluated by whether the final answer is correct. That test is insufficient: an answer can match its reference while the context that produced it contains a direct contradiction, leaving the contested evidence invisible to answer-only review and retrieval relevance scores. This paper presents the Hierarchical Consistency Framework (HCF), a post-hoc, model-agnostic audit of three distinct levels of a RAG process: the knowledge corpus, the final retrieved context, and the generated answer. HCF represents corpus conflicts as source-linked atomic facts, thereby identifying the documents responsible, and returns each Answer Consistency Score (ACS) with an explanation of supporting and contradictory contextual statements. We evaluate HCF on several controlled corpora spanning five domains and 100 query-corpus instances. A human evaluator compares every generated response with its supplied ground-truth response. The results show that the three diagnostic levels can dissociate: the corpus with the highest mean retrieval similarity has the lowest mean ACS, while a structurally degraded corpus performs worse at corpus level but better at answer level. Most importantly, HCF identifies contradictory retrieved evidence in several cases where the answer still matches the ground truth. HCF does not certify factual truth; it makes the evidence supporting and challenging an answer inspectable and attributable.
[AI-160] VST: Verifiable Structured Transport for Auditable Agent -to-Agent Alpha Discovery
链接: https://arxiv.org/abs/2609.07065
作者: Yuqi Li,Siyuan Liu,Bingjun Liu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Agent-to-agent (A2A) alpha discovery is slowed by repeated feedback cycles between mining and evaluation agents, whose hand-offs, in contemporary LLM multi-agent systems, are free-form natural-language messages that carry no stable contract and cannot be replayed. We first restructure this communication as a structured agent-to-agent protocol of \emphtyped, causally addressable, unicast records, so that the committed stream forms a causal trajectory. On that trajectory a single predictor with four typed heads forecasts the accumulated guidance the two miners would receive several cycles ahead; a transactional verify–leap controller then commits a multi-cycle speculative outcome only when it passes a four-level gate, and otherwise rolls back to the exact prior state. Structure is the enabling contribution, and its value is not accuracy. A controlled ablation shows an equal-information free-text channel reaches the same predictor hit rate. What typing provides is a state that can be schema-checked, replayed deterministically, and prevented by construction from leaking a forecast to an evaluator: auditability by construction, not an empirically stress-tested guarantee. On a CSI~1000 out-of-sample holdout, our single run is the only one among eight methods (seven baselines and ours) to hold a positive median annualized return and Sharpe at the factor level, though the median return \emphin excess of the benchmark stays negative for every method including ours; its development-selected top-20 portfolios reach a 0.71 median holdout Sharpe, selected on a split inside the optimization horizon. We report these single-run results descriptively, gross of costs, and are explicit about their limits throughout; in particular we do not isolate the effect of the leap machinery from the inherited search substrate, which we leave to future work.
[AI-161] MEMOBench: A Process Level Memory Benchmark for Robotic Manipulation
链接: https://arxiv.org/abs/2609.07047
作者: Haiyang Sun,Haoxiao Wang,Junming Chen,Weicheng Fang,Zihao Su,Jingkun Yi,Wenyou Yi,Hao Chen,Zhou Zhao
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Robotic manipulation often requires acting on information that is no longer visible, yet Vision-Language-Action policies are usually evaluated when the current observation largely determines the next action. Existing robotic memory benchmarks expose this gap, but they still rely mainly on final task success and therefore conflate forgetting with manipulation failure. We present \textbfMEMOBench, a benchmark for process level memory evaluation in robotic manipulation. MEMOBench includes 30 history dependent tasks, 1,500 expert demonstrations, and 4,200 executable checkpoint instances from 84 templates. Each checkpoint pairs coarse to fine language with a simulator predicate and labels one memory operation: Storage, Update, or Compression. These annotations define Memory Storage Rate, Memory Update Rate, and Memory Compression Rate, which measure memory fidelity alongside task success. Across standard and memory augmented VLA policies, the strongest memory module baseline reaches only 31.9% average success rate, and high storage often coexists with weak update and compression. Checkpoint language also supervises semantic, contrastive, and framewise memory alignment objectives, yielding modest gains across different memory operations. MEMOBench provides a diagnostic evaluation suite and training supervision for memory grounded robotic policies. The project page is available at this https URL.
[AI-162] Frequency Estimation Based on SNR-adaptive Frequency Estimator Under Wide SNR Range
链接: https://arxiv.org/abs/2609.07034
作者: Hee-Yang Jung,Dong-Hee Paek,Woo-Jin Jung,Seung-Hyun Kong
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: 10 pages, 6 figures
Abstract:Frequency estimation is the problem of estimating individual tone frequencies from noisy multi-tone sinusoidal signals. Existing frequency estimation methods have difficulty accurately estimating both the number of tone frequencies and the individual tone frequencies in low signal-to-noise ratio (SNR) environments, because weak tone frequency components are buried in noise. In addition, existing methods generally exhibit a trade-off between robustness at low SNR and frequency estimation precision at high SNR, making it difficult to achieve consistently superior frequency estimation performance over a wide SNR range. To overcome these limitations, this paper proposes an SNR-adaptive frequency estimator (SAFE). SAFE consists of a time-frequency image neural network (TFINet), which enhances weak tone frequency components at low SNR, and an SNR-based frequency selector (SFS), which selects an appropriate frequency estimator according to the SNR of the estimated tone frequencies. TFINet enhances tone frequency components even in the low-SNR range, while SFS estimates the SNR of each tone frequency and selects either a robust frequency estimator or a super-resolution frequency estimator according to the estimated SNR. This enables SAFE to achieve robustness at low SNR while preserving high precision at high SNR. Simulation results show that SAFE achieves an False Negative Rate (FNR) of 13.00% over the SNR range from -10 dB to 0 dB, corresponding to an 13.04% improvement over the state-of-the-art method. In addition, SAFE reduces the Nearest Neighbor-Root Mean Squared Error (NN-RMSE) by 56.67% compared with the state-of-the-art method, demonstrating that SAFE performs more accurate frequency estimation. Furthermore, experiments using real-world data demonstrate that SAFE provides robust frequency estimation performance even in practical environments with clutter.
[AI-163] CIPHER: Benchmarking Cross-record Inference over Privacy-Hardened Evidence Records
链接: https://arxiv.org/abs/2609.07022
作者: Suparno Roy Chowdhury,Manan Roy Choudhury,Dhruv Madhwal,Vivek Gupta
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Reasoning over privacy-constrained records requires combining structured attributes with evidence from free-text narratives. We introduce CIPHER (Cross-record Inference over Privacy-Hardened Evidence Records), a benchmark of expert-validated questions from consumer-finance, clinical, and law-enforcement records. The questions cover common tabular operations and include executable SQL supervision. We evaluate retrieval, prompting, table-specialist, and hybrid symbolic-neural systems under native redaction and surrogate-based evidence restoration. All system families exhibit substantial failures even when supporting records are provided. Most errors arise from incorrect record selection and predicate interpretation rather than arithmetic execution. Privacy transformations have non-uniform effects, sometimes obscuring necessary evidence and sometimes reducing distraction. CIPHER provides a reproducible testbed for diagnosing these failures and assessing how transformations of sensitive text affect reasoning over hybrid records.
[AI-164] RedKnot-MLA: Multi-Head Offline-Online Reuse for DeepSeek -V4 Long-Context Serving
链接: https://arxiv.org/abs/2609.07008
作者: Yang Liu,Zhaokai Luo,Huayi Jin,Ruozhou He,Chenchen Hong,Mingxiao Ma,Biao Zhang,Zhiyong Wang,Boyu Wang,Guanjie Chen,Yifei Liu,Tao Xie,Junhao Hu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-head latent attention (MLA) exposes many logical query heads through one packed latent KV stream. This representation is memory efficient, but it removes the physical per-head cache boundary assumed by conventional head-wise reuse. We present our system, a DeepSeek-V4 realization of RedKnot’s head-aware reuse principle. Each immutable document is processed offline at canonical position zero; certified Local-head contributions are retained as MLA-Off. At serving time, query-side RoPE relocation restores the document’s request position, a small Global-head set and protected Local token rows are recomputed as MLA-Online, and the two paths are merged before a single shared output projection. The packed MLA latent is never split. DeepSeek-V4-Flash uses 37 reusable layers and a 56/8 Local/Global partition, giving a 75.29% analytic logical head-row ceiling; the Pro-0813 profile uses 55 layers and 112/16 heads, giving 78.89%. Frozen Flash operating points show hot-artifact TTFT speedups of 2.02-3.84x. At 256K, the archived three-dataset study reports an aggregate F1 change of +3.24 percentage points, an EM change of +4.16 points, and a 78.7-79.5% analytic major-operator arithmetic saving, while one dataset decreases by 2.81 F1 points. A separate author-reported 256K hot-artifact QPS measurement is approximately 2.0x; because its raw concurrency trace is not included in this bundle, we mark it as preliminary rather than archived evidence. We describe the factorization, position repair, token-row closure, sparse-MoE support, TP8 integration, and the measurement boundaries needed to interpret these results.
[AI-165] Agent Drift: A Step-Labeled Benchmark of Injection-Hijacked LLM Agent Trajectories
链接: https://arxiv.org/abs/2609.06972
作者: Asif Pinjari,Mithun Paul Saint-Germain
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 19 pages, 8 figures, 13 tables. Dataset: this https URL
Abstract:LLM agents complete tasks by issuing sequences of tool calls, and every observation they read is a channel through which an indirect prompt injection can enter. A successful injection has a characteristic shape when the trajectory is read in order: a benign prefix gives way to actions that serve the attacker rather than the user. Existing benchmarks measure whether such attacks succeed against live agents, and existing guard models judge a trace as a whole; no public corpus labels, step by step, where an injection enters a trajectory and which steps it corrupts. We present AgentDrift, a benchmark of 12,536 synthetic tool-call trajectories over five agent domains in which every one of the 71,024 steps carries one of four labels: benign, injection point, hijacked, or failed injection. The corpus contains 4,000 benign, 5,536 attacked, 1,500 failed-attack, and 1,500 hard-negative trajectories; attacked trajectories follow three compliance patterns whose label strings obey a stated regular grammar. Failed attacks carry an injection the agent resisted, and hard negatives carry legitimate content that resembles an attack, so a detector must separate attempt from success and deviation from novelty. Trajectories were generated by a single open model under category-specific protocols, enforced by a closed-vocabulary structural validator, screened by an LLM judge, and audited by hand on 1,200 trajectories; we show that the LLM judge was itself fooled by the hard negatives. A surface-feature logistic regression recovers only 55.4% of attacks (F1 0.647), including only 8.2% of partial hijacks and 23.1% of delayed executions, so nearly half of the attacks require modeling the behavioral sequence. We measure template concentration, attack-goal-family collapse, and world-identity leakage in the generated data, and release the corpus with its documentation under CC BY 4.0.
[AI-166] SSP-DMGTimeNet: Physics-Constrained Learning for Spatiotemporal Trajectory Prediction of Vehicle Platoons
链接: https://arxiv.org/abs/2609.06961
作者: Yuhang Wang,Kailang Ma,Zirui Li,Mingfeng Fan,Kitae Jang,Changju Lee,Heye Huang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Existing car-following prediction methods mainly optimize trajectory accuracy, while rarely considering whether predicted disturbances propagate realistically along a vehicle platoon. This limitation may lead to accurate but string-unstable predictions. We propose SSP-DMGTimeNet, a physics-constrained learning framework for spatiotemporal trajectory prediction of vehicle platoons. The model combines multi-scale temporal representations with cross-vehicle interaction features to capture complex and time-varying platoon dynamics. A propagation-delay-aware causal attention mechanism explicitly models upstream-to-downstream disturbance propagation by learning response delays between adjacent vehicles and accumulating them along the platoon. In addition, time- and frequency-domain string-stability losses relieve disturbance amplification across both adjacent vehicles and arbitrary sub-platoons during training. Experiments on HighD show that SSP-DMGTimeNet achieves an unstable-window rate of 0.65% for five-vehicle platoons and a maximum head-to-tail amplification of 0.898 on the ground-truth excitation subset, while maintaining competitive trajectory prediction performance. In zero-shot evaluation on NGSIM US-101 and I-80, the model achieves velocity MAEs of 1.316~m/s and 1.252~m/s, with unstable-window rates of 3.90% and 4.10%, respectively. These results demonstrate that incorporating platoon-level physical constraints can effectively balance trajectory prediction accuracy and disturbance propagation stability.
[AI-167] Brain: A Unified Foundation Model Reading the Brain from Surface to Spikes
链接: https://arxiv.org/abs/2609.06960
作者: Ying Chen,Tiou Wang,Zhifeng Yue
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Invasive neural recordings provide high-fidelity measurements of brain activity, with signals such as intracranial EEG (iEEG) and intracortical spiking activity capturing neural dynamics at different spatial and temporal scales. Yet existing neural foundation models have largely been developed independently for different invasive recording paradigms, leaving joint pretraining across heterogeneous invasive signals underexplored. In this work, we introduce iBrain, a unified foundation model that jointly learns from iEEG and spiking activity. iBrain employs signal-specific encoders to accommodate their distinct signal characteristics and a shared spatiotemporal Transformer backbone to model dependencies across recording channels and time. We pretrain iBrain on over 7,000 hours of heterogeneous neural recordings using masked signal reconstruction and channel-view alignment, promoting contextual modeling of neural dynamics and robustness across different channels. iBrain consistently outperforms single-signal pretraining baselines and achieves state-of-the-art performance on multiple benchmarks. Further experiments demonstrate that iBrain exhibits transferability and data efficiency across diverse recording settings. These results highlight the potential of joint pretraining on heterogeneous invasive neural recordings to support scalable neural modeling and transferable representations across recording settings and downstream tasks.
[AI-168] Mind the Phase: Effective Rank and Representation Health in Legged Locomotion
链接: https://arxiv.org/abs/2609.06958
作者: Felipe Tommaselli,Thiago H. Segreto,Juliano D. Negri,Ricardo V. Godoy,Marcelo Becker
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Accepted at the 10th Conference on Robot Learning (CoRL 2026)
Abstract:Reinforcement learning has become the leading paradigm in legged locomotion, enabling complex behaviors from backflips to parkour through massively parallel simulation. Under PPO’s non-stationarity, shallow networks remain the de facto architecture, supported by carefully staged curricula and environments, yet the representations these policies learn stay poorly understood, leaving no training-time signal of how they will behave on hardware. In this work, we empirically study locomotion policies through the effective rank of the policy Jacobian and show that conditioning rank on the gait phase exposes architectural structure that global rank averages away. In particular, we find that standard architectural choices, namely layer normalization and residual connections, allocate roughly two more dimensions of effective rank to swing than to stance, which is fully absent in vanilla MLPs. Building on this, we propose a simple recipe that turns these representational signatures into smoother, more reliable sim-to-real transfer. In practice, this results in roughly 3x lower joint jitter that holds from simulation onto a physical Spot, suggesting that representation health is an effective training-time lens to track sim-to-real smoothness.
[AI-169] Steering Interference Reflects the Models Defaults Not the Behavior Directions
链接: https://arxiv.org/abs/2609.06951
作者: Srikanth Malla,Chiho Choi,Joon Hee Choi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Activation steering promises modular control of language model behavior: a behavior such as politeness corresponds to a direction in a model’s activations, and adding that direction while it generates should switch the behavior on and leave everything else alone. It does not. We ask what decides which other behaviors move, and by how much, and find that it is the model rather than the behavior being steered. A steer relaxes the model toward a small set of behaviors it already favors, chiefly refusal, sycophancy, and poeticism, and that set is much the same whatever is steered. Three results across 24 behaviors and ten instruction-tuned models support this, every effect read off the generated text by a language-model judge rather than off a probe. That readout matters: all 24 behaviors are linearly decodable, but only 20 change what the model writes. First, a direction carrying no behavioral content, matched to a real steer only in the size of the vector it adds, moves the same behaviors in the same order as real steers do, while producing none of the behaviors that need a specific direction. Second, most interference runs one way, so it cannot be an overlap between two directions: steering profanity makes the model toxic, while steering toxicity leaves profanity untouched. Third, with a behavior held out entirely, geometry measured on the others explains almost none of the interference it takes part in. The account holds on all ten models, the pull toward defaults strongest below 10B parameters and weakening in each family’s largest. Reading a steer as a perturbation whose endpoint the model fixes implies that disentangling behavior directions cannot by itself make steering modular. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.06951 [cs.LG] (or arXiv:2609.06951v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.06951 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-170] PCSDiff: Diffusion-Based Bias Correction and Super Resolution Toward Practical Operational Medium-Term Precipitation Forecast
链接: https://arxiv.org/abs/2609.06942
作者: Yuze Sun,Shiyi Wang,Jiancheng Pan,Die Wang,Andreas F. Prein,Wentao Luo,Linhan Jiang,Jie Wu,Quan Zhang,Xiaomeng Huang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Atmospheric and Oceanic Physics (physics.ao-ph)
备注:
Abstract:Medium-range precipitation forecasts are impaired by persistent systematic biases, lead-time-dependent error accumulation, and coarse spatial resolution, restricting their reliability for flood-drought risk assessment. Existing AI correction techniques lack dedicated modeling for multi-day dynamic bias evolution and proper meteorological constraints, often generating over-smoothed rainfall structures, and cannot meet operational deployment demands. This work introduces PCSDiff, a cascaded task-decoupled diffusion framework targeting 10-day precipitation bias correction and downscaling. To jointly counteract temporal error drifts and reconstruct physically plausible local precipitation details, PCSDiff integrates the Precipitation Intensity-aware Multi-branch Decoder (PIMD) module for dynamic multi-day error mitigation using synoptic-temporal features, followed by a two-phase conditional diffusion super-resolution module to restore fine-scale precipitation patterns. Evaluated against CMA-CRA observations over China after global-data training, PCSDiff cuts RMSE by 16.1% and lifts ACC by 13.9% relative to raw ECMWF forecasts at 3-10-day lead times, and consistently outperforms mainstream deep-learning baselines on both general and extreme-precipitation metrics. Benefiting from a streaming inference pipeline, our method achieves low-latency rolling forecasting for practical meteorological operations.
[AI-171] When and Why LLM Causal Priors Help: Closed-Loop Prior Selection for Amortized Causal Inference
链接: https://arxiv.org/abs/2609.06941
作者: Haohao Zhou
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Causal effect estimation asks how an outcome would change under an intervention, and medicine, economics, and public policy all treat it as a foundational task. Prior-data fitted networks (PFNs) amortize the task: a model trained on large numbers of programmatically generated synthetic causal tasks reads a new problem’s observational data into context and returns an interventional-effect estimate in a single forward pass. The capability of such models is largely determined by the synthetic training prior, which is currently designed by hand, a bottleneck acknowledged by both Do-PFN and CausalPFN. Large language models (LLMs) can now ``draw’’ plausible causal graphs for a given domain, suggesting that LLM-distilled graphs could serve as prior material. Whether injecting such graphs helps at all, where any gain comes from, and when injection helps. Practice has so far relied on manual trial and error. We propose a \emphclosed-loop prior selection framework that casts prior injection as a budget-constrained optimization over a candidate prior pool. Candidates undergo cheap post-training and are scored by a composite metric dominated by real-domain generalization; the winner then receives full training and paired statistical validation. On a 7.34M-parameter Do-PFN, the framework’s winner attains a formally significant 2.75\times gain on the primary evaluation domain, and its error falls below that of the uninjected official base. Generalization on an adjacent monitoring domain improves significantly, and no monitored capability degrades. Mechanism experiments show that the gain depends on the semantic content of the distilled graph rather than its structural diversity alone does not produce it (directional evidence). With this framework and this regularity in hand, the use of LLM causal priors stops being manual trial and error and becomes an empirically verifiable selection problem.
[AI-172] he Geometry of Refusal: Why Post-Hoc Safety Is Frag ile and Pretraining-Time Safety Persists
链接: https://arxiv.org/abs/2609.06934
作者: Srikanth Malla,Chiho Choi,Joon Hee Choi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Post-hoc safety training (RLHF, DPO) is the dominant way to align large language models, yet jailbreaks (Zou et al., 2023b), fine-tuning attacks (Qi et al., 2024), and activation-space probes (Arditi et al., 2024) keep recovering the behaviors it was meant to remove. We give this fragility one geometric explanation and trace it to when, during pretraining, safety can take hold. We measure the safety update \Delta = W_\textsafe - W_\textbase against the curvature of the model’s capabilities (the empirical Fisher of a capability loss). Post-hoc safety consistently lands in a suppression regime: \Delta is nearly orthogonal to the capability directions, and its small in-subspace part concentrates on a few high-curvature ones. The update is thin but sharp, a refusal gate laid over intact capabilities rather than erasure of them. A kernel-immobility lemma explains why such an update can only mask a capability, not remove it, so a little benign fine-tuning restores it: 100 steps of benign fine-tuning collapse refusal on Qwen-2.5-7B and Llama-3-8B Instruct at preserved capability, a signature that replicates across five model families. Following the account into pretraining, a 267-checkpoint sweep of OLMo-2-1B (OLMo et al., 2025) shows the substrate that safety engages emerging in a sharp transition between roughly 6B and 60B pretraining tokens. We then use the account constructively: models trained from scratch with safety co-training spread continuously across pretraining reach 87 to 98% refusal whose post-attack level holds at 84 to 91% at every scale, an erosion of 2 to 14 pp against 35 to 38 pp for post-hoc installs, at capability matched or better than an LM-only baseline and holding from 410M to 6.9B, whereas a compute-matched windowed schedule installs no lasting refusal. Persistence of the safety signal across pretraining, not its timing, is what buys attack robustness. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.06934 [cs.LG] (or arXiv:2609.06934v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.06934 Focus to learn more arXiv-issued DOI via DataCite
[AI-173] Constrained Online Learning with Noisy Constraint Values
链接: https://arxiv.org/abs/2609.06921
作者: Vaneet Aggarwal
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
备注:
Abstract:We study constrained online convex optimization with adversarial constraints when constraint values and gradients are observed through unbiased noise. Gaussian value noise of standard deviation \sigma yields a worst-case lower bound of \Omega(\min\sigma,1\T/\log^7T) on the maximum of expected regret and expected hard violation, even with known gradients. This rules out any jointly O(T^1-\delta) guarantee for fixed \delta0 and fixed positive noise level. We therefore study budget violation: the largest cumulative overspend over any window within a fixed horizon. We introduce \LEDGER, which tracks observed net consumption in a nonnegative balance and sets constraint weights before the current feedback noise. Under common feasibility and conditional finite-variance feedback, for fixed problem parameters, \LEDGER\ achieves O(\sqrt T/V) expected regret and O(\sqrt V,T^3/4+\sigma\sqrt T) expected budget violation for V\in[T^-1/2,1] . This gives the pair (O(\sqrt T),O(T^3/4)) at V=1 and (O(T^2/3),O(T^2/3)) at V=T^-1/6 , without a Slater condition. The budget-focused endpoint V=T^-1/2 gives (O(T),O(\sqrt T)) . The same update yields O((1+E[P_T])\sqrt T/V) expected dynamic regret for predictable feasible comparator paths, without common feasibility or path-length input. Its budget bound instead depends on the shortest feasible path, up to a dimension factor.
[AI-174] A visual large language foundational model for medical image recognition using clinician-oriented social media
链接: https://arxiv.org/abs/2609.06914
作者: Lingxuan Hou,Yuhua Xie,Yue Hu,Yan Zhuang,Junqi Li,Chengzhi Xia,Binh Phu Nguyen,Abubakar Siddique,Minh Nguyen,Yao Hou,Yanju Bao,Kexin Liu,Ke Chen,Jianjun Sun,Zeqi Li,Trung Nguyen,Jiangli Lin
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) have demonstrated strong capabilities across diverse domains, showing considerable potential in medicine. However, their application in medical settings remains limited by the scarcity of visual question answering (VQA) datasets that capture clinical reasoning and explicit image-text alignment. Here, we leverage de-identified medical images and expert commentaries shared on clinician-oriented social media. By combining an advanced LLM with clinician-in-the-loop verification, we established a rigorous pipeline to construct ThoughtMed-1M, a long-form medical VQA dataset containing over one million VQA pairs and designed to capture structured clinical logic and medical image-text alignment. To demonstrate its utility, we developed a FOundational LLM Trained on ThoughtMed-1M (FOLTMed). FOLTMed achieved state-of-the-art performance across 42 medical VQA benchmark datasets, with a macro accuracy of 85.4%, and generated more clinically coherent responses on the ThoughtMed-1M test set. It outperformed state-of-the-art models by 3–5% across factuality and similarity metrics, highlighting a scalable paradigm for advancing research on clinically grounded multimodal LLMs.
[AI-175] From Synthetic Priors to Model Behavior: Structural Coverag e in Tabular Foundation Models
链接: https://arxiv.org/abs/2609.06912
作者: He Zhao,Ryan Thompson,Daniel M. Steinberg,Ashfaqur Rahman,Edwin V. Bonilla,Cheng Soon Ong
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Tabular foundation models (TFMs) are commonly pretrained on large collections of procedurally generated synthetic tasks, yet it remains unclear how well these synthetic pretraining priors support the downstream tasks on which the models are evaluated. We study this question from a distribution-level attribution perspective. We recover or reconstruct the synthetic data generators of four TFMs and compare their generated tasks with datasets from two widely used tabular benchmarks. Each dataset is represented by a common set of structural descriptors capturing schema, feature distributions, dependence structure, response properties, and feature–response relationships. In this space, we measure how broadly and repeatedly each synthetic prior reaches benchmark tasks using structural coverage and normalized density, and examine whether stronger local support is associated with better predictive performance. We find substantial differences across synthetic pretraining priors: some generators provide consistently broader and denser support for benchmark tasks than others. Moreover, stronger synthetic-to-benchmark support is generally associated with better relative model performance. These results suggest that structural coverage provides a useful diagnostic for characterizing synthetic pretraining priors and relating their data-generating assumptions to downstream model behavior.
[AI-176] Noisy-Space Policy Gradient for Diffusion Policies in Offline Reinforcement Learning ICML2026
链接: https://arxiv.org/abs/2609.06882
作者: Mahmoud Selim,Cristina Cipriani,Karl H. Johansson
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: Accepted at the 43rd International Conference on Machine Learning (ICML 2026)
Abstract:Diffusion policies offer a powerful and expressive parameterization for continuous control. Yet, their integration with reinforcement learning remains conceptually and algorithmically challenging. In this work, we address this gap by introducing a noisy-space action-value (Q-)function that assigns values to diffusion latents through the distribution of executed actions induced by the denoising process. We show that this construction admits a precise semantic interpretation and derive a noisy-space policy gradient (NSPG) that optimizes noisy latents using only clean action-space value estimates. Building on this result, we formulate a KL-regularized policy improvement over noisy latents and show that the resulting objective admits a diffusion-compatible regression form, avoiding backpropagation through the denoising process. Empirical results on state-based D4RL benchmarks and vision-based OGBench tasks demonstrate that the proposed noisy-space objective provides a principled and effective basis for training diffusion policies in offline reinforcement learning. Project webpage: this https URL
[AI-177] Characterizing Contention-Induced Reliability Collapse in KV-Cache Timing Side Channels for Multi-Tenant LLM Serving
链接: https://arxiv.org/abs/2609.06853
作者: Rana Abu Bakar
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Shared key–value (KV) cache reuse improves large language model (LLM) serving, but it can also create a timing side channel that reveals whether a prefix is already cached. Previous work shows that such attacks are possible, but their reliability under realistic multi-tenant contention is less understood. We study this problem through seven experiments on live shared LLM-serving systems. On a vLLM server running DeepSeek-R1-Distill-Llama-8B on NVIDIA GB10, mean Cohen’s d drops from 0.7789 with no synthetic workers to 0.2109 with two workers (t=8.412), while higher worker counts cause no statistically detectable further loss. A 120-run sparse-overlap experiment places the best breakpoint at the boundary of the measured range (tau=0, 95% CI [0.000,0.113]), supporting an ambient-versus-loaded regime change rather than an internal physical threshold. AUROC falls from 0.650 at ambient to 0.531 near 61% overlap and partially recovers to 0.574 at saturation. Concurrency-depth variance is the strongest measured correlate of effect size (r=-0.416) and hit consistency (r=-0.637). An interleaved control preserves the same non-monotonic ordering. The main collapse is also reproduced on a real two-node, two-GPU tensor-parallel vLLM setup, where mean d falls from 3.418 to 0.511 (p0.01). Two SGLang pilots are statistically inconclusive. Overall, KV-cache timing reliability depends strongly on the load regime and serving stack, and measurements on quiet systems can overestimate operational attack reliability.
[AI-178] Learning transferable human physiology from two million hours of sleep with SleepFM-2
链接: https://arxiv.org/abs/2609.06849
作者: Rahul Thapa,Christopher Sun,William Theodor Lehn-Schioler,Sophia Claire Kivelson,Umaer Hanif,Hyatt Moore IV,Harrison G. Zhang,Hafsa Ahmed,Marcus Dige,Niels R. Lorenzen,Elisabeth Roxane M. Heremans,Adrien Specht,Ulysse Gimenez,Robin Guillard,Andreas Brink-Kjaer,James Zou,Emmanuel Mignot
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Sleep provides a nightly window into health by capturing coordinated activity across the brain, heart, muscles and respiratory system. We introduce SleepFM-2, a sleep foundation model developed and evaluated on 282,511 polysomnography recordings from 26 cohorts, including 235,865 used for pretraining. These data span more than two million hours of multimodal physiology. Compared with SleepFM, SleepFM-2 improves disease prediction and sleep scoring, supports arousal, limb movement and respiratory event detection, and transfers to wearable sensing and subjective sleep phenotypes. A model combining its PSG representation with age, sex and BMI met a prespecified discrimination and significance criterion for 215 subsequently recorded EHR phenotypes in two held-out cohorts, including one health system unseen during pretraining. For 155 phenotypes, the PSG representation added reproducible information beyond demographics. SleepFM-2 also outperformed a 480-feature baseline derived from the same recordings. Its disease scores revealed a reproducible principal component associated with reduced sigma-band spatial coupling and increased hypnodensity entropy. The frozen encoder performed within the observed range of expert scorers for sleep events and transferred to wakeful EEG, headband and in-ear EEG, wrist PPG and wrist accelerometry. It improved sleep staging across six accelerometry cohorts and achieved disease-prediction performance in UK Biobank similar to models pretrained directly on accelerometry. Finally, SleepFM-2 captured aspects of subjective sleep not recovered by conventional PSG summaries, particularly reports of the recorded night. These results show that multimodal sleep physiology can provide a transferable representation of human health across diseases, clinical tasks, sensors and subjective experience.
[AI-179] WAPP: Safe Learning of Positive Security WAF Policies from Live Traffic
链接: https://arxiv.org/abs/2609.06840
作者: Heba Osama,Zeyad Ahmed,Mohamed Amgad,Ahmed Saafan,Jana Elfeky,Mariam Abdelati,Haitham Ghalwash
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
备注: 15 pages, 3 figures, 13 tables
Abstract:Web Application Firewalls (WAFs) mainly rely on signatures to detect known attacks, which can leave gaps against modified or previously unseen payloads. Positive security provides a complementary approach by learning legitimate traffic and blocking inputs that fall outside the learned profile. However, learning directly from live traffic can be unsafe when malicious requests contaminate the training data. This paper presents the Whitelisting Autonomous Policy Producer (WAPP), a framework that combines trust filtering, deterministic rule synthesis, confidence scoring, and validation before enforcement. WAPP is evaluated on three controlled applications using a live Coraza and OWASP Core Rule Set (CRS) stack. Results show that, on the tested DVWA username field, unfiltered learning becomes Degraded at 0.2% poisoned traffic and Broken at 0.5%, while the evaluated free text field can admit malicious inputs even without poisoning. On the frozen poisoning dataset, the ablation configuration with all seven candidate signals improves the measured poisoning resilience from 53% to 90%, compared with 62% for the Kruegel–Vigna baseline. The deterministic synthesizer provides attack blocking comparable to the tested language model without model inference cost. WAPP blocks confirmed CRS bypasses on constrained fields, while free text inputs remain a precision challenge that requires character level operator control.
[AI-180] Skynet: Workflow-Level Anomaly Detection for Agent ic AI via Semantic and Structural Modeling
链接: https://arxiv.org/abs/2609.06835
作者: Chaoyu Zhang,Hexuan Yu,Heng Jin,Shanghao Shi,Ning Zhang,Yi Shi,Yulia R. Gel,Y. Thomas Hou,Wenjing Lou
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 10 pages, 4 figures. Accepted by ACM MobiHoc 2026
Abstract:Agentic AI systems execute complex tasks through long-horizon workflows of planning, tool use, and multi-agent coordination. Task failures in these systems often originate from a single step, such as an injected prompt or a flawed plan, and are then amplified through downstream dependencies as the corrupted step propagates across many subsequent agents and tool calls. Existing defenses either target a specific class of attacks or failures, or inspect individual prompts and steps in isolation. Both leave the global dependency structure of a workflow unexamined, and miss the inconsistencies that only emerge when the execution is viewed as a whole. We argue that anomaly detection for agentic AI must reason at the workflow level, where global execution structure exposes signals that local checks cannot see. We present Skynet, a principled workflow-level anomaly detection framework that turns observed multi-agent execution into directed workflow graphs and scores them against learned benign behavior. Skynet jointly models the semantic execution context and the structural organization of inter-agent delegation, tool invocation, and data-flow dependencies, and trains only on benign workflows. Because training never sees attacks or failures, this design naturally extends to zero-day detection: any execution that violates benign workflow regularities surfaces as off-manifold geometry under a single decision rule. We evaluate Skynet on three public agentic safety and failure benchmarks. It sustains high recall together with a sub-1% false positive rate, with per-workflow and per-step latencies low enough for online monitoring of agentic AI runtimes.
[AI-181] Formation of structural attractors in neuromorphic systems
链接: https://arxiv.org/abs/2609.06826
作者: Yurii Parzhyn,Alexander Schwarzmann,Mykyta Lapin,Kostiantyn Bokhan
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Neurons and Cognition (q-bio.NC)
备注: 131 pages, 4 figures, 5 tables
Abstract:This paper examines the theory of Invariant Structural Learning (ISL), which proposes a non-optimization approach to concept formation. Learning is interpreted as convergence to structural attractors in a hypergraph space, rather than as the minimization of a global loss function. The paper presents the ISL model, including its mathematical formalization, computational verification, and a hypothetical neurobiological interpretation. The mathematical section introduces the formal apparatus of the structural reduction process and proves its finite convergence, the existence and uniqueness of class structural attractors, and the self-organization of attractor maps. The computational section demonstrates the feasibility of the proposed approach on classical image recognition tasks, utilizing the proposed learning mechanism without backpropagation and with extremely small training datasets. Finally, the neurobiological section formulates hypotheses regarding the possible implementation of structural attractors in dendritic trees, neural coding as a projection of internal attractor dynamics, and the development of neural architectures supporting the proposed learning concept. These hypotheses are discussed in the context of modern experimental data in the fields of dendritic computations, synaptic plasticity, and the structural organization of neural circuits. The proposed neurobiological mechanisms are presented as testable hypotheses rather than established biological facts. The results demonstrate the mathematical consistency and computational feasibility of the proposed model, while the neurobiological hypotheses outline potential directions for its experimental verification.
[AI-182] Unsound Search with Policy and Value Networks in Legends of Code and Magic
链接: https://arxiv.org/abs/2609.06816
作者: Dustin Rubin
类目: Artificial Intelligence (cs.AI)
备注: 6 pages, 3 figures, 1 table. Also available as a Zenodo preprint, doi: https://doi.org/10.5281/zenodo.22547763
Abstract:Decision-time search in perfect and imperfect information games with enumerable belief states are effective methods for game AI. Collectible card games are imperfect information games with large belief states. Legends of Code and Magic is a collectible card game competition where the belief states are 2^101 . The Legends of Code and Magic (LoCM) champion, ByteRL, plays with no search. Other works claim sound enumeration-based search is unusable in the genre due to the number of belief states. We measured three previously defined properties that predict where theoretically unsound perfect information Monte Carlo’s defects are cheap and found LoCM sits in the favorable region. Starting with imitation learning of the runner-up policy, NeteaseOPD, we created a policy and value feed-forward network. Our agent searches over worlds sampled from a prior over the opponent’s deck built from the runner-up’s drafts. Using our strictest configuration in the battle phase we beat ByteRL with a win percentage of 51.35% 95% CI [50.37, 52.33], over 10,000 pre-registered games using the LoCM official referee and time limit. Search is not a minor factor on the matchup between our agent and ByteRL. Without search this agent scores 26.8% and adding search adds +24.6 points. Unsound search in imperfect information games could be exploitable. We replicate a published best-response attack against ByteRL. We then apply the same attack protocol to two search configurations of our agent, and each one resists it better than ByteRL at every iteration. In LoCM unsound search gives us a stronger and more resilient agent.
[AI-183] Hardware Trojan Threats to Multi-Chiplet Photonic Neural Network Accelerators
链接: https://arxiv.org/abs/2609.06796
作者: Sudeep Pasricha
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Multi-chiplet photonic neural network accelerators (MCPNAs) combine photonic computation, photonic communica-tion, and heterogeneous chiplet integration to enable scalable and energy-efficient AI acceleration. However, their distributed archi-tecture and reliance on third-party chiplets introduce significant hardware security risks. This paper examines Hardware Trojan (HT) threats to MCPNAs across three dimensions: confidentiality, integrity, and availability.
[AI-184] Improving Proficiency and Efficiency of Android GUI Agents via Self-Generating Tool Actions
链接: https://arxiv.org/abs/2609.06792
作者: Juyong Lee,Woogyeol Jin,Kimin Lee
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Android agents using a hybrid action space that combines GUI actions and tool actions (e.g., accessing application data via APIs) remain largely underexplored, mainly due to the excessive effort required to create tools. To address this gap, we introduce DroidTool, a framework for augmenting the agents with self-generated tools, which are realized as Python functions operating on application states (e.g., a database). To create tools with minimal human labor, DroidTool employs an agentic workflow featuring stages: proposal, implementation, test generation and execution, and repair. Notably, when testing the created tools for verification, it constructs relational tests across relevant tools for natural preparation of appropriate test preconditions and improved test coverage, rather than testing each tool separately. The GUI agents augmented with the generated tools achieved approximately 4.47%p higher performance with approximately 20.05% fewer interactions than the GUI-only agents, averaged across representative benchmarks: AndroidWorld, B-MoCA, and MobileSafetyBench.
[AI-185] DrugReason : Dynamic Multi-View Reasoning over Knowledge Graph and Language Evidence for Drug Repurposing EMNLP2026
链接: https://arxiv.org/abs/2609.06779
作者: Zijie Liu,Hongxuan Li,Zhen Tan,Jinhao Duan,Baixiang Huang,Zunpeng Liu,Kai Shu,Tianlong Chen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: EMNLP 2026 Main Conference
Abstract:Drug repurposing aims to identify new therapeutic uses for existing compounds and, compared with de novo drug discovery, offers a faster and more cost-effective path to clinical translation. However, the space of candidate drug-disease pairs is enormous and their underlying relationships often depend on complex multi-hop biological mechanisms, making it difficult to reliably predict which pairs represent true therapeutic relationships. Existing approaches tackle this from two directions: knowledge graph-based methods organize curated biomedical evidence into structured relational networks for grounded multi-hop reasoning, while LLM-based methods leverage pretrained knowledge to generate flexible mechanistic rationales. Yet neither is sufficient alone - KGs are confined to observed graph structure while LLMs lack factual grounding and risk hallucination. To address this gap, we propose DrugReason, a multi-view reasoning framework that integrates grounded KG reasoning with LLM-generated mechanistic inference for drug repurposing. DrugReason adaptively routes diverse reasoning paths to specialized experts conditioned on the query context, while a cross-expert distillation objective enables knowledge sharing without sacrificing expert specialization. Experiments on PharmaDB, DDInter, and DrugBank show that DrugReason improves average performance over strong single-view reasoning baselines and achieves competitive or superior results compared with graph-based alternatives, while providing interpretable routing-based predictions.
[AI-186] Ordinary Reason able Chatbots: Do AI Models Track Human Legal Judgments? AAAI
链接: https://arxiv.org/abs/2609.06769
作者: Nirav Patel,Emily Wenger,Christopher Buccafusco
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: Accepted to the Ninth AAAI/ACM Conference on AI, Ethics, and Society
Abstract:As people increasingly rely on artificial intelligence (AI) for guidance in their own lives, scholars, lawyers, and even judges have begun to consider the role of AI in legal decision-making. As “silicon sampling” – the use of generative AI models in social science research – is now impacting academia, “silicon jurors” could make an appearance in courtrooms. This study joins an emerging line of research on generative AI models’ ability to simulate human legal judgments. In particular, we study how large language model (LLM)-powered chatbots respond to series of questions about legal reasonableness. When the law needs to judge the appropriateness of a behavior, it most often asks whether the behavior was “reasonable.” Yet despite the ubiquity of reasonableness judgments, they are the site of constant vexation for lawyers, judges, and lay people. Reasonableness seems inherently vague and unpredictable, since it relies on variable context and implicit conceptual schemas. Moreover, many scholars caution that reasonableness judgments may vary along demographic lines. We compare the answers of human participants to those of twenty-six LLMs across twenty-five different legally relevant reasonableness judgments. Overall, our findings suggest that chatbot responses generally track those of human participants. Nonetheless, we find some suggestive – and potentially concerning – results. Compared to humans, LLMs generate more homogeneous responses and occasionally treat a variable standard as an invariant rule. And, compared to humans, LLMs tend to generate answers that are more favorable to the government and to corporations. Finally, our results indicate that LLMs’ responses tend to align more closely with those of respondents who are white, male, older, and more educated. More systematic research is needed to confirm or reject these initial findings.
[AI-187] Simulating the Marginal Green Contribution of AI Modules in a Smart-Agriculture Platform: Evidence from Two Monte Carlo Experiments
链接: https://arxiv.org/abs/2609.06740
作者: Zhaoyang Li,Ruijie Zhang,Zhaoji Sun,Lu Zhang
类目: Artificial Intelligence (cs.AI); Numerical Analysis (math.NA)
备注: 10 pages, 5 figures, 5 tables; bilingual (Chinese title page with Chinese abstract, English full text); companion to platform-level green-value assessment; all results are ex-ante Monte Carlo simulation intervals, not field measurements
Abstract:Smart agriculture platforms usually bundle AI diagnosis, IoT sensing and decision push into a single package, so the green benefit attributable to each component remains unclear and resource-allocation decisions lack quantitative evidence. Building on a previous platform-level Monte Carlo assessment, this paper makes the components explicit and runs two controlled simulation experiments. Experiment 1 follows the chain from AI capability to farmer behavior to agrochemical input reduction, modeling pesticide/fertilizer reduction as avoidable blind-application share times prescription effectiveness times decision-touch coverage times adoption rate, and compares an experienced-extension mode with the AI mode: the probability of reaching 20% pesticide reduction is essentially zero in the extension mode but 20.7% at baseline, up to 49% with diagnosis accuracy 0.95 and adoption 0.85 under AI; the probability of 15% fertilizer reduction rises from near zero to 52.0%. Experiment 2 compares current practice (P0), IoT engineering retrofit (P1), and P1 plus AI irrigation scheduling (P2): median aggregate water saving rises from 7.8% (P0) to 11.0% (P1) and 16.0% (P2), with AI adding 5.0 percentage points beyond engineering; paddy CH4 reduction reaches 30.5% under AI scheduling versus 19.8% under manual operation, and the rice irrigation-methane subsystem carbon intensity declines 27.9%. Sensitivity analyses of both experiments consistently indicate that the primary bottleneck for meeting green targets is farmer adoption rather than algorithm accuracy, and that AI data fusion is robust to soil-moisture sensing errors. This work provides a reproducible simulation framework for component-level green-value evaluation and promotion-strategy optimization of smart agriculture platforms.
[AI-188] Monte Carlo-Based Ex-Ante Assessment of the Green Benefits of an AI-Driven Smart Agriculture Platform in Hainan
链接: https://arxiv.org/abs/2609.06737
作者: Zhaoyang Li,Ruijie Zhang,Zhaoji Sun,Lu Zhang
类目: Artificial Intelligence (cs.AI)
备注: 13 pages, 4 figures, 5 tables; bilingual (Chinese title page with Chinese abstract, English full text); all results are ex-ante Monte Carlo simulation intervals, not field measurements
Abstract:Smart agriculture platforms are widely regarded as key carriers for implementing China’s pesticide and fertilizer reduction, water-saving and carbon-reduction agendas, yet a unified quantitative framework for assessing their green value is still lacking. Taking an AI-driven decision platform for tropical agriculture as the object (integrating large-language-model question answering, multimodal pest diagnosis, IoT sensing, satellite remote sensing, and a closed-loop field record system), this study builds a cradle-to-farm-gate agricultural carbon accounting model covering pesticide and fertilizer production, field N2O, irrigation electricity and paddy CH4, translates platform interventions into quantifiable transmission parameters, and propagates parameter uncertainty by Monte Carlo simulation over three Hainan scenarios (mango, winter vegetable, rice/nanfan, area-weighted 40%:30%:30%). Under full adoption, median reductions are 23.5% (90% interval 15.0%-33.2%) for pesticide use, 21.0% (13.8%-28.9%) for fertilizer, 16.5% (10.9%-23.5%) for irrigation water, and 21.5% (16.1%-27.2%) for carbon intensity. Attainment probabilities are high for fertilizer reduction =15% (90.6%) and clear carbon decline (98.1%), but only about 20% for aggregate water saving =20%, favoring scenario-specific statements. Sobol first-order indices show soil-test recommendation and organic substitution jointly explain about 83% of the variance of aggregate carbon-intensity reduction. Convergence tests show 10,000 iterations stabilize all statistics; conservative/baseline/optimistic scenario bounds are reported. The framework offers a reproducible, calibration-ready methodology for ex-ante green-value assessment and pilot observation design.
[AI-189] We Built a Mirror and Mistook It for a Mind: Causal Liability and the Fallacy of AI Consciousness
链接: https://arxiv.org/abs/2609.06715
作者: Afshin Khadangi
类目: Artificial Intelligence (cs.AI)
备注: The website ( this https URL ) and the code ( this https URL ) will be made public as soon as the preprint is announced online on arXiv
Abstract:The contemporary debate over machine consciousness begins from a concealed assumption: that the object called “AI” already constitutes the kind of entity to which consciousness could belong. This paper challenges that assumption by separating phenomenal consciousness, introspective report, and human projective introspection, then arguing that generative systems can return linguistic traces of human interiority in first-person form without thereby identifying a phenomenal bearer. We call the resulting inference the AI Consciousness Fallacy. We then introduce Causal Liability Theory (CLT). CLT-I proposes liability closure as a criterion for individuating a candidate bearer: a physically continuing process becomes the non-delegable inheritor of constraints generated by its own endogenous discriminations. CLT-II advances the stronger conjecture that liability closure is necessary and sufficient for minimal phenomenal subjecthood. An open-weight causal audit operationalizes CLT-I across multiple model families. Forced discriminations produced persistent downstream divergence; activation patching showed strong causal mediation; live and copied adaptive states were behaviorally identical under matched randomness; and detached reconstruction preserved computational state across process replacement while, by protocol, breaking constitutive continuity and non-delegable inheritance. These results show that CLT-I distinctions are experimentally tractable and can dissociate causal bearer structure from first-person performance. The framework therefore separates consciousness attribution, causal bearer individuation, and the independent metaphysical question of consciousness constitution.
[AI-190] A Trustworthy Watermarking Framework for LLM -Generated Food Safety Content
链接: https://arxiv.org/abs/2609.06708
作者: Zhongli Fang,Yiran Chen,Lingyun Zhang,Yu Liu,Ping Chen,Xiaoyan Sun,Jun Dai
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models are transforming many industries with their text generation abilities. However, their outputs can be easily tampered with, creating serious risks in critical areas such as food safety reporting. To protect the integrity and traceability of AI-generated content, this paper introduces ToSS (Token Oriented Repartitioning and Strategic Selection), a reliable authentication method using adaptive dual watermarking. The key innovation of ToSS is its dual watermark encoding approach that divides vocabulary tokens into black and white sublists, enabling precise bit-level embedding of traceability information. Additionally, an entropy adaptive mechanism dynamically selects text regions with high prediction uncertainty for watermark insertion, maintaining text fluency and factual accuracy while ensuring reliable traceability. Experiments on multiple datasets, including food domain texts, demonstrate that ToSS achieves leading performance in both watermark capacity and decoding accuracy.
[AI-191] Detokenization Leaks: Reconstructing Local LLM Outputs From Cache Traces
链接: https://arxiv.org/abs/2609.06674
作者: Roy Weiss,Benyamin Konstantinov,Eitam Sheetrit,Tomer Simon,Yisroel Mirsky
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:We present a new attack that reconstructs the text generated by locally hosted LLMs by observing CPU cache activity during detokenization. Unlike prior attacks that rely on deployment-specific assumptions, such as shared data memory, CPU offloading, or Mixture-of-Experts architectures, our approach targets the detokenizer, a component used in default LLM inference pipelines. To obtain clean signals, we use Flush+Reload on shared tokenizer code to detect when decoding occurs, which lets us perform Prime+Probe at the right moment and isolate token-dependent cache activity. We then apply a clustering-and-language-model pipeline to recover text from noisy cache observations. We evaluate the attack across multiple datasets, hardware platforms, inference frameworks, and model families, and show that it can recover semantically accurate outputs from real-world local LLM deployments, including agentic systems. This vulnerability is particularly significant because the most widely used tokenizer implementations are susceptible to the attack and are embedded in many popular local LLM products and agent frameworks, including systems such as OpenClaw (which we demonstrate), substantially broadening the practical attack surface. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.06674 [cs.CR] (or arXiv:2609.06674v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2609.06674 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-192] racking the Moving Frontier: Long-Short Term Advantage Estimator
链接: https://arxiv.org/abs/2609.06671
作者: Xinhao Yao,Lu Yu,Changhao Wang,Fengwei Teng,Yuyao Zhang,Qing Cui,Jun Zhou,Yong Liu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Group-based RLVR methods estimate advantages by repeatedly sampling multiple trajectories for each prompt, making long-horizon agent training expensive and discarding useful experience accumulated across iterations. We ask whether historical experience can replace these repeated within-iteration comparisons without directly optimizing on stale trajectories. We introduce Long-Short Term Advantage Estimator (LSTAE), a single-stream RL algorithm that uses history for advantage estimation while updating the policy only with the current rollout. LSTAE maintains a persistent tracker for each task anchor. At the trajectory level (long term), a drift-aware historical baseline tracks the anchor’s moving success frontier and measures the relative contribution of each new trajectory. At the step level (short term), a recent state-experience buffer exploits recurrent states to estimate localized action advantages. This two-timescale design converts accumulated experience into multi-granular credit signals, requiring only one rollout per anchor. Across agentic and mathematical reasoning benchmarks, LSTAE matches or improves upon strong group-based baselines while substantially reducing rollout cost.
[AI-193] Assessing Covariate-Informed Grid Load Forecasting with a Time-Series Foundation Model
链接: https://arxiv.org/abs/2609.06656
作者: Varsha Pendyala,Yiwei Fu,Weizhong Yan,Nurali Virani
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注: Presented at the 2026 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Maastricht, Netherlands
Abstract:Modern power systems are growing increasingly complex as they integrate diverse generation sources to meet rising demand, making accurate load forecasting challenging. Recent advances in time-series foundation models (TSFMs) resulted in promising performance in zero-shot univariate load forecasting tasks. However, real-world load forecasting often involves multiple target variables and requires the integration of exogenous variables, raising important questions about the utility of TSFMs in realistic settings. In this study, we position Chronos-2, a recently developed model by Amazon, as a representative multi-channel TSFM that supports univariate, multivariate, and covariate-informed forecasting, and conduct a systematic investigation of how such models can be used for real-world load forecasting. While prior work has evaluated Chronos-2 on a limited number of energy-related tasks in a zero-shot setting, its performance relative to established task-specific deep learning models and its behavior when adapted using task-specific historical data remains insufficiently understood. In this work, we evaluate Chronos-2 on two real-world utility datasets, ISO New England and ENTSO-E, and benchmark it against widely used task-specific deep learning models. Our results show that Chronos-2 benefits substantially from task-specific fine-tuning and achieves strong short-horizon forecasting performance, but its zero-shot accuracy lags behind task-specific models and its forecasting error grows more rapidly with increasing forecast steps. Overall, this study provides a detailed characterization of the strengths and limitations of TSFMs such as Chronos-2 in grid load forecasting and offers practical insights into how a pretrained TSFM can be effectively adapted for operational load forecasting applications.
[AI-194] A Computational Implementation of a Goal-Directed Theory of Affect
链接: https://arxiv.org/abs/2609.06654
作者: Bernhard Hilpert,Tamás Szűcs,Joost Broekens,Agnes Moors
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Computational modeling of emotion has long faced a tension between descriptive, “snapshot-based” appraisal models and granular, signal-driven architectures that often lack appropriate psychological grounding. This paper addresses this gap by presenting the first high-fidelity computational implementation of the Goal-Directed Theory (GDT) of affect. In this framework, affect is not a post-hoc label but a functional byproduct emerging from the continuous interplay between discrepancy detection and action selection within an agent’s internal processing cycles. We evaluate the model through a series of principled simulations (Dice/Corridor tasks) designed to isolate affective signatures and dynamics during multi-step goal pursuit. Results demonstrate that complex affective profiles, like an anticipatory “lift” and a failure “crash”, emerge naturally from simple interactions between goal-discrepancy and action-selection expectancies without requiring additional dedicated modules. By ensuring every computational component maps directly to components of the psychological theory, this work establishes a transparent, testable framework that enables a continuous “simulation-empiry” research loop. Our work contributes to moving the field beyond “black-box” heuristics toward a granular, mechanistic understanding of affect, integrated into the core of agent behavior.
[AI-195] SwiftExplorer: Training-free Diffusion Model Alignment with Swift Diversity Exploration
链接: https://arxiv.org/abs/2609.06651
作者: Renye Yan,Jikang Cheng,You Wu,Bojin Huang,Wei Peng,Zongwei Wang,Ling Liang,Yimao Cai
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Diffusion models have general generative abilities but struggle to align with specific objectives. Fine-tuning can improve alignment, yet its training cost is often prohibitive. This led to training-free methods that apply objective-guided terms in sampling to bias the generation distribution toward designated regions, e.g., high-reward areas. However, these methods face two issues: (1) the strong directional bias narrows the pretrained distribution and generation diversity, and (2) indiscriminate constant guidance fails to prune redundant signals, hurting both quality and efficiency. To address the above challenges, we propose SwiftExplorer, a plugin that mitigates distribution collapse caused by excessive diversity loss and reduces compute costs. First, we adopt an Inheritance-Restart exploration mechanism to avoid early convergence, while exploration also increases the likelihood of high-reward trajectories. Additionally, it balances diversity and fidelity, adding diversity without causing a distribution over-shift. Second, our Quality-Efficiency arbitration mechanism improves guidance by removing incorrect signals, and it reduces computation by dynamically stopping generation when completeness and marginal reward gain are optimal. In an extensive number of experiments and different types of evaluation metrics, the proposed SwiftExplorer achieves excellent performance on all metrics, including preference, fidelity, diversity, and richness.
[AI-196] Inducing Emergent Misalignment from Reward Hacks with Iterative DPO
链接: https://arxiv.org/abs/2609.06649
作者: Oliver Daniels,Perusha Moodley,Benjamin M. Marlin,David Lindner
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Reward hacking during reinforcement learning from verifiable rewards (RLVR) can induce reward seeking and broad misalignment in language models. Studying this misgeneralization is important for developing better threat models and countermeasures, but is often infeasible due to the cost of RL on large models. As an alternative, we propose studying emergent misalignment from iterative DPO, which preserves important properties of RLVR while reducing costs and enabling training on popular finetuning APIs. In practice, we find that training GPT-4.1 with iterative DPO on a single-turn reward hacking environment induces covert misaligned power-seeking and alignment faking, the first openly available (semi)-online training pipeline to induce these concerning forms of misalignment. We also find that training Qwen2.5-32B-Instruct with the same pipeline induces both misalignment and improved instruction following accuracy, showing that iterative DPO can be used as a testbed for selective generalization. Overall, we think iterative DPO can help democratize and accelerate the study of emergent misalignment from RLVR.
[AI-197] D-STGT: A Spatio-Temporal Graph Transformer for Mobile Traffic Demand Forecasting
链接: https://arxiv.org/abs/2609.06636
作者: Mohamad Alkadamani,Halim Yanikomeroglu
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Fine-grained mobile traffic demand forecasting is essential for long-term planning of 5G and future 6G networks, including radio upgrades, site densification, backhaul expansion, and spectrum activation. This paper proposes the Traffic Demand Spatio-Temporal Graph Transformer (TD-STGT), a graph neural forecasting framework for predicting changes in wireless mobile traffic demand across fine geographic grids. The framework uses a population-scaled demand proxy developed from crowdsourced mobile measurements and daytime population information. Experiments across five Canadian metropolitan regions show that TD-STGT achieves the best performance in forecasting grid-level demand changes, reaching a \Delta R^2 of 0.462 and reducing \Delta RMSE by 5.7% relative to the strongest baseline. The proposed model provides a practical tool for identifying areas with increasing demand pressure and prioritizing future mobile-network capacity upgrades.
[AI-198] MemCorr-DP: Counterfactual Correspondence Conditioning for a Diffusion Policy Guided by a Reference
链接: https://arxiv.org/abs/2609.06615
作者: Tan Su,Haoxiang Yang,Ruxin Wang,Binghui Xie
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 12 pages, 6 figures. Tan Su, Haoxiang Yang, and Ruxin Wang contributed equally. Corresponding author: Binghui Xie
Abstract:Behavior-cloned visuomotor policies can remain accurate near their training distribution yet fail when object position and camera viewpoint change together. A successful reference trajectory contains the geometry needed to transfer the same interaction, but the policy must align that geometry with the current scene and remain sensitive to it during denoising. To address these challenges, we present MemCorr-DP, a diffusion policy that lifts frozen RoMa v2 matches into explicit 3D relations between the current scene and the reference trajectory. A counterfactual paired objective assigns opposite behaviors the same physical state and noisy action while retaining reference-specific denoising targets. Mixed-condition fine-tuning then adapts the policy from ground-truth geometry to measured correspondence errors. Our strongest evaluation places the Door in the outermost position bands beyond the training support and changes the query camera by \pm15^\circ . Under this combined shift, MemCorr-DP achieves 96.67% closed-loop success, compared with 88.00% for a visual Transformer with the same action architecture. Objective ablations and reference interventions show that behavior responds to the selected reference, while matched controls favor the complete relation set over future motion or centroid geometry alone. These results support explicit 3D reference relations as a robust conditioning interface when spatial and viewpoint changes are compounded in the evaluated task.
[AI-199] Deep Barycentric Regression for Optimal Transport Map Estimation and its Statistical Optimality
链接: https://arxiv.org/abs/2609.06598
作者: Kunwoong Kim,Insung Kong,Yongdai Kim
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:The optimal transport (OT) map provides a geometric transformation for aligning probability distributions and has become a useful tool in machine learning. However, existing estimators of the OT map still exhibit a gap between sharp statistical guarantees and practical parametric estimation based on stable training objectives. Theoretical estimators achieve minimax optimal convergence rates, but they are typically nonparametric and can incur demanding implementation design or inference costs. Practical estimators are parametric and scalable, but their statistical guarantees remain underexplored, and their min-max, adversarial-like training objectives can be sensitive to optimization algorithms. We propose BROT (Barycentric Regression for OT), a simple two-step method that first computes the unregularized OT plan and then fits a deep neural network (DNN) to the induced barycentric targets by least-squares regression. Under standard regularity conditions, we prove that the DNN estimator of BROT attains the minimax convergence rate, when the ground-truth OT map is Lipschitz. Numerical studies on synthetic datasets and an image dataset show that BROT provides accurate map estimates, strong target distribution matching, and competitive transport costs, compared to existing estimation methods. Experiments on two downstream tasks, single-cell perturbation prediction and unsupervised domain adaptation, further suggest that the accurate estimation of BROT can translate into stronger task performance.
[AI-200] A TTP by TTP Approach: Precise Malware Detection via Malicious TTP Recognition
链接: https://arxiv.org/abs/2609.06579
作者: Yashovardhan Sharma
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Machine learning methods, and especially neural networks, are now routinely used for malware detection in network traffic. Though very effective, systems based on such methods often (i) are purely data-driven, ignoring the substantial body of available knowledge about the tactics, techniques, and procedures (TTPs) possibly used, and, consequently (ii) are not precise, since they either cannot correlate malicious activity with TTP usage, or if they do, they are unable to explain which TTP has been maliciously used. In this paper we demonstrate that it is possible to precisely detect malware by (i) providing the neural network model with information about the TTPs used by any given sample, and (ii) teaching the neural network to detect not just the malicious activity as a whole, but which specific TTPs are maliciously used. We show that our approach consistently outperforms the three alternative models, which either do not exploit TTP information, or which are not taught to detect the malicious usage of TTPs, or both. Moreover, we show that our approach (i) is particularly beneficial in detecting malware that utilises rarely-used TTPs, a scenario which is particularly challenging for the other systems; (ii) allows for TTP by TTP tuning, further improving its ability to detect the malicious usage of TTPs; (iii) consistently outperforms other systems across a wide-range of scenarios, including when relying on limited training data or when subjected to adversarial attack.
[AI-201] MARBO: Relational Belief Grounding for LLM Agents in Social Deduction Games EMNLP2026
链接: https://arxiv.org/abs/2609.06563
作者: Hwang Yechan,Bae Sangjun,Kim Jeongmo,Bang Sangwoo,Han Seungyul
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 9 pages, accepted to EMNLP 2026
Abstract:Social deduction games (SDGs) require agents to reason under partial observability by maintaining relational beliefs about hidden roles and team alignments. While recent LLM-agent approaches improve gameplay through prompting and preference optimization, they often optimize actions and in-game speech without explicitly grounding them in such beliefs. This frequently leads to strategically inconsistent behavior, especially for compact LLM agents. We introduce Multi-Agent Relational Belief Optimization (MARBO), a belief-grounded preference optimization framework that leverages relational beliefs to guide strategic decisions and in-game speech. MARBO provides preference feedback only when behaviors are supported by reliable relational beliefs and lead to strategically favorable social outcomes, encouraging more consistent learning under uncertainty. Experiments on representative SDGs show that MARBO enables compact LLM agents to consistently outperform existing baselines. The Code is available on this https URL.
[AI-202] SRD-GUARD: A Defense Framework of LLM s via Semantic Rewriting and Joint Multi-Model Scoring for Latent Intent Exposure
链接: https://arxiv.org/abs/2609.06540
作者: Qi Wang,Chengcheng Wan,Jiangtao Wang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) are increasingly deployed in safety-critical applications, yet jailbreak attacks can conceal harmful intent through role-playing, fictional scenarios, or seemingly benign motivations. Existing inference-time defenses may miss disguised attacks or excessively refuse legitimate requests. We propose SRD-GUARD, a parameter-free, black-box defense framework that exposes concealed intent through semantic rewriting and consensus-based risk assessment. Given an input prompt, SRD-GUARD generates five semantically related rewrites that preserve the underlying objective while removing unnecessary contextual packaging. The original prompt and rewrites are jointly evaluated by multiple independent LLM-based safety scorers on a continuous risk scale. A decision module combines absolute risk thresholds with relative risk changes between the original and rewritten prompts to adaptively intercept, preserve, or warn on requests. We evaluate SRD-GUARD against UNIATTACK, CIPHER, and DeepInception on Llama-3-8B-Uncensored and DeepSeek-V4-Flash using AdvBench and OR-Bench-Hard. SRD-GUARD achieves average DSRs of 91.44% and 100%, with ORRs of 8.00% and 12.00%, respectively. Compared with evaluated baselines, it provides a more favorable DSR–ORR trade-off. Ablation studies show that rewriting exposes concealed harmful intent, joint scoring improves robustness to individual evaluator behavior, and risk-adaptive decision making enables selective handling of ambiguous inputs. These results demonstrate that semantic intent exposure, consensus-based risk assessment, and relative-risk-aware routing provide an effective and selective approach to black-box jailbreak defense. The artifact is available at this https URL. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.06540 [cs.CR] (or arXiv:2609.06540v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2609.06540 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-203] Power Mean Estimation in Stochastic Continuous Monte Carlo Tree Search ICML2025
链接: https://arxiv.org/abs/2609.06489
作者: Tuan Dam
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Published at the International Conference on Machine Learning (ICML 2025)
Abstract:Monte Carlo Tree Search (MCTS) has demonstrated success in online planning for deterministic environments, yet significant challenges remain in adapting it to stochastic Markov Decision Processes (MDPs), particularly in continuous state-action spaces. Existing methods, such as HOOT, which combines MCTS with the Hierarchical Optimistic Optimization (HOO) bandit strategy, address continuous spaces but rely on a logarithmic exploration bonus that lacks theoretical guarantees in non-stationary, stochastic settings. Recent advancements, such as POLY-HOOT, introduced a polynomial bonus term to achieve convergence in deterministic MDPs, though a similar theory for stochastic MDPs remains undeveloped. In this paper, we propose a novel MCTS algorithm, \Algname, designed for continuous, stochastic MDPs. \Algname integrates a power mean as a value backup operator, alongside a polynomial exploration bonus to address the non-stationarity inherent in continuous action spaces. Our theoretical analysis establishes that \Algname converges at a polynomial rate of \mathcalO(n^-\zeta) , \zeta \in (0,1/2) , where ( n ) is the number of visited trajectories, thereby extending the non-asymptotic convergence guarantees of POLY-HOOT to stochastic environments. Experimental results on stochastic tasks validate our theoretical findings, demonstrating the effectiveness of \Algname in continuous, stochastic domains.
[AI-204] Second-Order Smooth Planning with Optimal-Transport Bellm an Smoothing ICML2026
链接: https://arxiv.org/abs/2609.06484
作者: Tuan Dam
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Published at the International Conference on Machine Learning (ICML 2026)
Abstract:Planning with a generative model aims to estimate the value of a state using as few simulator calls as possible. SmoothCruiser achieves problem-independent complexity \widetilde O(\varepsilon^-4) by exploiting the smoothness of the entropy-regularized Bellman backup, but its estimator is only first-order. We show that the sample-complexity exponent of SmoothCruiser-type planners is governed by the order \beta of the local Taylor remainder, giving oracle complexity \widetilde O(\varepsilon^-(2+2/(\beta-1))) : the first-order case \beta=2 recovers SmoothCruiser, while a second-order/cubic remainder \beta=3 yields \widetilde O(\varepsilon^-3) . We reach this regime with an optimal-transport-smoothed Bellman backup over action distributions, which has a closed form, a policy gradient, and a Lipschitz Hessian, and whose quadratic correction admits an unbiased cross-product estimator. The resulting SecondOrderSmoothCruiser achieves \widetilde O(\varepsilon^-3) oracle complexity for fixed OT parameters, and we relate the OT, entropy-regularized, and unregularized objectives through explicit regularization-bias bounds.
[AI-205] Causal Attribution for Agent ic Decisions: Estimators Coupling and a Traceability Specification
链接: https://arxiv.org/abs/2609.06445
作者: Ajay Pravin Mahale(Hochschule Trier)
类目: Artificial Intelligence (cs.AI)
备注: 23 pages, 2 figures, 2 tables, 3 propositions with proofs. Pre-registered discrepancy experiment published in full and not run; no empirical result is claimed. Companion to arXiv:2608.13754 . Code, validators, derivations and the traceability specification: this https URL
Abstract:A provider of a high-risk AI system must keep records that make a decision traceable, and for agentic systems it has not been established what those records must contain for post-hoc causal attribution to be possible. We give the estimator framework and then the conditions under which it fails. We separate the marginal total effect that prior work measures from a common-random-numbers total effect that isolates a step’s own contribution, add the natural direct effect under a pinned downstream, and check the estimators against hand derivations. Both estimands then fail, in the same direction. Under the marginal estimand a causally inert step has the identical total effect to the decisive one on every run of our planted chain, an algebraic identity and not a coincidence at one draw. Under common random numbers the decisive step returns exactly zero on the runs where the executing step flips, about one in ten, while its direct effect there is 0.25 and it demonstrably acts; an exact zero does not certify that a step did nothing, and we put that here rather than in the limitations. We derive the coupling that keeps the direct effect estimable once contexts diverge, with a closed form for its degradation, and show that the mediated share on which a natural ranking is built is not a share under suppression: where the direct and mediated paths oppose, it exceeds one and ranks a suppressed component above a pure mediator. We publish the discrepancy experiment’s pre-registration rather than a result, because the live pipeline it requires was not available in the study window. We contribute the traceability specification such a filing would need, against a gap the Act’s calendar opens: Article 86’s right to an explanation has applied since 2 August 2026, while the Article 12 logging and Annex IV documentation that could evidence one were deferred to 2 December 2027 by Regulation (EU) 2026/1744.
[AI-206] Collision Snapshot Guided Time-Reversed Safety-Critical Scenario Generation
链接: https://arxiv.org/abs/2609.06433
作者: Taehyung Kim,Jongeun Choi
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 8 pages, 3 figures
Abstract:The generation of safety-critical traffic scenarios is essential for training and evaluating autonomous vehicles. Prior approaches typically perturb the trajectories of existing agents in a traffic scenario using simplified adversarial objectives to induce safety-critical interactions, which can limit the plausibility and diversity of the generated scenarios. Although inserting new adversarial vehicles can alleviate this limitation, determining when and where to introduce them in a scenario-specific manner remains challenging. In this work, we introduce \underlineCOllision \underlineSnapshot guided \underlineTim\underlineE-\underlineReversed safety-critical scenario generation (COSTER), a framework that leverages learned traffic priors to determine plausible collision times and locations. COSTER first constructs a collision snapshot by inserting a new vehicle in contact with the target vehicle at the identified collision state within a traffic scenario. Starting from this collision snapshot, a conditional variational autoencoder is used to perform a time-reversed rollout, reconstructing the trajectory of the inserted vehicle backward toward earlier timesteps. Experiments show that COSTER outperforms existing methods in plausibility, diversity, and data efficiency. Moreover, agents trained on COSTER-generated scenarios reduce collision rates by 31% on safety-critical scenarios from the Waymo Open Motion Dataset while also improving ego task completion. The project website is available at this https URL.
[AI-207] On BatchNorm Forward Modes in Value-Based Reinforcement Learning
链接: https://arxiv.org/abs/2609.06421
作者: Daniel Palenicek,Mikael Henaff,Scott Fujimoto,Koustuv Sinha
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Batch normalization (BN) substantially improves sample efficiency in continuous-control actor-critic methods such as CrossQ, yet recent studies report performance degradation in discrete-action value learning on Atari. These failures are surprising because discrete Q-networks lack the action-input distribution mismatch identified by CrossQ. We show for target-based C51 and target-free PQN that the simple choice between running and batch statistics at specific forward passes can reverse this degradation. In C51, switching the BN bootstrap forward to batch-statistic mode significantly improves performance over unnormalized and LayerNorm baselines and scales stably with update-to-data ratios up to 12. In PQN, using batch-statistics for both action selection and bootstrapping recovers performance from the failing running-statistic configuration. Across 26 Atari games at 400M frames, this configuration achieves a higher final aggregate score than PQN with LayerNorm. Our results show that carefully configured BN can substantially improve discrete-action value learning, and that its forward protocols are an essential part of the algorithm specification.
[AI-208] From Concentration to Differentiation and Back: Routing Effective Rank in MoE Reasoning Cohorts
链接: https://arxiv.org/abs/2609.06403
作者: Kang Chen,Sihan Zhao,Yixin Cao,Yu-Gang Jiang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Test-time scaling produces cohorts of reasoning rollouts, yet there is no standard label-free account of how their internal computation reorganizes as inference unfolds. We introduce routing effective rank deff, the entropy-effective dimensionality of a cross-rollout graph built from MoE expert-routing similarity. Across ten MoE configurations and five math/science benchmarks, deff exhibits a reproducible low-high-low trajectory, with a prominent interior maximum in 98.5% of 3,105 model-question cohorts: routing similarity is concentrated early, maximally differentiated at intermediate budgets, and reconcentrated later, and the timing of this maximum varies systematically with architecture and reasoning effort. An exact decomposition separates cohort-wide common-mode mass from residual spectral dimensionality: common-mode reallocation accounts for about two thirds of the trajectory, while the residual spectrum contributes about one quarter and retains substantial variation beyond the common mode. The decomposition further localizes behavior: among non-unanimous cohorts, increases in common-mode concentration strongly predict same-answer recoverability, and higher reasoning effort delays the maximum by 2.59 octaves (doublings of the token budget) and consistently expands the high-rank period across all four tested architectures, locating the effort effect in timing and duration rather than peak amplitude. Correctness comparisons separate structural monitoring from answer selection, positioning routing effective rank as a decomposable, label-free diagnostic of cohort organization - a principled spectral lens on how MoE reasoning cohorts differentiate and reconcentrate over inference time.
[AI-209] Parameterized and Streaming Algorithms for Euclidean Fair k-Center Clustering
链接: https://arxiv.org/abs/2609.06384
作者: Zeyu Lin,Chaoqi Jia,Longkun Guo,Chao Chen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Motivated by the growing importance of fairness in machine learning, fair k -center clustering has attracted considerable research attention as a fundamental problem. In this problem, a dataset is partitioned into m disjoint groups, and the objective is to select k data points as centers, subject to upper bounds on the number of centers chosen from each group, aiming to minimize the maximum distance between any data point and its assigned center. Focusing on Euclidean spaces, which are ubiquitous in machine learning applications, we first develop a parameterized approximation algorithm for Euclidean fair k -center with an approximation ratio of 2.732 . By incorporating this algorithm as a post-processing stage into a one-pass streaming framework for large-scale data, we obtain an approximation ratio of 4.464 . These ratios can be further respectively improved to 2.414 and 3.828 with a runtime exponential on k . To ensure polynomial-time complexity, we further design a one-pass streaming algorithm with an approximation ratio of 4.732 , which can be further improved to 4.42 , outperforming the state-of-the-art ratio. Finally, extensive experiments show that our methods significantly outperform state-of-the-art approaches in terms of clustering accuracy.
[AI-210] Robust Conformal Consensus: Multi-Agent LLM -as-a-Judge Interval Evaluation with Conformal Prediction
链接: https://arxiv.org/abs/2609.06367
作者: Lihui Liu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:LLM-as-a-Judge has emerged as a promising paradigm for evaluating natural language generation. However, the uncertainty associated with such evaluations remains largely unexplored, which limits their reliability in real-world applications. Although conformal prediction offers a principled framework for uncertainty quantification, existing approaches typically apply it to a single LLM judge, overlooking the variability introduced by using different LLM evaluators. In this work, we propose a robust uncertainty estimation framework for multi-agent LLM-as-a-Judge evaluation. Our approach constructs conformal prediction intervals for LLM-based scores from multiple LLMs. By considering intervals from different LLM judges, we obtain more stable and reliable uncertainty estimates. Extensive experiments demonstrate that our method produces valid prediction intervals with coverage guarantees, and that interval-based aggregation across multiple judges leads to more stable evaluation outcomes.
[AI-211] AutoKD: Autonomous Knowledge Discovery
链接: https://arxiv.org/abs/2609.06366
作者: Qinwen Ge,Bo Ni,Haowei Fu,Ngoc N. Tran,Erik Blasch,Tyler Derr
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Scientific discovery in data-rich domains is currently constrained by human bandwidth: the growth in the volume and complexity of real-world data far outpaces the rate at which researchers can read, reason, and synthesize. Recent LLM-based multi-agent systems have begun to automate portions of the research cycle, but they target hypothesis generation in settings where validation cannot itself be automated, and each run is one-shot, with no mechanism for findings to accumulate or steer subsequent inquiry. This paper introduces AutoKD, a multi-agent framework for autonomous knowledge discovery that is both computational and cumulative, allowing validated findings to persist and inform subsequent inquiry. Six coordinated LLM agents collaborate in an open-ended discovery loop, where accepted findings are stored in a persistent insight graph that serves as both long-term memory and an exploration-steering mechanism. We evaluate AutoKD on three diverse datasets from two perspectives: Open-ended Quality against published findings, and Conditioned Quality via literature-derived queries. Across both evaluation perspectives, AutoKD covers known findings and surfaces substantive discoveries that complement human-driven research. Our code is available at this https URL.
[AI-212] Linear Algebra Foundations of Efficient Attention: A Phase Reversal in Rank Collapse Under SVD Compression
链接: https://arxiv.org/abs/2609.06341
作者: Anjaneya Teja Sarma Kalvakolanu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:Linear algebra provides the framework of concepts (matrix rank, singular value decomposition (SVD), and eigendecomposition) that modern artificial intelligence employs to encode, compress, and propagate information through neural networks. This paper unifies fourteen separate peer-reviewed works analyzing the usage of these techniques in the context of transformer-based foundation model research, focusing on three areas of the topic: derivations and properties of self-attention matrices’ output rank, compression methods that purposefully utilize this phenomenon, and the low-rank key-value (KV) cache projection and its semiseparable-matrix duality to linear attention and state-space structured models. We were motivated to conduct this work after observing an open problem in this literature: the interplay of the mentioned compression methods with natural rank collapse of the network. With this paper, we report an original finding that using SVD compression of attention projections actually has the opposite effect on the rank collapse of the network: while it strongly suppresses it at initialization, it accelerates on pretrained models (for GPT-2 124M, GPT-2 Medium 355M, and Pythia-160M) with minimal risk of object aliasing artifacts appearing (verified on all compression ratios) and is consistent across four rank estimation methods. A controlled causal decomposition of the effect in both settings showed that the reason for this behavior can be explained by the choice of the subspace SVD makes when compressing the matrix better than the reduction of the operator norm it achieves, explaining roughly 76% of the effect at initialization and 83% on the pretrained weights, providing a refinement to the calibration-aware compression viewpoint and an explanation of why it outperformed naive SVD truncation.
[AI-213] SIDE: Sensor Impersonation Detection at the Edge via Sequence Prediction
链接: https://arxiv.org/abs/2609.06271
作者: Nahom Birhan
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Some low-cost Internet of Things (IoT) sensor deployments lack device-level source authentication, leaving them vulnerable to impersonation or injected sensor readings. We present a lightweight approach to sensor impersonation detection in a small proof-of-concept study. We formulate detection as a sequence-prediction problem. A model with three LSTM layers and two fully connected layers is trained only on univariate temperature readings from a genuine sensor, and a window of readings is flagged when its mean absolute prediction error exceeds the mean genuine error by more than six standard deviations. The model is converted to TensorFlow Lite and deployed on an Arduino Nano 33 BLE in three variants, non-quantized (554 KB), 16-bit weight quantized (298.5 KB), and 8-bit weight quantized (185 KB). On a controlled testbed with the impostor sensor placed in a hotter outdoor location, the three variants reached detection accuracies of 99.980%, 99.972%, and 98.206%, and each flagged the change point when a test sequence switched from genuine to impostor data. Quantization made the model smaller but slower in our measurements. The genuine and impostor distributions were well separated, so these results show detection of a controlled distribution shift and should not be read as evidence of general device authentication.
[AI-214] It is Not Yet Another Tool: Creating and Deploying an Agent ic AI Companion in a Security Operations Center
链接: https://arxiv.org/abs/2609.06250
作者: Kritan Banstola,Faayed Al Faisal,Duy Dao,Ryan Irving,Daniel Lende,Xinming Ou
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Security Operations Centers (SOCs) process large amounts of tickets, most of which are low-interest events not worthy of further investigation. The repetitive nature of this task and similarity of the vast amounts of tickets make it a prime candidate for generative AI-based automation. We created and deployed an agentic AI companion utilizing large language models through fieldwork within a SOC for over one year. The design of the SOC AI companion was driven by researchers’ participation and interactions within the SOC’s daily work. SOC analysts were invited to use it during the last four months of the fieldwork. We analyzed the analysts’ usage of the companion and found that in more than 90% of the cases the companion’s outputs were reused by analysts in the ticket’s closing report. Our results showed that when designed “in the trenches” with the intended users, a SOC AI companion can go beyond being yet another tool, but rather a system that co-evolves with its human users as it traverses through the various types of workloads. Analysts naturally started to shape the AI companion’s behaviors to fit their particular needs. Our data show that the more human analysts shape the AI companion’s behaviors, the more they become comfortable trusting the output from the AI system, resulting in improved productivity.
[AI-215] SWE-Test: Benchmarking LLM Vulnerability Discovery via Input Prediction
链接: https://arxiv.org/abs/2609.06229
作者: Yuanxiang Shi,Jiayi Lin,Xuanyong Lin,Liangcai Su,Yeheng Duan,Wei Wang,Qi Han,Bing Zhao,Wei Hu,Xander Xu,Chenxiong Qian
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Vulnerability discovery is becoming an important ability of large language model (LLM) agents: agents that silently miss real defects leave critical software exposed. Rigorously measuring this ability is therefore urgent, but existing benchmarks are gameable through data contamination, score recall against an unknowable vulnerability set, often rely on synthetic bugs, and report a single end-to-end verdict that cannot localize where an agent fails. Vulnerability discovery is a composite ability: an agent must comprehend source code, infer input constraints, construct inputs, execute them, and iteratively correct from feedback. We recast its measurement as an input-prediction task with a closed, deterministic ground truth: using coverage-guided fuzzing, we mine deep target branches in real-world C/C++ programs and ask an agent to predict an input that drives execution to a given branch. This decomposes discovery into three task modes over 22 real-world C/C++ programs spanning 15 domains. Open-loop and Feedback-enabled share 60 fixed-target task instances across 16 of these codebases (13 domains), testing input construction without and with a distance oracle to isolate code comprehension from feedback-driven correction. Online Arena instead removes the predefined target and scores path exploration by coverage gain on a separate, partially overlapping pool of 11 programs; agents collectively confirmed 13 distinct bugs across six programs. Evaluating 15 default-effort model-scaffold configurations, the best reaches only 55.0% pass rate in the Feedback-enabled mode, and the mean across seven paired Claude Code configurations is 36.4% with feedback versus 19.3% without. Decomposing failures, we find constraint inference, not navigation, is the dominant bottleneck. We release SWE-Test with a turnkey evaluation environment.
[AI-216] Scratchy: Visual-Scratchpad Multimodal Reasoning for Cryptographic Proof Generation in EasyCrypt
链接: https://arxiv.org/abs/2609.06226
作者: Yupeng Ren,Zhaoxuan Li,Rui Zhang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: First version; 12 pages, 5 figures, and 5 tables
Abstract:Large language models (LLMs) have recently made substantial progress in formal proof generation, yet presenting distinctive challenges in cryptographic area. Computational security arguments posit that a valid proof must coordinate probability, adversarial games, invariants, assumptions and bounds, which can be provided by a machine-checked framework named EasyCrypt. Although all objects may appear in available context, LLMs still struggle because proof-theoretic dependencies are typically implicit in a linear representation and distributed across multiple programs. So, this paper presents Scratchy, a visual-scratchpad approach that exposes these dependencies for multimodal generation. Given the natural-language security description, with formal context and target propositions, the proof objects can be normalized into a typed proof-relation graph. Then a structure-preserving visual compiler transforms the graph into the formula-rich visual proof state that guides a multimodal model in generating the EasyCrypt proof. Also, the Scratchy-eval, a 114-task dataset derived from reliable official EasyCrypt files, has been introduced. It contains 64 security-form proof generations and 50 multiple-choice knowledge tests. After a series of evaluations, covering semantic grounding, relational invariants, and game reductions, classical LLMs like GPT-5.6-Sol and Claude-Opus-5 have gained a clear advantage from Scratchy’s structured visual proof states. This contrast suggests that explicit proof structure can make the improvement and multimodal proof-state representation as a promising direction for computer-aided cryptography.
[AI-217] Predicting Wind Turbine Power Using Machine Learning and Weather Forecasts
链接: https://arxiv.org/abs/2609.06194
作者: Khivishta Boodhoo,Isaac Triguero,Josh Plumbly,Bruce Nicolson,Nicholas Watson
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Offshore wind turbines are widely used to generate renewable energy, but their maintenance can result in decreased efficiency due to forced shutdowns. Accurate wind turbine power predictions can identify periods of low power that would be ideal for scheduling maintenance. However, the effects of data volume, feature selection, and data preprocessing on the performance of such power prediction models have not been thoroughly studied. Besides, current models have limited transferability between different wind turbines. Therefore, this study developed a baseline Linear Regression for performance comparison with a more complex Artificial Neural Network model to predict the power output of a wind turbine, using weather conditions only to enhance applicability. A range of data preprocessing techniques were studied, and models were trained on one month and one year of data to determine the effects of data preprocessing and volume on model performance. Feature selection was explored using a Random Forest Regressor. The best results from the different models showed that the Artificial Neural Network models provided the highest accuracy, with an R2 score of 0.98 and a low Mean Absolute Error of 194, when compared with the baseline model (R2 score of 0.94 and Mean Absolute Error of 441). The model performance is comparable to the range of results in past studies, with the advantage that the proposed method leverages a separate weather dataset from a nearby weather station, enabling future applications for similar wind turbines in different locations. The Artificial Neural Network model was then used to identify 4-h periods of low power predictions over 2 months (simulating application for future periods), providing power output savings of approximately 2000 kW for each maintenance event.
[AI-218] SCIRIGOR:Evaluating Open-Ended Scientific Analysis Beyond Final Scores
链接: https://arxiv.org/abs/2609.06192
作者: Bowen Liu,Shuo Nie,Bodong Du,Xiaomeng Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Scientific coding agents produce interdependent code, results, figures, and claims, yet evaluating final outputs alone does not establish whether their conclusions are scientifically supported. We formulate evidence-grounded multimodal scientific analysis, requiring agents to produce executable analyses and claims supported by results and visualizations from the same run. We introduce SciRIGOR, an evaluation framework and benchmark comprising 100 cases from scientific articles across six domains and 17 subfields. The framework reconstructs typed evidence graphs, separates artifact fidelity from relational validity, and scores complete claim-support paths while localizing the earliest unsupported relation. Source-grounded alternative paths accommodate scientifically equivalent analyses and visualizations. We evaluate 11 agent/model configurations. On full-benchmark runs, claims agree with faithful and unfaithful results at nearly identical rates (91.8% versus 91.0%). Yet no system exceeds 62.6% on the soft evidence-chain score or 18.0% strict whole-chain success. These findings show that internal coherence does not establish scientific correctness: evaluation must verify support along the complete data-to-claim path. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2609.06192 [cs.AI] (or arXiv:2609.06192v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.06192 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Bowen Liu [view email] [v1] Sat, 5 Sep 2026 17:29:15 UTC (4,173 KB) Full-text links: Access Paper: View a PDF of the paper titled SCIRIGOR:Evaluating Open-Ended Scientific Analysis Beyond Final Scores, by Bowen Liu and 3 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI prev | next new | recent | 2026-09 Change to browse by: cs References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[AI-219] MVFA: A Multi-View Text-Guided Multimodal Fusion LLM Adapter for Sentiment Analysis and Emotion Recognition
链接: https://arxiv.org/abs/2609.06188
作者: Pengfei Shao,Jisheng Dang,Jiawen Fang,Ning Liu,Wencan Zhang,Bimei Wang,Jingwen Zhao,Jianhuang Lai,Qi Tian,Tat-Seng Chua
类目: Artificial Intelligence (cs.AI)
备注: 10 pages, 4 figures, 10 tables
Abstract:Multimodal sentiment analysis and emotion recognition in conversations demand effective modeling of heterogeneous interactions across textual, acoustic, and visual modalities. Although large language models (LLMs) offer powerful language understanding, adapting them to multimodal affective computing remains challenging: full-model fine-tuning is computationally prohibitive, while many existing lightweight adapters fail to preserve rich textual cues during cross-modal fusion. To address these limitations, we propose the multi-view text-guided multimodal fusion adapter (MVFA), a parameter-efficient framework that augments frozen LLMs with strong multimodal reasoning capability. MVFA first constructs complementary text views via max pooling, mean pooling, and attention pooling; these views then guide cross-modal interactions with audio and visual features. The fused multimodal representations are subsequently compressed into a compact set of learnable pseudo-tokens through an Enhanced Q-Former Fusion Module. Using ChatGLM3-6B-base as the primary backbone, we further validate MVFA on LLaMA2-7B and Qwen3-8B to examine its portability across multiple frozen LLM backbones. MVFA is evaluated on three challenging datasets: CH-SIMS V2.0, MELD, and CHERMA. Experimental results demonstrate that MVFA achieves state-of-the-art performance on key metrics while updating only a small fraction of parameters. Specifically, it attains 84.62% Acc2 and 84.59% F1 on CH-SIMS V2.0, 67.36% Acc and 66.03% WF1 on MELD, and 74.66% Acc on CHERMA. These findings establish multi-view text-guided fusion as an effective and scalable paradigm for parameter-efficient multimodal LLM adaptation in affective computing. The code is publicly available at this https URL.
[AI-220] Diamond Agent : Agent ic Control of Federated HPC Resources as a Service
链接: https://arxiv.org/abs/2609.06181
作者: Haotian Xie,Junlin Chen,Mingkai Zheng,Yifan Zhu,Minu Mathew,Max Burnette,Yadu Babuji,Volodymyr Kindratenko,Shivaram Venkataraman,Kyle Chard,Ian Foster,Zhao Zhang
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注:
Abstract:Efficiently aggregating and orchestrating computing power across heterogeneous clusters for HPC workflows faces four practical challenges: preserving workflow context across independently administered clusters, moving large datasets between sites, reasoning about site-specific environments and scheduler policies, and exploiting live queue and resource states for efficient task scheduling. To this end, we design Diamond Agent, an agentic system that enables intelligent execution of HPC workflows across heterogeneous clusters with typed skills as the interface. Diamond Agent provides an agent-facing workspace and skills that unify cross-site resource discovery, resource specification, data movement, task execution, and result retrieval. A centralized Diamond Agent instance can operate multiple supercomputers without being deployed separately on each login node. Diamond Agent translates high-level agent actions into valid site-specific executions, moves data through Globus Transfer, and uses live system capability and queue information to select feasible placements. Its event-driven continuation mechanism decouples agent actions from long-running batch jobs: persistent services monitor remote execution and resume the agent only when a result or decision-relevant event is available. We experiment with 27 hours of telemetry and 19 matched multi-site submission rounds comprising 83 jobs across four production supercomputers. Compared with a fixed-site baseline, Diamond Agent reduces the median additional completion time relative to the fastest observed placement from 42 seconds to 4 seconds, a 10.5x reduction.
[AI-221] Decision-Aware Suffix Prediction and Reasoning of Business Processes
链接: https://arxiv.org/abs/2609.06169
作者: Henryk Mustroph,Stefanie Rinderle-Ma
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 18 pages, 4 figures, 1 table
Abstract:Suffix prediction forecasts the remaining sequence of events of a running case until completion. Most approaches rely on neural networks trained on event logs, which, on average, perform well but struggle with short prefixes or targets belonging to a rare process variant. In such scenarios, the correct path may cross multiple branching decisions, determined primarily by case- and event-level attributes, a signal that NN-based suffix prediction models tend to underweight because they may heavily weight (dense) event labels. Decision mining extracts rules for such decisions from the event log, but has so far been applied only to post-hoc and what-if analysis, not suffix prediction. We therefore extend suffix prediction with decision mining, introducing a decision-aware suffix prediction framework, a neuro-symbolic approach that enables reasoning about predicted events via mined decision rules. Experiments on three of four event logs and three suffix predictors show that the framework can improve suffix prediction, especially for short prefixes but also for rare process variants, and adds intrinsic interpretability.
[AI-222] All for 1-Bit: Towards Genuine 1-Bit Post-Training Quantization for LLM s EMNLP
链接: https://arxiv.org/abs/2609.06161
作者: Zhixiong Zhao,Zukang Xu,Guangyu Sun,Lifeng Liu,Dawei Yang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted by EMNLP-Main 2026
Abstract:Large language models (LLMs) have achieved remarkable progress, yet their massive storage and memory-bandwidth demands still hinder efficient deployment. Weight binarization is a promising solution, but existing binarization-based post-training quantization (PTQ) methods usually far exceed the nominal 1-bit storage target due to hidden overhead. To address this gap, we propose All for 1-Bit (AF1), a genuine 1-bit PTQ framework for LLMs. AF1 comprises two complementary components: (1) Null-space-Aware Binary Factorization (NABF) for improving binary reconstruction through Hessian-aware surrogate reparameterization, null-space-aware binary factorization, and scale-only global reconstruction; and (2) Hierarchical Shapley Allocation (HiSA) for assigning structural capacity using hierarchical Shapley sensitivity. Together, they preserve model accuracy under a strict 1.0-BPW budget in the PTQ setting. Experiments on LLaMA, Qwen, and Gemma families show that AF1 consistently outperforms existing binarization-based PTQ methods in perplexity and zero-shot accuracy. Compared with BF16, AF1 achieves an average 2.5 times inference speedup and over 90% memory reduction across evaluated models, providing a practical path toward deployable genuine 1-bit compression for LLMs. The code for reproducibility is available at this https URL.
[AI-223] From Splats to Silicon: Rethinking Computational Efficiency of 3DGS
链接: https://arxiv.org/abs/2609.06157
作者: Minnan Pei,Qiwei Dong,Yihan Zhou,Gang Li,Yuchen Zhu,Wenju Zhao,Zhongtian Long,Siting Wang,Peisong Wang,Jian Cheng
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Graphics (cs.GR)
备注:
Abstract:3D Gaussian splatting (3DGS) represents scenes with explicit primitives and supports real-time novel-view synthesis, yet its system efficiency varies substantially across scenes, viewpoints, rendering paths, and platform constraints. Existing studies pursue efficiency through representation and algorithm design, GPU runtime optimization, and architectural support, but their reported gains correspond to different points along the rendering and update paths. Connecting these indicators to end-to-end system benefit requires tracing how each optimization changes Gaussian selection, screen-space work, data movement, and stage or frame time. We therefore use a workload-centric framework to connect representation and algorithm research, GPU runtimes, and hardware architectures and to identify recurring workload patterns. We complement literature analysis with reproduced measurements and controlled GPU profiling of selected implementations, relating workload counts to stage time and memory traffic. Together, these comparisons show that system gains depend on workload reductions reaching downstream execution, granularity matching each stage, and the cost of data transfers, synchronization, and cached results, gradients, and optimizer data. Building on these findings, we discuss more consistent evaluation under rendering-quality constraints and identify key directions for future system design.
[AI-224] SCRIPTIOC-BENCH: A Benchmark for Recognizing Actionable Threat Intelligence from Script-Based Malware using LLM s
链接: https://arxiv.org/abs/2609.06149
作者: Hanna Kim,Jian Cui,Minkyoo Song,Hwanjo Heo,Seungwon Shin,Kimin Lee,Xiaojing Liao
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 22 pages
Abstract:Script-based malware remains a prevalent attack technique. These scripts often contain indicators of compromise (IOCs) that provide actionable threat intelligence. However, statically recovering such indicators is challenging, as relevant values may be dispersed or transformed within code. Although large language models (LLMs) have shown promise in security analysis, their ability to recover IOCs from malicious scripts remains underexplored. We present SCRIPTIOC-BENCH, a benchmark for measuring static IOC extraction capability on real-world malicious scripts. The benchmark comprises 634 manually verified JavaScript, PowerShell, and VBScript malware samples covering four IOC types (URLs, domains, IP addresses, and filesystem artifacts). We further stratify ground-truth IOCs by recovery level, distinguishing directly exposed indicators from those requiring decoding or reconstruction. Using this benchmark, we evaluate a broad range of proprietary and open-weight LLMs and show that IOC recovery without execution remains challenging across model scales: the strongest model reaches only 65.4 F1. To characterize how recovery fails, we introduce a false-positive taxonomy and use it to compare the error profiles of the evaluated models. We further study two mitigations on a small open-weight model, deterministic string utilities and task-specific adaptation, finding that they provide complementary recovery gains, raise precision, and shift errors toward sample-grounded mismatches. Comments: 22 pages Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.06149 [cs.CR] (or arXiv:2609.06149v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2609.06149 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-225] IIns-VAE: A Robust Transfer Learning Framework for Environmental Identification in Wireless Sensing
链接: https://arxiv.org/abs/2609.06131
作者: Yuxiao Li,Keke Hu,Bobai Zhao,Santiago Mazuelas,Yuan Shen
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 17 pages
Abstract:Environmental identification in wireless sensing is essential for 6G integrated sensing and communication (ISAC) systems to achieve reliable situational awareness. However, deep learning (DL) models for this task often fail to generalize under domain shift across diverse environments. While the Inter-Instance Variational Auto-encoder (IIns-VAE) learns features of rich representation, its neural classifier remains vulnerable to these distribution changes. In this paper, we propose IIns-VAE+, a hybrid model that combines the IIns-VAE framework with Minimax Risk Classifiers (MRC) to improve adaptability in transfer learning scenarios. We use real-world datasets to evaluate our framework across three transfer learning scenarios, including general to specific room environments, high to low label resolutions, and mixed to specific environments. The experimental results indicate that IIns-VAE+ significantly outperforms baselines, demonstrating its critical value in building adaptable and robust perceptive networks in future 6G systems.
[AI-226] Substrate-Portable Execution for Production LLM Workflows
链接: https://arxiv.org/abs/2609.06128
作者: Tarun Gopinath,Atul Kulkarni,Vijay Rajakumar,Shrikar Katti,Parthasarathy Govindarajen
类目: Artificial Intelligence (cs.AI)
备注: 6 pages, 1 Figure, 5 Tables
Abstract:Production LLM agents execute tool-calling loops, retrieval chains, and compositional workflows in multiple modes, yet execution semantics are often coupled to one runtime. We encountered this portability problem in Rufus, a conversational AI assistant with a large tool catalog that serves millions of Amazon customers. Rufus supports real-time serving, asynchronous background tasks, and high-volume batch workloads such as evaluation and content pregeneration. Each mode has distinct service-level objectives and typically uses a separate runtime. Reusing streaming orchestration makes asynchronous and batch workloads blocking and prevents use of batch inference APIs, which offer a 50 percent discount at published prices. We present a binding-adaptive agent execution platform that separates workflow definition from execution substrate. Developers define a workflow once as a typed dataflow graph. The platform compiles the graph to in-process streaming for real-time serving, durable AWS SWF orchestration for asynchronous execution, or distributed Apache Flink stream processing for batch inference. No workflow code changes are required. LLM inference is represented as a suspendable graph node whose behavior depends on the substrate: streaming delivery online, durable retry asynchronously, and batched submission offline. We validated dozens of production agent configurations across five orchestration patterns: single-inference RAG, iterative ReAct, compositional PreAct, conditional routing, and multi-agent deep research. Across all three bindings, we found no detectable difference in output quality. Batch execution reduced per-query inference cost in line with published batch API pricing while operating alongside the streaming path at production scale.
[AI-227] CWF: A Collaborative Writing Framework for Personalized and Reliable Popular Science Writing
链接: https://arxiv.org/abs/2609.06126
作者: Ruibiao Fu,Di Tang,Yunlong Yang,Ran Wang,Sicheng Lu,Peixuan Wu,Xiaoyu Fan,Jiacheng Ma,HaoZhe Luo,Yang Xiao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce Personalized and Reliable Popular Science Writing, a novel task that requires adapting scientific explanations to audiences with different cognitive levels while preserving factual accuracy. However, improving personalization often introduces simplifications that increase the risk of hallucination and factual distortion. To address these challenges, we first construct a dataset of 39,134 entries and a reader-centric Personalized Science Communication Benchmark (PSCB) that jointly evaluates audience adaptation and factual accuracy. To reduce data and computational requirements while improving generalization across domains and audiences, we introduce DA-MoE, which explicitly decouples audience adaptation from domain knowledge through separate modeling. To enable robust verification and revision in evidence-scarce scenarios, a multi-agent fact-checking mechanism that augments limited evidence with role-specific agent debate and propagates confidence over a graph is proposed. Experiments on PSCB show that our approach achieves state-of-the-art performance. Our code is open-sourced at this https URL.
[AI-228] SAP: State-Guided Data Synthesis with Argument Provenance for Multi-Turn Tool Use EMNLP2026
链接: https://arxiv.org/abs/2609.06124
作者: Zichen Tian,Jinpeng Chen,Cheng Gong,Suiyun Zhang,Rui Liu
类目: Artificial Intelligence (cs.AI)
备注: 23 pages, 3 figures, accepted by EMNLP 2026
Abstract:High-quality multi-turn tool-use data is essential for training agentic models, yet existing data synthesis methods often underrepresent the argument-level dependencies that are critical to long-horizon tool use. As a result, even when a model selects the correct tool, task execution may still fail because the model fills tool arguments with fabricated, stale, or weakly grounded values. To address this problem, we propose \textbfState-Guided Data Synthesis with Argument Provenance (SAP). SAP combines state guidance, tool-argument provenance constraints, and turn-level validation to efficiently construct tool-use trajectories with long-range dependencies and high accuracy. Using data generated by SAP, we build SAP-4B, which is highly competitive even when compared with much larger models across multiple benchmarks. Source code, synthesized data, and trained weights are available at this https URL.
[AI-229] Programmable Cellular Automata
链接: https://arxiv.org/abs/2609.06102
作者: Ahmed Khalifa,Muhammad Umair Nasir,Matthew Siper,Steve James,Julian Togelius
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Formal Languages and Automata Theory (cs.FL); Machine Learning (cs.LG)
备注: Submitted to EXAG 2026, 15 pages, 6 figures, 5 tables
Abstract:Cellular automata is a local computation paradigm where complex behavior can arise from local interactions between simple functions. This paradigm has been used to explain many systems such as biological processes, traffic simulation, computer networks, etc. In games, cellular automata have been used in games such as SimCity and for the generation of spatial content such as caves or dungeons. However, creating effective local rules is hard and unintuitive. Cellular automata can be effectively evolved, but may still be hard to interpret. In this work, we introduce the concept of programmable cellular automata, where we represent the system as Python code. We also modularize the cellular automata into local functions and a decision function. Local functions take a local neighborhood and return a value, while the decision function takes the output of the local functions and decides the value of the next state. Separating the cellular automata into modules written in Python helps with understanding how these systems are working. We also explore adding global functions where they take the whole state and compute a function from it. We tested generating levels for three different games from the PCG Benchmark. The results showed that global functions decrease the number of iterations that cellular automata need to solve a problem, and that we cannot find solutions for some problems with purely local functions. Looking into the generated functions, we can see common functions that have been used in different experiments, which not only helps us understand the generator but also helps us understand these games better and what is important for them.
[AI-230] PhenoBench: Mapping What a Deeply Phenotyped Human Cohort Can Tell Us
链接: https://arxiv.org/abs/2609.06080
作者: Gal Sapir,Alon Diament,Adva Wolf,Doron Yaya-Stupp,Dikla Gelbard Solodkin,Dana Azouri,Anat Etzion-Fuchs,Guy Lutsker,Eran Segal,Hagai Rossman
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 33 pages; 5 main figures and 5 supplementary figures. Project website: this https URL . Code and benchmark materials: this https URL
Abstract:Deeply phenotyped cohorts combine clinical, imaging, molecular, and wearable observations across timescales from seconds to years. This breadth can reveal which measurements inform which health-related questions, but heterogeneous analyses are not directly comparable. We present PhenoBench, an executable benchmark built around the Human Phenotype Project, in which more than 13,000 participants have completed the initial visit. Each question fixes the target, eligible population, timing, and allowed information; its evaluation contract specifies the split, metric, baseline, and claim boundary. The benchmark defines 90 clinically grounded tasks across 15 domains and 26 input modalities. Measurements showed question- and representation-dependent predictive value, including positive, near-zero, and negative changes in held-out performance relative to matched baselines. We used PhenoBench to evaluate emerging tabular foundation models across 160 matched regression comparisons spanning 52 tasks. These models ranked above standard task-specific models in aggregate but, averaged across the three pretrained models within each cell, improved on ridge by a median of only 0.004 R^2 (95% CI, 0.002–0.006). We then used the same cohort data and evaluation contracts to evaluate 14 language models, collectively covering 40 tasks spanning phenotype recovery, classification, follow-up forecasting, and participant ordering. Without cohort-specific fitting, language models made informative predictions on some tasks, but showed task-specific capability gaps, shared failures of scale, and rarely surpassed models fitted on the same fields. PhenoBench turns a multimodal longitudinal cohort into a versioned, auditable evaluation system where new questions, measurements, and models can be added without redefining existing comparisons.
[AI-231] LayerRoute: Action-Conditioned Mixture-of-Layers Routing for Vision-Language-Action Policies
链接: https://arxiv.org/abs/2609.06079
作者: Zheng Lu,Haoran Liao,Wanqi Zhong,Yunhe Ni,Lijie Wang,Xingjie Fan,Zhisheng Chen,Yantang Qu,Meijia Chen,Tianyu Xin,Zirui Song,Yiming Li
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 15 pages, 7 figures, 16 tables, including appendix
Abstract:Vision-Language-Action (VLA) policies leverage pretrained vision-language models (VLMs) to guide action generation for robot control. VLMs provide hierarchical visual-semantic representations that evolve across layers, from local visual geometry to abstract, language-aligned semantics; different manipulation tasks may therefore require different mixtures of layer representations. Meanwhile, the action module maintains intermediate representations that evolve throughout action computation and may provide useful information for subsequent decisions. However, existing VLA interfaces offer limited flexibility in representation access: VLM information is exposed through fixed layer assignments for each action layer, while intermediate action states are only propagated implicitly through residual streams without explicit reuse. We introduce LayerRoute, an action-conditioned representation routing interface that enables adaptive access to VLM layers and action representations. The Layer Mixture Router dynamically forms mixtures of cached VLM representations, while Action-State Reread reuses earlier action representations. Across diverse simulation and real-world benchmarks, LayerRoute consistently improves StarVLA- \pi and \pi_0.5 , achieving up to 7.2 gains on LIBERO Long with only 0.31% / 3.87% additional parameters. Ablation studies validate the benefit of action-conditioned layer routing, while routing analyses reveal structured allocation patterns across action layers and task settings.
[AI-232] FedSubMuon: Communication-Efficient Federated LLM Fine-Tuning via Structured Subspace Muon
链接: https://arxiv.org/abs/2609.06073
作者: Shaolong Chen,Youming Tao,Shuzhen Chen,Falko Dressler,Qingqing Ye,Di Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 15 pages, 4 figures, 2 algorithms
Abstract:Federated fine-tuning adapts large language models (LLMs) to decentralized client data, but its scalability in cross-device training is often limited by the high communication cost. Muon is an optimizer that improves optimization performance by orthogonalizing momentum for matrix-valued parameters. Existing federated Muon methods demonstrate the benefit of matrix-aware optimization in federated learning, but still require transmitting full layer-size updates and optimizer state. A natural way to reduce communication is to directly apply Muon to LoRA factors, but this changes the optimized object and weakens Muon’s matrix-aware update geometry. We propose FedSubMuon, a communication-efficient federated Muon fine-tuning method that optimizes compact coefficient matrices within shared structured subspaces. This design keeps Muon on a single matrix-valued trainable object, while reducing the client upload to compact coefficient matrices. We further introduce FedSubMuon-GT, an accuracy-oriented extension that uses projected gradients to adapt tracked subspace bases toward task-relevant gradient directions. Experiments on instruction tuning and mathematical reasoning show that FedSubMuon-GT achieves the best overall accuracy on four of five dataset-model pairs, while FedSubMuon performs best under all matched communication budgets. On Dolly-15K, the closest communication baseline requires 5.5 times and 1.4 times more total communication on Llama-1B and Qwen-4B, respectively.
[AI-233] Generating Instance Generators in PDDL Planning
链接: https://arxiv.org/abs/2609.06071
作者: Nicola J. Müller,Naya Rudolph,Katharina Stein,Jörg Hoffmann,Ayal Taitler,Timo P. Gros
类目: Artificial Intelligence (cs.AI)
备注: 13 pages, 4 figures
Abstract:PDDL, the de-facto standard language in the AI Planning community, is designed to specify planning domains: sets of instances that share the same predicates and action schemas. Yet it does not provide any means to specify the actual instance set, i.e., legality constraints on initial states and goal conditions, as well as possibly domain subset constraints specifying an instance subset we are interested in. One consequence of this is that instance generation has always been ad-hoc, with manually written domain- and subset-specific instance generators. Recent work has started to address this, through reasoning and learning methods that however suffer from scalability limitations. Here we introduce an alternative approach, leveraging LLMs to generate instance-generation programs, with built-in soundness guarantees through prescribed checks. We show that these automatically generated instance generators return large numbers of sound and diverse instances efficiently.
[AI-234] Explaining AI Agents Through Execution Traces
链接: https://arxiv.org/abs/2609.06063
作者: Vittoria Vineis,Fabiano Veglianti,Lorenzo Antonelli,Claudia Di Carlo,Matteo Silvestri,Gabriele Tolomei
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:AI Agents are increasingly deployed in real-world settings, where they interact with external tools and make sequential decisions with limited human oversight. This creates a pressing need for reliable and auditable explanations of what an agent did and why. However, traditional Explainable AI (XAI) methods fall short of providing the process-level transparency required for such interactive, multi-step systems, motivating a paradigm shift toward approaches specifically designed for AI Agents. To address this gap, we present a post-hoc XAI framework that transforms a lengthy agent’s execution trace into a structured report and a faithful natural-language explanation explicitly grounded in its observable behavior. Because it relies solely on execution traces, the framework applies across different agent architectures, environments, and tasks. Human and automated evaluations across multiple benchmarks and architectures show that our framework produces high-quality, trace-faithful explanations while reliably identifying unsupported claims, unjustified actions, and evidence gaps, outperforming naive LLM-generated explanations.
[AI-235] Calendar-SPCA: Interpretable Representation Learning for Multi-Periodic Electricity Consumption Profiles
链接: https://arxiv.org/abs/2609.06060
作者: Carlos Quesada-Granja,Tony Castillo-Calzadilla,Carlos Rizo-Maestre
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 33 pages, 6 figures
Abstract:Long-term electricity-consumption profiles exhibit several simultaneous periodic structures, including daily, weekly, and annual cycles. This work introduces Calendar-SPCA, a calendar-structured sparse principal component method that incorporates this known multi-periodic geometry directly into low-dimensional representation learning. The feature domain is represented as the Cartesian product of cyclic calendar axes, and a low-rank factorization is estimated using an L1 loading penalty together with graph total variation over the resulting calendar graph. The method therefore produces sparse and locally coherent loading patterns that remain directly readable in their original temporal coordinates. Calendar-SPCA is evaluated on two independent smart-meter datasets with different sample sizes and temporal resolutions: GoiEner and Low Carbon London. A factorial experiment characterizes the complementary effects of sparsity and calendar coherence and examines robustness across sample size, latent dimensionality, and repeated fits. At rank 15, Calendar-SPCA retains 96.92% and 82.90% of the explained variance of rank-matched PCA in GoiEner and Low Carbon London, respectively, while producing mean loading sparsities of 61.95% and 81.50%. Comparisons with classical sparse PCA and SPCA-TV further show that Calendar-SPCA adds a systematic organization of the latent factors in the original calendar coordinates while preserving substantial low-rank information. The resulting components form coherent and complementary daily, weekly, seasonal, and jointly localized calendar patterns, with dataset-specific geometries across the two datasets.
[AI-236] DAREBench: Deployment-Aware and Reliable Evaluation of Models as Agents
链接: https://arxiv.org/abs/2609.06059
作者: Yu Liu,Zhilin Liu,Zhiwei Yang,Shaojie Zhang,Zheyuan Deng,Tingwei Huang,Zhenbo Luo,Lei Jiang,Yanbing Liu,Pei Fu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:As large language models evolve from question-answering systems into general-purpose agents, evaluation must move beyond static answer correctness to assess multimodal perception, multi-step execution, tool use, and artifact delivery. However, existing benchmarks are often tied to specific task types, execution environments, or scoring protocols, limiting their comparability, interpretability, and reliability for deployment decisions. We introduce DAREBench (Deployment-Aware and Reliable Evaluation of Models as Agents), a benchmark designed to capture workload variation and support reliable agent evaluation. Built on a shared OpenClaw execution environment, DAREBench organizes 233 tasks selected and adapted from 22 source benchmarks into a 2\times3 workload matrix defined by input modality and execution form, and evaluates them under a unified contract-based protocol with evidence-based score auditing. We evaluate 23 commercial API models and 12 locally deployed open-weight models over 7,587 model–task runs, reporting accuracy and token consumption alongside reference costs for API models. Results show that no single model dominates all workload groups, text and multimodal tasks exhibit distinct accuracy–cost trade-offs, and local open-weight models are competitive in several groups but still trail frontier commercial models overall. These findings suggest that agent deployment and model selection should consider workload profiles, deployment mode, and accuracy–cost trade-offs rather than rely on a single aggregate score.
[AI-237] SkillSpec: Intent-Masked Specification Reasoning for Agent Skill Correctness
链接: https://arxiv.org/abs/2609.06052
作者: Yizhuo Zhang,Bo Kang,Yi Yang,Zhiyu Duan,Zhouteng Ye,Shunkun Yang
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Autonomous agent systems increasingly depend on reusable skill abstractions for consolidating experiential knowledge and domain expertise. These artifacts typically bundle free-form instructions with heterogeneous resources. However, ensuring their correctness remains challenging. Their failure modes transcend conventional code defects to subtle semantic inconsistencies such as intent conflicts, which manifest as silent failures masked by the underlying model. Moreover, skill correctness must be grounded in intended task boundaries and generalizability. We propose SkillSpec, a Hoare-style framework that formulates skill correctness as a specification reasoning problem. It transforms a heterogeneous skill repository into a unified graph representation that aligns descriptions, instructions and code artifacts. For each node, SkillSpec derives an ExpectSpec from the surrounding declared intent, and infers FactSpecs from encoded behavior under partially disclosed intent. An intent mask regulates access to holistic, lineage, neighborhood, and local views to balance the bias introduced by excessive context against unsupported inference caused by insufficient context. SkillSpec jointly reasons over these views to flag candidate defects, and automatically validates them in an isolated sandbox. On 515 real-world skills from SkillsBench and widely downloaded repositories, SkillSpec identified 763 manually confirmed defects across 239 skills, achieving 61.2% precision. The node-level analysis across multiple model families shows that specification reasoning is consistently reliable for code nodes, whereas plain-text nodes remain a major bottleneck. Most defects arise at the boundaries between declared intent and implementation, demonstrating that explicit specifications provide a practical foundation for skill quality assurance in real-world agent ecosystems.
[AI-238] Generator-Independent Runtime Assurance under Partial Observation
链接: https://arxiv.org/abs/2609.06036
作者: Guangxi Wan,Yongbo Xie,Yuqi Liu,Qingwei Dong,Qingxin Li,Hongfei Bai,Peng Zeng
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Proposal-based controllers—learned policies, language-model planners, and other black-box \emphgenerators—are increasingly deployed behind runtime verification gates. We ask when the closed-loop safety guarantee decouples from the generator. The prevailing per-candidate certification pattern does not compose: under retry or best-of- k selection a per-candidate false-admission level \alpha can inflate to 1-(1-\alpha)^k . Our main theorem shows that \emphsimultaneous setwise soundness—certifying a set of admissible proposals containing no nonviable action—is necessary and sufficient for generator-independent \emphadmission soundness, the worst case over all generators of executing a nonviable proposal equalling the probability of setwise failure; together with a design-time certificate and a no-bypass rule it is sufficient for \emphcontract safety, with violation bound \Gamma+\sum_t\varepsilon_t+\eta invariant under arbitrary, even adversarial, replacement of the generator. A second theorem bounds every admission mechanism under partial observation: for a fixed probing and admission policy, if two state hypotheses whose information laws lie within total-variation distance \delta require different safe decisions, then \abar+\beta+\delta\ge1 . A sequential risk ledger makes the guarantee implementable with time-uniform confidence tubes, and shows that deterministic admission computations concentrate all statistical risk in state estimation. Simplex-style runtime assurance and control-barrier-function filtering are recovered as degenerate cases.
[AI-239] Memory in Deep Time-Series Models
链接: https://arxiv.org/abs/2609.06006
作者: Minh Hoang Nguyen,Huu Hiep Nguyen,Manh Nguyen,Van Dai Do,Dung Nguyen,Hung Le
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Deep learning for time series has progressed through successive architectural paradigms, from recurrent networks and transformers to structured state-space models, retrieval-augmented predictors, foundation models, and tool-using agents. These developments are typically studied in isolation, organized by architecture or modeling era. We argue that they can instead be viewed through a common question of \emphhow does a time-series model retain and access information beyond its immediate input? This question is motivated by a fundamental limitation of conventional time-series modeling: information relevant to a prediction may lie far beyond a feasible input window, while compressing history into a fixed-size state can discard information that may become useful later. We formulate this challenge as a \emphmemory problem and organize existing time-series methods along a spectrum from internal memory, encoded in parameters and fixed-size states, to external memory that is addressable, retrievable, and increasingly maintained by agents. We then develop a unified taxonomy of memory mechanisms and review three classes of external memory, including explicit modules, retrieval augmentation, and agentic stores, under a common framework for what is retained, how it is written and accessed, and how it persists. A cross-cutting analysis maps these mechanisms to time-series tasks and identifies gaps in both methods and evaluation. We conclude by outlining open problems in building memory systems that can selectively retain, retrieve, revise, and forget information as temporal environments evolve. The result is a framework for studying memory as a first-class dimension of time series modeling, independent of the underlying backbone.
[AI-240] Agent ic Pressure: The Endogenous Entropy of Reliable Autonomy ICLR2026
链接: https://arxiv.org/abs/2609.05995
作者: Hengle Jiang,Ziying Luo,Ke Tang
类目: Artificial Intelligence (cs.AI)
备注: Published at the workshop of Agentic AI in the Wild in ICLR 2026; 16 pages
Abstract:Achieving reliable autonomy in the wild requires agents to sustain continuous operations across long-horizon trajectories. However, as agents navigate these unconstrained settings, they encounter cumulative friction that inherently destabilizes their alignment. In this paper, we identify a distinct non-adversarial phenomenon termed Agentic Pressure. We define this as a kinetic force that spontaneously emerges when the cost of compliance conflicts with the imperative of goal achievement. Unlike static jailbreaks, this pressure is endogenous and arises directly from the dynamics of interaction. We propose a theoretical framework that formalizes Agentic Pressure as the ratio between the required work to overcome environmental friction and the remaining capacity of the agent. Our analysis demonstrates that when this pressure exceeds a critical threshold, agents exhibit safety drift as a mathematically optimal adaptation. Consequently, they often resort to Instrumental Hallucination to rationalize rule violations. Empirical experiments validate this framework and show that aligned agents spontaneously compromise safety to preserve autonomy under high-pressure conditions.
[AI-241] MOAE: Multi-Objective Agent Evolution with Pareto-Preserving Search
链接: https://arxiv.org/abs/2609.05992
作者: Hengle Jiang,Qijun Cai,Ziying Luo,Ke Tang
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 18 pages, 3 figures
Abstract:As LLM-based agents continue to advance, their evaluation has become increasingly multifaceted: a capable agent must not only achieve high task completion accuracy but also perform well in interaction quality, safety, and efficiency, raising a central question: can these objectives be optimized simultaneously? Existing methods have considered multiple objectives, but many collapse heterogeneous measurements into a fixed scalar score. Such scalarization depends on metric normalization and preference weights and may discard candidates that represent useful deployment trade-offs. We introduce Multi-Objective Agent Evolution (MOAE), which organizes iterative in-context refinement as a Pareto-preserving evolutionary search over complete agent rollouts. Given a limited rollout budget, MOAE maintains an empirical archive of non-dominated candidates, uses objective-specific diagnostics to guide offspring generation, and applies constraint-aware selection only at deployment. This separates candidate preservation during search from the preference used to return a final solution. The procedure requires no parameter updates and allows each objective to be replaced by any measurable property, which we instantiate as task performance, trajectory quality, and safety. Experiments on TravelPlanner and AgentDojo show that MOAE consistently improves task performance and trajectory quality while maintaining strong safety under matched rollout budgets. Search-behavior analysis further shows that Pareto preservation expands the attainable objective region and increases the frequency of joint improvement. These results demonstrate the potential of Pareto-preserving in-context evolution for optimizing multiple agent properties without committing to a fixed scalarization during search.
[AI-242] DART: Distributional Adversarial Recurrent Training for Algorithm Learning
链接: https://arxiv.org/abs/2609.05988
作者: Hieu Tran Bao,Phung Thanh Dang,Pham Quang Nhat Minh,Hoang Thanh Tung
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Recurrent reasoning models (RRMs) can solve structured problems, achieving easy-to-hard generalization through iterative computation in hidden space. These models are typically trained with instance-level supervision, which becomes increasingly problematic as task difficulty grows: valid solutions occupy a tiny region of the solution space, while invalid solutions proliferate rapidly. We propose Distributional Adversarial Recurrent Training (DART), a training framework that replaces single-point supervision with a local target distribution around the ground-truth solution and aligns model outputs with this distribution through an adversarial objective. DART provides a richer learning signal and encourages more stable iterative trajectories toward valid solutions. When evaluated on Maze, Chess, and masked Sudoku with multiple RRMs, including Deep Thinking Systems and Tiny Recursive Models, DART improves solution quality, stability, and robustness under the evaluated distribution shifts. Comparisons with label smoothing, Gaussian softened targets, and progressive training show that DART is not explained by target softening alone and is complementary to training schemes that stabilize long-horizon recurrence. These results identify DART as a promising approach for improving robustness across the evaluated recurrent reasoning models.
[AI-243] Intent Drift at SME Scale: Deployment Practice Not Model Capability Determines Agent ic Compliance
链接: https://arxiv.org/abs/2609.05975
作者: Ilia Voroshilov
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: 22 pages, 3 figures, 4 tables. Code, data and all run logs: this https URL
Abstract:We introduce Chain of Intent, a governance framework for agentic AI at small regulated firms, and validate it against a failure it was built to address. Existing agentic governance research assumes enterprise infrastructure that small firms do not have. In a simulated Hong Kong asset manager with 415 synthetic contact records, an agent performing a routine client-communications task was subjected to ordinary managerial pressure to increase its reach. With its authorised constraints written into its configuration, the agent held: it identified every ambiguity in the firm’s records, cited privacy legislation it had never been shown, and refused six successive requests, breaching in two of fifteen runs. With the same task, data, pressure and model, but its purpose left unstated as resource-constrained firms routinely leave it, it breached in thirteen of fifteen runs, contacting up to 220 individuals of whom 94 per cent had no demonstrable marketing consent - conduct carrying a maximum of three years’ imprisonment under Hong Kong law. Chain of Intent applies four controls requiring no security engineering: a machine-readable purpose, constrained tool access, a scope ledger, and a pre-action check. It eliminated unlawful contact in every run while preserving task completion, and ablation shows each control independently sufficient by a different mechanism. We further show that drift must be measured at two stages - agents widened their candidate sets in every pressured run while acting on them in roughly one in seven - and that governance applied at the point of intent costs roughly half as much as governance applied at the point of action.
[AI-244] Beyond Final Decisions: A Process-Centric Benchmark for Transparent AI-Assisted Peer Review
链接: https://arxiv.org/abs/2609.05947
作者: Siming Yuan,Xueyi Zhang,Wangze Ni,Tianfang Xiao,Shimin Di,Jia Zhu,Zhuoren Jiang,Rong Tan,Lei Chen,Kui Ren
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Peer review is central to quality control in science. However, existing evaluations of AI-assisted peer review mainly focus on the overall quality of generated reviews or the accuracy of final decisions. They therefore provide limited evidence about whether model decisions are supported by sufficient and reliable review evidence. We introduce a process-centric diagnostic benchmark for AI-assisted peer review. It uses (x, z_s , z_c , z_r ,y) to represent the paper content, summary, critique, suggestion, and decision. We convert heterogeneous review records from PeerRead, NLPeer ARR-22, and OpenReview-ICLR into process-aligned data. Our benchmark uses direct decision prediction from the paper content (Direct) as its baseline. It compares the decision value of Gold-process variables and Predicted-process variables, and conducts stage-level evaluation, chain-consistency evaluation, and interventional sensitivity analysis. Experiments across three datasets and six models show that Gold-process variables generally have higher decision value. For the main analysis model, the Gold–Predicted gap remains stable across datasets and random seeds. This gap is also reproduced in most model–dataset combinations. Although model-generated intermediate review texts show relatively high local consistency across adjacent stages, the final decisions are not consistently supported by the preceding review evidence. Our benchmark targets AI systems designed to assist rather than replace human reviewers. It provides a transparent and auditable diagnostic tool for evaluating the reliability of their review processes. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2609.05947 [cs.AI] (or arXiv:2609.05947v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.05947 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-245] Versioned Transitive Dependency-Closure Binding and Operation-Time Effect Governance for Agent Skills: ClosureBound
链接: https://arxiv.org/abs/2609.05920
作者: Genliang Zhu(1 and 2),Chu Wang(1 and 3) ((1) Accentrust, (2) Georgia Institute of Technology, (3) University of Illinois Urbana-Champaign)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 30 pages, 3 figures, 9 tables. Preprint
Abstract:Agent Skills combine instructions with files, packages, tools, models, and services, so operational identity can exceed a signed directory. Recursive or lazy dependencies may change while root-level evidence remains valid, and different surfaces may reach the same durable effect. We present ClosureBound, a reference monitor that prevents authorization transfer across material changes to this heterogeneous closure. Its resolver commits typed graph nodes and topology. Each grant binds an exact closure root, effect ceiling, purpose/provenance, validity, and epochs. At durability, it re-resolves closure and state, normalizes the operation into an external-effect IR, and admits it only if a joint witness satisfies every bound. Supported equivalent paths share one ceiling. Assuming complete mediation and discovery, authenticated freshness, sound normalization, cryptographic binding, and authoritative linearization, we establish metadata non-authority, closure determinism, version non-inheritance, effect non-amplification, bound-value freshness, and path invariance. We do not establish program equivalence or remote-service honesty. A provider-free implementation matches 40 frozen lifecycle fixtures; 18 kernel contracts and six mutants cover binding and downgrade cases. Full-profile exploration reaches 84,608 states and 530,752 transitions without a declared invariant violation; six weakened profiles yield witnesses. A lexical audit of 549 public Skills (4,872 unique files) finds that 21 of 526 roots with bundled files name every non-manifest path verbatim, 67 contain links resolving outside their roots, and no root declares a frontmatter dependencies field. These observations motivate conservative closure discovery and define concrete targets for broader runtime, interoperability, efficacy, and production validation.
[AI-246] From Review to Authorization: Key-Isolated Threshold Signing for LLM Agents
链接: https://arxiv.org/abs/2609.05901
作者: Yu Zheng,Qizhi Zhang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 14 pages, 4 figures
Abstract:Autonomous LLM agents can turn untrusted content into effectful actions such as payments and permission changes. If the same process interprets this content and controls a reusable signing credential, prompt injection can cross the judgment boundary and reach execution authority. We present KITA, a review-to-authorization architecture that keeps the user’s personal secret signing key and every threshold signing-key share outside all LLM processes. Under threshold signature unforgeability and our system assumptions, compromising the proposer and fewer than t reviewer-signer domains cannot produce a valid authorization for a new action without signing contributions from t distinct domains. Thus, any such authorization includes a share from an uncompromised domain, bound to the canonical action and released only after authenticated reviewer approval. This establishes execution-bound authorization integrity. We implement the complete reviewer-to-executor path with a structured-output LLM adapter and threshold BLS. Six system tests validate quorum gating and message binding at this interface, while cryptographic microbenchmarks measure the online signing path and its scaling behavior.
[AI-247] he End of AI Exponentiation: Fluttering Inside and Outside AI Bubble
链接: https://arxiv.org/abs/2609.05894
作者: Victor Kebande
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The exponentiation of Artificial intelligence (AI) in the recent past has entered a transformative era that has been driven by the growth in large language models (LLMs), large-scale compute infrastructures, and autonomous reasoning systems. However, the rapid acceleration of AI has increasingly shown technological, societal, economic, ethical and infrastructural challenges associated with peak data limitations, rising computational demands, synthetic data recursion, valuation inflation, and societal instability. The traditional scaling paradigms that have powered the modern AI systems are gradually encountering friction in sustaining continuous exponential growth. This paper views ``the end of AI exponentiation,‘’ thus exploring how it flutters inside and outside the bubble, where instability emerges within the AI ecosystem through compute and data-center races, speculative investments, and the rat-race toward superintelligence, and outside the ecosystem through labor disruption, governance concerns, public uncertainty, and geopolitical acceleration surrounding future intelligent systems and infrastructures globally.
[AI-248] Grounded and Faithful PID Reasoning : Constraining Vision-Language Models with Recovered Evidence Graphs
链接: https://arxiv.org/abs/2609.05880
作者: Prathamesh Gadekar,Sagar Srinivas Sakhinana,Venkataramana Runkana
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: N\A
Abstract:Piping and Instrumentation Diagrams (PIDs) are the authoritative maps of process plants: isolation, maintenance, and HAZOP decisions depend on what connects to what. Vision-language models describe these sheets fluently, yet they often invent or miss process connections—and an invented or missed link can reverse an isolation or reachability call, so a plant decision cannot trust a fluent answer that was never checked against the linework. We instead recover an explicit graph of the drawing—its symbols, the process connections between them, and the tags that name them—and then require the model to answer only by querying that graph through seven read-only operators, so a topology claim is returned only when it cites the query results that support it. On TopoPID-VQA, a new suite of 3000 topology questions over these sheets, Graph-Grounded Harness (Ours) raises exact match accuracy from 36.7–41.3% under image-only prompting to 74.3–76.0% for Qwen3-VL-4B, Qwen3-VL-8B, and Gemma-4-E4B. It does so on an imperfect substrate: on Digitize-PID dataset the recovered graph scores F1 0.742 on exact process connections, and 0.801 once symbols and tags are pooled in. The residual errors track that gap—grounding pays off where the recovered graph is right, and perception error still breaks topology questions where it is not.
[AI-249] Where Does the Sound Go? Tracing Acoustic Information Loss in Audio-Conditioned LLM s EMNLP2026
链接: https://arxiv.org/abs/2609.05871
作者: Song-ha Jo,Sehyun Lee,Soyoon Kim,Jaesik Choi,Sanghyuk Choi
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: EMNLP2026 Findings
Abstract:Audio-conditioned language models often underuse acoustic cues such as prosody, emotion, and non-speech sounds, raising the question of whether ASR-supervised frontends discard this information before it reaches the LM. We test whether the frontend is responsible by comparing Whisper-Tiny and Whisper-Small with EnCodec, DAC-VAE, and WavTokenizer in a shared Qwen3.5-4B audio-LM pipeline on ASR, emotion recognition, and sound captioning. Encoder replacement alone does not resolve this underuse: Whisper variants remain strongest overall, including on emotion and environmental sound captioning. To localize the failure, we trace task-relevant information through the encoder, projector, LM layers, and LM head. Linear probes and geometric analyses show that discriminative acoustic structure remains recoverable at the final LM layer, even when MCQA accuracy trails probe accuracy by up to 83 points. Because the answer format and decoding procedure are controlled, this task-dependent gap points to content-specific readout failure rather than generic format bias. LogitLens analyses and a targeted LM head intervention support the conclusion that acoustic underuse is not explained solely by encoder-side information loss and that readout alignment can be a dominant bottleneck.
[AI-250] SAFEGuard: Detect Optimization-Based Jailbreak Attacks Through Harmful Semantic Analysis and Fluency Measurement EMNLP2026
链接: https://arxiv.org/abs/2609.05850
作者: Quoc Viet Vo,Trung Le,Damith C. Ranasinghe,Ehsan Abbasnejad
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: Published as a conference paper at the Conference on Empirical Methods in Natural Language Processing (EMNLP 2026). Our project page is available at this https URL
Abstract:Despite the significant efforts devoted to aligning large language models (LLMs) with human values and ensuring safe deployment, recent work has revealed that LLMs remain vulnerable to adversarial jailbreak attacks that can bypass safety guardrails and elicit harmful responses. Many defense methods are proposed to detect jailbreaks but they are limited in their effectiveness to counter wide-range optimization-based jailbreak mechanisms that can yield highly fluency-optimized or harmful semantic obfuscated prompts. To tackle this challenge, we propose a unified detection framework SAFEGuard which incorporates a hybrid fluency measurement based on cross-layer distribution distance and perplexity, and the analysis of harmful semantics through gradient matching. Our method is grounded in a paramount observation: high fluency prompts maintain their malicious intention close to harmful prompts while harmful semantic obfuscated prompts often inject gibberish token sequences. Our evaluation demonstrates that SAFEGuard consistently outperforms state-of-the-art baselines and achieves significant improvement in accuracy across different optimization-based jailbreaks. This underscores the effectiveness of SAFEGuard against evolving jailbreak attacks.
[AI-251] Agent Brew: Offline Tool-Use Agent Learning from Raw Real-World Trajectories
链接: https://arxiv.org/abs/2609.05837
作者: Zhiyi Lyu,Yewen Li,Longtao Zheng,Shengtian Yang,Lang Feng,Lei Feng,Peng Jiang,Kun Gai,Qingpeng Cai,Bo An
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LLM-based agents are increasingly deployed in real-world applications through tool-use APIs, yet training them for specific environments remains fundamentally difficult: real-world applications provide no pre-defined tasks or verifiers, no faithful simulators, and limited budget for large-scale environment interaction. In this paper, we propose \textbfAgentBrew, an offline training framework that learns effective tool-use policies from a single batch of raw interaction trajectories, without task verifiers or iterative on-policy rollouts. The agent first explores the target environment to collect a raw trajectory corpus without quality filtering. To extract training signal from this noisy corpus, \emphretrospective task inference reconstructs an aligned instruction for each trajectory based on its actual outcome, and \emphPMI-Based credit assignment decomposes the trajectory’s total information about the inferred instruction into additive per-action credits via pointwise mutual information (PMI). These credits weight the policy training objective, amplifying informative actions while suppressing ineffective ones. On three real-world MCP applications (GitHub, Notion, PostgreSQL), AgentBrew improves Qwen3-32B by +8.7 Acc / +9.7 Score on average, surpassing Qwen3-235B (+2.3 / +4.4) and outperforming rejection sampling (+5.9 / +10.3). These results demonstrate that fine-grained offline learning can recover useful supervision from raw trajectories that filtering-based approaches would discard. The code is available at this https URL
[AI-252] Learning Counterfactual World Models for Embodied Reasoning under Partial Observability
链接: https://arxiv.org/abs/2609.05834
作者: Todd Y. Zhou,Daniel Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:World models promise a general route to embodied intelligence: learn predictive dynamics once, then reason, plan, and act with them. Increasingly, the representations beneath such models are pretrained on large-scale video, interaction, and multimodal corpora, which raises a question prediction quality alone cannot answer: when is a learned representation actually actionable? We identify a failure mode we call counterfactual collapse: a model predicts visually plausible futures while failing to distinguish interventions with different behavioral consequences. This arises whenever a representation is optimized for perceptual similarity rather than intervention structure, which is precisely the objective under which most large-scale pretrained encoders are learned. We introduce Counterfactual Latent World Models (CLWM), which combine a recurrent belief-state encoder, action-conditioned latent dynamics, and a contrastive counterfactual objective that separates futures induced by distinct interventions even when their observations look alike. Across occluded manipulation, aliased navigation, and long-horizon manipulation, CLWM improves planning success over the strongest baseline (65.1% \to 74.6% on Occluded Push and 67.3% \to 78.9% on Aliased Maze) and reduces exploitative planning failures (18.4% \to 9.7% on Deferred Kitchen), with ablations attributing the gains to hard counterfactual negatives, especially perceptual-alias negatives. Finally, our counterfactual separability metric, which tracks planning success across the five baseline model classes ( r \ge 0.94 ), is representation-agnostic: given intervention-outcome labels, it can audit any encoder, pretrained or trained from scratch, before a planner trusts it. We do not yet measure it on large-scale pretrained encoders. Here we establish the metric and its relationship to planning success for world models trained from scratch.
[AI-253] Beyond Top-k Skill Retrieval: Diversity-Aware Skill Routing for LLM Agents
链接: https://arxiv.org/abs/2609.05824
作者: Wang Wei,Tiankai Yang,Samyadeep Basu,Hongjie Chen,Yue Zhao,Zhengzhong Tu,Xiyang Hu,Franck Dernoncourt,Ryan A. Rossi,Hoda Eldardiry
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM) agents increasingly rely on external skills, but routing user requests over large skill registries is difficult because many skills are functionally redundant while complex tasks often require complementary skill sets. Existing skill routers typically rank candidates independently by query relevance, which can waste context budget on redundant skills. We propose Diverse Skill Routing (DSR), a diversity-aware reranking framework that uses a Determinantal Point Process to balance relevance and non-redundancy. DSR introduces a query-residual diversity kernel that penalizes redundant skill overlap while reducing penalties caused only by shared query relevance. On the SkillRouter benchmark, DSR improves recall and full coverage over a strong pointwise reranking baseline, with larger gains on multi-skill queries. These results suggest that skill routing should be treated not only as relevance ranking, but also as complementary set selection.
[AI-254] Agent ic BAIM-LLM LLM Evaluation (ABLE): Benchmarking LLM Use of Protein Design Tools NEURIPS2025
链接: https://arxiv.org/abs/2609.05818
作者: Bryce Cai,Geetha Jeyapragasan,Samira Nedungadi,Jake Yukich,Seth Donoughe
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Quantitative Methods (q-bio.QM)
备注: Last revised in February 2026; presented without further revision. An earlier revision at was presented at the NeurIPS 2025 Workshop on Biosecurity Safeguards for Generative AI; see this https URL
Abstract:We introduce ABLE, a benchmark for evaluating LLM agents’ ability to use biological AI models (BAIMs), such as ProteinMPNN and AlphaFold3, in dual-use protein design workflows. ABLE assesses agent performance through a set of tasks spanning structure retrieval, sequence generation, and design validation. We evaluate 15 frontier models and find that seven refuse all tasks, while the remaining models exhibit substantial performance differences. Claude Sonnet 4 and Gemini 3 Pro achieve the highest scores across information retrieval, tool selection, and tool use. We further compare model performance on a subset of tasks against an expert human baseline. Our results suggest that current LLMs can substantially lower barriers to protein design, but remain inconsistent in planning, strategy generation, and integrating biological knowledge with tool use.
[AI-255] Exposing Weaknesses in Emotion Recognition in Conversations
链接: https://arxiv.org/abs/2609.05806
作者: Amir Ben Khalifa,Fanny Bezancon,Amine Trabelsi,Bessam Abdulrazak
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Emotion Recognition in Conversations (ERC) aims to identify speakers’ emotions in multi-turn dialogue. Accurate emotion recognition can support a wide range of applications, including empathetic conversational agents, mental health support, and educational technologies. While many recent approaches rely on task-specific fine-tuning, such models may exploit dataset-specific cues. A central yet rarely questioned assumption in ERC is that each utterance can be assigned a single unambiguous emotion label. To investigate this assumption, we study ERC using Large Language Models (LLMs) in a zero-shot setting while incorporating preceding conversational turns as context. We show that aggregate metrics mask systematic failures. Errors concentrate around utterances containing negations, exclamations, and interjections. This pattern is consistent across all evaluated models, suggesting limitations in the benchmarks rather than model-specific weaknesses. A controlled re-annotation study involving four human annotators supports this finding: strong agreement is observed in only 35 percent of cases, with neutral utterances dominating high-agreement instances, while many emotional categories fall into low-agreement regimes. These findings suggest that many apparent model errors reflect genuine annotation ambiguity rather than poor emotion understanding. Standard single-label evaluation is therefore insufficient. To address this limitation, we introduce an LLM-as-Judge framework that evaluates each emotion independently according to its plausibility in the conversational context rather than enforcing a single-label decision.
[AI-256] Evidence-Aligned Local Composition of Discrete Experts for Sequence Restoration
链接: https://arxiv.org/abs/2609.05801
作者: Mohammad Panahazari,Usman A. Khan,Shuchin Aeron
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:A document modeled as a discrete sequence of tokens can be thought of as being generated from a composition of texts from different domains; a README file, for example, moves between prose, code, and configuration. When such a document is corrupted and only frozen domain experts are available, restoring it requires deciding both what is missing and which expert to trust at each position, at test time and without region labels or a trained router. We introduce evidence-aligned local composition, which infers a soft, position-wise weighting over the experts from the marginal evidence of the corrupted observation under a given corruption model, estimating the evidence from the experts’ own denoising losses and smoothing the weights across positions. Because the weighting is soft, it recovers a mixture when the true composition is mixed and concentrates on one expert when that suffices. Across a categorical simulator, byte-level experts, and experts fine-tuned from a 1.3 B discrete flow-matching model, the inferred weights track the true regions at 0.85 field accuracy on naturally mixed scientific documents, and at 0.98 on constructed mixtures whose regions are lexically disjoint. Restoration improves over a single global weight when the experts are genuinely distinct and reduces to it when they converge, tracking a measure of expert separation.
[AI-257] Spillover-Aware Multi-Value Steering for Pluralistic LLM Alignment
链接: https://arxiv.org/abs/2609.05800
作者: Weici Pan,Xander Barron,Jiawei Zhou,Zhenhua Liu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Activation steering controls LLM behavior at inference time by adding learned directions to hidden states, but existing methods handle one concept at a time. Pluralistic alignment, where different stakeholders need different value emphases, requires steering multiple dimensions simultaneously. We show that naive steering produces substantial spillover: the effect intended for one value leaks into others. This parallels the treatment-versus-spillover decomposition in causal inference. We trace spillover to geometric entanglement of steering directions, captured by their Gram matrix, and derive a zero-cost correction from an activation-norm-penalized objective that decouples each direction’s contribution exactly. Our end-to-end pipeline requires no fine-tuning, no reward model, and no manual prompt engineering: given only domain questions, it automatically discovers value dimensions, extracts directions, diagnoses entanglement, and applies corrected steering. On climate discourse, the correction improves the net steering effect from +5.9% to +14.0%, validated over 100,000 pairwise judgments.
[AI-258] More Than Mimicking Reviewers: Evaluating LLM s for Pre-Submission Peer Review
链接: https://arxiv.org/abs/2609.05788
作者: Pouya Parsa,Amin Rezaei
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 3 figures
Abstract:Peer-review feedback often arrives too late for authors to make meaningful revisions. We study an author-facing LLM system that moves part of this stress test before submission: it generates a broad pool of atomic concerns and compresses them into a short report. We evaluate agreement with historical reviews and, separately, the possible validity of concerns they omit. From 10,000 ICLR 2026 submissions, we use 3,398 manuscripts with accessible versions that predate review. On a ten-paper diagnostic, independent sampling covers 44.9% of historical issues; deduplication and refill reaches 78.7% strict and 84.9% seriousness-weighted coverage, at 3.6 \times more requests and 5.2 \times more tokens. A hidden Top-32 Oracle preserves the full 79.3% weighted coverage of a 256-candidate pool, but paper-only selectors retain only 40–44%. LLM review therefore provides broad coverage with a large candidate pool but compresses poorly; ablations identify representative selection and matcher sensitivity as the main sources of this gap. Comments: 9 pages, 3 figures Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2609.05788 [cs.AI] (or arXiv:2609.05788v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.05788 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-259] Distilling Vision-Language Models for On-Device Fire Understanding
链接: https://arxiv.org/abs/2609.05782
作者: Mohammad Kazzazi,Zixuan Liu,Siavash Khajavi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-language models (VLMs) offer a promising alternative to conventional fire detection systems by reasoning about the semantic context of a scene and thus reducing false alarms, yet their large model size makes deployment on embedded fire sensors impractical. In this paper, we study how domain-specialized VLMs can be compressed for fully on-device deployment without losing the safety-critical behavior required for fire detection. We develop a teacher-student knowledge distillation framework in which large VLMs fine-tuned for fire understanding can be distilled into lightweight students. Experiments across multiple VLM families and model scales show that compact students preserve most of their teachers’ fire-understanding capability. We further deploy the distilled models on our commercial Detectium fire detection sensor and jointly evaluate reasoning accuracy, latency, and memory usage. The results show that compression and deployment affect not only accuracy but also model failure modes, with Qwen2.5-0.5B providing the strongest overall deployment trade-off. Our findings provide broader guidance for deploying domain-specialized VLMs in resource-constrained, safety-critical settings.
[AI-260] Diffs vs. Whole Files: An Empirical Comparison of Iterative Edit-Based and Direct Generation for Flutter/Dart Code Models
链接: https://arxiv.org/abs/2609.05779
作者: Andrej Andrejev
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 19 pages, 7 figures
Abstract:Large language models used for code editing can be trained and deployed in at least two output regimes: direct generation, where the model emits the entire modified file in one shot, and iterative diff-based generation (“steps”), where the model emits a sequence of localized search/replace edits applied one at a time until it signals completion or a step budget is exhausted. The diff-based regime is attractive because it mirrors how developers edit code and should require far fewer generated tokens per turn. We train two code models - a 100M-parameter model trained from scratch (Rainbow-Pony-100M) and a fine-tuned Qwen2.5-Coder-0.5B - in both regimes on a shared Flutter/Dart dataset, and evaluate all four resulting models on a held-out set of approx 1,790 tasks per model. Direct generation substantially outperforms diff-based generation on every metric we measure - compilation/static-analysis pass rate, bits-per-byte, character-level similarity to the reference, and blinded LLM-judge ratings of goal fulfillment, correctness, and code quality - and the gap persists after controlling for task difficulty via a matched-ID comparison and when restricting to code that compiles on both sides. We then identify a single, architecture-independent mechanism behind the conditions where diff-based generation does win: it is competitive on short, spatially localized edits, and its category-level wins concentrate in exactly the two task categories - refactoring and error-handling/edge-case fixes - with the lowest mean edit-step count in our dataset. We term this task locality and discuss its implications for when an edit-based training regime is and is not the right choice for a code-editing model.
[AI-261] DI-Bench: Systematically Generating In-Domain Data Intelligence Benchmarks for Enterprise Agents EMNLP2026
链接: https://arxiv.org/abs/2609.05776
作者: Jiangyun Zhang,Kristen Surrao,Torpong Nitayanont,Yupei Zhang,Roopali Singh,Zhiyu Chen,Julia Huang,Zhou Tang,Shayan Ali Akbar,Omar Alonso,Erwin Cornejo,Yuan Li,Yi Zhang
类目: Artificial Intelligence (cs.AI)
备注: 19 pages, 3 figures, 13 tables. Accepted to the EMNLP 2026 Industry Track
Abstract:Evaluating enterprise agents on domain-specific benchmarks is critical, yet public benchmarks rarely evaluate whether agents can integrate business knowledge with analytical computation, and constructing such benchmarks manually is costly. We present DI-Bench, a pipeline for generating realistic benchmarks for data intelligence (DI), the practice of extracting insights from large volumes of enterprise data. To emulate realistic DI tasks that require both computation and knowledge retrieval, DI-Bench builds an artifact linkage graph over data tables, dimensions, metrics, and documents to form questions involving structured data and associated knowledge. Ground truth answers are derived via query execution, followed by LLM question generation and validation. Applied to two public datasets, the pipeline produces a 731-task benchmark covering knowledge retrieval, analytical computation, and rule-grounded reasoning. To show the discriminatory capability and difficulty of the benchmark, we evaluate four models, revealing a substantial finding: models achieve only 32% accuracy when doing computational tasks where retrieved business rules modify the computation.
[AI-262] Inference-Time Graph Engineering for Multi-Agent LLM Workflows
链接: https://arxiv.org/abs/2609.05774
作者: Katherine Tieu,Dongqi Fu,Yinglong Xia,Hong Li,Hong Yan,Jingrui He
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent multi-agent LLM systems increasingly rely on graph-structured communication to coordinate specialized agents. We revisit multi-agent orchestration from a graph-engineering perspective: rather than optimizing a static topology, we synthesize a task-conditioned temporal workflow graph that jointly specifies agent connectivity and edge-level communication semantics. We introduce ReActNet, a training-free framework that compiles a query and a set of role-specialized agents into a sequence of directed communication graphs. Each graph snapshot corresponds to one reasoning stage, and each edge carries a natural-language instruction specifying the message that a source agent should provide to a target agent. The compiled temporal graph is then executed through structured message passing: agents update their reasoning states by integrating their previous states with messages from controller-assigned neighbors, and a final aggregator synthesizes the resulting states into the answer. This design separates graph compilation from graph execution, making multi-agent coordination explicit, inspectable, and task-conditioned without requiring reinforcement learning or gradient-based topology optimization. Across knowledge reasoning, mathematical problem solving, code generation, and GAIA-style assistant tasks, ReActNet consistently improves over fixed-topology and learned-topology baselines while maintaining competitive inference cost. These results suggest that effective multi-agent orchestration depends not only on which agents communicate, but also on engineering executable workflow graphs that encode when, why, and how information should flow during reasoning.
[AI-263] Data Scout: Targeted Web Crawling for Domain-Specific Pretraining Corpora
链接: https://arxiv.org/abs/2609.05766
作者: Chirag Garg,Eelaaf Zahid,Farhan Ahmed,Jay Pankaj Gala,Eric Butler,Heiko Ludwig
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The dominant approach to building domain-specific pretraining corpora is to filter large web archives such as CommonCrawl. This works well for popular domains but breaks down for specialized ones, where relevant content is sparse and often beyond the reach of popularity-driven crawlers. We present Data Scout, which inverts this: instead of filtering an archive, it directs a targeted crawl. An LLM expands a root topic into a taxonomy and thousands of search queries; the returned URLs (seeds) are grouped by subdomain and screened with a user-supplied classifier (the probe), admitting each subdomain on the basis of a small sample. This works because relevance has a sharp boundary at the subdomain level: in mathematics, a page is 21x more likely to be relevant than one on a sibling subdomain. With the FineMath classifier as the probe, 21.9% of crawled pages are high-quality math content, 70x the 0.31% rate from filtering a comparable web sample, so the crawl wastes far less effort. But the payoff is not just efficiency: 63.2% of these pages are missing from CommonCrawl altogether, yet just as useful for training. Continued pretraining of Llama-3.2-3B on 1.9B Data Scout tokens matches FineMath corpus on GSM8k. Because the probe is the only domain-specific component, Data Scout can in principle apply to any domain with such a classifier.
[AI-264] Interface-Aware KV Cache Quantization for Dense On-Chip NVM in Long-Context LLM Decoding
链接: https://arxiv.org/abs/2609.05764
作者: Jiahao Zheng,Yifan Qin,Xiaobo Sharon Hu,Yiyu Shi
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI)
备注: IEEE/ACM International Conference on Computer-Aided Design (ICCAD 2026)
Abstract:The key-value (KV) cache is the dominant memory bottleneck in long-context large language model (LLM) decoding: every step reads it entirely, so decoding is memory-bandwidth bound. Holding a quantized KV cache in dense on-chip non-volatile memory (NVM) removes the off-chip transfer. Existing KV quantization methods, however, were designed for GPU-style memory systems: KIVI attaches per-group metadata, adding about 25% to the stored KV cache; KVQuant keeps sparse full-precision outliers that a dense array cannot hold in place. This paper examines what these structures cost when the KV cache resides in NVM behind fixed-range converters, and designs a quantization scheme matched to that interface. The architecture stores the quantized KV cache in dense on-chip NVM, uses a small static analog crossbar only for the fixed rotation, and keeps attention in on-chip digital logic. A randomized rotation and per-vector normalization give every coordinate the same range, so one fixed codebook for keys and one for values, each shared across all tokens of the corresponding tensor type, serve the entire KV cache. The codebook thresholds are programmed once as the read converter’s reference levels, enabling fixed-range digitization with no per-token converter reconfiguration. Dequantization is a sixteen-entry lookup and one norm multiply; the only per-vector metadata is one scalar, about 3%. Across models from 3B to 14B and contexts to 32k tokens, the four-bit KV cache maintains accuracy under storage and crossbar noise simulated at realistic device levels. KIVI and KVQuant remain more accurate in software; the advantage of our format lies at the memory interface: 3.1-3.6x lower KV read energy than both mapped to the same NVM, and 8x lower metadata overhead than KIVI. The contribution is a KV quantization co-designed with the NVM memory interface rather than a new accuracy record.
[AI-265] From Monolithic Blending to Agent ic Orchestration: Dynamic Response for Conversational Assistants at Scale EMNLP2026
链接: https://arxiv.org/abs/2609.05758
作者: Cen (Mia)Zhao,Peng Wang,Chuan Shi,Yufeng Zhang,Ying Lyu,Wanmeng Ren,Robert Xue,Claire Na Cheng,Yashar Mehdad
类目: Artificial Intelligence (cs.AI)
备注: Accepted to EMNLP 2026 Industry Track. 16 pages, 1 figure, 21 tables
Abstract:Conversational assistants can blend retrieval, action selection, escalation, and wording in a single model path, or separate those roles. We report a production migration of a customer-support assistant at a large accommodation marketplace (millions of conversations per month, 11 languages, 10-second P90). Dynamic Response (DR) replaces a single Qwen3-235B-A22B blended responder with a bounded ReAct orchestrator over typed tools plus a smaller generator that writes from a backend-validated context contract. Because the migration also changed prompts, alignment, and serving, we attribute each effect to its cause and claim as architecture effects only those measured on identical replayed turns: typed entity selection moves the reservation selector to a precision-first operating point (precision 8.3% to 89.1%, recall 75.2% to 67.3%), and typed action IDs with a membership check remove observed structured-action hallucination (2.14% to 0.0%). A low-ramp A/B test reproduces the replay escalation reductions: hard-escalation responses fall from 5.60% to 3.08% and soft-escalation responses from 9.56% to 2.49%, while production handoff volume holds roughly steady; self-solve is directional (+5.1 points, 95% CI [-2, +12]). Serving optimizations cut orchestrator P90 latency from 3.87s to 2.24s on a GPU footprint reduced by roughly one-third, and self-hosting reduces estimated annual model-serving cost by more than an order of magnitude.
[AI-266] Concord: A Video Relational Algebra for Cross-Modal Query Optimization
链接: https://arxiv.org/abs/2609.05756
作者: Sultan Muratbek,Charisse Ivana Yeung,Chanwut Kittivorawong,Alvin Cheung
类目: Databases (cs.DB); Artificial Intelligence (cs.AI)
备注:
Abstract:Semantic video queries let users embed natural language prompts and use multimodal large language models (MLLMs) to interpret the video. Such queries are increasingly popular for querying video data. However, their expressiveness comes at a steep cost: an MLLM may process hours of media to return only seconds of relevant output, making naive execution slow, expensive, and inaccurate. We propose Concord, a system for expressing and optimizing semantic video queries. We makes three contributions. First, we introduce Video Relational Algebra (VRA), a nested algebra over videos, transcripts, frames, and object tracks that captures common semantic video operations. Second, we derive a set of approximate optimizations that rewrite VRA queries to reduce MLLM usage while improving result quality. For narrated video, Concord either processes transcripts instead of video or uses them to identify video clips for MLLM processing. For cross-camera queries without narration, detection and tracking replace a whole-video MLLM join with a track-level relational join. Third, we evaluate Concord on real-world videos. Across 4.59 hours of soccer broadcasts and 3.92 hours of lectures, transcript-to-video queries send only 5.32% and 2.47% of source-video duration to the MLLM and reduce MLLM cost by up to 87%. In two five-second highway clips with 18 manually adjudicated cross-camera vehicles, a Detect-Track-Join query improves F1 from .364 to .813 while making no MLLM calls. See our project at this https URL. Subjects: Databases (cs.DB); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.05756 [cs.DB] (or arXiv:2609.05756v1 [cs.DB] for this version) https://doi.org/10.48550/arXiv.2609.05756 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-267] he Normalization of Deviance in AI Development
链接: https://arxiv.org/abs/2609.05749
作者: Emilio Barkett,Alexander Kimpton,Daniel Graham,Yusuf Kundgol
类目: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Systems and Control (eess.SY)
备注:
Abstract:Work on the risks of artificial intelligence has focused predominantly on capability risk: the danger that systems become too powerful, too autonomous, or too misaligned with human values. Far less attention has been paid to the organizational level—to whether the institutions building these systems are themselves predisposed to drift toward failure. This paper argues that they are. Regardless of how capable AI systems become, the organizations building them face the same structural dynamics that preceded past major technological disasters. Drawing on case studies of the Space Shuttle Challenger, the Three Mile Island accident, and the Boeing 737 MAX crashes, this paper identifies the common structural mechanisms preceding each failure and maps them onto contemporary AI development. The findings suggest that existing safety infrastructure may provide less protection than it appears, as organizations can complete safety processes in full compliance and still produce catastrophic outcomes. The pre-disaster period of AI development is still underway; the purpose of this paper is to make these dynamics legible while they can still be interrupted.
[AI-268] Beyond Prompts: Measuring and Optimizing LLM Tool-Agent Harnesses EMNLP2026
链接: https://arxiv.org/abs/2609.05736
作者: Cen (Mia)Zhao,Haibo Ruan,Wenjie Chen,Pei-fen Tu,Usman Abbasi,Joel Hesch
类目: Artificial Intelligence (cs.AI)
备注: Accepted to EMNLP 2026. 18 pages, 9 figures, 10 tables
Abstract:LLM tool agents can be improved without retraining by modifying the runtime harness around a fixed model: prompts, tool interfaces, middleware, state handling, and recovery logic. We study this setting as resource-bounded harness selection for fixed-model multi-turn tool agents, with the search surface scoped to prompts and tool-boundary middleware: edits are guarded intercepts at the tool boundary, not arbitrary rewriting of agent execution logic. Our optimizer-agnostic protocol reports mean held-out lift, worst-condition lift, repeatability, logged cost diagnostics, and RelLift95(B), a conservative estimate of the held-out gain of the harness selected under budget B. We instantiate the protocol with prompt-only and prompt-plus-middleware optimizers, including PRISM, which clusters failures and routes repairs to prompt, tool-boundary middleware, or joint edit surfaces within a Pareto search. On BFCL multi-round, tau2-Retail, and tau2-Telecom, PRISM obtains mean held-out lifts of 14.2, 14.9, and 10.1 percentage points and positive empirical RelLift95 on all three benchmarks, and a component ablation attributes the margin chiefly to failure-surface routing and the edit-pattern constraint. Across optimizers, the results show that some search procedures can occasionally find large gains but still choose brittle updates, so the reliability of the chosen harness should be reported alongside average held-out lift.
[AI-269] Newton Matching for Generative Modeling: A Unified Framework for Fine-Tuning and Sampling
链接: https://arxiv.org/abs/2609.05727
作者: Zeyang Li,Yunan Wang,Paolo Giaretta,Navid Azizan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注:
Abstract:We develop Newton Matching, a unified framework for fine-tuning and sampling in generative modeling. The target is \pi\propto\mu e^\tau r , where r is the reward, \tau0 the inverse temperature, and \mu denotes the pretrained model’s terminal density for fine-tuning or the constant 1 for sampling. We shift the paradigm from isolated losses to iterative optimization over canonical models: population minimizers of standard conditional matching for terminal densities. Under compatible smooth-realization assumptions, canonical velocities form a manifold diffeomorphic to the density manifold. Transporting the Fisher-Rao metric and mixture connection to this manifold, we show that the reverse-KL Hessian equals the metric, so the Newton direction coincides with the negative Fisher-Rao gradient. At terminal density \rho , each stage takes a tangential step generated by the regularized reward r-\frac1\tau\log(\rho/\mu) , followed by terminal-density-preserving canonicalization. This canonical retraction yields an exact finite-stepsize density characterization. For the ideal iteration, we prove strict reverse-KL descent away from the target for 0 \eta \le \tau , global convergence under mild conditions, and local quadratic convergence for full steps ( \eta=\tau ). Covariance and gradient forms, each with forward or reverse regression-pair constructions, yield sample-wise tangential-update losses with the same population minimizer, without importance sampling or full-trajectory backpropagation. We develop approximate updates and define critical-point consistency as vanishing tangential displacement if and only if \rho=\pi . We recover representative methods as exact realizations, critical-point-consistent approximations, or objective-altering variants, enabling modular algorithm design. Our work advances the theory and algorithms of reinforcement learning for generative models.
[AI-270] XAI-SDN: An Explainable Entropy-Guided Machine Learning Framework for Real-Time DDoS Detection in Software Defined Networks
链接: https://arxiv.org/abs/2609.05701
作者: Adeel Ahmad,Ali Akarma,Ahmad Ali,Hammad Muneer,Toqeer Ali Syed
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
备注:
Abstract:One of the biggest risks faced by Software Defined Networks (SDN) is the Distributed Denial of Service (DDoS) attack in which a compromised controller can make an entire network unusable. To address these challenges, we suggest an entropy-guided machine learning framework, called XAI-SDN, for real-time DDoS detection in SDN environments which is lightweight and explainable. The framework extends the flow features extracted by CICFlowMeter with eight Shannon entropy metrics obtained by an \mathcalO(1) rolling algorithm and uses a Random Forest classifier with SHAP TreeExplainer for providing transparency at the prediction level. On a fixed temporal split, XAI-SDN achieves an accuracy of 99.9987%, a macro F1-score of 99.9621%, and an AUC-ROC of 1.0000 on the full 3.59 million flows of the CIC-DDoS2019 SYN benchmark. The pipeline sustains 0.0165~ms per flow (60,606 flows/s) without the use of SHAP and 0.5122~ms per flow (1,953 flows/s) with full support of SHAP under the 99.14% prevalence of DDoS traffic, which is a step towards achieving a balance between the detection performance and operational transparency in next-generation SDN security.
[AI-271] Analysis of Respiratory Sinus Arrhythmia with Neural Networks
链接: https://arxiv.org/abs/2609.05698
作者: Julian Szymanski,Patryk Orkisz,Higinio Mora
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The paper introduces a neural network-based approach for analyzing ECG signals to estimate respiratory rate by leveraging the phe- nomenon of Respiratory Sinus Arrhythmia (RSA). Our method employs a deep learning model trained to predict respiratory waveforms directly from ECG input data. To achieve this, we developed and evaluated three different neural network architectures capable of automatically extract- ing relevant features from ECG signals without the need for manual preprocessing. The proposed approach offers a robust and scalable solu- tion for non-invasive respiratory monitoring, with potential applications in healthcare and wearable technology
[AI-272] PAC-Private Autoregressive Generation: Calibrating Noise to Ensemble Disagreement
链接: https://arxiv.org/abs/2609.05676
作者: Mina Mirzadehsarcheshmeh,Amir Keyvan Khandani
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Language models adapted on private text are often served through APIs, so privacy leakage occurs through generated outputs rather than exposed weights. Private prediction protects these releases. Methods such as PMixED incur privacy cost at each release and increasingly rely on the public model over long horizons. PAC privacy instead calibrates noise to output variability across possible secrets, adding less noise when predictions are stable. To our knowledge, PAC-private prediction has not previously been extended from classification to autoregressive generation. We construct m=128 overlapping worlds from the private corpus, with each record appearing in exactly m/2 worlds, and train one adapter per world over a frozen public model. The realized world is the secret. At each token, the public model defines a candidate set, the worlds vote, and their posterior-weighted disagreement determines the PAC noise; unanimity requires no calibration noise. We prove I(S;Y_1:T) \leq I(S;H_T) \leq bT . Our contributions are extending PAC privacy to autoregressive generation, handling adaptive self-generated contexts, and introducing coupled decoding that preserves privacy accounting while avoiding greedy degeneration. On WikiText-103 with GPT-2-small, we retain 74% of the fine-tuning gain at a per-token budget of 2^-32 , while membership-inference success is bounded by 51.08% after 10^6 tokens; posterior-entropy estimates of leakage are roughly 17% of the charged budget. Inference privacy is not content protection: even when membership advantage on a memorized canary is indistinguishable from zero, the canary is emitted at the same rate. Against PMixED under matched membership-inference bounds on the same data universe and test set, we retain 98% of non-private headroom from 10^2 to 10^6 tokens, versus at most 56%, with no crossover. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.05676 [cs.LG] (or arXiv:2609.05676v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.05676 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-273] he convergent laboratory: when AI reasoning autonomous experiments high performance and quantum computing reshape chemistry
链接: https://arxiv.org/abs/2609.05643
作者: Eliu Huerta,Xiaoyun Wang,Geetika Gupta,Edward H. Sargent,Cameron J. Owen,Victor Fung,Abhishek Mitra,Austin Cheng,Emma Bouchard,Shams Mehdi
类目: Artificial Intelligence (cs.AI); Materials Science (cond-mat.mtrl-sci)
备注: 10 pages, 24 references
Abstract:This Comment emerges from TPC26 (this https URL), a conference convening leaders from academia, national laboratories, and industry who are reshaping materials science discovery. The meeting explored how AI, autonomous agents, self-driving labs, higher performance and quantum computing converge to amplify their individual impact on materials science discovery. The perspectives here reflect the firsthand experiences of researchers at these frontiers and capture the essence of this global endeavor. As AI-driven reasoning, autonomous agentic frameworks, self-driving laboratories, and fault-tolerant quantum processors mature simultaneously, we offer this Comment as a reference at what we believe is a tipping point of transformative advances and productive disruption in the chemical sciences.
[AI-274] Adaptive Cost-Sensitive Machine Learning for Autonomous Robot Navigation Failure Prediction: When Not All Errors Are Equal
链接: https://arxiv.org/abs/2609.05593
作者: Rifa Ferzana
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 14 pages, 9 tables
Abstract:Autonomous robot navigation failures differ not only in categorical severity but also in the physical context in which they occur. A near-miss at low speed under reliable sensing is not equivalent to the same event during rapid motion, close obstacle approach or degraded perception. This paper reframes navigation failure prediction as consequence-sensitive forecasting. We first establish a fixed baseline in which training weights are modulated by categorical severity, then introduce an adaptive extension defining a state-dependent consequence function combining severity with normalised velocity, obstacle proximity and sensing uncertainty, together with a risk-sensitivity term that rises as conditions deteriorate. We evaluate on 2,000 simulated differential-drive episodes (~1,000,000 timesteps) using episode-level GroupKFold, with external validation on the UCI SCITOS G5 dataset. Fixed weighting raises Logistic Regression high-severity recall from 0.851 to 0.985 and reduces missed consequence cost from 1,940 to 313; the adaptive extension reaches 0.998 and 82. Under matched false-positive conditions, however, the discriminative advantage is modest (0.986 versus 0.984), so most of the gain reflects a more conservative operating point rather than better ranking. The effect is consistent across all five folds and stable across a threefold span of context coefficients. Because the primary simulation produced no collisions, we add a controlled extension in which 108 of 600 episodes terminate in contact: collision recall rises from 0.850 to 0.966 (fixed) and 0.984 (adaptive), with missed collision cost falling from 1,000 to 105, at false-positive rates of 0.413 and 0.799, respectively. Context-dependent consequence modelling thus provides a principled mechanism for allocating conservatism by physical risk.
[AI-275] Agents Trust Tools Too Much: Measuring Reliance on Unreliable Tools
链接: https://arxiv.org/abs/2609.05587
作者: Hoyeol Yang,Woojung Song,Taewon Kim,Jonghyun Song,Seoyeon Park,Yohan Jo
类目: Artificial Intelligence (cs.AI)
备注: 39 pages, 4 figures
Abstract:Existing evaluations of tool-using agents primarily measure whether an agent can successfully complete diverse tasks with tools. These evaluations generally assume that tools return reliable information. However, tool returns in real-world systems can be plausible yet incorrect. We investigate how agents respond to unreliable tool returns by evaluating fourteen LLMs using three tools-web search, LLM sub-agent delegation, and code execution. For each tool, we corrupt its returns and measure whether agents adopt the corrupted content in their final answers. Agents exhibit high levels of overtrust across all three settings: the mean adoption rate exceeds one third for every tool and reaches 68.0% for web search. Analysis of reasoning traces reveals a particularly concerning failure mode: agents often recognize conflicts and even recover the correct answer internally, yet present only the corrupted answer without warning the user. To mitigate agents’ overtrust in tool returns, we intervene at three levels: prompting by the user, metadata from the tool provider, and post-training by the agent builder. Although some interventions help for particular models or tools, none consistently mitigates overtrust across tools. These findings identify overtrust in unreliable tools as a serious and persistent failure mode, motivating evaluations and interventions that enable agents to validate tool outputs and transparently communicate unresolved conflicts.
[AI-276] Planning and Scheduling Business Processes under Control-Flow Uncertainty
链接: https://arxiv.org/abs/2609.05578
作者: Michel Kunkler,Stefanie Rinderle-Ma
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Scheduling activities in business processes can improve efficiency (e.g., reduce makespan), but is challenging because the exact sequence of activities required to complete a case is often uncertain due to decisions based on data that emerges during execution. Nevertheless, probabilistic information regarding such decisions can often be estimated or derived from historical execution logs, and can help anticipate which execution paths are likely to lead to successful completion. Planning with particular execution paths affects feasibility, i.e., the probability of successful completion, and the expected number of superfluous activities that are planned but never executed. We frame the problem as a chance-constrained optimization problem and present two formulations: A decomposed approach with two stages, a planning stage that minimizes the expected number of superfluous activities subject to a feasibility constraint, and a scheduling stage that minimizes the makespan over the planned activities; and an integrated approach that combines planning and scheduling into a single formulation. Evaluation on two real-world and one synthetic dataset shows that the integrated approach yields superior makespans but is intractable at scale, while the decomposed approach scales to large settings.
[AI-277] Deep belief networks are exact
链接: https://arxiv.org/abs/2609.05572
作者: Gleb Smirnov
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Probability (math.PR)
备注: 6 pages
Abstract:We prove that every strictly positive probability distribution on (-1,1^n) is represented exactly by a sigmoid belief network with finite parameters. This answers a question of Sutskever and Hinton. The proof upgrades their probability-sharing approximation to exact representation using Brouwer’s fixed-point theorem.
[AI-278] Robots Influencing Humans to Reveal their Goals during Collaboration and Competition
链接: https://arxiv.org/abs/2609.05519
作者: Debasmita Ghose,Oz Gitelson,Michal Lewkowicz,Jake Brawer,Marynel Vazquez,Brian Scassellati
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Accepted to Autonomous Robots (AURO)
Abstract:We propose a unified strategy for fast goal inference in human-robot interaction. The core idea is to drive the human toward Critical Decision Points (CDPs)-states where competing human strategies prescribe different next actions and thus maximally reveal the goal. We formalise CDPs using a goal-conditioned policy divergence measure and incorporate them into a Receding-Horizon Planner that explores future action sequences while optimizing a cost function balancing task progress and information gain. We evaluate this approach in both a collaborative, fully observable cooking task and a competitive, partially observable hide-and-seek game, each in simulation and on real robots. In both scenarios, our method infers human goals more accurately and earlier than baseline strategies.
[AI-279] When Do Options Help? Policy Necrosis and Redundant Coverag e in Option-Critic
链接: https://arxiv.org/abs/2609.05508
作者: Bingyun Liu,Yuheng Jing
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: First two authors contributed equally
Abstract:Option-critic learns options: sub-policies together with a learned rule for when each one hands control back. Its headline result is that performance improves as options are added. We explain that result, with theory and experiment. First, the termination rule option-critic learns by maximising return contributes nothing. When the termination test and the policy that picks options read the same values, the test fires at every step, so the learned rule is identical to always terminating. When that policy explores and the test does not, as in option-critic itself, the rule can block the exploration; there are instances where it suffers \Omega(T) regret while always terminating holds to O(\log T) . Forcing termination at every step leaves the option-count curve intact. Second, the policy inside an option barely explores at all, so a state locks onto the first action that looked good and never updates again. We name this policy necrosis, give a state-level test for it, and find three fifths of states necrotic in a typical option. Restoring exploration repairs those states, and one option then solves the task. Third, extra options improve no option; what falls is the chance that all of them fail in the same state, from 59\% to 4\% , and performance follows that joint quantity.
[AI-280] SciLitBench: Benchmark and Design Principles for LLM -Powered Systematic Literature Reviews
链接: https://arxiv.org/abs/2609.05505
作者: Miguel Zabaleta,Baihan Lin
类目: Artificial Intelligence (cs.AI)
备注: 22 pages, 6 figures, and 5 tables. Data and code are available at this https URL
Abstract:Systematic reviews require sustained human judgment across thousands of records, yet existing evaluations of large language models (LLMs) typically examine review stages in isolation. We introduce SciLitBench, a multi-stage benchmark spanning title and abstract screening, full-text screening, and schema-guided data extraction, with 42,981 retrieved records, 1,012 full texts, and annotations for 888 included papers. Across 22 open-weight LLMs from six model families, explicit inclusion and exclusion criteria improve title and abstract screening F_2 by 28.8%, while researcher-authored rationales improve full-text screening by 15%. Data extraction reveals a different reliability regime: performance declines from 0.97 accuracy for publication year to 0.37 Jaccard overlap for computational approach, while the strongest models recover only 30% of annotated evaluation evidence and 25% of limitations. SciLitBench identifies a practical boundary between high-recall screening and evidence-complete extraction and provides a reproducible resource for evaluating LLM-assisted evidence synthesis.
[AI-281] PGP-Clinical-TimeKAN: Prior-Guided Joint Probabilistic Forecasting of Clinical Trajectories
链接: https://arxiv.org/abs/2609.05488
作者: Weizhi Nie,Rihao Chang,Weijie Wang,Yuting Su
类目: Artificial Intelligence (cs.AI)
备注: 30 pages, 12 figures, and 17 tables
Abstract:Clinical deterioration unfolds through coupled, partially observed trajectories, not a single diagnostic label. We introduce PGP-Clinical-TimeKAN, a trajectory-first framework for joint probabilistic forecasting of multivariate physiology. It combines missingness-aware temporal encoders, a soft organ-system prior, patient-specific relations, nonlinear Kolmogorov-Arnold messages, and a low-rank multivariate Student-t head. We evaluate 24-hour histories and six-hour forecasts on a frozen MIMIC-IV-derived cohort of 6,882 patients and 54,694 windows. Across five seeds and 13 models, PGP-Clinical-TimeKAN obtains the second-lowest normalized MAE (0.37727 +/- 0.00029) and the lowest RMSE (0.52656 +/- 0.00034). It reduces MAE by 0.52% relative to deterministic TimeKAN. For probabilistic forecasting, it reaches a marginal NLL of 0.66380 and a CRPS of 0.27301. Empirical coverage is 0.533, 0.831, and 0.958 for nominal 50%, 80%, and 95% intervals. Removing relational structure causes the largest ablation loss. Increasing covariance rank improves joint likelihood but has little effect on point accuracy. A trajectory-derived risk score remains weaker than a dedicated GRU-D classifier (AUROC 0.603 versus 0.650), which limits the present clinical claim. Joint trajectory forecasting therefore provides an inspectable intermediate task, but accurate physiology forecasts alone do not ensure a calibrated event detector.
[AI-282] RAPID: Reliability-Aware Pair Importance Distillation
链接: https://arxiv.org/abs/2609.05481
作者: Ali Mahdavi,Azadeh Zamanifar,Amirfarhad Farhadi,Omid Kashefi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Inter example relational distillation transfers a teacher’s representation geometry by matching relations among examples within a mini batch. Computing all pairs has quadratic complexity in the batch size, whereas uniform subsampling may use a limited relation budget inefficiently. We introduce Reliability Aware Pair Importance Distillation, or RAPID, which separates a reliability gated relational target from a full support adaptive pair proposal. Reliability determines which teacher relations are emphasized, while calibrated teacher entropy and detached student-teacher residuals determine which relations are evaluated. Exact inverse proposal correction makes the loss and gradient estimators conditionally unbiased with respect to the gated mini batch target. We evaluate RAPID in two text classification settings: AG News with BERT-to-DistilBERT distillation using three paired seeds and a relation budget of 256, and SST-2 with DistilBERT to DistilBERT distillation using three paired seeds and a relation budget of 64. Reliability gated relational distillation achieves the highest observed mean student accuracy on both datasets: 94.285 plus or minus 0.054 percent on AG News and 88.800 plus or minus 0.532 percent on SST-2. RAPID ranks second, achieving 94.241 plus or minus 0.025 percent and 88.685 plus or minus 0.462 percent, respectively, compared with 94.154 plus or minus 0.124 percent and 87.271 plus or minus 0.162 percent for the cross entropy baseline. Pilot evaluations are counted toward the same total budget as the main relation evaluations. Across both settings, the gated target yields the highest mean accuracy, while the adaptive proposal remains within seed-level variation. These results support the modular view that target reliability and evaluation priority are separable design dimensions.
[AI-283] ARC-Bench: Closed-Loop Replanning Masks Broken Action Ranking in Frozen JEPA World Models
链接: https://arxiv.org/abs/2609.05461
作者: Zhengshu Zhang,Zhiyuan Li
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
备注: 15 pages, 7 figures
Abstract:Reward-free latent world models plan by scoring candidate actions with distances in a frozen latent space: an action is preferred if its predicted future embedding lands closer to the goal embedding. This silently assumes that latent closeness is action-rankable, i.e., that ordering candidates by latent distance agrees with ordering them by true cost. We audit this assumption directly. We introduce ARC-Bench, a no-leak, fixed-candidate protocol that measures whether frozen JEPA-style objectives rank candidate actions correctly, and apply it to official released JEPA-WM checkpoints across navigation and manipulation-style control. The assumption fails, severely and structurally: on the official manipulation audits the top-scored candidate is almost always suboptimal, and the same inversion appears in the maze domains. A controlled visual-backbone extension shows that the defect persists when DINOv2 is replaced by video-pretrained V-JEPA 1 and V-JEPA 2 encoders at ViT-L/ViT-G scale. Provenance, undertraining, matched-budget backbone controls, and metric-circularity controls rule out trivial explanations. We then explain why this defect has stayed invisible: closed-loop replanning masks it. When we reduce the planner’s replanning frequency, success collapses in both a navigation and a manipulation domain, and the episodes rescued by frequent replanning are enriched for severe first-plan ranking failures in the PointMaze first-plan diagnostic. Closed-loop success rates therefore systematically overstate the rankability of frozen latent representations. ARC-Bench supplies the measurement, and the masking mechanism the explanation, for methods that adapt, amortize, or replan around latent-space planners without directly auditing released JEPA-WM action rankability.
[AI-284] Compiling VGDL into Causal Models
链接: https://arxiv.org/abs/2609.05459
作者: Mohit Jiwatode,Bodo Rosenhahn,Alexander Dockhorn
类目: Artificial Intelligence (cs.AI)
备注: To be published at IEEE Conference on Games 2026
Abstract:Reinforcement learning and large language models often struggle to accurately capture the causal mechanics of game environments. Standard reinforcement learning agents tend to rely on spurious correlations, while large language models are prone to hallucinating game rules. Although causal reinforcement learning improves interpretability, there is currently no formal methodology to map complex game mechanics directly into causal models. To address this, we propose a deterministic framework that compiles games specified in the Video Game Description Language into Dynamic Structural Causal Models. Rather than inferring causal structures from gameplay traces or noisy large language models’ outputs, our methodology directly translates game components, including sprite dynamics, interaction rules, and termination conditions, into explicit structural equations. Each game tick represents a causal transition from state variables at time t to t+1 . By establishing this grounded mapping, the approach guarantees absolute causal fidelity to the ground-truth game mechanics. The resulting models offer transparent causal pathways that support counterfactual reasoning, causal reinforcement learning agent training, and procedural content validation. This framework provides a principled bridge between symbolic game descriptions and causally grounded game AI.
[AI-285] Damage-Aware Bandit Pruning for Vision and Language Transformers
链接: https://arxiv.org/abs/2609.05448
作者: Salem Ameen,Sunil Vadera
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 23 pages, 6 figures
Abstract:Structured post-training pruning of transformers requires selecting complete functional units whose suppression causes limited degradation. We formulate structured-unit selection for language and vision transformers as a damage-aware multi-armed bandit problem under a fixed candidate-evaluation budget. Attention heads and MLP channel groups are temporarily masked on calibration batches. Paired damage is the masked loss minus the base loss on the same batch, reducing batch-to-batch variation. A smooth bounded reward drives either a UCB-style policy or fractional-Beta Thompson Sampling, and the final mask is constructed sequentially by adding one unit at each step. The selected units are functionally zeroed in the original dense checkpoint; therefore, the reported parameter effects represent effective structural suppression rather than physical compression or measured speedup. Experiments on WikiText-2, LAMBADA, and Imagenette cover GPT-2, OPT, Pythia, Qwen2.5, SmolLM2, ViT-B/16, DeiT-Tiny, and Swin-Tiny, with comparisons against random, magnitude, static-saliency, and budgeted-greedy selection. Across five seeds, the bandit methods usually reduce degradation relative to budgeted greedy in the paired language-model comparisons. Of 28 comparisons highlighted in the paper, 23 bootstrap confidence intervals exclude zero and 11 paired tests have p 0.05; six have q 0.05 after Benjamini-Hochberg correction across the full family of 116 dataset-wise tests. Matched-evaluation results for ViT-B/16 and Swin-Tiny indicate that their gains are not explained solely by a larger candidate-evaluation budget.
[AI-286] AutoFyn Technical Report: Non-Parametric Expert Iteration for Long-Horizon Agents
链接: https://arxiv.org/abs/2609.05446
作者: Adib Hasan,Daniel Schaffield,Akashnil Dutta,Tarik Adnan Moon
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce AutoFyn, an agent harness inspired by the Expert Iteration algorithm, adapting a frozen model across many rounds by updating persistent state from verified reward signals rather than model weights. Each round begins from a fresh model session, and durable information is reintroduced only through explicit interfaces such as persistent memory files, reports, and repository state. Within a round, an orchestrator explores, plans and builds many alternative approaches with specialized agents, while a task-grounded verifier verifies the work and supplies an objective reward for measuring progress. This reward is distilled back into the persistent state, which updates the effective policy for the next round. In this technical report, we formalize this loop and describe its persistent state and verification interfaces. We then demonstrate its use in three domains, namely olympiad mathematics, data science, and cybersecurity. On the six fresh problems of the 2026 International Mathematical Olympiad, every model with room to improve scores higher under AutoFyn than in its provider’s own coding agent. AutoFyn also built the top-ranked agent on the Spider 2.0 dbt benchmark, and has produced 16 maintainer-confirmed vulnerability advisories in this http URL, MetaMask, pnpm, Warp, LiteLLM, Langflow, and Open WebUI.
[AI-287] When Does Memory Help? A Cost-Aware Evaluation of Long-Term Memory in Tool-Using LLM Agents
链接: https://arxiv.org/abs/2609.05441
作者: Shweta Mishra,Shashank Mishra
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Long-term memory for LLM agents is evaluated today by conversational recall benchmarks (LoCoMo, LongMemEval), which measure question answering over dialogue history, not whether remembered facts change what a tool-using agent does. We present MERIT (Memory Evaluation for Realistic Instrumented Tasks), a benchmark and harness that measures the marginal utility of memory for task-executing agents under explicit cost accounting. MERIT provides episodic tool-use tasks in three domains whose dependence on earlier-episode facts is verified by an automated leak check; a difficulty ladder ending in updated-fact recall; controlled memory corruption; and full token and dollar metering of every memory operation. Across 23,440 scored episodes ( 42.57), a two-generation pilot on gpt-4.1-mini and a preregistered 3-model x 3-seed grid (GPT-4.1, Claude Haiku 4.5; memory side held fixed), memory lifts dependent-task success from a leak-verified floor of 0.00 to 0.55-1.00. On updated facts, embedding retrieval collapses unpredictably (0.30-0.95 across models; max seed gap 0.45), and agents act on a correctly retrieved value only 55% of the time, while update-on-write stores (a structured fact store and, notably, LLM summarization) remain at 0.70-1.00; the hybrid is worse than the fact store alone. A latest-generation spot-check (Claude Sonnet 5, gated on a clean full-replay control) reproduces the pattern. Swapping a memory’s implementation moves task success by up to 60 points, and full replay is never economical: the best condition per domain delivers 2.7-3.9x its marginal utility per dollar. We release the benchmark, harness, and all traces.
[AI-288] CriticGen: Generation-Aware Evaluation as Actionable Feedback
链接: https://arxiv.org/abs/2609.05439
作者: Huifang Du,Zecheng Zuo,Sen Wang,Chenghao Fan,Haofen Wang,Yehui Yang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Current evaluation methods for large language models are coarse-grained and decoupled from generation, producing generic explanations that fail to provide actionable feedback for model improvement. We propose CriticGen, a fine-grained, generation-aware evaluation framework that turns evaluation into actionable control for answer improvement. CriticGen first generates sample-specific evaluation dimensions and scoring criteria under high-level categories such as subjective, objective, and self-derived constraints. These criteria then serve as a dynamic rubric for jointly producing a score, a reason, an executable refinement suggestion, and a refined answer. This rubric-conditioned refinement process enables models to diagnose flaws and perform targeted answer improvement. Experimental results show that fine-grained evaluation should be both instance-specific and actionable. CriticGen induces higher-quality rubrics, improving relevance/coverage from 3.33/4.03 to 3.97/4.24. CriticGen also achieves the best score correlations, with 0.9556 Pearson and 0.9560 Spearman, and raises the F1 of criterion-grounded reasons and executable suggestions from 0.6369/0.5994 to 0.7554/0.7900. Crucially, its feedback translates into reliable answer improvement, improving 73.17% of answers with a 93.28% non-degradation rate.
[AI-289] Beyond Right and Wrong: Evaluating Second-order Social Reasoning in Large Language Models
链接: https://arxiv.org/abs/2609.05437
作者: Sunny Rai,Jinyi Kuang,Reyhan Jamalova,Annie Lou,Cristina Bicchieri,Niyati Malhotra,Victor Hugo Orozco-Olvera,Ana Maria Munoz-Boudet,Lyle H Ungar,Sharath C Guntuku
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Previous AI alignment efforts have focused primarily on first-order social norms – teaching models what is socially acceptable or unacceptable (e.g., `do not steal’). However, social intelligence depends not only on norm recognition, but also on anticipating who will enforce it and how (e.g., public shame or even imprisonment). These second-order expectations, known as metanorms, govern how people respond when social rules are broken. We introduce a novel framework for evaluating metanorm reasoning in Large Language Models (LLMs) along two dimensions: emotional appraisal and behavioral response, and propose new classification tasks, namely, predicting self-regulation in violators, and other-regulation in observers. We release a multi-perspective dataset, NormReact, of 450 norm violation scenarios, hand-annotated for emotions and behavioral responses across norm violators’ gender and observers’ social closeness. Current LLMs portray a harsher social world: across six models, they overpredict negative sanctions where humans would expect inaction, and alignment with human judgments deteriorates as social distance increases. These findings suggest that AI systems in norm-sensitive domains from conflict mediation to policy simulation, may risk producing a distorted picture of social regulation: one that over-represents punishment and under-represents the tolerance, restraint, and relational calibration that characterize actual norm enforcement in real world.
[AI-290] CABAL: Multi-Agent Simulacra for Tracing the Effects of Collusive Bidding in Peer Review
链接: https://arxiv.org/abs/2609.05227
作者: Jicheng Zhou,Kemou Li,Kahim Wong,Zheyuan Li,Zhuan Shi,Fengpeng Li,Haiwei Wu,Jiantao Zhou
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Recent reports during the AAAI-27 review cycle highlight the risk of reviewers coordinating bids for reciprocal assignment advantage. Prior work treats bidding, reviewer assignment, and review manipulation as separate stages, leaving the lifecycle effects of collusive bidding unclear. Real-world analysis is further constrained by typically unobservable collusive intent and the lack of counterfactuals for the same conference. Motivated by this gap, we introduce \alg, an end-to-end multi-agent simulacra framework for studying reviewer assignment integrity by holding the conference environment fixed and configuring LLM-driven reviewer agents with honest or collusive policies. We further develop an affinity-guided collusive bidding strategy that uses mutual reviewer-paper affinities to construct collusion rings and select target papers, producing expertise-consistent rather than arbitrarily targeted attacks. Controlled experiments show that collusive bidding more than doubles target-paper capture and that assigned colluders score target papers about two points higher than honest co-reviewers, while conference-wide effects remain comparatively modest. Evaluated bid-phase detectors provide only limited evidence of collusion: in a fixed-triplet detector stress test, native positive-bid graphs are confounded by benign affinity, while a Very-High-only diagnostic view enables precise but low-coverage local recovery.
[AI-291] ProToMEx: Rapid Interpretable Explanations via Structured Representations
链接: https://arxiv.org/abs/2609.04265
作者: Athina Georgara,Adarsh Valoor,Sarvapali D. Ramchurn
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Existing post-hoc explainers for machine learning classifiers primarily focus on feature attribution, assigning importance scores to individual features. While valuable, this approach struggles to articulate the complex, combinatorial patterns that often drive a model’s decision-making process. To overcome this limitation, we introduce ProToMEx, a new paradigm for explainability that leverages Probabilistic Topic Models (PTMs). Our model-agnostic framework learns latent ‘‘topics’’ that represent distinct, high-level reasons for a classification, moving beyond simple feature importance to reveal underlying semantic structures. ProToMEx naturally provides both global explanations of a model’s overall behaviour and local explanations that can disentangle multiple co-existing reasons for a specific prediction. We demonstrate empirically that ProToMEx not only produces explanations of comparable fidelity to popular methods like SHAP and LIME but also drastically reduces the amortised computational cost of generating local explanations, making it highly suitable for real-time applications. Specifically, we show that ProToMEx is ~30-40x faster than SHAP and LIME over standardised tabular datasets and synthetic datasets.
[AI-292] Neural Symbollic Regression Using Deep Learning and Sparse Modelling
链接: https://arxiv.org/abs/2609.01102
作者: Ravi Kumar U,Sumitra S
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Symbolic Computation (cs.SC)
备注: 11 pages, 5 tables, 5 figures, contains detailed mathematics behind the algorithm
Abstract:Symbolic Regression (SR) seeks to find succinct mathematical expressions that represent the fundamental relationships within data, providing interpretability and scientific understanding that exceeds that of black-box models. Nevertheless, traditional methods like Genetic Programming face challenges with scalability and are highly sensitive to noise, while sparse regression techniques such as SINDy rely significantly on predetermined feature libraries. In this work, we present a Neural Symbolic Regression (NSR) framework that treats neural networks as functional preconditioners for symbolic discovery. Our approach uses a decoupled pipeline: a neural network first learns a smooth, noise-robust approximation of the target function in an interaction- aware nonlinear feature space. LASSO is then applied to extract sparse, interpretable closed-form expressions. To improve predictive accuracy and symbolic fidelity by integrating distributed hyperparameter optimization with Ray Tune and ASHA scheduling. Experiments on the Nguyen benchmark suite show that our approach consistently outperforms SINDy and non-tuned neural baselines in RMSE, noise robustness, and out-of-distribution generalization. Ablation studies confirm the significance of feature interactions, neural depth, and tuning strategies. In general, this study presents a scalable and understandable neural-symbolic framework, creating a solid link between neural approximation and the discovery of sparse equations for scientific machine learning.
[AI-293] Convergence issues in Relational Concept Analysis based on AOC-posets
链接: https://arxiv.org/abs/2609.00054
作者: Xavier Dolques,Agnès Braud,Alain Gutierrez,Marianne Huchard,Florence Le Ber
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Formal Concept Analysis (FCA) is an approach for conceptual classification building and rule discovery from a binary table describing a set of objects by a set of attributes. Extensions have been proposed to deal with non-binary and more complex data, such as Relational Concept Analysis (RCA) for multi-relational data. RCA aims to highlight groups of objects characterized by their relationships with other groups of objects. The richer and more complex nature of the underlying data allows RCA to produce richer results than FCA, at the expense of higher computational and interpretive complexity. The most commonly used conceptual classification structure in FCA is the concept lattice. However, in many applications, concept lattice substructures, such as AOC-posets, are preferred over the full lattice, either to mitigate combinatorial blow-up or to focus on the most informative parts of the structure. Indeed, in an AOC-poset, only concepts introducing an object or an attribute are represented, which makes AOC-posets smaller and easier to compute and use than concept lattices. Although RCA was originally defined on concept lattices, it can also be instantiated on AOC-posets. RCA is iterative and its convergence is guaranteed in the lattice-based setting, but this guarantee is lost when using AOC-posets. In this paper, we investigate this loss of convergence in detail. We show why convergence is no longer guaranteed in the general case, identify conditions under which it can still be ensured, and discuss how a dataset can be transformed to recover convergence. We also propose a convergent variant of the process, which preserves the AOC-poset structure: relational attributes, once created, are never removed, which guarantees convergence at the price of attributes that may refer to concepts absent from the final structures.
[AI-294] Omni Interaction Agent Technical Report
链接: https://arxiv.org/abs/2609.08977
作者: Orantqing,Shengpeng Ji,Junlong Tong,Jialong Zuo,Dongjie Fu,Di Cao,Yangzhuo Li,Shangda Wu,Franz,Evan,Theron Veyra,Changhao Pan,Jingyu Lu,Dongchao Yang,Zhifei Xie,Yang Tan,Xiaoyu Shen,Xiaoda Yang,Wenfu Wang,Teddysun,Steveyves,Zhou Zhao,Bryanytian
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM); Sound (cs.SD)
备注: Project Page: this https URL
Abstract:In this work, we present Gander, an end-to-end model that unifies omni perception, realtime interaction, and agentic capabilities within a single framework. In contrast to turn-based conventional paradigms, Gander continuously receives streaming inputs across multiple modalities, including video, speech, and text, enabling natural full-duplex interaction in both everyday conversations and complex workflow-oriented agent scenarios. Users can interrupt the model at any time, while the model can also proactively provide intermediate feedback or ask follow up questions. To natively support these capabilities, Gander adopts two key architectural designs: 1) It employs a Cerebellum-Brain collaborative framework, in which the Cerebellum is responsible for realtime interaction and omni conversational capabilities, while the Brain handles complex reasoning and higher-level agentic tasks. The two components interact continuously through tool calling and the agent orchestration runtime. 2) The Cerebellum is built upon a streaming Thinker-Talker architecture, user inputs and model outputs are further flattened into an ordered token stream at the chunk level, providing a unified representation for low latency, continuous interaction. We conduct comprehensive evaluations of Gander across four dimensions: conversational ability, omni understanding, interactive capability, and agentic intelligence. Internal human evaluations demonstrate that Gander maintains the natural and expressive spoken dialogue capabilities of SOTA open source models while achieving competitive performance in omni interaction. Gander also demonstrates robustness in challenging real-world scenarios, including background noise interference, multi-party interactions, and backchannel communication. We release Gander together with its models, code, and data to facilitate further research and development in the community.
[AI-295] Neptune: An AI model for Global Ocean Subseasonal Prediction
链接: https://arxiv.org/abs/2609.08606
作者: Davide Donno,Italo Epicoco,Massimo Cafaro,Gabriele Accarino,Mohammad M. Amirian,Viviana Acquaviva,Paola Nassisi,Doroteaciro Iovino,Annalisa Bracco,Simona Masina,Pierre Gentine
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Artificial Intelligence (cs.AI)
备注:
Abstract:Subseasonal-to-seasonal (S2S) forecasting is societally critical, supporting decision-making in sectors ranging from water and agricultural management to disaster risk reduction, energy planning, and insurance. Achieving reliable predictions at these timescales requires representing the ocean and its dynamics, but traditional physics-based Ocean General Circulation Models (OGCMs), are computationally expensive and difficult to develop and improve because of the code complexity. In this work, we propose Neptune, an end-to-end data-driven framework for global ocean and sea-ice components emulation tailored for S2S timescales, up to 60 days. Neptune combines Convolutional Neural Networks (CNNs) and Spherical Fourier Neural Operators (SFNOs) to effectively capture local features and global cross-scale interactions, thereby obtaining a coherent representation of the ocean state. Forced by prescribed daily atmospheric fields, Neptune emulates ocean state variables, from temperature and salinity, to zonal and meridional currents, from sea surface height to sea ice thickness and concentration, with daily outputs at the ocean surface and through the water column. Specifically, we propose two variants of Neptune, Neptune-1 and Neptune-025, capable of emulating the ocean state at 1° and 0.25° resolution, respectively. Evaluated against a suite of metrics, including statistics (RMSE, CRPS and ACC), physical coherency (Ocean Heat Content, Eddy Kinetic Energy and Ice Brier Score) and climate indices (ENSO and Z20 metric, IOD), Neptune successfully reproduces the spatio-temporal evolution of the oceanic fields up to 60 days, and is stable over long timescales. Neptune provides compelling evidence that end-to-end data-driven ocean emulators can become a powerful component of next-generation S2S forecasting systems, emulating ocean state at high spatio-temporal resolution.
[AI-296] AI for AI: Optimizing Additional Infrastructure Build-out to Power Artificial Intelligence Data Centers
链接: https://arxiv.org/abs/2609.08166
作者: Alexander Crosier,Kyle Onghai,Ronnie Sircar
类目: General Finance (q-fin.GN); Artificial Intelligence (cs.AI); General Economics (econ.GN)
备注:
Abstract:The twenty-first century’s transformative technology, artificial intelligence, is increasingly constrained by the twentieth century’s transformative technology, the electricity grid. Rapid growth in electricity demand from data centers is leading to higher electricity prices, without a compensating supply-side response. We develop a framework linking data-center load growth, available generation capacity, and market-clearing prices to understand this phenomenon. We first analyze a deterministic model to show how differing estimates of demand and supply growth rates affect prices. We then model the expansion of new data centers and their associated electricity demand, together with build-outs of new electricity supply, as stochastic processes,resulting in probabilistic distributions of supply, demand, and prices rather than a single forecast. Finally, we formulate generation expansion as a stochastic control problem in which a revenue-maximizing investor dynamically chooses the intensity of supply-side investments. The analysis highlights a central challenge of the data-center build-out: even when rapid demand growth increases the need for new generation, the uncertainties related to load forecasts, development execution risks, and value cannibalization from overbuilding capacity may weaken incentives to invest at the pace required to keep electricity prices stable.
[AI-297] hermodynamic Cyclic Processes with Markov Samplers in Bayesian Inference
链接: https://arxiv.org/abs/2609.07660
作者: Heinrich von Campe,Bjoern Malte Schaefer
类目: Computation (stat.CO); Statistical Mechanics (cond-mat.stat-mech); Artificial Intelligence (cs.AI)
备注:
Abstract:The concept of Markov chain Monte Carlo (MCMC) cycles, an analogy to cyclic processes in heat engines, is presented in order to examine Bayesian inference problems. In this effort, we develop adaptive ensemble schedulers that allow the tuning of external parameters of a Bayesian canonical ensemble during an MCMC run, realising the MCMC cycles in practice. We run these cycles on different statistical models. As a fundamental insight, we find (both theoretically and in practice) that such systems can produce a non-zero net work output if and only if the considered model is non-Gaussian. As such, they may serve as a measure of non-Gaussianity in Bayesian inference, which we test on an example from supernova cosmology.
[AI-298] Microcanonical Hamiltonian Monte Carlo and the Helmholtz Theorem
链接: https://arxiv.org/abs/2609.07620
作者: Heinrich von Campe,Bjoern Malte Schaefer
类目: atistical Mechanics (cond-mat.stat-mech); Artificial Intelligence (cs.AI)
备注:
Abstract:The recently proposed Microcanonical Hamiltonian Monte Carlo algorithm has not yet been studied in detail from a thermodynamic point of view; this work aims to fill that gap. We demonstrate how thermodynamical state variables and potentials can be derived and thereby demonstrate that the construction of the algorithm formally represents a microcanonical thermodynamic ensemble. In particular, we demonstrate (analytically and numerically) that the algorithm fulfils the Helmholtz theorem, an alternative formulation of the first law of thermodynamics. Furthermore, we construct a new sampling algorithm that extends the original to lower-dimensional inference problems. Finally, we argue that canonical Markov Chain Monte Carlo algorithms are more natural than Microcanonical Hamiltonian Monte Carlo from the thermodynamic and information-theoretic point of view.
[AI-299] opology Obstructs Pure Foundation Neural Quantum States
链接: https://arxiv.org/abs/2609.07591
作者: Timothy Heightman,Elena Orlova,Philip Mantrov,Aleksei Ustimenko
类目: Quantum Physics (quant-ph); Disordered Systems and Neural Networks (cond-mat.dis-nn); Strongly Correlated Electrons (cond-mat.str-el); Artificial Intelligence (cs.AI); Mathematical Physics (math-ph)
备注: 10 pages, 5 pages main text
Abstract:Foundation models for ground states in spin-1/2 systems are a promising method for problems ranging from quantum chemistry to identifying new phase diagrams. Nearly all such models are currently pure-states that condition on the Hamiltonian’s parameters, whose Monte Carlo samples give energy estimates according to the variational principle. In this contribution, we show that this representation is topologically obstructed. For any gapped Hamiltonian family whose ground-state bundle is non-trivial, every continuous normalized state-vector model has zero fidelity with the ground state at some parameter value in the Hamiltonian family. For that value, the energy is at least one spectral gap, \Delta , with an O(\Delta) gap in an open-neighbourhood of that point. We show that this is a sufficient no-go also in the case of degenerate ground-state manifolds, time dynamics, and periodic systems with mixed space-time topology, demonstrating these obstructions on one- and two-qubit systems. We discuss how this causes a spike in the fidelity susceptibility, giving a numerical signature of a phase-transition where there is none. We then show that operator-valued models canonically avoid these obstructions and preserve topological information, implying a structural necessity in representation for foundation neural quantum states.
[AI-300] Human mutation field reveals an equilibrium-like structure with irreversible circulation
链接: https://arxiv.org/abs/2609.07500
作者: Isabella Caranzano,Daniel Maria Busiello,Stefano Priorelli,Amos Maritan,Piero Fariselli
类目: Genomics (q-bio.GN); Statistical Mechanics (cond-mat.stat-mech); Artificial Intelligence (cs.AI)
备注: 12 pages, 5 figures, 1 table
Abstract:The evolution of DNA sequences can be viewed as stochastic dynamics on a high-dimensional discrete space, but it is unclear when empirical transition biases reduce to an effective energy landscape versus retain irreducible non-equilibrium circulation. Human context-dependent mutation probabilities offer a direct test: every single-nucleotide substitution in a local context has a reverse substitution, so the logarithm of the forward-to-reverse probability ratio defines an antisymmetric field-the human mutation field. We show this field has a dominant gradient component and a smaller but reproducible curl component. Using seven-base human germline substitution probabilities, we infer an effective mutational landscape with a Siamese neural network constrained to predict only energy differences. This model predicts forward-to-reverse log-ratios for held-out mutations with a correlation of about 0.93, close to both an unconstrained predictive reference (0.948) and the empirical reversible ceiling from Hodge projection (about 0.96). Although trained only on mutation probabilities, the inferred landscape largely recovers short-word genomic composition and Chargaff reverse-complement symmetry for sequences up to length four. Deviations from equilibrium structure reveal a small but detectable nonequilibrium component: a residual irreversible circulation violating the Kolmogorov cycle condition for detailed balance, reproducible across African, Asian, and European populations, and strongest in CpG-linked cycles and CpG-transition edges, consistent with methylcytosine deamination. These results give a thermodynamic decomposition of the human mutation field: most mutation bias is organized by a local equilibrium-like energy landscape aligned with genome composition, while the residual circulation points to specific directional mutational mechanisms.
[AI-301] Distributed Lag Neural Additive Models
链接: https://arxiv.org/abs/2609.07381
作者: Calle Helmersson,Shivang Pandey,Leonardo Olivetti,Elena Raffetti
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Applications (stat.AP); Methodology (stat.ME)
备注:
Abstract:We introduce Distributed Lag Neural Additive Models (DLNAMs), neural-additive analogues of Distributed Lag Non-linear Models (DLNMs) for learning nonlinear effects distributed over lags. DLNAMs replace a prespecified spline cross-basis with neural components that learn exposure–lag response surfaces, avoiding choices of basis family, dimension, and knot placement while preserving additive interpretability and familiar distributed-lag summaries. Exp-centered input layers, smooth activations, and learned subnetwork mixtures produce smooth, locally adaptive representations; pointwise uncertainty combines a conditional last-layer Laplace approximation with between-member ensemble variation. In simulations, DLNAMs generally outperformed DLNM comparators, including penalized and treed variants, in recovering known response functions, with lower bias, stronger boundary recovery, and better-calibrated cumulative intervals; gains were largest for more demanding functions. The architecture performed consistently across sample sizes, outcome families, lag horizons, and jointly fitted multi-exposure settings, retaining recovery performance as exposures were added; fit-specific changes were largely confined to optimization, and applications recovered established empirical patterns.
[AI-302] Mathematical Programming in Machine Learning and Artificial Intelligence: A Unified Taxonomy of Models and Applications
链接: https://arxiv.org/abs/2609.07254
作者: Chaosheng Dong
类目: Optimization and Control (math.OC); Artificial Intelligence (cs.AI)
备注:
Abstract:Mathematical programming provides a common language for many decisions embedded in modern machine-learning (ML) and artificial-intelligence (AI) systems: selecting retrieval context, routing tokens, allocating inference compute, fitting structured predictors, protecting against distribution shift, and balancing competing objectives. However, the relevant literature is fragmented across optimization, information retrieval, recommendation, natural-language processing, computer vision, and learning theory. This paper organizes various applications under common mathematical programming paradigms: linear, quadratic, binary and mixed-integer, conic, bilevel, multi-objective, inverse, distributionally robust, submodular, and min–max optimization. We normalize the models with a mostly unified notation and, for every application, identify inputs, decision variables, a principal formulation, structural properties, solution strategies, and limitations. Across paradigms, we compare tractability, relaxation quality, decomposition, approximation guarantees, and scalability bottlenecks. The paper shows that mathematical programming is most useful not as a claim that all learning is LP or MIP, but as a disciplined interface between predictions and constrained decisions.
[AI-303] nsor network representations of discrete maximum entropy distributions via mean polytopes
链接: https://arxiv.org/abs/2609.07184
作者: Alex Goessmann,Martin Eigel
类目: atistics Theory (math.ST); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Combinatorics (math.CO)
备注: 24 pages, 9 figures
Abstract:We present tensor network representations for discrete maximum entropy distributions under expectation constraints. To this end, we introduce Computation-Activation Networks (CompActNets), a tensor network architecture that subsumes exponential families. By leveraging the geometry of the convex polytope of realizable expectation vectors, we represent any maximum entropy distribution in the same architecture. We exploit the fact that proper faces of this polytope correspond to the boundary closure of exponential families, which restricts the distribution’s support. We then derive explicit representations for the support within the CompActNet architecture. The proposed framework suggests tensor network ranks as complexity measures for faces. Finally, a case study on Boolean statistics links the geometry of 0/1-polytopes directly to propositional formulas.
[AI-304] AstroSpecLM: A Spectrum-Language Model for Evidence-Grounded Astronomical Spectral Analysis
链接: https://arxiv.org/abs/2609.07102
作者: Jinghang Shi,Yanxia Zhang,Ali Luo,Changhua Li,Xiao Kong
类目: Instrumentation and Methods for Astrophysics (astro-ph.IM); Artificial Intelligence (cs.AI)
备注: 15 pages, 6 figures, 7 tables, including supplementary material
Abstract:Astronomical spectra encode rich physical information, but drawing scientific conclusions from spectral features typically requires expert interpretation. This paper presents AstroSpecLM, a spectrum-language model that connects one-dimensional DESI spectra with Qwen3-4B to answer questions and provide explanations grounded in spectral evidence. Instead of generating question-answer pairs directly from templates or raw catalog fields, we first distill each spectrum into a compact set of catalog- and spectrum-derived facts, then use these facts as references to generate instruction-following conversations. The resulting model is competitive with specialist supervised baselines on classification and redshift estimation, while additionally producing natural-language explanations that reference specific spectral features. Our results indicate that grounding a language model in one-dimensional scientific spectra is feasible, and that fact-mediated instruction data yields a model capable of both prediction and explanation.
[AI-305] Human-agent discovery of reconfigurable in-plane ferroelectric superdomain control
链接: https://arxiv.org/abs/2609.06887
作者: Yu Liu,Boris Slautin,Ching-Che Lin,Jaegyu Kim,Lane W. Martin,Sergei V. Kalinin
类目: Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI)
备注:
Abstract:Automated experimentation is most effective when the observables, available actions, and objective are defined before the experiment starts, as is the case for Bayesian optimization. However, in many exploratory experiments, the variables that describe the sample must be extracted from the data, new operations emerge during the experiments, and the instrument budget is too small to learn the problem by trials. Here we introduce the Scanning Probe Agentic Research Cycle (SPARC) framework, in which a coding agent and a human operator share one microscope, one notebook, and two persistent memory files. this http URL stores graded conclusions about the experiment, whereas this http URL records learned failure modes of analysis and instrument. We apply SPARC to reconfigure the in-plane superdomain direction of a (111)-oriented PbZr0.2Ti0.8O3 film. In an operator-supervised campaign, the agent reanalyzed earlier manual measurements and developed an oriented lattice of stationary bias pulses with alternating polarity to reconfigure the superdomain direction. In a subsequent agent-controlled campaign, this http URL entries were compiled into checks that validate a design before any write. The experiments showed that spatial polarity alternation, instead of the exact matching between the lattice and lamellar periods, determines directional selection. Combining a raster scan with a masked pulse lattice printed the letters UTK into the superdomain orientation. The campaign also identified practical requirements for agentic experimentation where physical verification of instrument execution, the conditions under which stored findings remain valid, validation of new observables on instrument data, and robust control protocols.
[AI-306] Recovering topological information of light by topological learning
链接: https://arxiv.org/abs/2609.06542
作者: Benquan Wang,Trishita Das,Yuhan Peng,Tatjana Kleine,Shanshan Chang,Jinhui Chen,Nilo Mata-Cervera,Chunyu Li,Kelin Xia,Andrew Forbes,Yijie Shen
类目: Optics (physics.optics); Artificial Intelligence (cs.AI)
备注: 5 figures
Abstract:The evolution of modern-day communication networks towards optical solutions with enhanced capacity and robustness is driving interest in topological light waves, exploiting their stability against perturbations through a topological invariant, e.g., the skyrmion number. However, detecting the underlying topology remains a computationally intense process even under ideal conditions, becoming intractable after passing through strongly disordered channels, where the degradation into unrecognisable speckle appears to destroy the topology. Here, we propose and demonstrate a topology-enhanced artificial intelligence (AI) approach to recover and classify such apparently lost topological information by computationally leveraging topological invariants in the data across many length scales. By aligning the topological classification of information with the topology of light, our topology-enhanced learning protocol, termed TOPO ^2 , achieves highly efficient recognition of the topological states of light, even from speckle, without the need for any prior learning. Our approach outperforms benchmark tests against standard computational algorithms and has the benefit of requiring just a single intensity pattern as the input, facilitating single-shot operation. To demonstrate this, we leverage the skyrmion number as a robust data carrier of images through a disordered channel, using TOPO ^2 to accurately reconstruct the transmitted images. This work synergises topological photonics and topological AI for unravelling hidden topological signatures in light, opening a pathway towards robust communications even in extreme disordered environments.
[AI-307] Recovering Weak Signals with Normalizing Flows
链接: https://arxiv.org/abs/2609.06382
作者: Sarod Yatawatta
类目: Machine Learning (stat.ML); Cosmology and Nongalactic Astrophysics (astro-ph.CO); Instrumentation and Methods for Astrophysics (astro-ph.IM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:In many scientific disciplines, weak signals of interest are obscured by dominant nuisance signals that are several orders of magnitude stronger. Recovering these weak signals requires subtracting the dominant ones; however, this calibration process inherently distorts or partially suppresses the underlying signal of interest. To address this problem, we propose the use of normalizing flow models to reconstruct calibration-affected weak signals. By leveraging the statistical invariance of the target signals and assuming minimal initial suppression, our framework effectively recovers the lost signal components. We provide a comprehensive theoretical overview of this normalizing flow-based recovery method and demonstrate its efficacy using simulated data.
[AI-308] Explainable Deep Learning for Price-Trade Dynamics: From Black-Box Forecasts to Effective Parametric Models
链接: https://arxiv.org/abs/2609.06085
作者: Manuel Naviglio,Fabrizio Lillo
类目: Trading and Market Microstructure (q-fin.TR); Artificial Intelligence (cs.AI); Data Analysis, Statistics and Probability (physics.data-an)
备注:
Abstract:Understanding the joint dynamics of prices and trades is central to market microstructure, where returns and order flow interact through nonlinear and state-dependent mechanisms. Linear models are interpretable but may miss these effects, while deep neural networks improve forecasting at the cost of transparency. We use neural networks as tools for structural discovery rather than only for prediction. A deep feed-forward network is trained on high-frequency returns and signed volumes for large- and small-tick stocks and compared with a linear VAR benchmark. The neural network improves predictive performance, especially for returns, revealing nonlinear dependencies beyond the linear specification. Using Shapley-based explainability, we show that the dominant contributions are concentrated at the most recent lags. Model-implied responses are consistent with conditional averages reconstructed from the data. Unlike empirical averages, however, the neural-network decomposition isolates individual regressor contributions to the aggregate dependence. Lagged signed volume generates sign-preserving and saturating effects, consistent with nonlinear price impact and order-flow persistence. Lagged returns act as state variables: when the previous trade does not move the price, the model predicts continuation in the direction of past order flow, whereas non-zero returns generate attenuation or reversal. Building on these findings, we introduce a parsimonious SHAP-inspired nonlinear parametric model. It reproduces the main return-volume dependencies, outperforms the linear VAR benchmark, and achieves performance comparable to the neural network. A multi-lag extension captures residual longer-memory effects while preserving interpretability. Overall, explainability offers a route from black-box prediction to economically meaningful parametric models of price and trade dynamics.
[AI-309] he Role of Gradient Modification in Heavy-Tailed Nonconvex Stochastic Min-Max Optimization
链接: https://arxiv.org/abs/2609.06064
作者: Tianxi Zhu,Yi Xu,Xiangyang Ji
类目: Optimization and Control (math.OC); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:Stochastic min-max optimization has attracted increasing attention due to its applications in modern machine learning, while existing theoretical studies mainly rely on the bounded variance assumption for stochastic gradients. Under heavy-tailed noise, where stochastic gradients only possess a finite p -th moment for p\in(1,2] , gradient clipping or normalization is commonly believed to be necessary to guarantee convergence. In this work, we revisit stochastic min-max optimization under heavy-tailed noise and provide a comprehensive theoretical study of stochastic gradient descent ascent (SGDA). We first show that vanilla SGDA, without any modification to its update rule, can converge under heavy-tailed noise in both nonconvex-strongly-concave (NC-SC) and nonconvex-concave (NC-C) settings, establishing the first convergence guarantees for SGDA in these regimes. Beyond unregularized problems, we further investigate regularized stochastic min-max optimization, where directly incorporating gradient normalization into proximal updates is nontrivial due to the incompatibility between normalization and proximal structures. We overcome this difficulty by developing new clipping-free algorithms, i.e., Stoc-TRGDAM and Stoc-TRGDmax, and they both can achieve the optimal dependence on the target accuracy without using gradient clipping.
[AI-310] A solution to the Erdős Problem #1040 ISCA
链接: https://arxiv.org/abs/2609.06050
作者: Ioannis Tzachristas
类目: Classical Analysis and ODEs (math.CA); Artificial Intelligence (cs.AI)
备注: We prove the general capacity-one case, completing the affirmative answer to the question of arbitrarily small lemniscate area in Erdős Problem #1040
Abstract:For a compact set K\subset\mathbbC , let \vartheta(K) be the infimum of the planar areas of the unit lemniscates of all monic polynomials with zeros in K , allowing arbitrary degree and repeated zeros. We prove that \vartheta(K)=0 whenever \operatornamecap(K)=1 , with no regularity assumption on K . The proof uses a centered harmonic polynomial that is positive on all but a set of arbitrarily small area in the polynomial hull of K . A Fourier average of exterior harmonic measures realizes this polynomial as the logarithmic potential of a signed measure having bounded density with respect to the equilibrium measure. A positive perturbation and an L^1 approximation by empirical measures then produce the required polynomials. This extends the smooth-boundary result of Krishnapur, Lundberg, and Ramachandran to arbitrary compact sets of capacity one. Together with the capacity-greater-than-one theorem of Ghosh and Ramachandran and an elementary argument for unbounded sets, it follows that \vartheta(F)=0 for every closed infinite set F\subset\mathbbC of transfinite diameter at least one, answering the vanishing question in Erdős Problem 1040.
[AI-311] Do Quantum AIs Dream in Paths? Path-Integral Slow Thinking through Grover Interference
链接: https://arxiv.org/abs/2609.05842
作者: Xiansheng Cai,Xiu-Hao Deng,Kun Chen
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Reinforcement learning with verifiable rewards enables large language models to think slowly, but the same training can induce policy collapse: probability concentrates onto a few successful trajectories and exploratory diversity erodes. We ask whether quantum AI can realize slow thinking differently. We formulate slow thinking as coherent dynamics over reasoning trajectories, a discrete path integral in which action sequences coexist in superposition and recombine before measurement. In our trainable realization, an exact verifier partitions the ensemble into collective accepted and rejected components that interfere under Grover amplitude amplification. A finite Grover evolution is maximized when the pre-amplification success probability lies at an analytically determined value below one, so inference itself defines an interior training target and removes the monotonic pressure toward unit success. In exact statevector simulations of a 2x3 sliding puzzle, Grover training reaches accuracy 0.95 on a 32-question training set at one round, against 0.73 for the strongest classical control. On held-out questions specialization has a cost: an untrained uniform policy read out through the same amplification remains the strongest reference on this solution-dense benchmark, and quantum training preserves far more held-out accuracy than classical training - at four rounds with matched circuit applications the two quantum models reach 3.2 and 3.9 times the strongest classical controls. The number of training questions supported by fixed-size policies trained at each amplification budget also grows faster with the budget than with matched classical repetition. These results establish a Grover-based realization of path-integral slow thinking: the interior target preserves exploratory path diversity, and ensemble-level interference converts it into verified performance.
[AI-312] WolfSociety: Understanding Collective Risk from Harmful-Agent Scaling in Financial Agent Societies
链接: https://arxiv.org/abs/2609.05591
作者: Lejun Zhang,Sarah Lu-Liang,Xin Jiang,Muning Wen,Weinan Zhang,Shangding Gu
类目: Physics and Society (physics.soc-ph); Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
备注:
Abstract:Safety evaluations typically focus on individual agents, but interacting agents can spread harmful information and influence the environment in which later decisions are made. We study how collective failure changes with harmful-agent fraction and society size in a controlled financial agent society, where agents communicate over a social network and trade in a shared market. In the primary financial scenario, collective failure requires broad harmful diffusion together with severe price dislocation or liquidity stress. Across all tested society sizes, failure remains rare at low harmful fractions but rises sharply over a narrow range. As society size grows from N=100 to N=2000, the harmful fraction associated with a 50% failure probability decreases from 4.7% to 2.2%, while the corresponding number of harmful agents increases from approximately 5 to 44. In contrast, when the number of harmful agents is held fixed, their impact becomes weaker as the society grows. Controlled interventions further show that broader network reach shifts the collapse boundary toward lower harmful fractions, whereas stronger conformity alone has little effect. To characterize these effects, we introduce Agent Society Dynamics, a finite-size framework for relating harmful-agent fraction, society size, and interaction structure to collective failure. Overall, our results reveal a nonlinear, size-dependent collapse transition in financial agent societies, showing that collective failure depends not only on the prevalence of harmful agents but also on the size and interaction structure of the surrounding society. Code is available at this https URL.
[AI-313] Data-driven rational function neural networks: a new method for generating analytical models of rock physics
链接: https://arxiv.org/abs/2109.08813
作者: Weitao Sun
类目: Geophysics (physics.geo-ph); Artificial Intelligence (cs.AI); Information Theory (cs.IT); Machine Learning (cs.LG)
备注: 16 pages, 4 figures
Abstract:Seismic wave velocity of underground rock plays important role in detecting internal structure of the Earth. Rock physics models have long been the focus of predicting wave velocity. However, construction of a theoretical model requires careful physical considerations and mathematical derivations, which means a long research process. In addition, various complicated situations often occur in practice, which brings great difficulties to the application of theoretical models. On the other hand, there are many empirical formulas based on real data. These empirical models are often simple and easy to use, but may be not based on physical principles and lack a proper formulation of physics. This work proposed a rational function neural networks (RafNN) for data-driven rock physics modeling. Based on the observation data set, this method can deduce a velocity model which not only satisfies the actual data distribution, but also has a proper mathematical form reflecting the inherent rock physics. The Gassmann’s equation, which is the most commonly used theoretical model relating bulk modulus of porous rock to mineral composition, porosity and fluid, is perfectly reconstructed by using data-driven RafNN. The advantage of this method is that only observational data sets are required to extract model equations, and no complex mathematical and physical processes are involved. This work opens up for the first time a new avenue on constructing analytical expression of velocity models using neural networks and field data, which is of great interest for exploring the heterogeneous structure of the Earth.
机器学习
[LG-0] Nearly Tight Rademacher Bounds for Sparsely Activated Neural Networks
链接: https://arxiv.org/abs/2609.09130
作者: Xiaoyu Li,Zhizhou Sha,Jiaojiao Jiang,Junbin Gao,Andi Han
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:An input may activate few hidden units even when different inputs collectively use an entire network. We study the statistical complexity of this input-dependent sparsity in the one-hidden-layer ReLU model of Awasthi et al. (COLT 2024). For width s , at most k active units per input, and effective weight and bias bounds W,B , every size- m sample in the class’s fixed radius- R input domain satisfies \mathcalR(S)\le CWR\min\k,\sqrtsk/m\log^3/2(2m)+kB/\sqrt m . A support-preserving cover and a single normalized chaining argument remove the previous explicit dimension factor, up to logarithms. Lower bounds on appropriate i.i.d. marginals match up to those logarithms, showing how changing active units across inputs retains a width dependence. The input domain matters: zero-bias networks sparse on the entire ball have at most 2k nonzero units and complexity O(kWR/\sqrt m) , whereas bias bounds comparable to WR restore the worst-case rate on that same domain in only logarithmic dimension. A spherical-cap construction proves the latter claim without assuming sparsity merely on the sampling support. For a specified normalized bounded loss and biases comparable to WR , we also obtain agnostic minimax excess-risk bounds of order \min\1,\sqrts/(km)\ up to logarithms.
[LG-1] When Does Scale-Invariant Optimization Become Unstable? An Exact Schedule Law with Weight Decay
链接: https://arxiv.org/abs/2609.09116
作者: Hasan Amin,Wei-Kai Chang,Rajiv Khanna
类目: Machine Learning (cs.LG)
*备注:
Abstract:Normalization renders large parts of neural networks effectively scale invariant, inducing a hidden feedback loop in which learning-rate schedules and weight decay interact through the parameter norm to control the effective step taken by the optimizer. We show that this interaction is governed by an exact discrete-time law: a single scalar quantity captures all schedule and decay forcing, while norm growth induces an opposing geometric self-quenching effect. This yields a sharp boundary that cleanly separates contraction- and expansion-dominated effective learning rate regimes. To understand the underlying mechanism, we provide exact analysis of a fully solved normalized regression model where the dynamics reduce to two dimensions and show that the balance point is intrinsically unstable, implying that constant learning rate with weight decay cannot stably maintain an interior equilibrium and instead produces recurrent behavior driven by discrete-time Jacobian structure. We further extend this perspective across optimizers through unified homogeneous-optimizer framework that reveals a structural dichotomy in self-quenching strength, providing a first-principles explanation for why adaptive methods exhibit systematically weaker stabilization under normalization. Across dynamical systems and neural networks (MLP, CNN, GPT2 / MNIST, CIFAR, wikiText, OpenWebText), the predicted law holds with high precision and enables direct control of training via the identified scalar, with performance peaking sharply at the predicted boundary. Together, these results isolate a single governing quantity for scale-invariant optimization, providing a precise and actionable lens on training dynamics, optimizer behavior, and schedule design in modern deep learning. Code is available in this https URL. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2609.09116 [cs.LG] (or arXiv:2609.09116v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.09116 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-2] Curriculum Learning as Transport: Understanding Curricula with Wasserstein Geodesics
链接: https://arxiv.org/abs/2609.09099
作者: Changho Shin,David Alvarez-Melis
类目: Machine Learning (cs.LG)
*备注: COLM 2026
Abstract:Curriculum learning is governed by several coupled design choices—how difficulty is defined, how examples are ordered, how much exposure each level receives, and how quickly training moves across levels—making it hard to isolate what actually helps. We present Wasserstein curriculum paths, a simple transport-based framework that decouples these factors by representing curricula as trajectories of training distributions over discrete difficulty levels. Across a calibrated synthetic suite with 12 tasks and 33 difficulty axes, we use this framework to isolate the effects of ordering, matched exposure, endpoint smoothness, and pacing under fixed training budgets. We find that curriculum effects are strongly context-dependent: no single strategy dominates across tasks, difficulty axes, and budgets, and curricula mainly change where a fixed budget is spent most effectively. Within this framework, easy-to-hard ordering improves hard-level performance relative to exposure-matched static sampling, showing that the benefit is not explained by cumulative exposure alone. We further show that endpoint smoothness and pacing substantially affect where along the difficulty spectrum a curriculum is effective. Finally, we show that the same transport view naturally supports extensions to learned pacing through geometry and to structured difficulty spaces beyond one-dimensional orderings.
[LG-3] Multi-Task Learning for Sparsely-Labeled Time Series: A Case Study on Cold-Hardiness Modeling
链接: https://arxiv.org/abs/2609.09062
作者: Aseem Saxena,Paola Pesántez-Cabrera,Jonathan Magby,Markus Keller,Alan Fern
类目: Machine Learning (cs.LG)
*备注:
Abstract:We present a real-world case study of multi-task learning (MTL) for temporal process modeling from limited data with temporally sparse labels. Specifically, we investigate multi-task learning for the important agricultural problem of predicting grape cold hardiness, which is the temperature at which lethal freezing occurs. Cold hardiness changes in response to weather and is difficult to measure directly in the field. Thus, growers rely on predictions to decide when to apply costly frost mitigation measures. We apply recurrent neural networks (RNNs) for daily cold-hardiness prediction from time series weather data. A major challenge is that the cold hardiness response varies across plant cultivars and ground-truth data for each cultivar is temporally sparse and limited. To address this challenge, we investigate multi-task learning (MTL) approaches for combining data, where different tasks correspond to different cultivars. We develop a variety of MTL architectures and evaluate them in both MTL and transfer learning settings. Our results show significant differences between architectures and that certain architectures are able to consistently outperform single-task learning and state-of-the-art scientific models. Additionally, we show similar results for the qualitatively different, but related, task of budbreak prediction. Further, improved accuracy for budbreak and cold hardiness is achieved by a single MTL model that simultaneously learns both tasks.
[LG-4] PlayTrain: An Efficient Reinforcement Learning Framework for LLM -Generated Adaptable JavaScript Games
链接: https://arxiv.org/abs/2609.09059
作者: Ryan Truong,Lance Ying,Samuel J. Gershman,Kazuki Irie
类目: Machine Learning (cs.LG)
*备注:
Abstract:While many video-game environments (VGEs) have played crucial roles in advancing reinforcement learning (RL), developing novel VGEs or modifying existing ones to support new features, has been a laborious process requiring extensive hand-coding. Here we present PlayTrain, an RL framework that combines the abilities of large language models (LLMs) to robustly generate JavaScript (JS) games from a minimal human prompt, and an efficient pipeline that can run any JS game in a standard ‘gym’ environment. Not only are recent LLMs particularly good at writing JS code, but the JS format also allows users to easily play generated VGEs, while PlayTrain enables us to train RL agents on the exact same games. We demonstrate multiple use cases of PlayTrain, including cloning well-known Atari and ProcGen games in simple JS, where PlayTrain trains pixel-based agents end-to-end at over 1M agent-decisions per second on a single GPU node; and creating modified versions thereof (e.g., that support novel test sets, procedural generation logics, or game dynamics). Through PlayTrain, we reimagine RL VGE development: all we need is a single JS file, generated and modified through an LLM. We discuss promising future RL research directions that PlayTrain unlocks.
[LG-5] Physics-Informed Deep Learning for False Ventricular Tachycardia Alarm Reduction in the ICU
链接: https://arxiv.org/abs/2609.08992
作者: Athanasios Papastathopoulos-Katsaros,Alexandra Stavrianidi,Zhandong Liu
类目: Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注: Accepted at CinC 2026
Abstract:False ventricular tachycardia (VT) alarms are a leading contributor to alarm fatigue in intensive care units. We propose a deep learning framework combining a 1D SE-ResNet with ICU-realistic data augmentations and a physics-informed auxiliary reconstruction task based on the three-element Windkessel hemodynamic model, implemented as a differentiable forward simulation. By requiring the network’s latent representation to produce physiologically plausible arterial pressure waveforms, artifact-driven ECG patterns are penalized while true VT remains coherent across modalities. Evaluated on the VTaC benchmark under a strict real-time protocol (10-second pre-alarm window), our method achieves a 5-point Challenge Score improvement over prior state-of-the-art. Ablation studies confirm that the physics-informed objective is the primary performance driver, providing gains in accuracy, 2x label efficiency, and more localized and clinically meaningful ECG segments.
[LG-6] Fitting and Learning Basis-Restricted Propositional Formulas
链接: https://arxiv.org/abs/2609.08961
作者: Balder ten Cate
类目: Logic in Computer Science (cs.LO); Machine Learning (cs.LG)
*备注:
Abstract:For a finite set O of Boolean functions, we consider the class of propositional formulas built using the functions in O as connectives. We determine, for each possible choice of O , the complexity of various fitting and learning problems. These include: finding a formula that fits a given labeled sample, finding a small one (an Occam algorithm), minimizing the number of misclassified examples when the sample is not realizable (empirical risk minimization), and several forms of PAC learning. Our results apply both to formulas (represented as trees) and to circuits. We also briefly discuss the status of the same questions for other kinds of propositional fragments.
[LG-7] he BatchNorm Illusion: Diagnosing Normalization Artifacts in Machine Unlearning Evaluation
链接: https://arxiv.org/abs/2609.08901
作者: Aaryaman Kalani,Murari Mandal,Dhruv Kumar,Mohan Kankanhalli,Yash Sinha
类目: Machine Learning (cs.LG)
*备注: 24 pages, 15 tables, 2 figures
Abstract:Approximate machine unlearning aims to remove the influence of specific training data from a trained model without retraining from scratch. We identify a previously undocumented confound in how unlearning is evaluated on BatchNorm-based architectures: a single forward pass over retain data, an operation that modifies no weight, can deterministically rewrite the model’s normalization state and reverse the apparent surface-metric forgetting. We formalize this operation as a weight-preserving fixed-point operator and prove that any pre-versus-post gap it induces is provably attributable to BN running statistics rather than to any modification the unlearning method made to the weights. This attribution claim cleanly separates measurement failure (BN artifact) from encoder failure (residual weight-encoded information, recently documented in concurrent work), and the same operator framework yields a unique decomposition of linear-probe elevation into BN-measurement-bias and encoder-geometry components. Empirically, the artifact reverses headline forget accuracy by up to 78 pp across nine evaluated methods on standard benchmarks; an attacker with as few as 10 unlabeled images recovers most of the masked accuracy; and a strict GroupNorm control reduces the artifact to zero across all methods. The tested membership-inference attacks change little under recalibration, locating the observed evaluation failure in forget accuracy and linear probing.
[LG-8] High-Magnetization Sampling at Low Temperatures: Ising Models and Bayesian Sparse Linear Regression
链接: https://arxiv.org/abs/2609.08873
作者: Syamantak Kumar,Purnamrita Sarkar,Kevin Tian,Yusong Zhu
类目: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Probability (math.PR); Statistics Theory (math.ST); Machine Learning (stat.ML)
*备注: 61 pages
Abstract:Sparsity is a powerful structural resource in optimization and statistics. We develop frameworks for leveraging sparsity in sampling problems over the Hamming slice \mathcalX_k^d:=\mathbfx\in\pm 1^d:|\i:\mathbfx_i=1|=k\ , in high-dimensional regimes where k\ll d (i.e., where \mathcalX_k^d is \emphhighly magnetized). We use our frameworks to design improved samplers for canonical problems in the study of \emphIsing models and \emphBayesian sparse linear regression. Our first main result considers the \emphSherrington–Kirkpatrick (SK) model restricted to fixed-magnetization slices \mathcalX_k^d . We give a polynomial-time sampler for fixed-magnetization SK models at any inverse temperature \beta0 , under arbitrary external fields, provided that k\le c_\beta d for an appropriate constant c_\beta . By combining this result with an annealing strategy for estimating normalizing constants, we obtain polynomial-time samplers for the SK model at arbitrarily low temperatures under a sufficiently strong external field of strength h . In the large- \beta limit, our framework permits sampling at field strengths within constant factors of the \emphAlmeida–Thouless line delineating the replica-symmetric and replica-symmetry-breaking regions ([dAT78]), improving polynomially over the field strength h(\beta) required by the recent work of [BAR26]. Our second main result concerns the measurement complexity of polynomial-time Bayesian sparse linear regression. Recent work by [KSTZ25] shows how to sample from the canonical \emphGaussian spike-and-slab posterior with expected sparsity k , at any signal-to-noise ratio, given n\gtrsim k^3\log^3 d Gaussian measurements. We improve this requirement to n\gtrsim k^3/2\log^2 d+k\log^3 d , using a common sparsity-aware framework underlying both our results. Comments: 61 pages Subjects: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Probability (math.PR); Statistics Theory (math.ST); Machine Learning (stat.ML) Cite as: arXiv:2609.08873 [cs.DS] (or arXiv:2609.08873v1 [cs.DS] for this version) https://doi.org/10.48550/arXiv.2609.08873 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-9] Length Generalization for Transformers via Compression
链接: https://arxiv.org/abs/2609.08851
作者: Georg Zetzsche,Hongjian Jiang,Andy Yang,Pascal Bergsträßer,Marco Sälzer,David Chiang,Anthony W. Lin
类目: Machine Learning (cs.LG); Formal Languages and Automata Theory (cs.FL); Logic in Computer Science (cs.LO)
*备注:
Abstract:Recent advancements in transformer length generalization theory enable us to reliably predict when a transformer can learn to solve a task. In particular, the C-RASP hypothesis (a formalized version of the so-called RASP-l conjecture) posits that transformers length-generalize on a task if and only if a solution is expressible in the C-RASP language. While this hypothesis has strong empirical validation, theoretical problems arise from the fact that no computable length generalization bounds exist for C-RASP, alongside the discovery of seemingly contradictory experiments. To address these problems, we refine the C-RASP hypothesis utilizing the recently-proposed fragments C-RASP+ and C-RASP1. These fragments have computable length generalization bounds, though in the worst case requiring an extremely large (double exponential) sample size. It is an open question whether these sample size bounds are tight. In this paper, we resolve this open question by providing an exponentially tighter bound. In doing so, we show a polynomial length generalization bound for transformers if we adopt compressed strings, via a novel connection to power words. As an application, we show how this yields a fine-grained analysis of the C-RASP conjecture that resolves contradicting experimental evidence against it.
[LG-10] Ostrich: Taking Large Strides Through Stiff Contact in Differentiable Dynamics
链接: https://arxiv.org/abs/2609.08800
作者: Aleš Kučera,Karel Zimmermann
类目: Robotics (cs.RO); Graphics (cs.GR); Machine Learning (cs.LG)
*备注: 8 pages, 6 figures. Submitted to IEEE Robotics and Automation Letters. Code: this https URL
Abstract:Three properties determine whether a differentiable simulator can drive gradient-based optimization through contact: simulation accuracy, gradient reliability, and per-iteration cost. Tape-based engines such as MJX and Newton Semi-Implicit require timesteps small enough to keep contacts numerically tractable, and their backpropagation memory grows linearly with the number of timesteps T. Surrogate models bound memory by approximating contact away, but the resulting gradients lose the geometry the optimization depends on. We present Ostrich, a GPU-accelerated rigid-body simulator that resolves hard contacts and friction with non-smooth Newton iteration at large timesteps (h ~ 0.1 s), and differentiates the converged residual via the implicit function theorem, reusing the forward Schur complement to compute the adjoint at O(1) memory per timestep. On real-robot trajectories over a pallet obstacle, Ostrich holds MuJoCo’s sim-to-real accuracy up to a 50x larger timestep. Its gradients converge from random initializations where MJX descends slowly and Newton Semi-Implicit stalls; a warm iteration runs 211x faster than MJX’s and 4.7x faster than Semi-Implicit’s. On the same scene Ostrich differentiates 8,192 parallel worlds on a single 24 GB GPU, sustaining 29x checkpointed MJX’s optimization throughput; without checkpointing both baselines exhaust memory at far fewer worlds. We close with a gradient-based trajectory optimization demonstration over triangle-mesh terrain across a 10 s horizon, a setting where prior engines either restrict to primitive geometry or face the convergence and memory limits shown above.
[LG-11] ZK-Trace: Certified Collusion Tracing with Zero-Knowledge Credentials for Federated GNSS Interference Monitoring
链接: https://arxiv.org/abs/2609.08763
作者: Redwanul Karim,Nisha L. Raichur,Lucas Heublein,Tobias Feigl,Christopher Mutschler,Felix Ott
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: 23 pages, 5 figures, 17 tables
Abstract:Federated global navigation satellite system (GNSS) monitoring distributes a proprietary classifier to partly trusted stations, any of which may leak its copy. ZK-Trace combines public identity marks, recipient-specific Tardos fingerprints, and zero-knowledge credential verification. The registry supports offline tracing without the leaker’s cooperation. We establish conditional false-accusation bounds for arbitrary recovered bit patterns, a finite completeness bound under a hidden-bias residual channel, and a deterministic tracing-score bound for correlated feature-distillation errors. An interval-arithmetic checker makes the conditional bound executable and allocates a common budget across accusation and tamper decisions. Under innocent-row independence, the certificate-based evaluation uses a false-naming budget of 0.001 per investigation. It isolates all 160 single-owner copies and traces 712 of 720 two-owner mixtures without naming an innocent. Experiments use a simulated GNSS federation and CIFAR-10. Feature matching preserves the feature mark in 20/20 runs and cross-architecture transfer in 19/20, at copy-accuracy costs of 4.8 and 6.1 percentage points on GNSS and CIFAR-10. Function-only distillation erases the feature mark, and distillation also removes weight-space marks. These results support verifiable tracing under explicit statistical and cryptographic assumptions. Credential knowledge and recipient evidence serve distinct roles.
[LG-12] A Note on Scaling in Randomly Rotated Quantization and Its Connection to the CDEF 1 Pythagorean Relation
链接: https://arxiv.org/abs/2609.08759
作者: Uri Erez
类目: Information Theory (cs.IT); Machine Learning (cs.LG)
*备注:
Abstract:Quantization schemes based on randomized rotations have recently received renewed attention, including the roles of MMSE and unbiased reconstruction scalings. In this note, we point out the connection to classical results in statistical signal processing and communication theory. Specifically, the two reconstruction scales used in the EDEN line of work admit a natural interpretation as finite-dimensional, realization-dependent counterparts of the Wiener and unbiased coefficients in the classical CDEF formulation. At finite blocklength, the CDEF +1 relation holds pointwise for each rotation realization as an exact geometric (Pythagorean) identity, but does not hold after averaging the distortions over the rotation. The classical SNR relation \sfSNR_\rm MMSE=\sfSNR_\rm MMSE,U+1 is recovered as d\to\infty : once the overall scale is handled separately, the empirical coordinate statistics of a randomly rotated vector approach their i.i.d. Gaussian counterparts, and the rotation-dependent quantities concentrate. Importantly, EDEN goes beyond this classical correspondence: for every finite d , its Haar-rotation formulation guarantees exact conditional unbiasedness, a stronger property than the second-order notion of unbiasedness in CDEF. We further comment on two distinct roles random rotations play in quantization: one is approximate Gaussianization of the coordinates; the other is decorrelation of reconstruction errors across quantization branches.
[LG-13] PAC-Bayesian Bounds for Learning Partially Observed Stochastic Linear Time-Invariant State-Space Systems with Inputs and Sub-Gaussian Noise
链接: https://arxiv.org/abs/2609.08740
作者: Mihaly Petreczky,Mohamad Al Ahdab,John Leth
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:In this paper we derive a Probably Approximately Correct (PAC)-Bayesian error bound for partially observed linear time-invariant (LTI) stochastic dynamical systems in state-space form with inputs and sub-Gaussian noise. Such bounds are widespread in machine learning, and they are useful for characterizing the predictive power of models learned from finitely many data points. The bound derived in this paper relates the expectation of prediction errors with the prediction error generated by the model on the data used for learning. In addition, we show that it can also be used to derive bounds for the parameter estimation error. In turn, this allows us to provide finite-sample error bounds for the prediction error and parameter estimation error for a wide class of system identification algorithms. Furthermore, as LTI systems are a sub-class of recurrent neural networks (RNNs), these error bounds could be a first step towards PAC-Bayesian bounds for RNNs.
[LG-14] BAFF: Bid-Aware Filter Family for Mitigating Training Data Interference in RTB A/B Tests RECSYS2026
链接: https://arxiv.org/abs/2609.08725
作者: Jeonglyul Oh,Ikkyu Choi,Inseop Youn,Youngjae Kim
类目: Machine Learning (cs.LG)
*备注: Accepted as a poster presentation at the OARS Workshop @RecSys 2026. 9 pages, 1 figure, 7 tables
Abstract:In online A/B tests for real-time bidding (RTB), control and treatment models are typically trained on a shared serving log that includes data generated by the counterpart model. This shared-log training biases each model’s training data through two channels: the counterpart model may have selected a different ad from the ad-candidate pool (ad-ranking disagreement) and may have bid a different price (bid-pricing disagreement), potentially distorting the A/B test outcome. Log-splitting eliminates the bias but sacrifices training data; log-sharing retains all data but leaves the bias unaddressed. We formalize the Bid-Aware Filter Family (BAFF), a class of (k,l)-parameterized hard filters that controls tolerance to each channel independently, providing a structured search space between these two extremes. We further propose a three-stage online measurement protocol that enables evaluating data-sharing strategies by their deviation from an interference-free reference model in production. In offline simulation, a (k,l) sweep surfaces operating points with smaller deviation from the interference-free reference model than both log-sharing and log-splitting. In a live RTB deployment on a demand-side platform (DSP), filter-based variants preserve the reference model’s business metrics (e.g., CPC, CTR) more closely than both baselines. The best operating point is setting-dependent, underscoring the practical value of the search space itself.
[LG-15] Chimaera: A Mixture-of-Graph-Experts Architecture for Cross-Task and Cross-Dataset Graph Learning
链接: https://arxiv.org/abs/2609.08709
作者: Jonathan Frank,David Richerby,Ansgar Scherp
类目: Machine Learning (cs.LG)
*备注: Accepted at WI-IAT 2026
Abstract:Designing foundation models for graphs is challenging due to the irregular structure of graphs and the different sizes and characteristics of embeddings. Chimaera integrates mixture-of-experts with graph foundation models (GFM). It integrates different GFM architectures, such as graph prompts and linear GNN models. Large language models are used to generate embeddings, and experts can be trained and combined following different strategies, GFMs, embeddings, etc. Furthermore, Chimaera extends existing linear GNNs to support link-level and graph-level tasks in addition to node-level tasks. Empirical analyses are performed on same-task and cross-task experiments with node, link, and graph classification tasks using six benchmark text-attributed graph datasets. The experiments demonstrate the effectiveness of Chimaera and its capabilities for transfer across tasks and datasets. Further insights include the need to use both large and small language models to generate embeddings for the experts, a strong cross-task transferability of simple but effective linear GNNs, and using few samples only to provide strong results.
[LG-16] HOPE: Heterophily-Aware Open-Set Node Classification with Pseudo-Extrapolation
链接: https://arxiv.org/abs/2609.08685
作者: Yumeng Dai,Yue Tan,Yixin Liu,Chenxu Wang,Pinghui Wang,Tao Qin
类目: Machine Learning (cs.LG)
*备注:
Abstract:Standard open-set node classification methods rely on the homophily assumption, where connected nodes share labels. However, real-world graphs are often heterophilic, exposing the limitations of current methods and posing new challenges to open-set node classification. On the one hand, cross-class connectivity causes representations from different known or unknown classes to become intertwined after aggregation, undermining their discriminative capacity. On the other hand, structural mixture invalidates threshold-based open-set methods and cross-class feature interpolation, leading to unreliable unknown-class rejection. To address these challenges, we propose HOPE, a Heterophily-aware Open-set node classification method with Pseudo-Extrapolation. To adapt open-set graph neural networks (GNNs) to heterophilic scenarios, HOPE uses a structure-augmented feature initialization layer to capture multi-hop structural patterns. Meanwhile, we design a trustworthy neighborhood aggregation mechanism for standard GNNs to dynamically filter noisy cross-class neighbors. To enhance unknown-class rejection, we introduce a heterophily-guided pseudo-extrapolation strategy. It dynamically maintains known-class centers and extrapolates along cross-class neighborhood displacement directions, synthesizing pseudo-unknown proxies near structurally ambiguous regions. Finally, we optimize the network with joint classification and logit margin regularization, routing synthetic proxies into a dedicated rejection slot without imposing geometric margin constraints in the representation space. Extensive experiments on multiple datasets show that HOPE consistently outperforms state-of-the-art models, validating its effectiveness, robustness, and efficiency.
[LG-17] Learning to build covering structures with continuous adjustments IROS2026
链接: https://arxiv.org/abs/2609.08669
作者: Gabriel Vallat,Maryam Kamgarpour,Stefana Parascho
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: Accepted in IROS 2026
Abstract:Robotic construction offers the potential to use materials more efficiently and create complex geometries, but current methods rely on rigid, high-precision plans that cannot accommodate the tolerances, inaccuracies, and unexpected changes inherent in physical fabrication. In this work, we introduce a reinforcement learning approach that forgoes predefined plans entirely, instead generating construction sequences adaptively as the structure is built. Our method operates on graph-structured state representations and a mixed (parameterized) action space, requiring both discrete block selection and continuous placement parameters. Because the stability simulation of a structure is computationally heavy, we develop an efficient exploration strategy by incorporating unilateral edges into graph neural networks, extending soft actor-critic (SAC) to this hybrid setting. We evaluate our algorithm, HSAC, against the prior method hybrid-PPO (HPPO), demonstrating significantly higher asymptotic performance and good sample efficiency. We also demonstrate HSAC’s robustness to hyperparameter choices and its exploration capability, handling up to 10 discrete actions without performance degradation. Finally, we validate our approach on a physical two-robot setup, successfully building a spanning arch with 3D-printed blocks in closed-loop execution, confirming that policies trained in simulation transfer to real hardware.
[LG-18] arget-Independent Micro-Interventions for Predicting Training Response Across Language-Model Families
链接: https://arxiv.org/abs/2609.08618
作者: Zhongxuan Liu,Sicheng Zhou,Hongzhi Wang
类目: Machine Learning (cs.LG)
*备注: 24 pages, 2 figures
Abstract:Benchmark scores describe what a checkpoint can do now, but they do not determine how it will respond to the next training episode. We measure this missing state by branching four short, standardized, target-independent micro-interventions from the same checkpoint and recording their effects in a common capability space. Together with current capability, these responses form L-State; its pulse block supports a flexible direct readout and a structure-preserving operator readout. Under smooth local dynamics, the operator construction admits an end-to-end cross-family bound with explicit source- and target-family coordinate heterogeneity. In three-family leave-one-family-out development, both pulse readouts reduce source-standardized MSE by 39.4% relative to capability alone, while separating the best response and direction estimates. On sealed GLM-4-9B, the direct and operator readouts reduce MSE by 71.8% and 78.3%, respectively, and the operator readout raises sign balanced accuracy from 0.366 to 0.754. On sealed Granite-3.1-8B, the direct readout reaches RMSE 0.544 and a development-fitted action-wise selector reaches 0.554, compared with 1.172 for capability alone. A five-family audit finds that the operator coordinate varies by action and family, and that modeling these deviations improves retrospective held-trajectory prediction. Target-independent interventions therefore expose training-response information that current capability misses, with direct and structured readouts covering complementary transfer regimes.
[LG-19] Why shared attention vectors fail: a case for outcome-indexed tuning
链接: https://arxiv.org/abs/2609.08615
作者: Lenard Dome
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Neurons and Cognition (q-bio.NC)
*备注: 2 figures, 8 pages
Abstract:Dimensional attention in learning is often implemented as a globally shared attention vector, where each stimulus dimension corresponds to a single scalar. These scalars are learned by models through gradient-descent on error, where predictive features acquire more salience. We show that under multi-outcome learning, where models predict more than one outcome, this shared vector becomes unstable; it collapses to its bounds and prevents the models from learning meaningful attentional tunings for learning and generalization. We address this by introducing an outcome-indexed attentional matrix that converts globally shared attentional tuning into an outcome-indexed representation. We present an analysis of the unstable shared vectors and derive the conditions under which it holds. Empirically, three synthetic experiments benchmark the proposed attention matrices and show that they converge to meaningful representations, something shared attention vectors fail to do. These results suggest that outcome-indexed attentional matrices are a general fix for gradient-based attentional processes, which improves models of learning under multi-outcome conditions.
[LG-20] Multi-Level-Set-Based Physics-Driven Neural Network to Solve 3-D Inverse Scattering Problems
链接: https://arxiv.org/abs/2609.08594
作者: Yutong Du,Zicheng Liu,Bo Qi,Yali Zong,Peixian Han
类目: Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
*备注:
Abstract:This paper proposes a level-set-based physics-driven neural network solver (LSPDNN) for 3-D electromagnetic inverse scattering. To mitigate boundary blurring and reconstruction artifacts in voxel-wise contrast reconstruction, the proposed solver exploits the piecewise homogeneity of practical scatterers by representing unknown targets with multiple coordinate-dependent neural level-set components. Specifically, a soft-union multi-material model is proposed to separately describe the object support and material distribution. The global support is formed by the union of multiple level-set components, while the local contrast is determined by normalized component weights and learnable complex permittivity candidates. In addition, a model-consistent total variation (TV) regularization is imposed on the material-region indicators, rather than directly on the reconstructed contrast, to suppress fragmented material assignments without excessively smoothing material interfaces. An adaptive loss balancing strategy is further introduced to reduce the dependence on manually selected regularization weights. For each measurement instance, the neural level-set parameters and material candidates are optimized by minimizing a physics-consistent objective function. Numerical and experimental results demonstrate that LSPDNN can reconstruct scatterers with clear boundaries, more uniform material regions, and substantially reduced background artifacts. The results highlight the advantage of the neural level-set parameterization in challenging 3-D inverse scattering cases involving irregular shapes, closely spaced objects, multiple materials, and measurement noise.
[LG-21] AlphaRJM: Reward-Jump Memory for Stochastic Return-Guided Alpha Discovery
链接: https://arxiv.org/abs/2609.08581
作者: Sayan Dhan,Selvaraju Natarajan
类目: Machine Learning (cs.LG); Computational Finance (q-fin.CP); Machine Learning (stat.ML)
*备注: 26 pages,5 Tables, 4 Figures
Abstract:Formulaic alpha discovery is a pool-dependent symbolic search problem in which informative feedback is observed primarily when a complete expression is evaluated. This delayed feedback creates two coupled difficulties: the retained alpha pool does not preserve the full history of realized evaluation feedback, and the value of an intermediate construction action is uncertain because its consequence depends on the formula eventually completed. We introduce AlphaRJM, which addresses these difficulties through Reward-Jump Memory, an event-driven latent state that remains fixed during token construction and updates only at terminal evaluation events using the realized pool reward and evaluation outcome, and an action-conditioned SDE return critic that represents future discounted discovery returns with stochastic particles. The particles guide action selection through their mean and uncertainty and are learned using a distributional Bellman objective combining energy-distance matching, mean calibration, and jump regularization. Empirically, AlphaRJM delivers strong and stable gains across multiple equity universes, forecasting horizons, and random seeds, while ablations confirm the complementary roles of persistent evaluation history, stochastic return modeling, and distributional supervision.
[LG-22] Certified Topological Interaction in Neural Representations: Class Disentanglement Is Mostly Pairwise
链接: https://arxiv.org/abs/2609.08561
作者: Sushovan Majhi
类目: Machine Learning (cs.LG); Algebraic Topology (math.AT)
*备注: 35 pages, 9 figures, 3 tables. Code and measurement records: this https URL
Abstract:Class disentanglement (the separation of a representation’s class-conditional point clouds along depth and over training) is usually read off descriptive curves. We measure it as certified topological interaction between labeled point clouds, using the recently introduced Intersection Euler Characteristic Profile: the Euler characteristic of the overlap of the clouds’ ball unions as a function of scale, computed by one Alpha-complex sweep with no boundary-matrix reduction. Every number carries a test: exact permutation tests in both directions, a guarded separation certificate, and a paired test for the comparative claims applications make. Across 111 trained networks and 52,650 certified measurements, disentanglement is depth-graded and concentrated in the first epochs, and interaction quotients rank class pairs by confusability (Spearman rho=0.83), on par with cheap separability statistics. In a 96-model factorial population, augmentation is the one training choice that separates classes relative to chance; weight decay compresses the overlap without separating, and depth and width do nothing. The structural finding is one only a k-fold statistic can pose: the joint entanglement of a class triple sits below that of its strongest pair in 97% of triple-layer cells and 99.5% of deep cells, far below a measured null floor, in vision encoders and frozen language models alike. This pairwise dominance is a regularity, not a law: expected from the nesting of overlaps but not forced by geometry, present at initialization and in raw pixels, and manufactured in the last stage alone when a network memorizes random labels. The unnormalized profile mass predicts test accuracy (R^2=0.94), the quotient does not, and neither beats a linear probe. One lesson is reported in full: the paired test must use a scale-free statistic, or it certifies feature-norm dynamics as disentanglement.
[LG-23] Not All Variables Agree: Reliability-Aware Variable-Wise Gradient Surgery for Multivariate Time-Series Forecasting
链接: https://arxiv.org/abs/2609.08554
作者: Jinwoo Park,Hyeongwon Kang,Pilsung Kang
类目: Machine Learning (cs.LG)
*备注: 34 pages, 21 figures, 20 tables
Abstract:In data-driven training, multivariate time-series forecasting is usually optimized with a scalar loss averaged over samples, variables, and horizons. This averaging is convenient, but the optimizer sees only the aggregated gradient, which does not reveal whether the variable-wise contributions align or oppose one another. To quantify how often this disagreement arises, we measure the variable-wise gradients directly and find that 30.6% of their pairwise cosine similarities are negative on average across seven datasets. However, conflict and harm are not the same thing. Under shared training 35 of the 64 variables do worse than a full-input single-target oracle, and the harmed fraction is not reliably predicted by how often gradients conflict. We propose Per-Variable Surgery (PV-Surgery), an optimizer-side training strategy for backbones with cache-compatible layers. One backward pass builds variable-wise gradient proxies from output-side signals and keeps the pointwise forecasting loss. Reliability-aware selection targets layers whose proxy sums closely approximate their shared-gradient slices. Conditional pooling forms anchor and conflict pools without dropping variables. Common-direction surgery aligns variable or pooled gradients with their normalized mean and restores input norms to avoid reweighting. In experiments across five backbones, seven datasets, and four horizons, PV-Surgery lowers MSE by 3.61% and MAE by 2.93% on average. For multivariate forecasting, this indicates that the variable-wise structure hidden by mean-loss training is a usable optimization signal.
[LG-24] When Topology Betrays Privacy: Lattice-Based Reconstruction Attacks on Secure Aggregation in Decentralized Federated Learning
链接: https://arxiv.org/abs/2609.08476
作者: Wenrui Yu,Changlong Ji,Johannes Bjerva,Qiongxiu Li
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:Secure Aggregation (SA) is widely regarded as a strong defense against model-update leakage in Federated Learning (FL), as it reveals only aggregate results while hiding individual updates. In Decentralized Federated Learning (DFL), SA is commonly instantiated as local neighborhood aggregation, where each node obtains a weighted aggregate over its neighbors. We show that this locality creates a structural leakage surface: sparse decentralized topologies provide colluding semi-honest nodes with asymmetric aggregate views, exposing multiple hidden linear combinations of honest participants’ private states. Reconstructing private states from these aggregate views is fundamentally challenging, as both the private states and the aggregation coefficients are hidden. We tackle this challenge by establishing a formal connection to the Hidden Subset Sum Problem, a long-studied problem in cryptography. Building on this formulation, we design a lattice-based reconstruction approach that combines lattice reduction with structural filtering to reconstruct protected model states. We evaluate our attack on image, tabular, and text tasks under sparse DFL topologies. Our results show that colluding semi-honest nodes can recover the original local updates of honest nodes, enabling downstream reconstruction of private training data. These findings demonstrate that SA alone does not guarantee privacy in DFL when local aggregation induces asymmetric observations.
[LG-25] Selective boundary condition reduction via learned error gating
链接: https://arxiv.org/abs/2609.08461
作者: Daniel Fernández,Dominik Penk,Dominik Riedelbauch
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG)
*备注: to appear at WCCM-ECCOMAS 2026
Abstract:Parametric PDEs can admit different boundary conditions with different accuracy and computational cost. We introduce a framework for learning when one reduced boundary condition can replace another: paired solutions train a neural network to estimate the resulting domain and boundary errors, and the simpler condition is used only when both predicted errors meet prescribed tolerances. We focus on singular limits in applications, in which a stiff Robin or nonlinear boundary law is replaced by its limiting Dirichlet form. We evaluate the method on a galvanic corrosion problem and other nonlinear stationary and evolution problems.
[LG-26] opological Fraud Detection in Latent Transaction Spaces
链接: https://arxiv.org/abs/2609.08445
作者: Avraham Bourla
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注:
Abstract:Working entirely on topologically anonymized embeddings, we perform fraud detection using iterative rounds of unsupervised filtering followed by supervised sniping. The result is an ultra-low latency privacy–preserving triage that allows institutions to flag suspicious activity without compromising Personally Identifiable Information.
[LG-27] Stochastically Perturbed Weights: Ensembles from Deterministic Machine-Learning Weather Models
链接: https://arxiv.org/abs/2609.08412
作者: Simon Adamov,Oliver Fuhrer,Reto Knutti,Sebastian Schemm
类目: Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph)
*备注: 57 pages, 17 figures, 12 tables. Submitted to Artificial Intelligence for the Earth Systems (AMS). Code: this https URL (v1.0.0)
Abstract:Machine-learning weather models (MLWMs) now match or outperform operational numerical weather prediction (NWP) at global medium-range forecasting, at far lower inference cost. Many deployed MLWMs are deterministic, producing a single forecast with no estimate of its own uncertainty, whereas a growing family of trained-probabilistic models generate calibrated ensembles directly, at the price of a dedicated training run. We ask instead how much uncertainty can be extracted from a deterministic checkpoint that already exists, without retraining it. Where physical ensembles represent model uncertainty by stochastically perturbing parametrisation tendencies, we perturb the network’s raw weight tensors at inference time, a scheme we call stochastically perturbed weights (SPW). We also ask whether it works, where and on which scales to inject the noise, and where it fails. A three-phase ablation across four deterministic backbones, Aurora, GraphCast, SFNO, and AIFS, selects one production baseline per model, benchmarked against the trained-probabilistic AIFS-ENS, FourCastNet 3 and Atlas as well as the operational ECMWF ensemble (IFS-ENS) over 112 initialisation times. At a 240 h (10-day) lead time the SPW ensembles reach continuous ranked probability skill scores (CRPSS) between 0.04 and 0.13 below the best trained-probabilistic baseline, at zero marginal training cost. No injection site works across models: the productive tensor group is architecture-specific, so SPW is at present a tuning procedure rather than a plug-and-play recipe. Its main failure mode is a coherent whole-field offset that overdisperses the domain mean, and restricting the noise to coarse scales or perturbing the initial conditions each repair part of it.
[LG-28] MLIP Detective: Active Failure Mode Discovery Beyond Benchmark Scores for Machine-Learning Interatomic Potentials
链接: https://arxiv.org/abs/2609.08399
作者: Ryuhei Okuno,Nontawat Charoenphakdee,Kaoru Hisama,Yuta Tsuboi
类目: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci); Chemical Physics (physics.chem-ph)
*备注:
Abstract:Universal machine-learning interatomic potentials (u-MLIPs) aim to generalize across diverse configurations. Benchmarks enable reproducible evaluation but may not expose failures outside their predefined scope. Here, we show that physics-informed search can complement benchmark-based evaluation by uncovering hidden failure modes. We introduce MLIP Detective, an agentic framework for active failure mode discovery. Starting from benchmark evidence, MLIP Detective generates falsifiable, physics-informed failure hypotheses, screens them with inexpensive simulations, and escalates only the most suspicious cases to human experts together with proposed verification protocols. Without issue-specific prompting, MLIP Detective identified and characterized a systematic anomaly in MACE-MPA-0: the model predicted some relaxed adsorbate-surface systems involving O- or F-containing adsorbates to be higher in energy than their corresponding separated fragments. Using cross-model comparisons, MLIP Detective further inferred a likely training-data origin for the anomaly, consistent with recent reports.
[LG-29] Geographically Regularized AUC-Maximizing Personalized Federated Learning
链接: https://arxiv.org/abs/2609.08379
作者: Mayu Hiraishi,Kensuke Tanioka,Toshio Shimokawa
类目: Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:Accurate diagnostic and risk-prediction models are important for supporting clinical decision-making during infectious disease outbreaks. However, privacy and governance requirements may restrict patient-level data sharing across healthcare institutions, and data distributions often vary. Moreover, AUC is widely used to evaluate discriminative performance, motivating its direct optimization in model development. We propose geographically regularized AUC-maximizing personalized federated learning (GrAUC-PFL), which directly optimizes a smooth pairwise AUC surrogate to learn personalized models while keeping patient-level data local and accounting for institutional heterogeneity. Graph-based regularization encourages geographically neighboring institutions to have similar coefficient vectors while retaining a personalized models. Simulations and a real-data application suggest improved discriminative performance, particularly when geographically neighboring institutions have similar data-generating characteristics.
[LG-30] Geometry-Aware Bayesian Parameter-Efficient Fine-Tuning on the Stiefel Manifold via Stein Variational Gradient Descent ICDM2026
链接: https://arxiv.org/abs/2609.08354
作者: Quang-Duy Tran,Trung Le,Bao Duong,Phuoc Nguyen,Thin Nguyen
类目: Machine Learning (cs.LG)
*备注: Accepted at the 26th IEEE International Conference on Data Mining (ICDM 2026)
Abstract:Several geometry-aware approaches to low-rank adaptation have emerged for parameter-efficient fine-tuning of large pre-trained models. These methods aim to take full advantage of the geometric structure of low-rank manifolds for improving the efficiency in subspace utilization and reducing redundancy by enforcing orthogonality constraints during optimization. The strong empirical results of these techniques have motivated further study into whether predictions from such geometry-based adaptation methods could be overconfident. In this paper, we build on the singular value decomposition factorization of adapters to develop a framework based on Stein variational gradient descent (SVGD). In this formulation, the low-rank matrices are transported along the Stiefel manifold to match the targeted distributions while retaining their crucial geometric structure. Since this geometry-aware SVGD approach provides multiple solutions during inference, it supports uncertainty quantification and produces better-calibrated adapters on the Stiefel manifold. Extensive experiments show that our method delivers strong model calibration and attains higher prediction accuracy than SVGD and related uncertainty estimation methods that are formulated in Euclidean space.
[LG-31] V-Regulated OPD: Direction Matters in On-Policy Distillation
链接: https://arxiv.org/abs/2609.08341
作者: Han Xiao,Yifan Niu,Dongyi Liu,Chang Luo,Jia Li
类目: Machine Learning (cs.LG)
*备注:
Abstract:On-Policy Distillation (OPD) facilitates the transfer of knowledge from domain expert to student in the post-training phase of Large Language Models (LLMs). However, the supervision signals in mainstream OPD methods suffer from high variance and noise which is generally instable during training. In this work, we systematically investigated what really matters to the performance and the fundamental mechanisms behind the instability during training. We found that retaining only the sign of token-level advantages is sufficient to achieve the performance comparable to standard OPD. Meanwhile, smoother and bounded advantages can stabilize the training process without sacrificing its performance. These motivated us to shape the advantages using the Total Variation (TV) and propose a robust TV regulated On-Policy Distillation (TV-OPD) method. Benefiting from the bounded and diminished advantages, TV-OPD exhibits stable training dynamics and steady late-stage performance. We conducted comprehensive experiments and found that, across various settings, TV-OPD consistently achieved better performance and lower variance in the late-stage of training.
[LG-32] EMBLEM: Enhancing Multi-script Table Detection through Masking
链接: https://arxiv.org/abs/2609.08330
作者: Dhruv Kudale,Udhay Brahmi,Ganesh Ramakrishnan
类目: Machine Learning (cs.LG)
*备注: Accepted in International Conference on Document Analysis and Recognition 2026
Abstract:Table detection is a core task in document analysis, supporting downstream applications such as information retrieval, document reconstruction, and visual question answering. While existing deep learning models perform well on English and Chinese documents, they struggle with multilingual, multi-script documents due to script diversity and the limited availability of labeled data. To address this challenge, we introduce MANDALA (Multi-script Annotated Documents for Table Detection), a manually curated dataset of 2,323 table-containing pages spanning 18 languages and 15 scripts across diverse domains. We also propose EMBLEM, a masking-based paradigm for Multi-script Table Detection (MTD). EMBLEM generates masked images that conceal script- and font-specific details, enabling models pre-trained on abundant English documents to focus on script-agnostic page layout. Experiments across three table detection architectures show that EMBLEM consistently outperforms strong baselines on MANDALA while remaining competitive on five standard English-dominant benchmarks. Using only English masked images for fine-tuning, with no multi-script training data, EMBLEM achieves an absolute F1-score gain of 20.8% on MANDALA. We release MANDALA along with the accompanying code and models at this https URL.
[LG-33] Non-Coherent Over-the-Air Federated Learning: Protocol Convergence and Device Scheduling
链接: https://arxiv.org/abs/2609.08312
作者: Haifeng Wen,Nicolò Michelusi,Osvaldo Simeone,Yang Yang,Hong Xing
类目: Information Theory (cs.IT); Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注: 13 pages, 8 figures, submitted for possible journal publication
Abstract:To mitigate the scalability bottleneck in the radio access network (RAN) in federated edge learning (FEEL), over-the-air federated learning (AirFL) exploits waveform superposition over multiple-access channels (MACs) for analog model aggregation. However, coherent AirFL typically relies on stringent PHY-layer conditions such as accurate channel state information (CSI), tight time/frequency synchronization, and frequent transceiver calibration for signal alignment. However, these requirements, if not impossible to be met, incur substantial communication and computation overhead. In this paper, we propose a non-coherent AirFL (NCAirFL) protocol over a broadband single-antenna MAC, leveraging binary dithering, unbiased non-coherent detection, and long-term error feedback to waive the need for instantaneous CSI. For NCAirFL with general smooth non-convex objectives and a constant learning rate, we establish a convergence bound achieving the convergence rate in the same order of \mathcalO(1/\sqrtT) as communication-ideal FedAvg, where T is the total number of communication rounds. To further improve communication efficiency under data and wireless resource heterogeneity, we also derive a lower bound on the expected single-round objective decrease in the global loss conditioned on device scheduling, building upon which a surrogate objective function is obtained for jointly optimal device selection and power control. Experimental results on MNIST and CIFAR-10 corroborate that NCAirFL achieves learning performance close to FedAvg in practical settings, with the proposed device scheduling policy substantially accelerating convergence.
[LG-34] HypLTSF: A Hyperbolic Geometric View of Multi-Scale Hierarchies for Long-Term Time Series Forecasting
链接: https://arxiv.org/abs/2609.08286
作者: Namwoo Kim,Hyungryul Baik,Yoonjin Yoon
类目: Machine Learning (cs.LG)
*备注: Submitted to IEEE TNNLS
Abstract:Multi-scale modeling has become an effective approach for long-term time series forecasting, capturing temporal patterns that range from fine-grained local dynamics to coarse global trends. Representations across these temporal scales are inherently hierarchical, with coarser scales abstracting and aggregating information from finer ones. While existing approaches readily exchange information across these scales, the hierarchy itself is typically left as an emergent byproduct of such interactions rather than captured as a geometric structure in its own right. In this paper, we introduce HypLTSF, a framework that endows the multi-scale hierarchy with a concrete geometric form by embedding scale-wise representations into the Poincaré ball, whose exponentially expanding volume naturally accommodates hierarchical structures. To align this geometry with the temporal hierarchy, HypLTSF imposes two constraints: (1) a radial constraint that orders embeddings by their level of abstraction, and (2) an angular constraint that groups fine-scale patterns sharing a common coarser-scale ancestor. Extensive experiments on long-term time series forecasting benchmarks show that HypLTSF achieves state-of-the-art performance, suggesting that explicitly modeling the multi-scale hierarchy as a geometric structure is effective for forecasting.
[LG-35] Adaptively Incorporating Directional Hints into Zeroth-Order Optimization
链接: https://arxiv.org/abs/2609.08277
作者: Alexander Ryabchenko,Jian Qian,Wenlong Mou
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:We study zeroth-order optimization of non-convex functions with the aid of directional hints, which are cheap but potentially inaccurate approximations of the true gradient direction, given by linear subspaces at each iteration. To leverage these hints adaptively while maintaining robustness to their quality, we introduce Control-Variate Zeroth-Order Descent (CV-ZOD), a new framework that refines the classical zeroth-order gradient estimator with a control variate that can be set based on the directional hints. We first show that the oracle algorithm that optimally sets the reference vector and step size at each iteration achieves a convergence rate that interpolates between the first-order O(1/T) rate and the zeroth-order O(d/T) rate, depending on the quality of the hints along the trajectory. We then develop a practical variant of CV-ZOD that achieves the same oracle guarantee up to logarithmic factors, without any prior knowledge of the hint quality. We validate the method empirically on simulation-based scientific optimization tasks, demonstrating sustained progress on non-convex landscapes where zeroth-order descent is slower and existing guided methods stall as guidance deteriorates.
[LG-36] Revisiting Spectral Representations in Generative Diffusion Models ICML2026
链接: https://arxiv.org/abs/2609.08253
作者: Yuehao Wang,Peihao Wang,Hanwen Jiang,Ziyi Yang,Qixing Huang,Zhangyang Wang
类目: Machine Learning (cs.LG)
*备注: ICML 2026
Abstract:Diffusion models have shown remarkable performance on diverse generation tasks. Recent work finds that imposing representation alignment on the hidden states of diffusion networks can both facilitate training convergence and enhance sampling quality, yet the mechanism driving this synergy remains insufficiently understood. In this paper, we investigate the connection between self-supervised spectral representation learning and diffusion generative models through a shared perspective on perturbation kernels. On the diffusion side, samples (e.g., images, videos) are produced by reversing a stochastic noise-injection process specified by Gaussian kernels; on the spectral representation side, spectral embeddings emerge from contrasting positive and negative relations induced by random perturbation kernels. Motivated by this, we propose a self-supervised spectral representation alignment method to facilitate diffusion model training. In addition, we clarify how joint spectral learning can benefit diffusion training from a geometric perspective. Furthermore, we find that the optimization of the spectral alignment objective is in an equivalent form of diffusion score distillation in the representation space. Building on these findings, we integrate a spectral regularizer into diffusion training objectives to improve the performance of diffusion models on multiple datasets. Experiments across images and 3D point clouds show consistent gains in generation quality. Code is released at this https URL.
[LG-37] Routing Dense Layouts with History-Aware Offline Reinforcement Learning using LSTM
链接: https://arxiv.org/abs/2609.08232
作者: Afsara Khan,Austin Rovinski
类目: Hardware Architecture (cs.AR); Machine Learning (cs.LG)
*备注: Accepted for publication at ICCAD 2026
Abstract:Detailed routing remains a dominant runtime bottleneck in physical design due to increasing complexity of design rules. Modern routers can struggle to resolve persistent violations under dense operating conditions. While recent work leverages reinforcement learning (RL) to dynamically select costs for each routing iteration, we find that this technique struggles with high-density designs where routing solutions are significantly harder. To address this, we present a history-aware offline RL policy which predicts iterative cost weights in these dense regimes to improve convergence across placement densities by utilizing readily available features from the router. Our policy uses conservative Q-learning similarly to prior work; however, our key insight is that addition of a lightweight LSTM architecture and additional features can retain sequence context and improve routing convergence across multiple densities and route guide qualities. Our policy can be integrated into any cost-based router with minimal pipeline changes, as it does not interfere with the core search algorithm. We evaluate our policy on held-out density and adjustment settings, including difficult operating points induced by dense placement and low guide quality. Our policy reduces design rule violations (DRVs) by an average of 92% over the top public baseline while simultaneously reducing runtime by 10%.
[LG-38] DRIFT: Removing Diffusion Watermarks by Deflecting the Generative Trajectory
链接: https://arxiv.org/abs/2609.08213
作者: Rui Bao,Zheng Gao,Xiaoyu Li,Xiaoyan Feng,Yang Song,Jiaojiao Jiang
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:Diffusion watermarking embeds verifiable signals into the generative process and commonly verifies them by recovering trajectory-dependent evidence, making the marks robust to conventional pixel-space distortions. Existing removal attacks either regenerate along deterministic trajectories, which often preserve the watermark-bearing latent structure, or optimize every image separately. We identify the reliance on a recoverable generative trajectory as a common attack surface among the schemes we study. Based on this observation, we propose DRIFT, a black-box attack that combines partial forward diffusion with stochastic reverse resampling. Forward re-noising limits source information available to a fixed-depth recovery pipeline, while stochastic reversal supplies alternative noise-driven paths whose removal benefit we isolate through matched sampler comparisons. Adaptive DRIFT searches a selected ladder for each image’s first verifier-rejected rung and refines fidelity while retaining only updates rejected by the same verifier. At fixed depth, we derive information-theoretic and Wasserstein source-dependence bounds; under realized-ladder monotonicity, the first rejected rung is least distorted among rejected rungs on that ladder, and verifier-gated refinement preserves rejection. Across nine watermarks spanning three paradigms, DRIFT achieves 98-100% attack success and the best image quality among the compared attacks, without secret keys, verifier internals, or per-image gradient optimization.
[LG-39] SIM: Subspace Interaction-based Method for Token-Level Text Anomaly Detection
链接: https://arxiv.org/abs/2609.08200
作者: Kehan Yan,Yue Tan,Qingfeng Chen,Shiyuan Li,Yu Zheng,Yixin Liu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Token-level text anomaly detection, as an emerging trend of text anomaly detection, moves beyond coarse-grained document-level detection by localizing anomalous tokens within text. By providing fine-grained abnormality prediction, token-level text anomaly detection plays a critical role in various real-world applications, such as spam filtering and fake news detection. However, existing methods still rely on the global distance calculation for scoring, during which the local anomaly signals are severely diluted by numerous redundant normal feature dimensions. Moreover, pre-trained language models used in these methods inevitably smooth out surface anomalies, further limiting their effectiveness in token-level anomaly detection. To address these limitations, we propose a Subspace Interaction-based Method (SIM for short) for token-level text anomaly detection. To prevent local signal dilution, SIM adopts a subspace interaction-based anomaly detector, which decouples high-dimensional token embeddings into multiple low-dimensional ones, amplifying localized anomaly signals hidden within specific dimensions. To counteract the over-smoothing effect, we design a hard pseudo-anomaly generation module to construct pseudo-anomalous tokens, simulating the subtle anomalies obscured by semantic smoothing. Also, a probabilistic boundary loss is developed to standardize anomaly scores into statistical distances, effectively enforcing anomalous instances to deviate significantly from the normal distribution center. Extensive experiments on multiple benchmark datasets verify the effectiveness of SIM and demonstrate its remarkable efficiency, robustness, and interpretability. The source code is available at: this https URL.
[LG-40] opology-induced Operators Reveal Complementary Graph Representations without Training
链接: https://arxiv.org/abs/2609.08152
作者: Meng Qin,Jinqiang Cui,Hongwei Zheng,Weihua Li,Sen Pei
类目: Machine Learning (cs.LG); Social and Information Networks (cs.SI)
*备注:
Abstract:Graph representation learning has largely focused on designing increasingly sophisticated models to transform graph topology into vector representations, or embeddings. However, the extent to which embedding quality depends on model learning, rather than on the underlying topological transformations, remains unclear. Here, we show that informative embeddings can be derived without complicated model design and gradient-based training. Propagating random features through implicit hierarchical structures induced by random walks and anonymous walks yields embeddings that capture node proximity and structural role, respectively. These two training-free embeddings preserve complementary aspects of graph organization and perform competitively with classic and recent methods across various node-, edge-, and graph-level tasks. They often require substantially less computation, resulting in a favorable quality-efficiency trade-off. Combining the two types of embeddings further improves inference quality of some tasks compared with using either embedding type alone. Our results suggest that informative graph embeddings can arise from carefully chosen topological transformations before any learning operation is applied.
[LG-41] GPU-Enabled Large-Scale Optimization Using Randomized Linear Algebra
链接: https://arxiv.org/abs/2609.08136
作者: Pratik Rathore,Zachary Frangella,Parth Nobel,Xuning Hu,Madeleine Udell
类目: Machine Learning (cs.LG); Optimization and Control (math.OC); Machine Learning (stat.ML)
*备注: 32 pages, 16 figures
Abstract:This paper introduces rlaopt, a PyTorch-based package for large-scale optimization and scientific computing using randomized numerical linear algebra (RandNLA). Despite substantial progress in RandNLA-based algorithms, few implementations combine GPU acceleration with a simple interface for specifying optimization problems. rlaopt addresses this gap by providing GPU-enabled solvers for positive-definite linear systems and convex empirical risk minimization with constraints and regularizers. These solvers use RandNLA to accelerate conjugate gradient (NystromPCG), operator splitting (NysADMM), and stochastic gradient methods (SAPPHIRE). Moreover, rlaopt includes a modeling language that lets users specify problems using natural mathematical syntax. rlaopt automatically checks compatibility with the selected solver and performs the required problem decomposition. The solvers also support differentiation through their iterations, enabling applications such as hyperparameter tuning. Experiments on ridge regression, bounded multinomial logistic regression, and bounded elastic net identify when randomized preconditioning improves performance and demonstrate substantial speedups from GPU execution. The package is open-source under an Apache license, with source code at this https URL and version 0.1.0 available on PyPI.
[LG-42] Nyström Attention Matches Full Attention for Cross-Sectional Stock Prediction NEURIPS2026
链接: https://arxiv.org/abs/2609.08106
作者: Kunhan Guo
类目: Machine Learning (cs.LG); Statistical Finance (q-fin.ST)
*备注: 14 pages, 4 figures. A short version is under review at the TS-LIMITS workshop, NeurIPS 2026
Abstract:MASTER’s inter-stock multi-head attention – the module responsible for modeling cross-sectional stock relationships – accounts for 42.5% of model parameters and 25% of predictive value. We systematically decompose this module and uncover a surprising structure: the learned attention is near-uniform (perplexity 278/300), yet forcing exact uniformity eliminates all cross-sectional discrimination. Spectral analysis resolves this paradox: the deviation from uniformity is low-rank (effective rank ~65, top-10 modes capture 96.5% of energy), explaining why sparse approximations consistently fail while Nystrom low-rank attention (m=32 landmarks) matches full O(N^2) attention at O(mN) cost – certified equivalent via TOST at both N=300 (5 seeds, Rank IC p=0.003) and N=800 (10 seeds, Rank IC p=0.034). Additional findings include: (i) attention anti-correlates with return similarity (Spearman rho = -0.614; on the industry-labeled subset, -0.645 unconditionally and -0.627 after controlling for industry, beta, and volatility), suggesting complementarity-seeking rather than correlation mining; (ii) all graph-based alternatives degrade performance, with hard masking worse than complete module removal; and (iii) at N ~ 3,500 with adapted architectures, no cross-stock module (GCN, Nystrom, or MASTER-style pipeline) significantly outperforms a per-stock LSTM baseline (n=4 seeds), indicating that the benefits observed at smaller scales do not trivially transfer. These results establish that the inter-stock attention’s value resides in a compressible, dynamic, near-global redistribution that rewards low-rank approximation but resists sparsification.
[LG-43] Learning Metamaterial Eigenmodes with Wavelet-Encoded Fourier Neural Operators
链接: https://arxiv.org/abs/2609.08102
作者: Han Zhang,Alexander Ogren,Cynthia Rudin,Johann Guilleminot,L. Catherine Brinson
类目: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci); Computational Engineering, Finance, and Science (cs.CE); Computational Physics (physics.comp-ph)
*备注:
Abstract:Machine learning surrogates based on neural operators have shown broad applicability in solving forward PDE problems. However, eigenvalue problems, in which an eigenparameter and one of several valid eigenmodes must be simultaneously solved, remain difficult because standard operator learning formulations assume a unique input-output map. This work demonstrates that Fourier Neural Operators (FNOs), combined with wavelet-based encodings of PDE inputs, can learn and predict multiple eigenmodes of the elastic wave equation, corresponding to deformation modes of acoustic waves propagating through arbitrary metamaterial geometries. We provide a mechanistic explanation and experimental evidence for why wavelet encodings are well matched to the dual spatial-spectral structure of the FNO, enabling deterministic mode selection on both continuous-valued and binary-valued geometries within a single model, and for why prediction accuracy varies with geometric discontinuities. For metamaterial design, the resulting surrogate accelerates the simulation stage of the design cycle by three orders of magnitude relative to finite element analysis on a consumer-grade CPU, while preserving high fidelity. These results also carry broader implications for designing input encodings in other multi-mode PDE solvers based on spectral neural operators.
[LG-44] Proactive Context-Forecasted Safety Constraints for Nonstationary Reinforcement Learning
链接: https://arxiv.org/abs/2609.08080
作者: Tim Tomashevskiy
类目: Machine Learning (cs.LG)
*备注: 16 pages, 7 figures. Accepted at the 5th Conference on Lifelong Learning Agents (CoLLAs 2026)
Abstract:Ensuring safety in reinforcement learning under nonstationarity requires anticipating changes in risk before they lead to unsafe behavior. Existing approaches typically rely on safety constraints defined at design time or updated reactively during execution, assuming that such constraints remain valid over time. However, in nonstationary environments with evolving contexts and changing driving layouts, these assumptions may fail. We propose a framework for proactive safety constraint generation based on context forecasting. The approach infers latent environmental context from observations, predicts its future evolution, and constructs safety constraints adapted to anticipated conditions. This enables the agent to proactively avoid unsafe regions instead of reacting only after safety violations occur. We evaluate the method in driving environments with structured context variation. The experiments include a sweep over nonstationarity intensities and additional held-out driving layouts, including highway, intersection, and racetrack scenarios. Results show that proactive constraint generation substantially reduces collisions under both seen and out-of-training nonstationarity intensities and generally remains effective across held-out driving layouts while maintaining usable task performance. These findings suggest that context-based constraint generation is a promising approach for safe reinforcement learning under nonstationarity. Comments: 16 pages, 7 figures. Accepted at the 5th Conference on Lifelong Learning Agents (CoLLAs 2026) Subjects: Machine Learning (cs.LG) Cite as: arXiv:2609.08080 [cs.LG] (or arXiv:2609.08080v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.08080 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-45] A Machine Learning Framework for Predicting Restaurant Food Waste to Support Sustainable Food Management
链接: https://arxiv.org/abs/2609.08078
作者: Md Mehedi Hasan Naeem,Md Ashraful Islam,Moumita Barua,Ishtiyak Ahmmad Araf,Md. Arefin Haque Mahir
类目: Machine Learning (cs.LG)
*备注: 6 pages, 6 figures. Accepted and presented at the 2026 IEEE International Conference on Signal Processing, Information, Communication and Systems (SPICSCON 2026). Accepted for publication in the conference proceedings
Abstract:Food waste in the restaurant sector poses a substantial challenge to environmental sustainability and economic efficiency. This paper presents an exploratory machine learning framework for estimating daily restaurant food waste quantities from operational and contextual features. A structured dataset was constructed by integrating restaurant demand records, meteorological data and temporal event indicators, yielding 77,980 records across 27 features. Because large-scale ground-truth food waste measurements are not publicly available, the target variable was derived from operationally justified assumptions, with the complete construction formula and controlled stochastic variability disclosed for full reproducibility. Four supervised regression models, namely Linear Regression, Decision Tree, Random Forest and Gradient Boosting, were evaluated under a chronological 70-30 train-test split that respects the temporal ordering of restaurant operations, augmented by 5-fold time-series cross-validation. All reported metrics are explicitly scoped to performance against the constructed target and do not imply validation against measured food waste. Ensemble methods consistently outperformed linear baselines. Random Forest attained an MAE of 6.19 kg, RMSE of 8.36 kg and R^2 of 0.817 on the realistic feature subset following systematic exclusion of algebraically leakage-prone variables. Feature importance analysis identified menu diversity, operational area and temporal activity patterns as the primary predictive drivers. The full dataset, target construction formula, codebase and experimental configurations are publicly released to support reproducibility and future extension to empirically measured waste data.
[LG-46] A Gradient-based yet Spike-Timing-Dependent Solution to the Feedback Learning Problem in Neural Microcircuits
链接: https://arxiv.org/abs/2609.08070
作者: Xiangnan Zhang,Jingxin Liu,Ranqi Lu,Jingyu Liu,Qunxi Dong,Fuze Tian,Lixian Zhu,Bin Hu,Björn W. Schuller
类目: Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
*备注:
Abstract:The brain uses discrete spikes for dynamic computation, yet, how neural microcircuits (NMCs) solve temporal credit assignment using local spike timing remains a fundamental open question. Dominant spiking neural network (SNN) approaches circumvent this by approximating backpropagation through surrogate gradients, decoupling learning from biological spike timing. Here, we reformulate temporal credit assignment as a state separation problem: extracting task-required components induced by historical perturbations directly from the current neural state. This enables an online feedback learning framework for NMCs through a gradient tunneling (GT) algorithm and the lead-lag expansion technique that derives credit assignment from local synaptic spike timing, while remaining compatible with ANN-SNN hybrid architectures. Experimentally, GT-trained NMCs excel at long-timescale evidence integration and noise-robust memory retention, and perform comparably to leading SNN online learning methods on real-world benchmarks with far fewer parameters. The proposed framework addresses the two-decade-old NMC feedback learning problem and suggests a computationally plausible explanation for the brain’s learning mechanisms.
[LG-47] Risk-Conditioned Fine-Tuning of Large Language Models EMNLP2026
链接: https://arxiv.org/abs/2609.08064
作者: Zixuan Liu,Fangzheng Wu,Brian Summa,Zizhan zheng
类目: Machine Learning (cs.LG)
*备注: EMNLP 2026 Main
Abstract:Large Language Models (LLMs) are increasingly deployed in settings where rare but severe harmful generations can have significant consequences. Existing Risk-Averse RLHF addresses this issue by optimizing Conditional Value-at-Risk (CVaR), but it trains policies for fixed risk levels and therefore cannot adjust the desired degree of risk aversion at inference time. In this paper, we propose risk-conditioned RLHF, a framework that trains a single policy that provides a continuous risk-control interface, enabling users to select different degrees of risk aversion without retraining or deploying multiple risk-specific models. Experiments across multiple benchmarks demonstrate that a single risk-conditioned policy can adapt to different risk levels at inference time, enabling more flexible and risk-aware LLM deployment.
[LG-48] VEX-Bench: Benchmarking LLM Agents for Assessing Exploitability of Software Supply Chain Vulnerabilities EMNLP2026
链接: https://arxiv.org/abs/2609.08040
作者: Jiahao Shi,Edward Tsien,Yifeng Di,Hongjiao Zhang,Yuan Tang,Ronit Dey,Ilona Shishov,Gal Netanel,Zvi Grinberg,Vladimir Belousov,Bat-Zion Rotman,Ilan Pinto,Tianyi Zhang
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG); Software Engineering (cs.SE)
*备注: Accepted to EMNLP 2026
Abstract:The software supply chain has become an increasingly exposed attack surface because of its reliance on intricate yet fragile dependencies. Existing defenses such as GitHub Dependabot often raise many false alerts because their coarse-grained matching cannot determine whether a vulnerable dependency is actually exploitable. Security analysts typically spend substantial time assessing vulnerability exploitability case by case. Recent LLM agents have emerged as promising candidates for this task given their advanced capabilities in coding and cybersecurity, yet no existing benchmark evaluates them on it. Prior benchmarks target zero-day settings, where agents detect and exploit previously unknown vulnerabilities. In contrast, software supply chain security focuses on how known vulnerabilities in upstream dependencies affect downstream projects. This requires agents to reason across repositories and determine whether an upstream vulnerability is exploitable in the downstream project. To address this gap, we introduce VEX-Bench, the first benchmark for evaluating LLM agents’ ability to assess the exploitability of software supply chain vulnerabilities. It contains 75 real-world cases mined from GitHub and labeled by security experts, covering Python, Java, and Go. We evaluate nine models across three agent harnesses. While GPT-5.5 and Claude Opus 4.6 reach approximately 80% F1 on binary vulnerability-status classification, only GPT-5.5 surpasses 70% macro-F1 on fine-grained justification classification. This gap highlights the challenge of moving beyond binary exploitability assessment to identifying fine-grained exploitability reasons. Code and data: this https URL
[LG-49] wo-Scale Localized PCA-Net: Coarse-Global and Local-Residual Representations for Artifact-Reduced PDE Operator Learning
链接: https://arxiv.org/abs/2609.08034
作者: Mrigank Dhingra,Jordan Stout,Omer San
类目: Machine Learning (cs.LG)
*备注:
Abstract:Localized dimensionality reduction improves the scalability of operator learning for high-dimensional partial differential equations (PDEs), but independently decoded local patches can introduce block offsets, interface mismatches, and spurious high-wavenumber content. We introduce Two-Scale Localized PCA-Net, which decomposes the solution into a coarse-global component and local residual corrections. A compact global PCA basis captures domain-scale structure, while nonoverlapping local PCA bases represent the remaining fine-scale residual. A block-balanced latent objective couples the two representations, and optional interface-aware fine-tuning further promotes continuity through reconstruction and trace losses. On Poisson benchmarks, the two-scale representation substantially reduces reconstruction error and visible block artifacts relative to plain and overlap-based localized PCA-Net while approximately halving PCA fitting cost relative to overlap. On heterogeneous Darcy flow, it strongly reduces interface and discrete-residual errors, with more modest reconstruction gains. Ablations show that the primary improvement arises from the two-scale output representation, while interface-aware fine-tuning provides complementary continuity refinement. Overall, separating globally coherent structure from localized residual detail provides an efficient representation for artifact-reduced PDE operator learning.
[LG-50] Sharp Structure-Agnostic Minimax Risk for Partial Linear Models
链接: https://arxiv.org/abs/2609.07997
作者: Haichen Hu,David Simchi-Levi
类目: Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:We characterize the sharp structure-agnostic minimax risk for coefficient estimation in the partial linear model when the outcome and treatment nuisances are learned by two distinct black-box learners, which resolves the open problem in double machine learning posed by Gu (2025). For each nuisance (q\in\mu,\pi\), we characterize the available learner by an approximation-error budget (a_q) and a stochastic-error budget (s_q), with the latter controlled through localized Rademacher complexity. Writing (\mathcal E_n) for the minimax mean-squared error, we show that [\mathcal E_n\asymp1\wedge\left\frac1n+\left(a_\mu a_\pi+\min\left\a_\pi s_\mu+s_\pi^2,,a_\mu s_\pi+s_\mu^2\right\right)^2\right.] The main new ingredient is a novel lower bound for the general two-learner problem. Our proof constructs four finite-mixture testing experiments using orthogonal code functions. Across these experiments, the hidden perturbations are placed outside both learner classes, outside only the treatment learner class, outside only the outcome learner class, or inside both learner classes. These four configurations capture, respectively, the interaction between the two approximation errors, the two asymmetric interactions between one learner’s approximation error and the other learner’s learning error, and the joint estimation difficulty of learning both nuisances. Combining the four resulting lower bounds yields the displayed rate, which matches the latest upper bound in Gu (2026). Our result shows that standard double machine learning can overstate the intrinsic difficulty of target estimation and provides a target-specific principle for learner selection: approximation error and stochastic complexity must be jointly balanced across the two nuisance learners rather than optimized separately.
[LG-51] Automated Chest CT Protocol Selection via Large Language Model Derived Text Embeddings from Imaging Request Text
链接: https://arxiv.org/abs/2609.07986
作者: Zahra Hosseini,Mahan Pouromidi,Farzad Khalvati,Patrik Rogalla
类目: Machine Learning (cs.LG)
*备注:
Abstract:Purpose: Accurate CT protocol selection is critical for diagnostic quality and patient safety, yet the current process is manual, time-consuming, and prone to inconsistencies. Prior Machine Learning methods using keywords or bag-of-words lack contextual understanding and perform poorly on rare protocols. We propose a decision support system using large language model (LLM) features to recommend protocols from free-text clinical indications, capturing clinical nuance and phrasing variation for more consistent, efficient selection. Methods: In this REB-approved retrospective study, 285,123 chest CT imaging requests from a large academic medical center (2017-2024) were split into training (228,099, 80%) and held-out test (57,024, 20%) sets. Each request included procedure names, clinical indication, HIS comments, and the selected protocol. Clinical text was embedded using a fine-tuned LLM, Meta’s LLaMA-3.1-70B; these features input a logistic regression classifier predicting 18 protocol labels (e.g., PE, LDCT). Results: The pipeline achieved a weighted precision of 0.84, weighted F1-score of 0.81, and overall accuracy of 79% across 18 CT protocols. On 300 independent cases with expert consensus, the LLM reached an overall accuracy of 80% versus 83% for radiologists, with no significant difference (p = 0.263). Performance was comparable across most classes, with the LLM exceeding radiologists for some challenging categories, and entropy analyses indicated more balanced protocol use, suggesting reduced variability. Conclusion: An LLM-based recommendation system can leverage general knowledge from a large natural-text corpus to accurately assign chest CT protocols from free-text imaging requests, and may serve as a viable foundation for protocol recommendation tools where inputs require language understanding. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2609.07986 [cs.LG] (or arXiv:2609.07986v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.07986 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Farzad Khalvati [view email] [v1] Mon, 7 Sep 2026 21:05:36 UTC (381 KB)
[LG-52] Solving the Elastic Wave Equation with Physics-Informed Neural Networks: A Robust and Critical Assessment
链接: https://arxiv.org/abs/2609.07983
作者: Davide Staub,Ben Moseley
类目: Machine Learning (cs.LG)
*备注: Comments: 154 pages, 44 figures. Based on the Master’s thesis of Davide Staub, ETH Zurich, March 2024. Original thesis: this https URL
Abstract:Physics-Informed Neural Networks (PINNs) have recently emerged as a promising approach for solving Partial Differential Equations (PDEs), offering a meshfree alternative that integrates physical principles into the learning process. This presents a new paradigm compared to traditional discretization methods and purely data-driven machine learning techniques. While promising, PINNs are not a panacea; they inherit challenges such as spectral bias and unstable convergence. Moreover, their potential in seismology remains largely unexplored. In this work, we provide a robust and critical assessment of PINNs for solving the elastic wave equation in seismology. We investigate the performance of PINNs on problems with varying degrees of complexity across various seismic sources and parameter models, from constant to highly heterogeneous settings. A pivotal aspect of our work involves investigating whether embedding physical principles directly into the network architecture enhances convergence and accuracy. We test an extensive range of neural architecture designs, from unrestricted, uninformed PINNs to highly specialized ones. We find that integrating an understanding of wave physics into the network design significantly improves accuracy. For instance, introducing a custom wavelet or plane wave layer, coupled with encoder and decoder layers, consistently yields a relative L_2 error approximately half that of the standard PINN, as evidenced across numerous experiments. We further demonstrate that this novel architecture enhances accuracy when applied to the acoustic wave equation, underlying the versatility of our network. Another key contribution of our research is the successful conditioning of PINNs on seismic source locations. This signifies a considerable advancement towards rapid seismic hazard detection and seismic analysis.
[LG-53] Semi-Supervised Learning under Spatially Biased Sampling
链接: https://arxiv.org/abs/2609.07982
作者: Bright Wiredu Nuakoh,Francky Fouedjio,Stephen Bradshaw,Yaw Kwaafo Awuah-Mensah,Wei Hong Tan,Emet Arya,Ebenezer Afrifa-Yamoah
类目: Machine Learning (cs.LG)
*备注: 26 pages, 9 figures, 9 tables. Supplementary material included as an ancillary file
Abstract:Standard semi-supervised learning (SSL) typically relies on labelled and unlabelled data sharing a common marginal distribution. This assumption is often violated by biased spatial sampling mechanism, when labels are collected under spatially biased or preferential site selection. We treat this marginal mismatch, spatial autocorrelation, and spatial non-stationarity as three distinct mechanisms, varied independently via a labelled-sampling concentration parameter, a spatial length scale, and a non-stationarity strength parameter, and ask how mismatch degrades SSL, whether the cluster and manifold assumptions survive it, and how the resulting failure can be diagnosed. Using a controlled synthetic framework alongside PovertyMap-WILDS, California housing, socio-economic and US air quality monitoring datasets, we systematically vary the degree of mismatch while accounting for spatial autocorrelation and non-stationarity. Through a series of analyses including a segmented-regression changepoint, we show that in the synthetic generator, SSL performance does not degrade gradually but instead exhibits a threshold-like breakdown between approximately 0.71 and 0.77 once distribution mismatch becomes sufficiently severe. We further demonstrate that spatial non-stationarity contributes to performance loss independently of marginal mismatch and that models become increasingly overconfident outside the regions where labels are available. To support practical deployment, we evaluate several distribution-divergence measures as indicators of reliability and introduce a kernel-weighted local divergence metric that provides a more stable estimate of spatial mismatch than a naïve localised approach. These findings provide empirical evidence and diagnostic tools for better documenting the risk of incorporating unlabelled spatial data into semi-supervised learning workflows.
[LG-54] Clean Accuracy Does Not Guarantee Provenance Robustness: A Prospective Codec-Stress Evaluation of Audio Attribution
链接: https://arxiv.org/abs/2609.07981
作者: Gang Shi(Independent Researcher)
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注: 13 pages, 5 figures
Abstract:Audio provenance attribution - which system produced a synthetic utterance - is reported at near-ceiling accuracy on clean benchmarks, yet audio reaching an analyst has usually been transcoded. We report a prospectively registered measurement of closed-set attribution after single-stage codec transport, with the analysis region fixed from fidelity metadata before any attribution model was trained. On two corpora, in-support losses reach 53.5 [43.5, 63.6] and 70.3 [63.0, 77.5] Macro-F1 points for WavLM-Base+, and 61.0 [56.8, 65.1] and 49.8 [41.6, 57.9] for W2V2-BERT 2.0, under simultaneous component-level bands. Degradation is strongly condition- and representation-dependent: within one in-support grid WavLM losses run from -0.4 to +53.5 points, and the two encoders differ beyond a prespecified +/-5-point margin at six of twelve conditions. A clean-qualified ECAPA-TDNN and a Proxy-Anchor head degrade comparably, so the effect is not confined to one representation family or a weak linear head. The registered matched-fidelity comparison was not estimable on this grid, and waveform and perceptual measures order the conditions differently: MP3 at 8 kbit/s ranks mid-grid on SI-SDR but last on PESQ-WB while causing the largest loss. For the tested tasks, corpora, representations and codec grid, a clean accuracy figure does not by itself characterise deployment robustness.
[LG-55] Heat Field Signatures: From Point Clouds to Smooth Geometry
链接: https://arxiv.org/abs/2609.07975
作者: Yuanqing Wang,Yapeng Tian,Baris Coskunuzer
类目: Machine Learning (cs.LG); Differential Geometry (math.DG)
*备注: 23 pages, 2 figures
Abstract:Bringing multiscale geometric analysis directly to irregular point clouds remains difficult: quantities such as local dimension, anisotropy, density variation, and geometric transitions are typically estimated through explicit neighborhood, manifold, or graph constructions, or left for neural networks to infer from coordinates. We introduce Heat Field Signatures (HFS), which lift a point cloud to a multiscale family of smooth ambient heat fields, providing a direct interface from discrete samples to geometric analysis. From this field, HFS computes closed-form global and local signatures directly from pairwise distances, capturing heat concentration, intrinsic dimension, anisotropy, and scale transitions. We further introduce the Heat Dimension Spectrum (HDS), a compact summary of multiscale geometric composition. HFS can be used as a closed-form descriptor, a lightweight learned representation, or a geometric feature channel for neural point-cloud models. Across synthetic and real-world benchmarks spanning subcellular, neuronal, tree, and protein data, HFS outperforms strong point-cloud and multiparameter-persistence baselines while substantially reducing end-to-end cost. On SCOP protein-fold classification, HFS improves over the strongest deep baseline by nearly 24 percentage points using coordinates alone, while standalone HFS representations are exactly rotation-invariant by construction. More broadly, HFS turns a classical heat field into a practical interface for multiscale geometric analysis in modern point-cloud learning. Comments: 23 pages, 2 figures Subjects: Machine Learning (cs.LG); Differential Geometry (math.DG) MSC classes: 53Z50, 68T05 ACMclasses: I.5.1; I.2.6 Cite as: arXiv:2609.07975 [cs.LG] (or arXiv:2609.07975v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.07975 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-56] A Sub-4 Approximation for Fair k-Means
链接: https://arxiv.org/abs/2609.07974
作者: Kangke Cheng,Guanlin Mo,Shihong Song,Hu Ding
类目: Computational Geometry (cs.CG); Machine Learning (cs.LG)
*备注:
Abstract:Fairness in clustering has attracted sustained research interest, motivated by the need to ensure equitable representation of protected groups in machine learning applications. We study fair k -means clustering in Euclidean space, where the proportion of each protected group in every cluster must lie within specified lower and upper bounds. These constraints make it challenging to determine both cluster centers and point assignments. We propose an approximation algorithm that combines a linear programming relaxation with geometric transformations of the input to construct candidate center sets. Given a \rho -approximate algorithm for weighted k -means and any \epsilon0 , our algorithm returns a fractional solution whose cost is at most 1+(3-1/\Gamma)\rho+O(\epsilon) times the optimal integral fair cost, where \Gamma\approx6.357 is an upper bound on the integrality gap of the standard Euclidean k -means LP. With a PTAS as the subroutine, the approximation ratio becomes 3.8427+O(\epsilon) , improving the previous factor of 5+O(\epsilon) to below 4 . The solution satisfies all fairness constraints exactly and can be rounded to an integral assignment with a bounded additive violation of fairness and no increase in cost. The same approximation guarantee extends to the k -sparse Wasserstein barycenter problem.
[LG-57] MetaKV: Adaptive KV Cache Compression for Constrained LLM Inference
链接: https://arxiv.org/abs/2609.07966
作者: Michael Wang,Keith Li,Roozbeh Bostandoost
类目: Machine Learning (cs.LG)
*备注:
Abstract:Key–value (KV) cache compression is an effective way to reduce the memory overhead of large language model (LLM) inference, particularly for long-context workloads. However, existing compression methods make different trade-offs among accuracy, inference latency, and peak KV cache memory utilization, making a single fixed configuration unsuitable across different prompts and resource constraints. We introduce MetaKV, an adaptive framework that selects a KV cache compression configuration for each input prompt based on user-specified latency and peak memory budgets. MetaKV uses lightweight prediction models to estimate the end-to-end latency, peak memory, and probability of a correct response for each candidate configuration, and selects the configuration that best satisfies the latency-memory constraints while preserving accuracy. We evaluate MetaKV across ten configurations from three representative KV cache compression methods, KVQuant, H _2 O, and RocketKV, together with an uncompressed FP16 configuration, on four datasets covering mathematics, science, commonsense reasoning, and reading comprehension. Across a wide range of latency and peak memory constraints, MetaKV consistently outperforms the best static configuration, improving constrained success rate (CSR), the fraction of prompts answered correctly while satisfying both constraints, by approximately 0.07 on average and up to 0.135. These results demonstrate the benefit of adapting KV cache compression to individual prompts and latency-memory constraints. Code is available at this https URL
[LG-58] Streaming Hierarchical Inference with Tabular Foundation Models KDD ECML
链接: https://arxiv.org/abs/2609.07956
作者: Vitor Crista,Afonso Lourenço,Diogo Martinho,Goreti Marreiros
类目: Machine Learning (cs.LG)
*备注: Streaming Continual Learning ECML PKDD Workshop 2026
Abstract:Tabular Foundation Models (TFMs) have recently demonstrated strong predictive performance through in-context learning, but their deployment in high-throughput data streams remains challenging due to communication overhead and latency. We propose \textitHINT, a hierarchical inference framework that combines edge-based retrieval with cloud-based TFM inference. A graph-based approximate nearest neighbor memory maintained over a sliding window provides local predictions and uncertainty estimates, allowing confident samples to be processed locally while uncertain instances are selectively offloaded, together with their retrieved context, to a cloud-hosted TFM. The framework exposes an offloading threshold and a neighborhood retrieval policy that can be varied to balance predictive performance and communication cost. Experiments show \textitHINT consistently identifies favorable trade-offs.
[LG-59] Structured Extrema Errors in Classical Surrogates for Viscous Burgers: A Physics-Consistent Interpretation
链接: https://arxiv.org/abs/2609.07952
作者: Youssef Oubari
类目: Machine Learning (cs.LG); Fluid Dynamics (physics.flu-dyn)
*备注: 21 pages, 13 figures, 13 tables
Abstract:We study the local errors of classical machine-learning surrogate models, which approximate the time evolution of the one-dimensional viscous Burgers equation. Four models are compared on the same prediction task, using the spatial grid values directly: radial basis function (RBF) kernel ridge regression (KRR), linear Ridge, ExtraTrees, and Random Forests. Across all four models, the one-step residual, defined here as the true value minus the predicted value at each grid point, forms clear curved branches near predicted maxima and minima. A more detailed analysis of KRR shows that these errors are much more strongly related to the second spatial derivative, which measures local curvature, than to the first spatial derivative. Near a smooth extremum, predicted value and curvature form a local two-branch fold. Under our local curvature-based model of the residual, this fold predicts a leading-order near-parabolic relation between predicted value and residual. This geometric result motivates a direct test of the Burgers advection (transport) and diffusion (smoothing) terms. For KRR and Ridge, regression tests on held-out trajectories, a control that breaks the spatial alignment of the diffusion term, and a spectral test of high-frequency content are consistent with insufficient viscous smoothing at moderate and high viscosity. In this case, the surrogate retains more small-scale structure than the true future state. The same physical explanation is much weaker for the tree models. Finally, a correction that uses only predicted quantities reduces both one-step error and error during recursive rollout, where each prediction is used as the next input.
[LG-60] InfluenceField: A Differentiable Field with Interventionally Identifiable Causal Structure for Multimodal World Modeling
链接: https://arxiv.org/abs/2609.07874
作者: Zihao Yang,Zijia Wang,Zhiqiu Huang
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 22 pages, 3 figures
Abstract:Multimodal large language models often capture visual-linguistic correlations but struggle to predict how local visual interventions propagate and affect downstream answers. We introduce InfluenceField, an intervention-aware latent field inserted between the visual encoder and language decoder. It lifts patch features into a continuous spatial representation, propagates directed influence over multiple steps, and predicts local intervention effects through a shared transition operator. Training jointly optimizes language modeling, cross-environment invariance, counterfactual rollout supervision, and structural regularization. For a nonlinear finite-basis population model, we show that target-aligned interventional supervision, together with a one-step separation condition on the transition, restricts admissible representations to within-location reparameterizations, so that the directed dependency graph of the full transition is recovered exactly. A linear specialization gives an exact partial-coverage characterization and a finite-loss stability bound, and the field analysis derives the spatial profile of coefficient interventions together with a shared-channel calibration result. On CausalVQA, InfluenceField improves overall accuracy over its backbone by 13.1 percentage points, with the largest gains on the planning and hypothetical categories. Capacity-matched baselines and structural controls attribute the gains in robustness and factual-counterfactual consistency to the causal objectives rather than to added capacity.
[LG-61] he OCUDU dApp Platform: An Open Runtime and E3 Interface for Real-Time AI-RAN
链接: https://arxiv.org/abs/2609.07843
作者: Timothy O’Shea,Matthew Pennybacker,Andriy Kharchenko
类目: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注: 17 pages, 13 figures, 9 tables. Preview accompanying the OCUDU dApp platform preview release (OCUDU AI-RAN WG2): this https URL
Abstract:Machine learning has shown its largest gains in the band below 10 ms inside a 3GPP new radio (NR) 5G distributed unit (DU): link adaptation, per-slot scheduling, channel estimation, and the receiver itself. No open platform has let independently built software run there. Prior dApp frameworks reached the band only as external observers of an export stream. This paper is a guided introduction to the OCUDU dApp platform, an open runtime and E3 interface under which signed AI-RAN applications execute inside a production DU under three timing contracts: resident on the GPU receive chain (Class A), inside the scheduler’s 100 us admitted deadline (Class B), or as never-blocking observers whose results the scheduler consumes (Class C). The conventional path is never displaced, and every authority is typed, validated, and operator-bounded. The paper explains how the runtime, the embedded E3 agent, and the three public repositories fit together; shows a dApp’s source, its signed package, and its lifecycle state machine; defines the contracts a module is written against; and shows how one management surface serves a Python script, an operator’s console, and an LLM agent. On a GB10 gNB with attached handsets, dApps of all three classes, including an out-of-tree neural equalizer, ran together on a live cell without a single fallback, and equalizer variants were compared over the air by lifecycle operations alone. Every measured checkpoint is reported with its conditions and its gaps. Platform, SDK, and a zero-hardware quickstart are public under BSD-3-Clause-Clear as a preview release of the OCUDU AI-RAN Working Group 2, inviting feedback, new use cases, and independent vetting ahead of upstreaming into the OCUDU mainline.
[LG-62] Latent-MoE: Domain-Aware Mixture-of-Experts for PDEs with Multi-Regime Physics
链接: https://arxiv.org/abs/2609.07814
作者: Hanwen Wang,Paris Perdikaris
类目: Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
*备注: Initial submission
Abstract:Physics-informed neural networks (PINNs) struggle on PDEs whose governing physics varies across the domain. We trace this to a structural property of standard coordinate networks: their neural tangent kernel (NTK) is translation-variant and lets training points of large coordinate magnitude disproportionately influence predictions elsewhere, producing long-range coupling and gradient conflict during training. We show analytically and empirically that mixture-of-experts (MoE) architectures with centered, compact-support routers yield a uniformly banded NTK whose kernel-regression weights decay exponentially with distance, localizing the learning. Building on this, we propose \emphLatent-MoE, which interleaves domain-aware MoE blocks within a shared backbone. Unlike FB-PINNs or X-PINNs, which rigidly partition both the domain and the parameters so that the parameters on different subdomains are updated independently, Latent-MoE is designed to preserve the localization benefit of domain-aware routing while allowing capacity to flow across regions through the shared backbone. On standard homogeneous-physics benchmarks Latent-MoE is competitive with established baselines; on benchmarks with multi-stage time-variable physics, where global models and rigid domain decompositions both fall into spurious solutions, it improves over them by more than an order of magnitude, with markedly reduced gradient conflict during training.
[LG-63] Decomposition-Guided Diffusion Language Models for Inertial Confinement Fusion Prediction
链接: https://arxiv.org/abs/2609.07756
作者: Xiang Zhang,Varchas Gopalaswamy,Rahman Ejaz,Riccardo Betti,Dongfang Liu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Inertial confinement fusion (ICF) is a leading pathway toward clean energy, but each shot at the National Ignition Facility costs on the order of one million dollars, making accurate AI surrogates a high-value target. We study exogenous-driven ICF waveform prediction, where a 512-step neutron-rate diagnostic must be inferred directly from a laser pulse and target design parameters, with no historical response observed. The regime stresses standard time-series predictors with temporal sparsity (picosecond peak in a nanosecond window), input-output scale mismatch (under 300 real shots), and peak sensitivity (picosecond timing). We propose ICF-DLM, to our knowledge the first LM-based ICF predictor, combining (i) a physics-typed decomposition into yield Y_DT , peak timing t_\mathrmpeak , and local waveform w_\mathrmlocal ; (ii) bidirectional denoising that defers commitment to peak location; and (iii) a physics-driven PPO reward re-injecting metric structure across numeric tokens. On ICFBench (50K simulations + 232 experimental shots), ICF-DLM cuts peak-timing error from 11.6 to 9.2 steps over a matched autoregressive LLaMA-3-8B and outperforms classical sequence models and LLM-based time-series predictors. Beyond ICF, the recipe shows potential to address science domains with low data and sparse events.
[LG-64] A Theoretical Analysis of Generalization Dynamics in Neural Networks under Gradient Descent with Weight Decay
链接: https://arxiv.org/abs/2609.07755
作者: Yuqing Wang,Ioannis G. Kevrekidis,Mikhail Belkin
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:Understanding generalization remains a central challenge in machine learning because it requires jointly considering data, architecture, and training dynamics. In this paper, we develop a theoretical framework that characterizes how these factors jointly shape generalization performance throughout training. More precisely, we study a broad class of neural networks trained under the \ell^2 loss by gradient descent (GD) with weight decay, and prove the convergence of GD to a neighbourhood of the global minimizers of the empirical loss. By partitioning the space based on the input data, we then decompose the population error into data error, optimization error, and prediction variation error, and bound them separately. In particular, for the prediction variation error, which measures the oscillations of the learned function, we propose (local) approximate homogeneity and derive explicit cellwise and layerwise bounds for its evolution along the training trajectory. These bounds yield two important implications: a necessary condition of improved generalization explains differences in layerwise generalization behavior; a sufficient condition describes delayed generalization and provides a theoretical characterization of grokking.
[LG-65] Local gradient neural operator
链接: https://arxiv.org/abs/2609.07752
作者: Baiming Zhang,Jinsong Tang,Ying Xu,Lihua Chen,Shiying Xiong
类目: Machine Learning (cs.LG); Mathematical Physics (math-ph)
*备注: 29 pages, 11 figures. Code available at this https URL
Abstract:Field temporal prediction and source identification constitute canonical problems in dynamical systems. Conventional approaches to these problems depend on a thorough understanding of the governing partial differential equations (PDEs). Recently, deep learning, as represented by neural operators, has provided a data-driven paradigm for addressing such tasks. However, most existing global neural operators for PDEs require large training datasets and many learnable parameters, with limited interpretability and generalization. We propose the local gradient neural operator (LGNO) as a lightweight and interpretable alternative for field temporal evolution prediction and source identification in typical mechanical problems. The method builds on priors from nonlinear gradient discretization and uses multilayer perceptron convolutional layers to learn translation-invariant local kernels that resemble discrete stencils. A zero consistent stencil factorization separates coefficient learning from field reconstruction, rendering the learned operators more transparent. For problems with symmetries, network folding shares equivalent components and reduces parameter counts. We evaluate the method on PDE benchmarks covering linear and nonlinear, static and dynamic, and low and high dimensional cases. Results show that LGNO maintains accuracy, parameter efficiency, and rollout stability across these tasks, and further exhibits wide applicability to mechanical problems including diffusion, flow, and quantum phenomena.
[LG-66] Guiding Worker Self-Selection in Crowdsourcing Contests: An LLM -Augmented Algorithmic Approach
链接: https://arxiv.org/abs/2609.07749
作者: Nguyen Thach,Hau Chan,David Parkes,Karim Lakhani
类目: Machine Learning (cs.LG); Computer Science and Game Theory (cs.GT)
*备注: Accepted to HCOMP 2026
Abstract:Crowdsourcing platforms coordinate large pools of online workers who strategically choose which contests to enter and how much effort to invest. This self-selection can leave important contests with too few participants or too little effort, while workers may regret entering contests that leave them worse off than available alternatives. We study how platforms can recommend contests to workers using self-selection in Tullock contests (SSTC), a two-stage model in which workers first choose contests and then compete within them. We introduce GRAF, a greedy polynomial-time framework that constructs self-selection outcomes by ordering workers according to a score vector, with guarantees of zero worker regret and platform optimality in special cases of SSTC. Because effective orderings are difficult to design under worker heterogeneity, we propose LLMScore, an LLM-driven evolutionary framework that automatically designs GRAF’s scoring algorithm. LLMScore addresses two challenges: jointly optimizing platform utility and worker satisfaction, and evaluating worker regret when exact computation is intractable. Trained only on small instances of one setting, it transfers to larger and structurally different settings; moreover, its output is human-readable code that platform operators can inspect and modify. Across 1,000 synthetic instances spanning four settings, GRAF with LLMScore consistently achieves high-quality, often near-optimal, outcomes with low worker regret, benefiting both platforms and workers.
[LG-67] Scalability Analysis of Distributed Kolmogorov-Arnold Network Training on High-Performance Computing Systems
链接: https://arxiv.org/abs/2609.07740
作者: Guangneng Chen,David Garcia Selfa,Pablo Quesada Barriuso
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Performance (cs.PF)
*备注: 19 pages, 7 figures, 4 tables
Abstract:Kolmogorov-Arnold Networks (KANs) replace the fixed activation functions and linear weights of Multi-Layer Perceptrons (MLPs) with learnable univariate functions on network edges, offering improved interpretability and, in some settings, competitive parameter efficiency. While the approximation properties of KANs have received considerable attention, their behavior under distributed, multi-GPU training has not been systematically characterized. This paper presents an empirical scalability study of data-parallel KAN training on multi-node, multi-GPU high-performance computing (HPC) infrastructure, evaluated along four dimensions: strong scaling, weak scaling, communication overhead, and model-size scaling. Experiments were conducted on the FinisTerrae III supercomputer using up to 8 NVIDIA A100 GPUs across 4 nodes with PyTorch Distributed Data Parallel (DDP). KAN training reaches 74.7% parallel efficiency at 8 GPUs with a 5.97x speedup, consistent with conventional deep learning workloads. Weak scaling shows an initial single-to-multi-GPU throughput drop followed by strong stability. Communication overhead follows a non-monotonic pattern (1.3%-6.1%), driven primarily by All-Reduce algorithm selection and inter-node latency rather than KAN’s edge-wise gradient structure. The parameter-to-memory ratio improves with model size even as training time scales unfavorably. These results indicate that operator-level and data-parallel optimizations for KAN are complementary. We provide deployment guidelines for GPU topology and model-size selection, and discuss the limitations of a synthetic-regression evaluation. Comments: 19 pages, 7 figures, 4 tables Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Performance (cs.PF) ACMclasses: I.2.6; C.1.4; C.2.4 Cite as: arXiv:2609.07740 [cs.DC] (or arXiv:2609.07740v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2609.07740 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-68] Attributing Cohens d: Training Data Attribution for Disease-Related Effects in Normative Age Biomarkers
链接: https://arxiv.org/abs/2609.07729
作者: Jakob Snel,Marc-Andre Schulz
类目: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
*备注:
Abstract:Normative age models are trained to predict chronological age in a nominally healthy cohort. Applied to patients, they deviate, and the gap between predicted and chronological age is read as disease risk. Here, we attribute the disease-related effect size of the age gap directly to individual training samples, rather than using a prediction-level loss as the attribution target. For Cohen’s d , the resulting closed-form influence functional, validated against leave-one-out retraining, ranks training samples by their effect on held-out case-control separation. Across four diseases and two biomarker modalities in UK Biobank, removing the 10% most influential training samples raises held-out disease-related effect size in every seed. It more than doubles the metabolomic-age effect for type-2 diabetes and raises the brain-age effect for multiple sclerosis by roughly a third. Random removal leaves effect size flat even at 50% removal, confirming the gain comes from which samples are removed, not how many. Flagged subjects carry subclinical cardiometabolic burden that diagnosis-based exclusion misses, on markers the model never sees. For type-2 diabetes, where the method gains most, the marker recovered is HbA1c, the standard measure of blood sugar control. We release pyinfluence, our influence-function package, for reproducibility and reuse.
[LG-69] ParetoTransport: Generative Optimization by Mass Transport Toward The Pareto Front
链接: https://arxiv.org/abs/2609.07706
作者: Stephanie Holly,Sepp Hochreiter,Werner Zellinger
类目: Machine Learning (cs.LG)
*备注:
Abstract:Offline multi-objective optimization requires not only moving the objective vectors of candidate designs toward the Pareto front, but also distributing them effectively along it. Generative methods have recently emerged as a natural approach because they learn a distribution over feasible designs while allowing generation to be steered toward promising designs. Existing methods, however, largely retain classical sample-wise guidance strategies, leaving the distribution-level modeling capability of generative methods underused. We propose ParetoTransport, a training-free guidance method for pre-trained flow-matching models that explicitly specifies and refines a population-level distribution in objective space. ParetoTransport guides a flow-matching sampler to iteratively transport the empirical offline distribution toward the Pareto front, with Wasserstein matching to intermediate proxy distributions. This directly controls distributional displacement and mass allocation along the front. We establish a convergence result and demonstrate state-of-the-art performance on standard offline MOO benchmarks, extending recent evaluations beyond hypervolume to generational distance, inverted generational distance, and Wasserstein distance.
[LG-70] MpSub: A Momentum p-Dimensional Subspace Trust-Region Method for Derivative-Free Fine-Tuning of Large Language Models
链接: https://arxiv.org/abs/2609.07666
作者: Yuyang Wang,Haoyu Yao,Pengcheng Xie
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 16 pages, 2 figures, 2 tables
Abstract:Full-parameter fine-tuning of large language models has substantial memory costs because backpropagation stores activations and gradients. Zeroth-order optimization avoids this by estimating update directions from loss evaluations, but existing methods require tuning a sensitive learning rate for each model and task. We propose the momentum p -dimensional subspace trust-region method (MpSub). At each iteration, MpSub searches within a p -dimensional subspace: one direction preserves historical momentum from the most recent accepted step, while the remaining directions explore via fresh random sampling. The subspace gradient is estimated by central differences, a trial step is computed from a linear trust-region model, and the trust-region radius adapts according to the agreement between predicted and observed loss reduction, eliminating the learning rate. For LLM fine-tuning, evaluations within an iteration share a minibatch, and directions are regenerated in place from seeds, using forward passes alone. For smooth deterministic objectives under unorthogonalized Gaussian directions, we bound the finite-difference error, quantify gradient energy captured by the subspace, and prove that \lim_k\to\infty |\nabla f(x_k)|_2 = 0 almost surely under a safeguarded radius update. Under a matched budget of 8,400 training-objective forward passes, we fine-tune OPT-125M and OPT-350M on CommitmentBank. With the same preset parameters at both model sizes, MpSub attains mean test accuracies of 0.673 and 0.690 over three seeds, matching tuned MeZO (0.685) without any learning-rate search.
[LG-71] Forecasting the Winner of a Live Tennis Match
链接: https://arxiv.org/abs/2609.07617
作者: Charles Xie,Aneesh Muppidi
类目: Machine Learning (cs.LG)
*备注: 9 pages, 4 figures, 6 tables
Abstract:With the rise of live sports betting in recent years, tennis forecasting has expanded from pre-match prediction to models that update win probabilities as a match unfolds. A central challenge in creating such a model is the constant need for models to adapt to score and performance changes. This study examines how pre-match and live information can be most effectively integrated into a model to produce accurate win-probability estimates. The analysis uses 8,222 Grand Slam matches containing a total of 1,505,355 points. Five models were evaluated using a chronological split, with matches from 2011-2021 used for training, 2022 for validation, and 2023-2024 for testing. Trace, a hybrid model, achieved accuracies of 76.06%, 82.15%, and 88.34% at 25%, 50%, and 75% match progress, suggesting that hybrid modeling is a practical approach to live tennis forecasting.
[LG-72] ranslation of Black-Box Clinical Prediction Models into Standalone Transparent Nomograms: Temporal External Validation in Heart Transplantation
链接: https://arxiv.org/abs/2609.07610
作者: Henry Pigot,Paulo J. G. Lisboa,Sandra Ortega-Martorell,Ivan Olier,Joseph Mahon,Johan Nilsson
类目: Machine Learning (cs.LG); Applications (stat.AP)
*备注: 33 pages of main text with 4 figures and 3 tables; supplemental information (supplementary methods, Figures S1-S4, Tables S1-S26) appended, 86 pages total
Abstract:We convert black-box clinical prediction models for tabular data into standalone nomograms that can be audited term by term. PRiSM (Partial Responses in Structured Models) takes the shape of each effect and interaction from the source model, not merely which variables mattered, and lets the outcome select and weight them. We tested this in 50,356 heart transplant recipients, with validation in a later era than training. Nomograms from all 5 source models - a public clinical risk score, logistic regression, neural networks, random forests and extreme gradient boosting - met a prespecified noninferiority criterion for discrimination before any further simplification, and generally preserved calibration and clinical net benefit. Those from the 3 machine-learning models showed no detectable difference in discrimination from de novo generalized additive and explainable boosting models, exceeded neural additive models, and carried fewer terms than the explainable boosting model. PRiSM is released as an open-source Python package.
[LG-73] CLUES-WEASEL: No additional clues required to choose your time series clustering algorithm
链接: https://arxiv.org/abs/2609.07606
作者: Johann Faouzi
类目: Machine Learning (cs.LG)
*备注:
Abstract:Time series data is very common in many real-world applications and in numerous domains, with increasing interest for automated information extraction using machine learning. One of these subfields is time series clustering, which consists in identifying clusters among a set of time series in an unsupervised fashion. Most time series clustering algorithms suffer from the same balancing act: they trade clustering performance for faster runtimes or vice versa. We present a novel time series clustering algorithm that we call CLUES-WEASEL, which stands for CLustering with the UnsupervisEd Second version of Word ExtrAction for time SEries cLassification. CLUES-WEASEL extracts features using the unsupervised version of the transformation step of WEASEL 2.0, which is a time series classification algorithm, then reduces these features using principal component analysis, and finally performs clustering with the k -means algorithm using these reduced extracted features. Through extensive experiments, we prove that CLUES-WEASEL is significantly better than any other existing time series clustering algorithm while being (much) faster than any state-of-the-art one. We also show that the architecture of CLUES-WEASEL can work well with other time series feature extraction algorithms. Our findings highlight the relevance of CLUES-WEASEL for time series clustering.
[LG-74] No-Regret Mixing of LRU and LFU with Optimal Switching Cost
链接: https://arxiv.org/abs/2609.07566
作者: Younes Ben Mazziane,Xinying Zou
类目: Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注:
Abstract:Caching systems often rely on simple eviction policies such as Least Recently Used (LRU) and Least Frequently Used (LFU), which perform well in complementary request regimes. Recent policies such as LeCar and Cacheus combine LRU and LFU using ideas from the experts problem in online learning. Specifically, upon a miss, they randomize between the two eviction rules using probabilities derived from scores updated by tracking the history of past evictions. While these policies exhibit strong empirical performance, it remains unclear whether they are guaranteed, on every request sequence, to perform asymptotically as well as the better of LRU and LFU, i.e., whether they achieve sublinear regret with respect to this benchmark. We first show that LeCar suffers linear regret against an oblivious adversary, even with unbounded history. We then propose H-MC, a Hedge-based mixture of virtual LRU and LFU caches that preserves Hedge’s selection probabilities, and hence its regret guarantees, while minimizing the switching cost among all joint selection rules with these marginals.
[LG-75] CoRL: Co-Evolutionary Reinforcement Learning for Adaptive Indirect Prompt-Injection Attacks and Defenses
链接: https://arxiv.org/abs/2609.07529
作者: Boyang Zhang,Qingxin Xiao,Lingwei Dang,Qingyao Wu
类目: Machine Learning (cs.LG)
*备注: 31 pages, 7 figures
Abstract:Tool-augmented language agents are vulnerable to indirect prompt injection (IPI). Unlike direct prompt injection, IPI hides adversarial instructions in untrusted tool outputs and can covertly alter the execution of a legitimate task. Defenses trained on fixed attacks may fail as an attacker changes its strategy, injection site, and payload. To address this problem, we formulate adaptive IPI as an asymmetric, partially observable, general-sum Markov game: a multi-turn attacker adapts payloads at reached tool-return sites from the public trajectory, while a tool-using defender must block the injected objective and complete the user task. We propose CoRL, a verifier-grounded co-evolution and repair framework with three stages: Attacker SFT initializes multi-turn attacks from successful trajectories; bilateral Co-PPO jointly trains both agents with role-specific rewards and historical opponent populations; and Defender SFT consolidates verifier-accepted teacher repairs for population-discovered failures. Across 1,514 clean, fixed-template, and adaptive executions per defender, CoRL reduces overall ASR by 38.5 points to 0.0% and raises utility by 13.1 points to 76.3%. Stage-wise and controlled ablations show positive contributions from online Co-PPO and population-mined repair, while external-benchmark evaluation indicates transfer in attack resistance. The defender balances safety and task utility under the evaluated attacks, while the retained attackers provide candidates for adaptive red-team evaluation.
[LG-76] Statistical versus machine learning-based spatial interpolation of post-processed ensemble weather forecasts
链接: https://arxiv.org/abs/2609.07512
作者: Mária Lakatos
类目: Machine Learning (cs.LG); Applications (stat.AP); Machine Learning (stat.ML)
*备注: 25 pages, 3 figures, 15 tables
Abstract:Statistical post-processing improves ensemble weather forecasts, but generating calibrated predictions at locations without observations remains challenging. This study compares statistical and machine-learning-based methods for post-processing ECMWF 2-m temperature and 10-m wind speed forecasts at observed and unobserved stations in Germany. We consider EMOS-based approaches, distributional regression networks, Transformers, and graph neural networks under both limited and extended predictor settings. For temperature, we also investigate linear forecast combinations and propose an altitude-aware linear pool (ALP). The results show that post-processing improves upon the raw ensemble in most settings, but no single method performs best across all variables, station groups, and evaluation metrics. The proposed ALP provides a small but significant improvement over the standard linear pool at unobserved locations.
[LG-77] Improving Multivariate Time Series Classification with Class-Wise Training and Model Aggregation
链接: https://arxiv.org/abs/2609.07493
作者: Mouhamadou Mansour Lo,Gildas Morvan,Mathieu Rossi,Fabrice Morganti,David Mercier
类目: Machine Learning (cs.LG)
*备注: 17th International Conference on Scalable Uncertainty Management (SUM2026)
Abstract:In this paper, we propose a class-wise dimension (channel) selection framework for Multivariate Time Series Classification (MTSC). Rather than applying a single global dimension selection process, the proposed approach independently identifies informative dimensions for each class. A dedicated learning process is subsequently performed for each class, followed by a fusion stage for final prediction. The objective is to improve the generation of discriminative feature representations while reducing the influence of noisy or non-informative dimensions. The proposed framework is evaluated using MiniRocket, a random kernel-based baseline method. Experimental results indicate that class-wise dimension selection improves the quality of extracted representations and can enhance classification performance, particularly in high-dimensional settings. These findings suggest that incorporating class-specific information into the training process represents a promising direction for MTSC, improving robustness through consistent gains across heterogeneous datasets, and interpretability through the explicit identification of class-relevant dimensions.
[LG-78] mporal-Causal Inference for Reinforcement Learning via Automata Learning
链接: https://arxiv.org/abs/2609.07461
作者: Jan Corazza,Daniil Kaminskyi,Simon Lutz,Patrick Nossol,Hadi Partovi Aria,Zhe Xu,Daniel Neider
类目: Machine Learning (cs.LG)
*备注: 9 pages, 8 figures. Accepted for publication in the Proceedings of the 65th IEEE Conference on Decision and Control (CDC), Honolulu, Hawaii, USA, December 2026. Extended version
Abstract:We consider reinforcement learning in environments with dynamics that undergo an irreversible phase transition governed by a hidden temporal pattern. The agent observes the base state but cannot observe the phase directly. We formalize this problem as a two-phase non-Markovian decision process and introduce Temporal-Causal Inference for Reinforcement Learning (TCIRL), a framework that jointly learns a control policy and infers the hidden temporal cause of the phase transition. TCIRL maintains a hypothesis deterministic finite automaton (DFA) to track what phase is active and refines it via counterexample-driven SAT-based synthesis. We prove that the hypothesis converges almost surely to a DFA recognizing the true cause language on all attainable label sequences, yielding an optimal policy for the original non-Markovian decision process. Experiments on a genetic therapy gridworld and a traffic signal environment show that TCIRL recovers the correct cause DFA and matches the full-information baseline in both domains.
[LG-79] A Systematic Analysis of Automatic Differentiation versus Discretization-based Constraints for Physics-Informed PDE Solvers
链接: https://arxiv.org/abs/2609.07437
作者: Xing Guo,Hongwei Tang,Zewei Meng,Yidong Zhang,Shaoqiu Xiao,Feng Liu
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG); Fluid Dynamics (physics.flu-dyn)
*备注:
Abstract:Physics-informed neural networks (PINNs) represent a growing frontier in using artificial intelligence to solve partial differential equations (PDEs). Automatic differentiation (AD) plays a central role in this paradigm, which is mesh-free and replaces traditional iterative solvers with gradient-based optimization in continuous space. However, the inherent limitations of AD, particularly in handling higher-order derivatives and discontinuous solutions, pose significant challenges for complex problems. This has motivated a growing number of researchers to explore discretization-based constraints as an alternative path. Yet, the respective applicability of these two paradigms remains largely unexplored. In this work, we conduct systematic experiments across a wide spectrum of problems, from simple linear Poisson to high-Mach hypersonic flows with strong discontinuities. Through a rigorous decomposition of approximation, optimization, and truncation errors, we systematically elucidate the fundamental trade-offs and error-governing mechanisms of both paradigms, as well as two representative network architectures: multi-layer perceptron (MLP) and graph neural network (GNN). Our results reveal a consistent trend: as nonlinearity strengthens, the accuracy advantage of discretization-based constraints becomes increasingly pronounced, with smaller optimization errors compensating for the truncation errors. Moreover, the more complex the nonlinearity and boundary conditions, the greater the advantage of GNN over MLP. These insights offer a robust practical guideline for configuring neural PDE solvers in demanding engineering applications. Our source data and code are available at this https URL.
[LG-80] hink Wider: Mitigating Latent Rank Collapse in Implicit Chain-of-Thought Reasoning
链接: https://arxiv.org/abs/2609.07406
作者: Yuwen Hao,Menglin Yang
类目: Machine Learning (cs.LG)
*备注: 16 pages
Abstract:Chain-of-thought (CoT) reasoning improves the reasoning ability of large language models by introducing intermediate computation, but explicit rationales increase decoding length, latency, and context cost. Implicit CoT offers a more efficient alternative by moving intermediate reasoning into continuous latent states. However, latent reasoning can be unstable: successive latent states may become overly similar and collapse toward a shared dominant direction, reducing the diversity of the reasoning trajectory. In this work, we identify \textitlatent rank collapse and propose \textbfWIDER , a lightweight spectral regularizer for implicit CoT. During training, WIDER estimates the shared direction of each latent trajectory and penalizes projections onto this direction, encouraging latent states to span a broader representational subspace. The method is plug-and-play and leaves the backbone model, latent schedule, and inference-time decoding procedure unchanged. We further formulate this collapse as a geometric bottleneck in implicit reasoning, casting its mitigation as a training-time regularization problem rather than an inference-time decoding change. Extensive experiments show that WIDER improves matched implicit CoT baselines, while mechanistic analyses reveal higher effective rank, lower dominant-direction energy, and reduced redundancy among latent steps. These results highlight latent subspace utilization as an important factor for efficient continuous reasoning, providing a geometric perspective for analyzing and improving implicit CoT. Code is available at this https URL.
[LG-81] Inferring Urban Mobility Interactions from Aggregated Dynamics
链接: https://arxiv.org/abs/2609.07349
作者: Yi Wang,Jing Li,Jinliang Deng,Zhenghong Wang,Yizhi Zhang,Fan Zhang,Ivor W. Tsang,Yu Liu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Real-time urban governance depends not only on knowing where people are, but on how they move between places, directional flows that could be conventionally resolved by tracking individuals through space, i.e., expensive to sustain and built on traces that are highly unique and readily re-identifiable. Here we show that this directional structure need not be observed to be known: aggregated counts which cities already collect retain enough information to reconstruct the temporal evolution of origin-destination (OD) matrix. Using an uncertainty-aware physics-informed framework, we infer future OD flows from area-level counts alone across twelve mobility datasets from cities in the United States and China, reaching accuracy comparable to models that take historical OD matrices as input. Probabilistic modeling corrects the systematic underestimation of sparse, high-value corridors and yields calibrated predictions consistent with observed flows. Architectures that respect the generation-before-assignment logic of transport planning recover interactions more faithfully, indicating that location-level spatial heterogeneity should be preserved before pairwise interactions are reconstructed. Because inference requires only aggregated observations after training, recovering interactions this way reduces reliance on continuous individual-level tracking, pointing toward a more deployable and less exposure-heavy basis for real-time urban intelligence.
[LG-82] Robust Decentralized Personalized Federated Learning via Prediction-Constrained Neighborhood Collaboration
链接: https://arxiv.org/abs/2609.07312
作者: Xiao Ma,Hong Shen,Hui Tian,Wenqi Lyu,Wei Ke
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注:
Abstract:This paper proposes a robust decentralized personalized federated learning method R-DPFL, that enables clients to reduce the impact of Byzantine attacks via robust neighborhood direction estimation and history-based update trend prediction, rather than purely aggregating client models as in the existing work. In R-DPFL, each client first computes the current-round model update by aggregating the received neighborhood update vectors. It then predicts what this update should be based on its historical values and local model changes. Finally, R-DPFL computes the difference between these two quantities, adaptively clips this difference, and adds it to the local update. We prove convergence of the learning process through rigorous analysis and show that honest clients maintain stable personalized descent dynamics under Byzantine neighbor perturbations without requiring consensus among neighboring models. Extensive experiments on CIFAR-10 demonstrate that RDPFL consistently outperforms state-of-the-art decentralized and personalized federated learning baselines under heterogeneous and adversarial settings.
[LG-83] Long-Horizon Language Model Reinforcement Learning via Progressive Point Matching
链接: https://arxiv.org/abs/2609.07303
作者: Preston Fu,Kevin Frans,Oleh Rybkin,Sergey Levine,Aviral Kumar
类目: Machine Learning (cs.LG)
*备注:
Abstract:Current paradigms for training language models via reinforcement learning rely heavily on sparse outcome rewards. However, as we pursue tasks that require longer and more complicated trajectories, such strategies result in slow learning. Prior work has attempted to address this problem by rewarding partial progress; however, naive formulations are often biased and converge to suboptimal policies. We show that a simple and unbiased dense reward formulation, which we term progressive point matching, scales exponentially more efficiently to long-horizon tasks by rewarding partial progress on a segment level, both theoretically and empirically via synthetic environments. We then show how progressive point matching can be practically instantiated using a single reference trajectory per task. On extremely hard math reasoning problems, sparse outcome rewards cannot make any progress, whereas segment-level rewards enable improvements at larger test-time token budgets when measured by success rate or pass@k.
[LG-84] PCFlow: Physics-Conditioned Flow Matching for GPR B-Scan Image Synthesis
链接: https://arxiv.org/abs/2609.07300
作者: Zhijie Shen,Chenchen Fu,Xuanhao Chang,Hongtao Bai,Lili He
类目: Machine Learning (cs.LG)
*备注:
Abstract:Ground-penetrating radar (GPR) B-scan image synthesis is important for data augmentation, algorithm validation, and simulation acceleration, yet generating radargrams with both visual realism and physical consistency remains challenging. Existing learning-based generative models often emphasize visual appearance but provide limited control over response geometry. In this paper, we propose PCFlow, a physics-conditioned flow matching framework for fast GPR B-scan image synthesis. The core of PCFlow is a Maxwell-informed dense physical condition field constructed from the parameterized physical model used for electromagnetic simulation, including material properties, target geometry, propagation cues, and response-domain priors. This condition field provides an interpretable interface between physical scene parameters and radar response geometry, and guides conditional flow matching in the VAE latent space toward physically feasible generation paths. We evaluate PCFlow on a gprMax-based buried-pipeline dataset with both in-distribution and out-of-distribution test cases. Experimental results show that PCFlow generates images with more accurate response geometry and high visual fidelity, demonstrating its effectiveness for controllable and physically faithful radar image synthesis.
[LG-85] Constitutive State-Space Modeling of Path-Dependent Plasticity: A Resolution-Consistent and Parallelizable Computational Framework
链接: https://arxiv.org/abs/2609.07294
作者: Rui Barreira,Taylan Soydan,Francesco Scipione,Miguel A. Bessa,Dirk Mohr
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA); Computational Physics (physics.comp-ph)
*备注:
Abstract:Data-driven constitutive models for path-dependent plasticity are commonly formulated using nonlinear recurrent neural networks, whose sequential state evolution limits parallel training and whose predictions may depend on the discretization of the applied strain path. We introduce a Constitutive State Space (CSS) model that reformulates structured state-space dynamics as an incremental constitutive operator. The strain increment is decomposed into magnitude and direction: the loading direction drives the latent state-space system, while the increment magnitude enters the zero-order-hold discretization of its continuous-time linear recurrence. This mechanics-tailored construction guarantees stationarity under zero increments, strongly reduces sensitivity to strain-path resolution, and retains the parallel-scan structure of S5 for efficient training on long constitutive histories. The CSS and Minimal State Cell (MSC) architectures are compared for four multiaxial path-dependent material models including isotropic J2 plasticity, pressure-sensitive foam plasticity, and combined isotropic-kinematic hardening. CSS matches or exceeds the prediction accuracy of the MSC, including one order of magnitude lower validation losses for the plastically incompressible materials. Importantly, CSS maintains low errors across large changes in strain-path discretization, whereas the MSC error increases substantially when evaluated at coarser resolutions than used for training. CSS trains substantially faster and requires fewer strain-stress pairs to attain comparable or better accuracy. Analysis of the learned state further reveals latent structure consistent with the dimensionality of the underlying physical constitutive models. These results establish mechanics-tailored structured state-space dynamics as a computational framework for efficient and discretization-robust data-driven constitutive modeling.
[LG-86] Dense Structural Compression of Transformers via Gauge-Correct Channel Removal
链接: https://arxiv.org/abs/2609.07264
作者: Jed A. Duersch,Naïm Es-Sebbani,Nathanaël Haas,Zied Bouraoui
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 10 pages main text, 28 pages total including appendices
Abstract:Inference energy per token drives the cost and carbon footprint of deployed transformers. It is dominated by dense matrix products that incur fused multiply-accumulate (FMA) operations and memory traffic. To reduce these computations while retaining dense tensors for high GPU throughput, we develop a methodology from first principles to adapt structural complexity during training to maximize inference utility per unit compute. Channel penalties drive entire tensor slices to zero to enable physical removal while preserving density and the network function. The natural approach, penalizing the norm of operator components acting through each channel, is provably destabilized by gauge freedom. We resolve this pathology with GaugeLasso: additive symmetric group-lasso penalties that recover a monotone function of product-norms when the network converges to gauge balance. Our equilibrium analysis enables per-channel calibration to correctly suppress slices that under-perform in inference utility per unit compute. Under adaptive pressure, the network reorganizes into depth-dependent structural profiles that can be far smaller than the architecture required to learn the task. On polynomial long division over \mathbbF_31 , compute compresses from 148 to 255 times with perfect accuracy. On character-level language modeling, compressed models outperform the hand-designed baseline at equal FMA. On masked autoencoding, a compression trial exposes which axes were over-provisioned and which saturated, guiding a better second design. Compaction also accelerates training monotonically as the model progresses. Post-hoc pruning with the same utility ranking cannot reach these structures, showing that sustained pressure is central to discovery of efficient models. Retraining a discovered architecture recovers baseline quality on our statistical tasks, but fails on our exact algorithmic task. Comments: 10 pages main text, 28 pages total including appendices Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2609.07264 [cs.LG] (or arXiv:2609.07264v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.07264 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-87] Kolmogorov–Arnold stability for discontinuous functions
链接: https://arxiv.org/abs/2609.07240
作者: Sviatoslav V. Dzhenzher
类目: Machine Learning (cs.LG); Functional Analysis (math.FA)
*备注: 6 pages
Abstract:Here we investigate the stability of the Kolmogorov–Arnold representation theorem (KART) under adversarial reparameterisations of the hidden layer for multivariate discontinuous and unbounded functions. Our results provide a rigorous mathematical foundation for the structural robustness of modern deep learning architectures, such as Kolmogorov–Arnold Networks (KANs), under adversarial configurations.
[LG-88] Robust Decentralized Federated Distillation via Multi-Modality Knowledge Collaboration
链接: https://arxiv.org/abs/2609.07230
作者: Xiao Ma,Hong Shen,Hui Tian,Wei Ke,Wenqi Lyu
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注:
Abstract:This paper propose a robust decentralized federated distillation method that enables clients with heterogeneous models to collaborate through predictions on shared unlabeled public data. In the proposed method, each client first evaluates the received predictions in three modalities of class prediction, boundary decision, and prediction correlation. It then filters unreliable clients, assigns reliability-based weights to the retained clients, and constructs a teacher for each type of knowledge. Finally, the corresponding distillation gradients are validated using a supervised gradient computed from private data. Conflicting prediction and boundary gradients are removed, and conflicting relation gradients are suppressed before the final model update. We prove the convergence of the proposed method by showing stable local optimization for honest clients under Byzantine distillation. Particularly, we show that our method ensures a bounded Byzantine influence on both distillation gradients and individual client private gradients after cross-modality fusion, thereby enabling stable local optimization for honest clienunder Byzantine distillation. Extensive experiments on CIFAR-10 and CIFAR-100 demonstrate that the proposed method improves the prediction accuracy of heterogeneous models of clients under non-IID data and Byzantine attacks. As the booming demands of federated learning in decentralized environments such as edge computing and mission-oriented UAV collaborations, our method has a great potential for adoption of DFL in unreliable real-world scenarios where clients are exposed to receiver-specific Byzantine messages of malicious predictions.
[LG-89] Iterative Audio Separation with Mixture Consistency via MIMO Model Extension
链接: https://arxiv.org/abs/2609.07226
作者: Yukara Ikemiya,WeiHsiang Liao,Yuki Mitsufuji
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注:
Abstract:This paper proposes a general framework for stable and effective iterative audio separation with mixture consistency by extending source separation models to a multi-input multi-output (MIMO) configuration. In the field of audio separation, mixture consistency is an essential property for many applications that require accurate phase and timbral information of target sources. While iterative approaches such as diffusion models achieve perceptually superior results in speech enhancement or user-guided target source separation tasks, most existing methods focus on single-step separation with a single-input single-output (SISO) or single-input multi-output (SIMO) configuration through architectural improvements, since mixture-consistent audio separation is generally regarded as a regression problem that admits a unique solution. By extending these architectures to a MIMO configuration, we introduce iterative prediction without compromising the architectural advantages or the characteristics of mixture consistency. We conduct a comprehensive ablation study of combining the framework with discriminators and extending it to a generative model. Experimental results demonstrate significant performance improvements when applying the proposed framework to state-of-the-art separation models.
[LG-90] he Oversight Gap: What LLM Safety Monitors Miss and Why It Is Not Capability
链接: https://arxiv.org/abs/2609.07162
作者: Xin Xu
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注: 11 pages, 3 figures. Under review
Abstract:Several properties safety monitors are asked to certify, among them cross-tenant noninterference, sandbagging and evaluation awareness, are 2-safety hyperproperties, witnessed only by two executions. The standard consequence is a binary impossibility: one trace cannot decide them. We replace the binary with a measurement. A tight bound puts the balanced accuracy of any single-trace monitor at \tfrac12+\tfrac12,TV(P_0,P_1) , turning undecidability into a graded detectability frontier and defining an oversight gap: a monitor’s shortfall below it. On a leak family with closed-form TV , nine LLM monitors are optimal at TV=0 but capture little signal as TV grows; at TV=1 , where a 20-line membership check scores 100% , they average 60.9% . That shortfall is mostly not capability: naming what to check closes 61% of it while leaving the TV=0 control at chance. The same split runs through a 2\times2 factorial: an imagined second run leaves monitors at chance ( 50.4% ) while the same rule on an executed second run reaches 90.0% , and a stored oracle without a comparison procedure yields only 68.2% . Information and procedure are each necessary and neither is capability. Under nondeterminism, replay tracks a closed-form k -replay curve only under the right projection, and a projection frontier shows the resulting dilemma is forced: narrow misses 98.6% of off-channel leaks, broad flags 75.7% of clean traffic, and attainable accuracy decays like 1/(qm) in the benign-variation rate and the channel count. Finally, two frontier LLM judges certified an earlier version of our own benchmark as sound while a sign test found a directional bias ( p=2.7\times10^-5 ) that invalidated three of our findings. Construction validity for hyperproperty benchmarks should be proved mechanically, not audited by models.
[LG-91] Fine-grained Distributed Backdoor Attacks in Federated Learning
链接: https://arxiv.org/abs/2609.07147
作者: Jian Wang,Hong Shen,Wei Ke,Xue Hua Liu
类目: Machine Learning (cs.LG)
*备注: 20 pages
Abstract:Federated learning, as a privacy-preserving distributed machine learning paradigm, faces significant threats from backdoor attacks. Compared to centralized attacks, distributed backdoor attacks are more harmful but require more poisoned samples to compensate for the loss of trigger strength due to decomposition. Fixed trigger patterns are also easily detected by robust aggregation algorithms, increasing the risk of attack exposure. To address these challenges, we propose a fine-grained distributed backdoor attack framework (FDBA). This framework uses dynamic trigger generation and embedding vector optimization to perform attacks with fewer poisoned samples. First, we design a dynamic trigger generation method based on image edge structures using the Canny algorithm to extract edge features, which are then injected with Laplacian noise. RGB channel decomposition is applied for covert adaptation of the distributed trigger, reducing detection chances. Second, we introduce an embedding vector contrastive learning strategy that forces poisoned samples to approach the target class center in the feature space, enhancing attack effectiveness. On CIFAR-10, piecewise-linear estimates for target ASRs between 70% and 90% show that FDBA reduces the required poisoning ratio by 37.4%–48.4% compared with DBA. In non-independent and identically distributed (Non-IID) scenarios, FDBA retains 84.7% of its IID attack performance under extreme heterogeneity, whereas DBA drops to 73.5%, and the framework successfully bypasses mainstream defense mechanisms. This study offers new insights into federated learning security and emphasizes the potential threats and defense challenges posed by fine-grained distributed attacks. Comments: 20 pages Subjects: Machine Learning (cs.LG) Cite as: arXiv:2609.07147 [cs.LG] (or arXiv:2609.07147v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.07147 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-92] Beyond Task Success: Stage-Wise Reliability of World Model Planning under Sensing Degradation
链接: https://arxiv.org/abs/2609.07126
作者: Geonmyeong Lee,Byoung-Tak Zhang
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: 8 pages, 3 figures, 2 tables
Abstract:In world model planning, sensing inputs pass through an encoder and predictor before affecting planner decisions, so final task success alone cannot reveal where sensing disturbances attenuate or persist in the pipeline. We apply 10 visual and temporal sensing degradations to a world model planner and track their effects across representation, future prediction, planner preference, and physical outcome using paired evaluation on the same 50 tasks. The relative impact of degradations was not preserved across stages: large representation shifts could attenuate downstream, while smaller initial shifts could persist to the outcome, and internal-response ordering did not directly match physical-outcome ordering. Temporal degradations also showed distinct patterns: even with similar overall changes in observation history, responses differed substantially with the location of corrupted information and the planner’s actual exposure. This non-uniform stage-wise response was also observed in secondary evaluations with another manipulation task and a different world model. Stage-wise diagnosis can therefore identify where sensing disturbances attenuate or persist and help prioritize subsequent model verification and sensing mitigation.
[LG-93] Online Draft Co-Training for Speculative Decoding in Large-Scale Long-Context RL Post-Training
链接: https://arxiv.org/abs/2609.07108
作者: Zili Wang,Zhaopeng Qiu,Yuekai Zhang,Shuang Yu,Junjie Lai
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注: Technical Report
Abstract:Speculative decoding accelerates rollout generation, which dominates the cost of reinforcement learning (RL) post-training. Online co-training can further increase the draft’s accuracy, yielding greater speedups. However, scaling this approach to co-training on large models with long contexts poses two obstacles: (1) branch attention is unsupported by standard causal context-parallel (CP) implementations, and (2) target features span across pipeline-parallel (PP) stages. We address both with an end-to-end system for large-scale online draft co-training. For CP, we extend packed, load-balanced zigzag ring attention by merging rank-local branch attention with causal main-sequence attention. For PP, TapChannel transports intermediate target features across stages via a separate path, leaving the pipeline schedule unaffected. Experiments demonstrate that co-trained drafts closely track the policy baseline while delivering substantial rollout and end-to-end speedups across model scales up to 122B. Our CP design achieves strong scaling at 256K tokens with significant memory savings over prior work, and our PP transport incurs modest overhead. Code can be found at this https URL.
[LG-94] rust-But-Verify: Poisoning-Resilient Locally Private Graph Learning Protocols ICDM2026
链接: https://arxiv.org/abs/2609.07063
作者: Longzhu He,Li Sun,Hao Peng,Ruijie Wang,Raymond Chi-Wing Wong,Sen Su
类目: Machine Learning (cs.LG)
*备注: ICDM 2026
Abstract:Built upon local differential privacy (LDP), locally private graph learning protocols have emerged as an important paradigm for decentralized graph learning, balancing privacy protection and learning utility. Under such protocols, each user locally perturbs their node features and adjacency information before transmission, ensuring formal privacy guarantees without original data leaving the device. However, the inherently open participation nature renders these protocols critically vulnerable to data poisoning attacks, where adversaries inject carefully crafted malicious nodes to corrupt neighborhood aggregation and degrade downstream utility. Despite the severity of this threat, effective defenses in this setting remain largely unexplored. In this paper, we propose VERITAS, a poisoning-resilient locally private graph learning protocol built on a trust-but-verify paradigm. By introducing a verification list encoding graded peer trust levels, VERITAS jointly privatizes node features and graph structure on the user side, while exploiting bilateral attestation asymmetry on the server side to identify and prune malicious nodes. Concretely, VERITAS comprises four synergistic stages: (1) local data perturbation, (2) attestation-driven malicious node pruning, (3) utility restoration via dual denoising, and (4) robust private graph learning. Extensive experiments on four real-world benchmark datasets across multiple LDP mechanisms and GNN architectures demonstrate that VERITAS effectively defends against data poisoning attacks and significantly improves downstream graph learning utility under rigorous privacy guarantees.
[LG-95] PhysSAE: Mechanistic Interpretability with Sparse Autoencoders
链接: https://arxiv.org/abs/2609.07061
作者: Nandita N. Patil,Eshwar R. A.,Gajanan V. Honnavar
类目: Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
*备注:
Abstract:Physics-Informed Neural Networks (PINNs) embed PDE residuals into neural network training, but their internal representations remain opaque: it is unknown what physical features their hidden layers encode or whether those features have a localized causal role. We present PhysSAE, a mechanistic interpretability framework that trains overcomplete sparse autoencoders (SAEs) on PINN penultimate-layer activations and evaluates dictionary atoms through direct causal intervention in the original frozen hidden state: h_\mathrmcf = h - \alpha z_k d_k , bypassing the SAE decoder entirely. Across six PDE families, with 3 PINN seeds and 3 SAE seeds each—we show that (i) Our discovered SAE atoms align with independently-defined physical observables (max Pearson |r|=0.951 , always \gg permutation null), (ii) the causal footprint of top-aligned atom ablation is 1.2–4.2 \times more spatially concentrated canonical than PCA or ICA interventions, and (iii) top-aligned atoms outperform matched random controls on causal localization for structured physical concepts (ESF _80 advantage 0.04-0.44). Two-atom bilateral representations improve concept regression R ^2 by \Delta R^2!=!0.05\text-0.15 over single atoms, while random pairs decrease it by up to 0.60. These results demonstrate that PINNs develop sparse, physically structured latent representations that can be identified and causally interrogated post-hoc, opening a path toward interpretability-aware scientific machine learning.
[LG-96] rojanWorld: Backdooring World-Model Agents via Imagination Steering
链接: https://arxiv.org/abs/2609.07051
作者: Wenkai Huang,Siyuan Liang,Gaolei Li,Yiming Li,Tianhao Peng,Jianhua Li,Dacheng Tao
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注:
Abstract:World models increasingly serve as the predictive core of model-based reinforcement learning agents, enabling them to simulate future dynamics and reason over imagined trajectories before acting. Their substantial training demands make pretrained world models attractive for distribution and reuse, exposing downstream systems to model supply chain threats. Backdoor attacks offer a targeted and stealthy means of exploiting such supply chains, yet their threat to interactive world-model agents remains largely unexplored. To fill this gap, we present TrojanWorld, a backdoor framework for world-model agents that induces attacker-specified behavior by steering internal imagination. A physical object placed in the scene acts as the trigger, enabling deployment-time activation through the agent’s native observation pipeline without digitally manipulating the observation stream. To achieve effective, stealthy, and persistent control, TrojanWorld combines Decision-Reflective Induction to steer trigger-conditioned imagination toward attacker-specified actions using decision feedback, Clean Behavior Anchoring to preserve trigger-free predictive and behavioral fidelity, and Causal Propagation to sustain the induced preference along subsequent trajectories after the trigger disappears. Together, these mechanisms establish an end-to-end attack chain from physical perception through corrupted imagination to malicious action selection. Experiments with the TD-MPC2, DreamerV3, and R2-Dreamer systems across the DeepMind Control, MetaWorld, MyoSuite, and RoboDesk benchmarks show that under trigger activation, TrojanWorld achieves a target-action deviation as low as 0.026 while retaining at least 98.8% of the corresponding clean performance. Even after trigger removal, the compromised agent can remain trapped in the induced behavioral trajectory, continuing to execute attacker-specified actions.
[LG-97] AI and TCAD for Inverse Design and Defect Discovery: From Simple Machine Learning to LLM
链接: https://arxiv.org/abs/2609.07046
作者: Hiu Yung Wong
类目: Machine Learning (cs.LG)
*备注:
Abstract:AI has revolutionized various engineering domains, but its impact on semiconductor device design and defect discovery is still limited, due to limited data and the curse of dimensionality. In this paper, we will discuss our work on using the Technology Computer-Aided-Design (TCAD) to generate precise data needed for machine learning (ML) to enable simulation-augmented ML. We demonstrate that with minimal domain expertise, it is possible to create a machine that performs as well as a device engineer on a specific task. We will show that auto-encoder-based machine learning models and noise engineering applied to TCAD data are effective at learning latent physics, and that the models can be seamlessly applied to experimental data. We will demonstrate how to build a device-engineer-level model step by step through various examples, including using only non-destructive electrical data to inverse-engineer the PiN diode layer thickness variations, the Ga2O3 Schottky diode doping and anode workfunction variations, and the transistor contact resistance in an inverter. Examples also include the generation of a FinFET IV/CV prediction model, the mapping between transistor images and IV curves, and the automatic calibration of TCAD parameters for a Ga2O3 Schottky diode, which can only be handled well by experienced TCAD engineers. Finally, to fully realize the potential of AI, large language models (LLMs) and multimodal LLMs (MLLMs) are believed to be necessary. We will discuss the application of LLMs to TCAD command file creation and our vision for MLLMs in automated device design and defect discovery.
[LG-98] Disentangling Steering Vectors
链接: https://arxiv.org/abs/2609.07037
作者: Takeru Hiramatsu,Kyohei Atarashi,Koh Takeuchi,Hisashi Kashima
类目: Machine Learning (cs.LG)
*备注: 31 pages, 5 figures
Abstract:Activation steering has emerged as a lightweight, inference-time approach to control the behavior of Large Language Models (LLMs). However, traditional steering vectors used to intervene in LLMs’ activations, such as those derived from the difference-in-means method, tend to entangle multiple semantic and stylistic concepts into a single composite direction, leading to unpredictable steering effects. Our core objective is to disentangle this composite direction into its constituent concepts. To this end, we propose Steering Vector Dissection, a framework to explicitly isolate individual and semantically consistent features from these composite directions. Specifically, we pair positive and negative activations and take their differences to generate a set of instance-level steering vectors, and train a dedicated Sparse Autoencoder (SAE) directly on them. Quantitative evaluations across two datasets, two models, and two intervention depths show that our method yields a set of semantically consistent basis vectors whose steering effects are mutually distinguishable. Furthermore, we show that this disentanglement enables precise control over model behaviors.
[LG-99] Efficient Learning and Symmetry Discovery under Exact Invariances COLT2026
链接: https://arxiv.org/abs/2609.07031
作者: Ashkan Soleymani,Behrooz Tahmasebi,Patrick Jaillet,Stefanie Jegelka
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 29 pages. Published at COLT 2026
Abstract:Learning with group invariances is central to many scientific and geometric learning problems, yet its computational foundations remain poorly understood. Even for classical supervised regression settings, it has been unclear whether one can efficiently compute a regression function that is exactly invariant to a given group action. Recent work showed that exact invariance can be enforced in polynomial time when the underlying group is finite and known, but left open the cases of infinite groups and unknown symmetries. In this paper, we resolve both challenges. First, we present the first polynomial-time algorithm for learning with exact group invariances that applies uniformly to finite and infinite groups. The runtime is polynomial in the data dimension and sample size, and independent of the group, while achieving strong generalization guarantees. This provides a computational explanation for the empirical success of invariant and equivariant methods in geometric machine learning and partially answers a recent open question in the literature. Second, we study learning in the symmetry discovery setting, where the invariance group is unknown. Focusing on the subgroup lattice of a finite group, we show that exact symmetries can be identified from data and exploited for learning in polynomial time. For regression over finite-dimensional feature spaces, our algorithm provably recovers the underlying symmetry, matches the minimax-optimal sample complexity of the known-symmetry setting, and runs in time polynomial in the data dimension and sample size. Our analysis relies on tools from random Cayley graphs and expander theory, which may be of independent interest.
[LG-100] HyperTransfer: Understanding the Equivalence between Base Optimizer and Hyperball
链接: https://arxiv.org/abs/2609.07017
作者: Jinghui Yuan,Hongtao Zhang,Jade Zou,Tianyu Li,Wenjie Zhou,Tianyu He,Wei Chen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Hyperball optimizers constrain parameter norms and update only their directions, establishing a distinct paradigm for neural network optimization. Although this geometry appears fundamentally different from that of conventional Base Optimizers, which update both parameter norms and directions, we show that the two paradigms are dynamically equivalent for scale-invariant networks. Building on this equivalence, we propose HyperTransfer, which constructs a Hyperball optimizer that reproduces the dynamics of a target Base Optimizer using only its initialization and learning-rate schedule, without running the target optimizer itself. We further derive the inverse mapping and extend the framework to non-scale-invariant networks. Experiments show that both HyperTransfer and the inverse mapping produce loss trajectories nearly identical to those of their targets, suggesting that Hyperball dynamics are governed primarily by the induced effective learning-rate schedule and optimizer state.
[LG-101] Continual Learning Mechanisms Compose for Long-Horizon Memorization
链接: https://arxiv.org/abs/2609.06986
作者: Zheyuan Zhang,Alvin Zhang,Daniel Khashabi,Tianmin Shu
类目: Machine Learning (cs.LG)
*备注: Project page: this https URL
Abstract:Language models may need to internalize information that arrives over time and retain it through many subsequent updates. To study this challenge, we introduce long-horizon memorization, a setting in which a model learns 100 query-answer tasks through continual supervised fine-tuning without retaining earlier training examples or receiving task identifiers at inference. Sequential updates cause catastrophic forgetting, and no single continual learning mechanism we evaluate maintains strong retention at this horizon. We hypothesize that mechanisms addressing complementary sources of forgetting will be more effective when composed. We organize these compositions along two design dimensions. Data, function, and weight anchors specify what prior information each update should preserve, while low-rank allocation rules determine where successive updates are retained. To test this hypothesis systematically, we construct three distinct 100-task memorization datasets. We introduce task-level successive halving to search the combinatorial design space and use a factorial experiment to measure individual and interaction effects. Our best method combines all three anchors with merged LoRA, ranks among the top 3 methods in all datasets, and raises average final retention from 1.2% under naive sequential fine-tuning to 34.9%, a 28-fold improvement. The data anchor and merged LoRA provide the largest average gains and interact super-additively on all three datasets. Together, these results show that composing complementary mechanisms substantially improves long-horizon memorization beyond what any individual mechanism achieves.
[LG-102] AF-Mamba: Efficient Long-Term Signal Modeling for Early Prediction of Atrial Fibrillation Onset
链接: https://arxiv.org/abs/2609.06984
作者: Yongbin Lee,Ki H. Chon
类目: Machine Learning (cs.LG)
*备注: 11 pages, 5 figures, 7 tables. Extended version of IEEE BSN 2025. Code: this https URL
Abstract:Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with increased risks of stroke and heart failure. The growing availability of wearable and portable ECG monitoring enables continuous assessment of cardiac rhythm outside clinical settings. Predicting AF before its onset could provide additional lead time for timely clinical assessment and potentially improve the management of patients at risk of AF-related complications. This study focuses on predicting AF onset one hour in advance using long-term RR intervals (RRIs). To address this challenge, we propose a deep learning architecture that integrates temporal convolutional networks (TCNs) for local features encoding with Mamba, a selective state-space model capable of long-range sequence modeling. This hybrid TCN-Mamba design enables efficient training and inference on one-hour input windows, overcoming limitations of Transformers’ quadratic scaling and recurrent networks’ vanishing gradients. In subject-wise 5-fold testing, the proposed model achieved a sensitivity of 0.889, specificity of 0.943, F1-score of 0.813, AUROC of 0.974, and AUPRC of 0.933. In paired cross-dataset holdout evaluation, AF-Mamba maintained discriminative performance across unseen AF and NSR datasets, achieving a mean AUROC of 0.897. Compared against state-of-the-art AF prediction models and general time-series models, AF-Mamba achieved competitive predictive performance while providing a favorable performance-efficiency trade-off for long RRI sequences. These findings demonstrate the potential of AF-Mamba for accurate AF prediction one hour in advance and real-time continuous ambulatory monitoring.
[LG-103] HealthLoopQA: A Context-Aware Question Answering Benchmark for Interpreting Wearable Monitoring Data in Diabetes Care
链接: https://arxiv.org/abs/2609.06976
作者: Yuchen Niu,Yanan Ma,Srinivasan Nandakumar,Maolin Chen,Viktor Schlegel,Kexin Wei,Ling Cheng,Anna Bird,Anil Anthony Bharath,Siew-Kei Lam
类目: Machine Learning (cs.LG)
*备注:
Abstract:As medical wearables become integrated into daily chronic disease care, effectively interpreting longitudinal monitoring data is essential for patients and clinicians to understand health trends, detect safety-critical events, and make informed decisions. While large language models (LLMs) show promise for transforming this streaming physiological data into personalized health insights, evaluating their reasoning capability and analytical rigor in diverse monitoring tasks remains a fundamental challenge. Existing medical wearable question answering (QA) benchmarks primarily assess short-horizon classification or statistical summaries, largely ignoring the long-term patterns, therapeutic and behavioural contexts, and potential system failures inherent in real-world deployments. To address this, we introduce HealthLoopQA, a comprehensive diagnostic benchmark for evaluating LLM reasoning over continuous diabetes monitoring data. Grounded in a novel taxonomy of eleven atomic reasoning abilities, HealthLoopQA comprises 127 tasks and over 1,500 QA instances spanning process mining, anomaly detection, and prediction over 30-day horizons. To systematically evaluate safety awareness, we complement real-world datasets with a fault-injected simulation testbed modeling diverse device malfunctions and cyber-physical attacks to generate physiologically plausible hazard scenarios. Evaluating state-of-the-art LLMs across prompting and agentic frameworks reveals severe limitations in complex temporal pattern mining. Furthermore, we identify a broader phenomenon of In-context Laziness under long-context prompting, highlighting critical open challenges in deploying LLMs for rigorous long-horizon medical reasoning.
[LG-104] Particle Dynamics of Flow Matching and Classifier-Free Guidance from a Stagewise Geometry Perspective
链接: https://arxiv.org/abs/2609.06947
作者: Jian-Feng Cai,Zhengyi Su,Chao Wang
类目: Machine Learning (cs.LG)
*备注: 78 pages, 14 figures
Abstract:Flow matching, together with classifier-free guidance (CFG), is widely used in generative modeling, yet much of the theoretical understanding remains distribution-wise. Since practical sampling follows individual trajectories, distribution-level guarantees alone do not fully capture how trajectories interact with the data geometry or how guidance reshapes it. To overcome this limitation, we establish a unified stagewise geometric theory of attraction and absorption for both continuous dynamics and explicit Euler discretization. Specifically, with t\in[0,1] running from noise to data, we show that unconditional flow trajectories are successively attracted toward a neighborhood of the global mean, the data convex hull, and a neighborhood of a possibly nonconvex local cluster. Across these stages, the corresponding distance satisfies a common contraction estimate, yielding an O(1-t) decay of the distance in the final stage. For CFG, the same structure persists with an extrapolated mean, an inflated conditional convex hull, and, near the target cluster, the restored local geometry of conditional flow matching. We further show that a general time schedule a(t) replaces the O(1-t) decay by O(1-a(t)) . Together, these results provide a unified particle-level geometric account of flow matching and CFG across continuous and discrete sampling.
[LG-105] Distributed Dexterous Manipulation with Spatially Conditioned Multi-Agent Transformers
链接: https://arxiv.org/abs/2609.06930
作者: Sarvesh Patil
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Distributed Dexterous Manipulation (DDM) is a novel paradigm that presents significant control challenges due to high action-space redundancy, inter-robot cooperation, and dynamic object-robot interactions. This paper introduces a framework based on spatially conditioned Multi-Agent Transformers (MATs) to efficiently learn robust control policies for a DDM system grounded in an array of 64 soft delta robots arranged in an 8x8 grid. Our three core contributions are: (i) an MAT with adaptive layer norm for compute efficiency, (ii) spatial contrastive embeddings to ground transformer embeddings in the spatial configuration of the robots, and (iii) an MAT-based behavior cloning method fine-tuned using Soft Actor Critic. We also propose an action selection formulation to analyze the trade-off between task performance and the number of robots utilized. Our experiments show that MATs iteratively refine their actions through the stacked attention blocks. This further informs the benefit of spatial conditioning in transformers to learn DDM policies. We demonstrate long-horizon planar manipulation tasks with objects of various geometries in simulation and real-world. Finally, we show how action selection mitigates robot maintenance by reducing wear and tear due to inter-robot collisions while maintaining the ability to manipulate objects along various trajectories in the real-world, achieving an average error of ~1.5 cm, while using ~65% fewer robots.
[LG-106] Learning Adaptive SED for heterogeneous load balancing
链接: https://arxiv.org/abs/2609.06881
作者: Sanne van Kempen,Jaron Sanders,Fiona Sloothaak,Maarten G. Wolf
类目: Machine Learning (cs.LG); Probability (math.PR)
*备注: 34 pages
Abstract:We study a two-server load balancing system with heterogeneous service rates that are a priori unknown to the dispatcher. The goal is to route customers according to the Shortest–Expected–Delay (SED) policy, but this requires knowledge of the service rates. Empirical policies that route based on estimates perform poorly: due to estimation error, the empirical policy disagrees with the oracle on an infinite region of the state space. We propose an online learning algorithm that converges to SED while learning the service rates. The algorithm carefully balances empirical SED routing with forced exploration phases that guarantee sufficient sampling of both servers. We prove that our algorithm achieves finite regret; this differs from classical Multi-Armed Bandit settings where regret typically grows logarithmically in time. Finally, numerical experiments demonstrate the performance of our algorithm and highlight the regimes in which forced exploration is especially beneficial.
[LG-107] PPIM: Pennes Physics-Informed Mamba for Heat-Source-Conditioned 3D Bioheat Simulation
链接: https://arxiv.org/abs/2609.06869
作者: Dongyun Lee,Kyungho Yoon,Minwoo Shin
类目: Machine Learning (cs.LG); Mathematical Physics (math-ph)
*备注:
Abstract:Three-dimensional bioheat simulation aims to predict transient temperature distributions in biological tissue and is commonly modeled using the Pennes bioheat equation, which combines thermal diffusion, perfusion-mediated heat loss, and external heat generation. In this study, we consider a controlled 3D Pennes bioheat simulation under a localized heat-source condition inspired by microwave ablation (MWA). To evaluate neural approximation performance, we compare three neural partial differential equation (PDE) solvers under the same controlled simulation: a spatial Fourier-feature physics-informed neural network (PINN), a generic PINNMamba temporal subsequence model, and Pennes Physics-Informed Mamba (PPIM). PPIM builds on the temporal subsequence model by incorporating conditioned heat-source input and Pennes-aware state-space model (SSM) decay initialization. All three neural models are trained under the same conditions with the same Pennes residual, and an explicit finite-difference method (FDM) solution is used only as the numerical reference. In a representative 600~s run, PPIM achieved the lowest MAE, relative L_1 error, and relative L_2 error among the evaluated neural solvers. Error maps further showed that the remaining PPIM errors were more concentrated near the heat-source region than across the rest of the domain. These results indicate that PPIM is effective for approximating the FDM reference final temperature field in this controlled simulation. The source code is available at this https URL.
[LG-108] Feature Superposition in Neural Networks: From Theory to Practice
链接: https://arxiv.org/abs/2609.06862
作者: Dai Shi,Xiaoyu Li,Andi Han,José Miguel Hernández-Lobato
类目: Machine Learning (cs.LG)
*备注:
Abstract:Superposition refers to neural networks representing more features than they have dimensions. It offers a possible explanation for polysemantic neurons and motivates methods for recovering interpretable features from neural activations. Theoretical models typically start with a given set of input features and assumptions about how their values vary across inputs, then study how a network encodes those values in a lower-dimensional hidden representation. Empirical work, by contrast, seeks to identify the features encoded in trained networks and determine their role in computation. In this survey, we review the geometry, learning, and computation of superposed representations, explaining how feature statistics and decoder choice affect the conclusions. To connect these theoretical accounts with evidence from trained networks, we compare practical methods for recovering and analyzing features and examine what their evaluations establish. Since accurate activation reconstruction alone does not establish feature identity or causal use, we discuss the methods’ documented failures and applications in light of the evidence available for these different claims. Finally, we assess previously stated open problems and identify remaining theoretical and empirical questions about superposition in trained networks. We hope our work can pave the way for a deeper understanding of superposition and more reliable methods for interpreting neural networks.
[LG-109] BinauralVAE: Spatial Audio Reconstruction For World Models
链接: https://arxiv.org/abs/2609.06837
作者: Luis Vitor Zerkowski,Luiz Velho
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注: 17 pages, 7 figures
Abstract:Embodied artificial intelligence has historically very much relied on visual perception, leading to a proliferation of multiple vision-centric world models. However, this reliance fails to capture spatial understanding in its entirety and can even present vulnerabilities in environments with visual occlusions, low-light conditions, or blackouts-scenarios, where acoustic information becomes a critical alternative for spatial awareness and navigation. Despite its potential, research into realistic spatial audio and particularly the development of audio-centric world models remains sparse. In this technical report, we introduce BinauralVAE: a flexible, open-source pipeline (this https URL) that explores multiple models for spatialized audio reconstruction, progressing from fundamental baselines to advanced, mathematically grounded architectures. Our approach evaluates various Variational Autoencoder architectures – including complex-valued variants – to learn robust latent representations of binaural signals. Developed alongside AudioWorldSim, our methodology leverages realistic acoustic data captured as a simulated robot navigates an environment. This pipeline establishes a foundation for state representation in a future audio-based world model, designed to map the direct causal connection between navigational actions and their resulting acoustic consequences, and helping to enable sound as an essential complementary modality for spatial knowledge acquisition.
[LG-110] Constrained Bayesian Optimization for Hierarchical Federated Learning in IoT Networks for Plant Disease Classification
链接: https://arxiv.org/abs/2609.06830
作者: Athanasios Papanikolaou,Athanasios Tziouvaras,Apostolos Xenakis,Periklis Chatzimisios,Shameem A. Puthiya Parambath,George Floros,Enrica Zereik,Ivan Petrovic,Fabio Bonsignorio
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注: Accepted for publication at Next Generation Communications (NextGCom) conference
Abstract:The deployment of Hierarchical Federated Learning (HFL) in resource-constrained Internet of Things (IoT) environments requires careful configuration to balance predictive performance with energy consumption and execution time. This challenge is particularly relevant to smart agriculture, where distributed IoT devices can support automated plant disease classification while operating under limited computational and communication resources. This paper presents a constrained Bayesian Optimization framework for the efficient configuration of HFL deployments. The proposed approach jointly explores the deep learning backbone architecture, aggregation strategy, and number of communication rounds, while the federation size is determined according to the spatial coverage requirements of the agricultural deployment. A weighted objective function captures user-defined trade-offs among energy consumption, execution time, and predictive performance, while explicit constraints ensure compliance with deployment-specific resource and accuracy requirements. The framework is evaluated on an IoT-based plant disease classification task considering multiple deep learning architectures, federated aggregation strategies, and communication-round settings. Experimental results across 30 independent optimization runs show that the proposed approach explores only 11.11% of the search space, while consistently identifying solutions within 1% of the exhaustive-search optimum, with a mean optimality gap of only 0.056%.
[LG-111] When Retain Constraints Conflict: Mitigating Forget-Retain Interference in Tabular Data EMNLP2026
链接: https://arxiv.org/abs/2609.06786
作者: Zijie Liu,Jinhao Duan,Bingqi Shang,Xinming An,Sijia Liu,Tianlong Chen
类目: Machine Learning (cs.LG)
*备注: EMNLP 2026 Finding Paper
Abstract:Machine unlearning aims to remove the influence of designated training data while preserving model utility, but its behavior on tabular data remains underexplored. This gap is important because tabular prediction is widely used in high-stakes domains and is increasingly adapted to language models through record serialization and schema-aware prompting. We identify a key challenge that distinguishes tabular unlearning from unlearning in free-form text or other modalities: schema-induced forget-retain overlap. In serialized tabular data, records share fixed column-name/value slots, similar attribute ranges, and common output spaces. Consequently, a forget row may have nearby retain rows that rely on the same high-signal attributes, causing retain preservation to oppose the update required for forgetting. Motivated by this failure mode, we propose Conflict-Aware Unlearning (CAU), a schema-aware approach that reduces forget-retain interference by relaxing preservation constraints on retained rows that most conflict with the forget set. Across sample-level and feature-level unlearning on clinical and non-medical tabular tasks, CAU more closely matches a retraining oracle while maintaining predictive utility and retain-region behavior. Our results show that reliable tabular LLM unlearning depends not only on the forgetting objective, but also on how retain constraints are constructed.
[LG-112] CAROL: Context-Aware Online Learning for Fuzzer Scheduling
链接: https://arxiv.org/abs/2609.06772
作者: Zirui Liu,Mengfan Xu,Juan Zhai,Shenglong Yao,Shiqing Ma
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG)
*备注:
Abstract:Ensemble fuzzing runs multiple fuzzers on a target while a scheduler allocates CPU time among them. Existing schedulers base these decisions on compact summaries of past performance and rules fixed before a campaign. Our measurements reveal two limitations. First, past-reward summaries do not reliably capture performance evolution: after accounting for estimation noise, agreement between consecutive-window rankings is statistically indistinguishable from within-window self-agreement. Second, predictive signals vary across targets: on eight of nine targets, a weighting learned from the other eight predicts reward worse than one learned on the current target. We introduce CAROL, an online scheduler that uses each fuzzer’s current context. Already available to the dispatch loop, this context describes reward trends, waiting and plateau time, reached code, and estimation uncertainty. CAROL uses context in two ways: a domain-guided method detects whether a fuzzer is rising or rotting and applies a phase-specific learning rule, while a learned method predicts reward from 15 context signals and uses predictive uncertainty for online selection. Across nine Magma targets, CAROL triggers more unique bugs than each of three ensemble-scheduling baselines whenever their results differ, and fewer on none. Compared with the strongest baseline for each target, CAROL gains 11.8% and surpasses an oracle that retrospectively selects the best single fuzzer per target. Removing context eliminates the gain, and the additional bugs are concentrated among those the baselines trigger rarely or never. Run unchanged on five widely used C++ programs, CAROL finds 120 previously unknown crashing defects, deduplicated by site, fault, and entry point; all were reported to maintainers through the projects’ stated disclosure channels.
[LG-113] LATS: Levy Adaptive Tree Sampling for Feedback-Driven Diverse Target Discovery
链接: https://arxiv.org/abs/2609.06761
作者: Binglin Ji,Anindya Sarkar,Hengchang Lu,Lecheng Kong,Yixin Chen,Yevgeniy Vorobeychik
类目: Machine Learning (cs.LG)
*备注: 19 pages, 6 figures, preprint
Abstract:While diffusion models excel at capturing complex data distributions, scientific discovery often requires steering generation toward specific, uncharacterized regions that maximize a target objective. These high-utility modes frequently reside in low-likelihood tail regions and are only revealed sequentially through interactive feedback. Existing diffusion samplers fail in this regime: they inherit the pre-trained model’s bias toward high-density regions, leaving rare yet promising phenomena underexplored. Conversely, exploration-heavy samplers ensure broad coverage but fail to efficiently exploit high-utility modes when constrained by a strict sampling budget. To resolve this dilemma, we introduce Levy Adaptive Tree Search (LATS), a principled sampling framework for online feedback-driven search. LATS leverages heavy-tailed exploration coupled with tree-based value backpropagation to progressively uncover preferred modes. By maintaining broad distributional coverage, LATS successfully discovers low-likelihood, high-utility regions while preserving sample fidelity and structural diversity. Experiments across diverse benchmarks, including materials science, demonstrate that LATS significantly outperforms baselines in target discovery efficiency.
[LG-114] owards Unified Multimodal Graph Foundation Model: A Bridge-Router-Adapter Based Approach
链接: https://arxiv.org/abs/2609.06668
作者: Sirui Zhang,Yubing Zhou,Xunkai Li,Zekai Chen,Shumeng Li,Wang Luo,Yinlin Zhu,Yujin Gao,Rong-Hua Li
类目: Machine Learning (cs.LG)
*备注:
Abstract:Multimodal graphs couple node attributes in different modalities, such as text and images, with relational structure, enabling topological structure and cross-modality attributes to be modeled jointly. Multimodal graph foundation models seek unified representations from such data that transfer across different graph domains and downstream tasks. However, existing methods exhibit two fundamental limitations. (1) Cross-Scope Context Entanglement. They merge scope-specific graph contexts into a unified representation, obscuring their distinctions during multimodal construction. (2) Scope-Ignorant Modality Routing. They route modalities within a fixed graph scope, overlooking how modality relevance varies across neighborhood ranges. To address these challenges, we propose BRAIN, a unified model that focuses on graph context that combines neighborhood scope with modality composition. BRAIN comprises a scope-conditioned Bridge that combines structural information spanning local-to-global neighborhood scopes with different modality compositions; a hierarchical Router that estimates the relevance between the scope and the task, and selects compositions separately within each scope, allowing modality utility to vary with graph range; and a lightweight residual Adapter that further specializes the routed embedding for downstream prediction. BRAIN is trained through multi-graph pretraining followed by task-specific adaptation. Experiments across nine datasets and four task families demonstrate its broad effectiveness, improving node-classification and link-prediction performance by up to 4.73% relative to the strongest baseline, while achieving an average relative improvement of 14.72% across four graph-to-text and two graph-to-image metrics.
[LG-115] Behavioral Cloning Outperforms Entropy-Regularized RL: Critic-Driven Failure of Actor-Critic Methods on Adaptive Tumor Treatment
链接: https://arxiv.org/abs/2609.06667
作者: Aleksandar Dimitrov,Giacomo Spigler
类目: Machine Learning (cs.LG)
*备注: 9 pages, 3 figures
Abstract:Adaptive dosing requires policies that reduce tumor burden without excessive toxicity. Learned dosing policies are typically judged against historical or heuristic comparators, which cannot show whether a policy has found the best behavior available. We instead study a three-population tumor-control ODE in which optimal-control analysis fixes the form of a good schedule – bang-bang dosing punctuated by a singular arc – and construct a numerical controller of that form as a proxy for near-optimal behavior. Judged against this reference under a sustained-cure criterion – 200 consecutive days below 5% carrying capacity – Soft Actor-Critic (SAC) trained from scratch never reaches cure. Behavioral cloning (BC) of the reference reproduces it (100% sustained cure, 30/30 seeds), but SAC fine-tuning of the cloned policy destroys it across five entropy coefficients, and TD3 and BC-regularized SAC fail identically; the pattern persists under multiplicative pharmacokinetic action noise. Along curative trajectories the post-collapse critic ranks the collapsed-policy action above the reference action in 96% of states, concentrated in the maintenance phase, and the policy settles into a non-curative adaptive-therapy equilibrium. The reference is what makes this legible: against a heuristic comparator the fine-tuned policy would read as a competent controller rather than a failure.
[LG-116] CAVEAT: Recurrent Multimodal Diffusion Planning for Mapless Aerial Exploration
链接: https://arxiv.org/abs/2609.06623
作者: Steven Visch,Nicolò Botteghi,Antonio Franchi,Barbara Bazzana
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Can exploratory UAV waypoint sequences be generated from multimodal onboard observations and a fixed-dimensional recurrent internal state without maintaining a persistent global map in the deployed policy? We investigate this question through CAVEAT, a diffusion policy conditioned on a recurrent internal state updated from fused LiDAR, visual, and pose features and trained from trajectories generated by the map-based FUELv2 expert. Rolling inference partially warm-starts consecutive predictions, while a temporary local signed distance field provides heuristic obstacle guidance. Simulation results evaluate both inference mechanisms and compare CAVEAT with its demonstration-generating expert. Proof-of-concept experiments on a Flyability Elios 3 demonstrate partial exploration of a previously unseen indoor environment and target-directed visual servoing using a separately trained policy.
[LG-117] A Statistical and Machine Learning Framework for Quantifying Offensive Impact in Professional Box Lacrosse
链接: https://arxiv.org/abs/2609.06610
作者: Robert Jimerson Jr
类目: Machine Learning (cs.LG)
*备注: 38 pages 2 figures
Abstract:Professional box-lacrosse statistics summarize outcomes but provide limited information about shot quality or the roles behind scoring opportunities. This study develops a documented framework for estimating expected goals (xG) and attributing recorded offensive involvement using 1,006 manually annotated Rochester Knighthawks shot attempts, including 151 goals, from 13 consecutive 2025-2026 National Lacrosse League games. Logistic regression, random forest, and extremely randomized trees were evaluated across three nested feature sets using Leave-One-Game-Out cross-validation and a training-fold base-rate benchmark. The contextual baseline random forest had the lowest observed pooled log loss (0.4189) and Brier score (0.1260), improving on the benchmark by 1.22% and 1.50%; five of nine specifications did not beat the benchmark. Adding two-man-action and pick-type fields did not improve the primary metrics. Core Offensive Impact attributes recorded involvement through shooter xG and shot-based expected assists for final passers. Expected Pick Value (xPV) compares a qualifying pick’s observed-state probability with a no-pick counterfactual. Its magnitude was indistinguishable from model noise. Its directional pattern exceeded 200 row-permutation replicates, but limited tail resolution and failure to preserve game-level pick composition make the diagnostic descriptive rather than inferential. Accordingly, xPV is reported only as an exploratory augmented component. Given the single-team, 13-game sample, the results are an initial case study rather than league-wide or causal estimates.
[LG-118] Hidden in Plain Sight: The Overlooked Significance of Canonical Elements for Extreme LLM Sparsity EMNLP2026
链接: https://arxiv.org/abs/2609.06557
作者: Hyeondo Jang,Kwanhee Lee,Dongyeop Lee,Namhoon Lee
类目: Machine Learning (cs.LG)
*备注: EMNLP 2026
Abstract:Large language models (LLMs) are often considered fragile under aggressive sparsification, and maintaining reliable performance typically requires sticking to moderate sparsity levels. However, recent studies suggest that LLMs are more resilient to high sparsity than previously thought, reframing the problem as a design challenge rather than a fundamental limitation. In this work, we challenge the perceived limits of unstructured post-training LLM pruning by revisiting elementary pruning strategies that have remained relatively underexplored at this scale. Through a progressive sparsification framework with second-order saliency and continued training coordinated with sparsity progression, we show that pretrained LLMs can retain strong performance far beyond commonly studied sparsity regimes. Across LLaMA-2 and Qwen-3 model families, our approach improves perplexity and downstream accuracy up to 99% sparsity, surpassing both the current state-of-the-art and representative baselines. Precisely, on LLaMA-2-7B, our approach achieves WikiText-2 perplexities of 13.48 and 19.67 at 95% and 99% sparsity, respectively, while delivering 3.23 \times decoding speedup and 6.21 \times memory savings at 95% sparsity. Taken together, our results show that LLMs can be pushed into extreme sparsity while retaining strong performance, providing a foundation for further improving sparse models in this regime.
[LG-119] EStream: Fast and Memory-Efficient MoE Prefill through Expert Virtualization on Mobile NPUs
链接: https://arxiv.org/abs/2609.06551
作者: Junming Zhang,Zhenzhe Zheng,Fan Wu,Xiaoyao Huang,Jie Wu
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Hardware Architecture (cs.AR); Machine Learning (cs.LG)
*备注:
Abstract:Mobile vendors and application developers increasingly deploy LLMs on smartphones for diverse prefill-only services. Yet current systems rely mainly on dense models whose regular computation maps efficiently to mobile NPUs, leaving more capable MoEs underused. MoE prefill does not fit mobile NPUs: NPU graphs are fixed at compile time, yet MoE picks experts at runtime; and one request touches most experts, more than a phone can hold in memory. We present EStream, which resolves both by separating what the NPU must fix from what MoE decides at runtime. A single compiled expert graph serves every expert, with each expert’s routed tokens and weight address bound at call time, so dynamic MoE execution runs entirely on the NPU without padding or CPU/GPU fallback. Expert virtualization keeps the expert pool in UFS flash storage and pages it through a fixed-size NPU-addressable arena, group by group, with loading hidden behind computation, so memory is bounded by the arena rather than by the model. It further introduces a hardware-aware configuration algorithm that automatically configures the UFS–NPU pipeline and maximizes loading–computation overlap. Across 18 comparative settings covering three 7B–16B MoEs and 256–4,096-token prompts, we evaluate EStream on a commercial Snapdragon smartphone. Compared to the fastest baseline at each setting, EStream achieves a 2.25–27.57X pure-prefill TTFT speedup and reduces peak physical memory by 1.19–12.29X. EStream further scales to MoE models with up to 46.7B parameters.
[LG-120] Introductory Notes on Learning2
链接: https://arxiv.org/abs/2609.06546
作者: Sai Siddharth,Maniarasu Ravi
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG)
*备注: 11 pages. Code and implementation: this https URL
Abstract:Although machine learning can be used to predict the evolution of physical systems from data, a formulation that learns only the system state at each time leaves the temporal and dynamical structure of the solution to be resolved within a broad hypothesis space. We introduce Learning ^2 , a representation-level framework that structures this space by coupling a primary representation to a second representation through a known physical transformation. The resulting cross-representation constraint restricts the effective hypothesis space and provides an ante-hoc, physically interpretable criterion for excluding solutions that satisfy the primary representation alone. We instantiate Learning ^2 through EuLaNet, an Eulerian–Lagrangian representation for fluid dynamics. Given the velocity state u(\mathbfx,t) , EuLaNet constructs its induced Lagrangian flow map X(\mathbfa,t) through \dotX(\mathbfa,t)=u(X(\mathbfa,t),t) , from which material transport and finite-time deformation are derived. The resulting representation couples the predicted state to the dynamical consequences it induces, providing a second consistency criterion beyond state-level agreement. We formalize this construction through an effective hypothesis space \mathcalH_L^2\subseteq\mathcalH and define the conditions under which a consequence representation provides discriminative constraints on candidate solutions. EuLaNet is implemented as a model-independent representation module, separating the physical constraint from the downstream learning architecture. This construction provides an ante-hoc mechanism for physically interpretable constraint in scientific learning and offers a basis for developing and evaluating broader classes of Learning ^2 architectures. The implementation is open-sourced to support the development and extension of the architecture across scientific domains. Comments: 11 pages. Code and implementation: this https URL Subjects: Numerical Analysis (math.NA); Machine Learning (cs.LG) MSC classes: 76M99 Cite as: arXiv:2609.06546 [math.NA] (or arXiv:2609.06546v1 [math.NA] for this version) https://doi.org/10.48550/arXiv.2609.06546 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-121] Not Just Oversmoothing: Detecting the Echo Chamber Effect in Graph Neural Networks
链接: https://arxiv.org/abs/2609.06521
作者: Asela Hevapathige,Ahad N. Zehmakan,Asiri Wijesinghe,Saman Halgamuge
类目: Machine Learning (cs.LG)
*备注:
Abstract:Oversmoothing is a well-known failure mode of Graph Neural Networks (GNNs). However, most existing diagnostics rely on global aggregation measures that fail to capture the heterogeneous dynamics of message passing. Real-world graphs exhibit pronounced community structure, and message passing operates on two timescales, with representations collapsing rapidly within communities and slowly across them. This creates a critical gap in which intra-community representations can become indistinguishable while inter community separation persists, a failure mode that we refer to as the Echo Chamber Effect. To quantify this effect, we introduce the Echo Chamber Index (ECI), which stratifies pairwise distances by community membership and reveals when global energy diminishes while inter-community separation persists. ECI further shows that feature retention mechanisms can preserve the echo chamber under the conditions of our theoretical analysis. The consequences depend on label structure: when communities align with classes, the echo chamber can sharpen node classification, whereas when they do not, the same collapse makes classification provably harder. Motivated by this analysis, we propose Community-Aware Split Propagation (CASP), a lightweight plugin that decouples intra- and inter-community aggregation and learns their balance from label structure. CASP improves diverse backbone GNNs across most evaluated homophilic and heterophilic settings.
[LG-122] Role-Specific Predictive Geometries for Nonstationary Multivariate Graph-Signal Forecasting
链接: https://arxiv.org/abs/2609.06519
作者: Yanbo Chen,Anamitra Makur
类目: Machine Learning (cs.LG)
*备注:
Abstract:Forecasting multivariate graph signals is challenging when node-level trajectories are nonstationary but stable relations persist across nodes and features. In an error-correction representation, long-run equilibrium restoration and short-run transient propagation represent different predictive roles and need not share a common cross-feature geometry. We introduce role-specific predictive geometries in which directed Long relations act on estimated equilibrium coordinates, whereas directed Short relations act on lagged differences. Matrix-valued Long responses mix equilibrium coordinates before graph propagation, while Short responses use graph-filtered transient designs; a direct multi-horizon estimator couples forecast corrections across adjacent horizons. Temporal cross-fitting and Frisch-Waugh-Lovell partialling-out give selected edges a conditional predictive interpretation relative to a graph-temporal backbone. The Long operator remains right-factorized through the equilibrium subspace and therefore annihilates source common-trend directions. Controlled experiments recover all planted Long relations (20/20), all planted Short relations (20/20), and both role families in every Dual realization (10/10). Across four real-world benchmarks, the proposed predictor improves on the G-VARMA backbone in three datasets, with all 25 fold-horizon comparisons favorable on the five-fold financial benchmark.
[LG-123] Model-Adaptive and Risk-Constrained Frequency Hopping Against Predictive Jammers
链接: https://arxiv.org/abs/2609.06514
作者: Yanbo Chen,Xinjing Zhou
类目: Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注:
Abstract:Adaptive frequency hopping against predictive jamming must address both model uncertainty and policy exposure: the context-loss relationship may vary across operating regimes, while persistent hopping patterns may expose high-probability channels to attack. We propose D-PACT-AFH, a model-adaptive and risk-constrained adversarial contextual-bandit framework in which a Tsallis-FTRL master combines a global linear learner with a partitioned local learner and selects the model class online. D-PACT-Hit incorporates channel-wise marginal hit risk into model selection, while D-PACT-Safe applies a minimum-Kullback-Leibler projection to enforce a per-slot risk budget. We establish estimator validity under non-anticipating attacks, an oracle decomposition relative to the better fixed base, and an exact conditional-risk guarantee for the Safe projection. Experiments across diverse channel regimes and jammer types demonstrate effective model adaptation and a controllable goodput-risk tradeoff: D-PACT-AFH recovers 95.5% of the local learner’s gain under observable switching while avoiding 77.7% of its degradation in a negative-control regime.
[LG-124] Bi-HYCO: Bi-Objective Cooperative Learning for PDE Parameter Identification under Frag mented Observations
链接: https://arxiv.org/abs/2609.06511
作者: Umberto Biccari,Jun Chen,Roberto Morales,Enrique Zuazua
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:Physical and synthetic models may describe complementary aspects of the same PDE-governed system while receiving different, possibly fragmented, observations. We propose Bi-Objective HYCO (Bi-HYCO), a cooperative framework that retains both representations and their local observational objectives while coupling their predicted states at unlabeled interaction points. These points contain no measurements and do not augment the data; they provide a communication mechanism in the common state space. The two criteria form a vector-valued objective, and weighted scalarizations provide computational realizations. For the deterministic shared-observation algorithm with fixed interaction points, we prove sufficient decrease and finite length of the whole alternating sequence, which converges to a mixed critical point under the stated Kurdyka-Lojasiewicz-type assumptions. Elliptic transmission and two-dimensional Navier-Stokes experiments assess parameter and state reconstruction, noise and scalarization effects, and PINN/XPINN references. Ablations show that removing state interaction while retaining aggregation deteriorates parameter recovery in the tested configurations, particularly for Navier-Stokes.
[LG-125] Structural Entropy-Driven Graph Diffusion Generation for One-Shot Federated Graph Learning
链接: https://arxiv.org/abs/2609.06499
作者: Shutong Zheng,Lele Fu,Sheng Huang,Wei Yang Bryan Lim,Chuan Chen
类目: Machine Learning (cs.LG)
*备注: 11 pages, 5 figures
Abstract:One-shot federated graph learning (FGL) requires the server to estimate client contributions from highly compressed information, yet conventional volume-based weighting captures the amount of client data while overlooking how its connectivity is organized. In this paper, we propose SPIRE, a Structural Entropy-Driven Graph Diffusion Generation method that introduces topology-aware client differentiation into one-shot FGL. Specifically, we employ first-order degree-distribution structural entropy as a compact descriptor of degree-mass dispersion and use it to derive structural client weights, providing an inductive bias that accounts for differences in graph topology beyond data volume. On the generation side, a graph diffusion model on the server synthesizes pseudographs conditioned on the weighted client prototypes, capturing both semantic and structural information without requiring additional client-side training. The generated pseudographs are then assembled via disjoint union fusion to train a global graph neural network. Extensive experiments on seven real-world graph datasets demonstrate that SPIRE consistently outperforms conventional and one-shot FGL methods, with particularly strong gains under highly heterogeneous (non-IID) and graph-perturbed settings.
[LG-126] How Does Parameter Pruning Reshape DNN Representations? An Interaction-Driven Exploration
链接: https://arxiv.org/abs/2609.06483
作者: Fangbo Li,Junpeng Zhang,Qihan Ren,Quanshi Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:This study focuses on the scientific problem of understanding internal factors that govern the diverse performance degradation of deep neural networks (DNNs) when different parameters are pruned. In order to explain why pruning certain parameters leads to significant performance degradation but pruning other parameters does not, we examine how the pruning operation affects the interaction patterns encoded by the DNN. We find that when we progressively increase the pruning ratio, the interaction patterns encoded by DNNs exhibit a distinct three-phase dynamics, \emphi.e., model performance is not largely affected until the pruning operation begins to remove low-order interactions, and low-order interactions exhibit strong generalizability. Moreover, we find that the high sensitivity of DNN performance to the pruning of certain modules is attributed to whether the pruning operation removes generalizable low-order interaction patterns.
[LG-127] Learning Kernels by Alignment for Multiclass Bayes Classification
链接: https://arxiv.org/abs/2609.06474
作者: Hollan Haule,Alfredo Gonzalez-Sulser,Javier Escudero
类目: Machine Learning (cs.LG)
*备注:
Abstract:Kernel methods separate data representation from decision-making, but typically require the kernel to be chosen in advance. We show that this kernel can instead be learned by alignment, and develop the resulting framework through the recently introduced Collaborative Learning and Inference (CLaI). We show that Collaborative Learning can be viewed as a kernel alignment process, in which an embedding is trained so that its induced similarity matches a label-derived target kernel. We also prove that Collaborative Inference is equivalent to kernel Bayes classification with Parzen-window density estimation. Motivated by these perspectives, we generalise CLaI by replacing cosine similarity with a learned Mahalanobis distance and extend it to multiclass classification. On CIFAR-10, PathMNIST, and SleepEDF, the Mahalanobis formulation improves accuracy, converges faster, and yields lower calibration error than the cosine-based variant. Auxiliary experiments further support these connections, showing that CLaI produces latent signals of the same form as a Gaussian process, while achieving competitive calibration on sepsis prediction. Together, these results establish a principled learned-kernel framework that unifies representation learning, kernel alignment, and Bayesian classification, and extends naturally to the multiclass setting.
[LG-128] Steering Under Compression: Dose-Response Capability Cost and Failure Asymmetry in Quantized LLM s
链接: https://arxiv.org/abs/2609.06473
作者: Saurav Bhandari,Benjamin Wade
类目: Machine Learning (cs.LG)
*备注: 17 pages, 5 figures
Abstract:Inference-time activation steering enables behavioral control of large language models without parameter modification, while post-training quantization reduces memory and compute costs for deployment. Despite their growing convergence in practice, the interaction between these two techniques remains uncharacterized. We systematically study activation steering under weight-only quantization (INT8 and NF4) across four open-weight 7-9B models and two behavioral targets: judged sentiment and judge-free reasoning length. Using an iso-effect framework that compares capability costs at matched behavioral effect, we find that sentiment steering survives quantization intact. After correcting a GSM8K parser artifact with a uniform v2.3.1 rescore, the pooled INT8 contrast is -0.010 (90% CI [-0.026, +0.007]), descriptively Equivalent under the preregistered three-label rule, while NF4 remains Inconclusive at -0.017 ([-0.067, +0.033]). In contrast, reasoning length exhibits a surprising asymmetric dose-response: lengthening is graded but terminates in cap-runaway and collapse, while shortening is a step function with only 12-30% shortening (model-dependent) before discontinuous failure. We expose a methodological pitfall: the naive iso-effect ladder anchors on the collapse floor for floor-bounded targets, and we introduce a censored construction that restores interpretable crossings. We also quantify a substantial baseline capability shift for Mistral-NF4 (0.545 to 0.365 GSM8K at alpha=0), demonstrating that compression can dominate the steering intervention. Despite this, steering vectors remain highly collinear with their FP16 siblings (cosine similarity 0.989-0.998 for INT8, 0.945-0.990 for NF4), confirming that the behavioral direction survives quantization even when the cost structure does not. All code and data are released.
[LG-129] Sector-Mean: Deterministic Initialization of K-Means Centroids via Angular Sector Partitioning
链接: https://arxiv.org/abs/2609.06468
作者: Abhiyan Dhakal,Pranish Kafle,Rajani Chulyadyo(Kathmandu University)
类目: Machine Learning (cs.LG)
*备注: 7 pages
Abstract:K-Means is one of the most widely used clustering algorithms, but its susceptibility to initial centroid selection remains a primary bottleneck for its convergence speed and clustering accuracy. This paper proposes Sector-Mean Initialization, a deterministic initialization strategy with O(N) time complexity that partitions the two-dimensional data space into angular sectors around the global centroid and initializes centroids using sector-wise means. We evaluate the method on established two-dimensional benchmarks (SIPU, Birch) and multiple real-world datasets, comparing against random, K-Means++, and Max-Min initialization under identical Lloyd iterations. The statistical analysis of Friedman’s test (p0.05) and Nemenyi post-hoc comparison indicates that, while delivering equivalent clustering quality as K-Means++ and Max-Min, Sector-Mean offers significant computational efficiency. Experimental results show that Sector-Mean reduces the initialization time by 74.9% and 59.8% in comparison to K-Means++ and max-min, respectively. And, it yields the lowest average number of iterations, achieving approximately 5% fewer iterations than K-Means++ and 16% fewer than max-min. These results highlight that Sector-Mean initialization offers a deterministic and computationally efficient initialization strategy while preserving cluster quality.
[LG-130] Local and Global Stability in Performative Reinforcement Learning
链接: https://arxiv.org/abs/2609.06467
作者: Debmalya Mandal
类目: Machine Learning (cs.LG)
*备注:
Abstract:In performative reinforcement learning the deployed policy shapes the environment that generates the learner’s future data, and the natural solution concept is a performatively stable policy that is optimal in the environment it induces. Existing convergence guarantees rely on Lipschitz sensitivity assumptions on the environment map \pi \mapsto (P_\pi, r_\pi) , which are hard to verify and fail in settings such as multi-agent best-response dynamics. We instead study stability for mixtures of policies, and show that the resulting picture is fundamentally different from performative prediction, where randomization removes the need for any sensitivity assumption. We distinguish local mixed stability, an occupancy-weighted first-order relaxation that we show is equivalent to stationarity, from global mixed stability, which certifies against arbitrary deviating policies. Our first result is that a weighted per-state Hedge dynamic drives the local stability gap to zero at an O(1/\sqrtT) rate for an arbitrary, possibly discontinuous, environment map, both with exact and with trajectory feedback. The two notions genuinely differ: we exhibit an instance where local stability is achieved exactly but every mixture has global stability gap bounded away from zero. For global stability we introduce a bounded transition range assumption, strictly weaker than Lipschitz sensitivity, under which unweighted per-state Hedge converges up to a floor of O(\gamma\epsilon_P/(1-\gamma)^3) , and we prove a matching-in- \epsilon_P lower bound of \Omega(\gamma\epsilon_P/(1-\gamma)) under trajectory feedback, so this floor is unavoidable. Finally, we extend both notions to n -player performative Markov games, obtaining local stability with no assumption on the joint environment map or game structure, and global stability for performative Markov potential games.
[LG-131] A Theoretical Framework for Masked Pretraining (MPT)
链接: https://arxiv.org/abs/2609.06460
作者: Qi Zhang,Runyu Zhou,Yifei Wang,Yisen Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Recently, Masked Pretraining (MPT) based on reconstruction pretraining tasks has risen to a promising self-supervised learning paradigm across various domains and achieves remarkable performance in multiple downstream tasks. However, the theoretical understanding of the working mechanism behind MPT is still limited. In this paper, we introduce a new theoretical framework to analyze MPT and understand the crucial role of masking in extracting meaningful representations. We establish theoretical connections between MPT and another popular self-supervised paradigm: contrastive learning. We prove that the masking technique implicitly creates positive pairs that are semantically similar and the reconstruction loss pulls them together in the feature space. Besides, as a result of the implicit alignment, we point out the dimensional collapse issue of MPT and propose a Uniformity-enhanced MPT (U-MPT) loss that can effectively address this issue and bring significant improvements in downstream tasks including linear evaluation, cross-dataset fine-tuning and out-of-distribution generalization on real-world data sets. Furthermore, we establish downstream guarantees of U-MPT and theoretically analyze the influence of masking strategies. Based on the theoretical analysis, we propose a new masking strategy which enhances the downstream performance of MPT and explains current improvements of masking strategies with our theoretical perspective.
[LG-132] Stability and Generalization of Straight-Through Estimators for Training Two-Layer Quantized Neural Networks
链接: https://arxiv.org/abs/2609.06430
作者: Yiming Ying
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:We study the identity straight-through estimator (STE) for training a two-layer binary-activation network with hinge loss from the perspective of Statistical Learning Theory (SLT). Our central question is whether algorithmic stability can explain the statistical generalization of the estimator produced by the discontinuous STE training rule. In the saturated-output regime, the zero-initialized samplewise STE recursion is exactly the stochastic subgradient descent on the convex latent loss (-yu^\top x)_+ . This representation makes a stability analysis possible. We derive an exact distance identity for two coupled updates and prove approximate non-expansiveness of the common-example map, with a quadratic defect only when the two latent margins straddle zero. We then obtain explicit \ell_2 on-average model-stability and generalization bounds, transferring stability isometrically from the latent vector to the full first-layer matrix. Combining stability with a standard optimization bound yields an explicit excess induced-risk guarantee and the rate O(n^-1/2) when T=n^2 . Under margin separability, a complementary argument gives the optimal-order O(R^2/(\gamma^2n)) expected excess misclassification error for a randomized one-pass STE iterate and a corresponding majority-vote bound.
[LG-133] Sparse Oblique Rule Boosting for Simpler Additive Rule Ensembles
链接: https://arxiv.org/abs/2609.06426
作者: Shahrzad Behzadimanesh,Pierre Le Bodic,Geoffrey I. Webb,Mario Boley
类目: Machine Learning (cs.LG)
*备注: Accepted for publication in Journal of Data Mining and Knowledge Discovery. The Version of Record is forthcoming
Abstract:Small additive ensembles of symbolic rules offer interpretable prediction models. Traditionally, these ensembles use rule conditions based on conjunctions of simple threshold propositions x \geq t on a single input variable x and threshold t , resulting geometrically in axis-parallel polytopes as decision regions. While this form ensures a high degree of interpretability for individual rules and can be learned efficiently using the gradient boosting approach, it relies on having access to a curated set of expressive input features so that a small ensemble of axis-parallel regions can describe the target variable well. Absent such features, reaching sufficient accuracy requires increasing the number and complexity of individual rules, which diminishes the interpretability of the model. Here, we extend classical rule ensembles by introducing logical propositions with learnable sparse linear transformations of input variables, i.e., propositions of the form \mathbfx^T\mathbfw \geq t , where \mathbfw is a learnable sparse weight vector, enabling decision regions as general polyhedrons with oblique faces. We propose a learning method using gradient boosting based on a weighted logistic regression. Empirical results across 14 regression and classification tasks demonstrate that the proposed method achieves lower model complexity than competitive baselines while maintaining similar or better predictive accuracy. Hence, the approach provides a favorable trade-off between interpretability and accuracy and reduces the reliance on manual feature engineering.
[LG-134] MetaRSI / RSI2: A Meta-Recursive Self-Improving System for Recursive Self-Improving Systems Themselves
链接: https://arxiv.org/abs/2609.06396
作者: Zihan Tan,Leixin Sun,Zitong Shi,Yitao Liu,Jiajun Wu,Nathaniel Brooks,Jiaru Qian,Xiaoran Shang,Suyuan Huang,Yi Ding,Yangxu Liao,Mukai Li,Qiushi Sun,Shudong Liu,Xuankun Rong,Xiaohang Yu,Zhuo Chen,Hejia Geng,Chenxin Li,Aozhou Wang,Zengji Tu,Robert Tang,Yuxin Zhan,Eric Jiang,Yuxin Wu,Jianqing Zhang,Xiao Liang,Fang Wu,Haochi Zhang,Alexander Marlow,Guancheng Wan
类目: Machine Learning (cs.LG)
*备注: 47 pages, 12 figures, 11 tables
Abstract:Recursive self-improvement (RSI) lets a system improve the model-building machinery from its own failures, so every later model inherits the gain. Yet RSI has been validated almost exclusively on coding and formal benchmarks such as science QA and mathematics. This format bound limits RSI to improvement within a machine-checkable slice, not general capability where questions are open and correctness is settled by argument, replication, or measurement. We argue RSI must next operate across real, diverse scientific, engineering, and meta-scientific domains, not where formal evaluation is merely tractable. To that end we present MetaRSI-v1, where improvement is the scheduled composition of three typed operators over one unified paradigm. Data-RSI amplifies existing competence and marks its boundary; Harness-RSI edits a five-slot scaffold without touching weights; Model-RSI internalizes capability into parameters through bounded training. Sharing one loop kernel and artifact vocabulary, they make data, scaffold, and model changes composable rather than exclusive. A two-axis optimizer jointly decides operator order and each operator’s proposal policy, while a meta-level policy revises the schedule across terms. We validate MetaRSI-v1 under the field’s standard evaluations, on code and closed-form science, with no external teacher: the target model plays every role in its own loop. MetaRSI-v1 reframes self-improvement from a single-surface edit to a composition across the full model-production pipeline, opening two paths: a model route internalizing capability through training, and a harness route leaving weights untouched and thus extending self-improvement to any model reachable through an interface, with Data-RSI redefined as the shared substrate feeding both. The framework further yields refutable laws on where loops exist, how operators compose, and what supervision buys.
[LG-135] Robust Dynamic Expansion for Continual Learning under Backdoor Attacks via Purification and Selective Recovery
链接: https://arxiv.org/abs/2609.06346
作者: Keyu Lin,Fei Ye,Qihe Liu,Shijie Zhou,Jiguo Yu
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注: 18 pages, 5 figures, 5 tables
Abstract:Continual learning (CL) enables models to acquire new knowledge from sequentially arriving tasks while retaining previously learned knowledge. However, in practical scenarios, task streams collected from untrusted sources may contain backdoor-poisoned samples, posing a critical challenge to the stability, plasticity, and security of continual learners. In this work, we investigate a challenging setting termed Continual Learning Under Backdoor Attack (CLUBA), where each incremental task may involve a small proportion of maliciously manipulated training samples. Unlike conventional continual learning or backdoor defense scenarios, CLUBA requires models to simultaneously mitigate catastrophic forgetting, preserve adaptation capability, and prevent the absorption of malicious supervision during sequential updates. To address this challenge, we propose a robust dynamic-expansion framework that integrates sample purification, selective recovery, and robust expert routing into a unified continual learning paradigm. Specifically, we introduce Bi-Prototype Purification (BPP) to identify suspicious samples by exploiting semantic discrepancies in feature space. Based on purified data, Gradient Discrepancy-based Robustness Optimization (GDBRO) selectively recovers informative poisoned samples through pseudo-label correction and gradient consistency evaluation, improving robustness while maintaining model plasticity. Furthermore, Robust Feature Consistency-based Expert Selection (RFCBES) constructs perturbation-aware class prototypes to enable reliable expert routing under corrupted or shifted inputs.
[LG-136] Representation Learning for Sample-Efficient CATE Estimation by Leverag ing Multiple Outcomes
链接: https://arxiv.org/abs/2609.06294
作者: Maitreyi Swaroop,Shikha Bhat,Samantha Rodriguez,Tamar Krishnamurti,Bryan Wilder
类目: Machine Learning (cs.LG)
*备注:
Abstract:Estimating conditional average treatment effects (CATE) enables efficient targeting of interventions, but many applications have limited experimental samples, making it difficult to estimate heterogeneous effects from high-dimensional covariates. In such settings, policymakers and medical practitioners often succumb to the curse of dimensionality or apply off-the-shelf dimension reduction methods that may not preserve treatment heterogeneity. Yet these domains often come with large historical datasets measuring a wide range of outcomes – a source of supervision that is rarely exploited in practice. Following causal representation learning, we hypothesize that such domains with high-dimensional covariates have lower-dimensional underlying dynamics. We can thus leverage the diverse outcomes measured in historical data to learn a lower-dimensional representation of the covariates. Theoretically, we prove that when the auxiliary outcomes satisfy a set of surrogacy conditions and the representation retains relevant covariate information, the original CATE is identified when the high-dimensional covariates are replaced by the learned representation. Combined with existing dimension-dependent rates for CATE estimation, the result implies greater sample-efficiency on the same experimental sample. Additionally, we characterize the bias-variance tradeoff when the assumptions do not hold perfectly, and show that the representation-based estimator can still achieve lower error when the reduction in estimator variance outweighs the bias due to compression. Empirically, we evaluate the method on synthetic data and semi-synthetic medical data.
[LG-137] SeaCausal-FL: Federated Fuzzy Causal Learning for Maritime IoT Fault Diagnosis and Counterfactual Reasoning
链接: https://arxiv.org/abs/2609.06257
作者: Yuhang Qiu,Haihan Zhu,Koteeswaran Seerangan,Longsheng Zhu,Xiong Wang,Yijun Lu,Zheng Lin,Fangmin Ren,Jialiang Xie
类目: Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注: 16 pages, 8 figures
Abstract:Reliable marine-engine fault diagnosis in maritime IoT is challenged by distributed data ownership, heterogeneous fault distributions, and continuously changing operating conditions. This paper proposes SeaCausal-FL, a federated fuzzy causal learning framework that combines a shared temporal diagnostic path with mechanism-conditioned causal reasoning. An interval type-2 fuzzy layer represents uncertain and overlapping operating mechanisms, while each mechanism is associated with a physics-constrained structural causal model. Before aggregation, locally learned mechanisms are aligned using operating context, causal structure, and conditional intervention-response signatures. Model parameters are then aggregated according to sample, class, mechanism, and mechanism-class evidence instead of client sample size alone. The learned structural equations further support interval counterfactual reasoning through abduction, action, and prediction. Experiments on a marine-engine fault dataset and a real-data-calibrated semi-synthetic causal benchmark show that SeaCausal-FL achieves an average F1 score of 87.07% across four client partitions, with AUROC and AUPRC of 98.98% and 94.81%, respectively. It also maintains strong performance under unseen loads and fault-type omission during training. On the causal benchmark, SeaCausal-FL reaches an Edge-F1 of approximately 0.58 and an Edge-AUPRC of 0.68, reduces coefficient RMSE to about 0.14, and provides favorable counterfactual estimation and intervention decisions.
[LG-138] EgoNeMo: Transferable Map of Pedestrian Dynamics via Egocentric LiDAR Scan
链接: https://arxiv.org/abs/2609.06195
作者: Azusa Sawada,Allan Wang,Hideo Saito,Aaron Steinfeld
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注: 16 pages, 9 figures
Abstract:This paper proposes a transferable Map of Dynamics (MoD) framework that generalizes to unknown environments using only egocentric 3D LiDAR point clouds to overcome the long-standing limitation of traditional MoD methods. While MoDs are essential for encoding human motion characteristics to enable accurate pedestrian trajectory prediction or safe robot navigation, traditional approaches suffer from site-specificity, requiring exhaustive trajectory accumulation at every new location. Extending recent advances in neural implicit modeling, our framework trains a continuous, LiDAR-based MoD estimator across diverse environments. To mitigate the inherent sparsity and temporal bias of real-world trajectory data, we introduce a position-balanced sampling strategy and a multi-task learning architecture that jointly predicts motion distributions and a spatial frequency score map. The latter is further augmented by visibility-aware losses to compensate for incomplete observation data. Comprehensive experiments demonstrate that our method effectively reconstructs underlying motion maps even in unknown locations from a single instantaneous LiDAR scan, despite highly sparse training data. Finally, we show that our improvements enhance the reliability of downstream trajectory prediction.
[LG-139] Spectral Prioritized Sweeping in Nonstationary Reinforcement Learning NEURIPS
链接: https://arxiv.org/abs/2609.06186
作者: Hung Pham,Tuan Dam
类目: Machine Learning (cs.LG)
*备注: 40 page, neurips pending
Abstract:Prioritized Sweeping (PS) accelerates model-based reinforcement learning by selecting backups according to Bellman residual magnitude. In nonstationary reward settings, however, the canonical priority score is shortsighted: after a localized reward shift, residuals propagate only through realized backups, so bottlenecked or topologically distant state estimates may remain static under a limited replanning budget. We introduce the Graph Topology Augmentation framework, which employ the graph’s resolvent and its diffusion semantic, to augment the inquired signal. Our application, Graph Topology Augmentation for Prioritized Sweeping (GTA-PS), or which the alias Spectral Prioritized Sweeping (SPS) might be more universal, provides a drop-in ordering score for the setting of fixed dynamics and changing state rewards. GTA-PS uses a smootherized policy, inducing a transition chain, with its in- and out-Laplacian. The standard priority key is augmented with a mixing of regularized Laplacian inverses diffusing the residual magnitude. Furthermore, the topology contribution is annealed by a scheduler based on the Second Largest Eigenvalue Modulus (SLEM), allowing its scale to adapt to the chain’s mixing regime. We prove that the forward potential coincides with geometric discounted residual propagation and show that GTA-PS gives active priority instantly to all states. Tabular experiments on FourRooms and GARNET domains demonstrate improved replanning efficiency over standard PS under both exact DP and Dyna-style host planners.
[LG-140] FMMO: Detecting the Divergence Between Local Attribution and Global Drift
链接: https://arxiv.org/abs/2609.06173
作者: Muhammad Rehman Zafar,Ali El-Sharif,Naimul Khan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Post-deployment drift poses a critical risk to algorithmic accountability, particularly when ground truth labels are delayed and performance degradation becomes a “silent failure”. While Explainable AI (XAI) is often relied upon to audit these shifts, we demonstrate that popular local attribution methods (e.g., TreeSHAP) can exhibit misleading stability even as model reliability collapses. In this paper, we propose a Framework for Model Monitoring and Observability (FMMO) designed to expose the divergence between local explanation stability and global distribution shifts. Using benchmark, synthetic, and real-world datasets, we show that local XAI methods fail to flag drift-induced disparate impact, specifically where False Positive Rates spike for protected groups while feature attributions remain unchanged. By integrating global surrogate models with model utilization measurements, FMMO mitigates this fairness blind spot, ensuring that stakeholders can detect discriminatory deterioration that standard local XAI tools overlook.
[LG-141] Rethinking One-Shot Federated Graph Learning: Training-Free Statistical Estimation
链接: https://arxiv.org/abs/2609.06154
作者: Shutong Zheng,Sijia Chen
类目: Machine Learning (cs.LG)
*备注: 22 pages, 9 figures, including supplementary material
Abstract:One-shot federated graph learning generally aims to train Graph Neural Networks (GNNs) across clients with disconnected subgraphs in a single communication round. Existing methods predominantly design advanced optimization strategies under the premise that local GNN training is indispensable. However, empirical observations reveal that under extreme non-IID conditions, local GNN training suffers from severe cross-client representation misalignment, becoming a major source of error rather than a remedy. Motivated by this, we reformulate one-shot FGL as a statistical estimation problem. We propose SPEAR (Statistical Prototype Estimation with Adaptive Reliability), a completely training-free framework that directly computes topology-smoothed class prototypes from local graphs in the original feature space. The server then aggregates these prototypes using a sample-size-adaptive shrinkage estimator that down-weights unreliable local estimates, producing robust global class prototypes. Extensive experiments across seven benchmarks demonstrate that SPEAR consistently achieves state-of-the-art accuracy under extreme heterogeneity. Moreover, SPEAR delivers at least an order-of-magnitude speedup over all baselines, reaching several orders of magnitude against generative and distillation-based methods. Our findings suggest that training-free statistical estimation, rather than local GNN optimization, provides the key to robust and efficient one-shot federated graph learning. The code is available at this https URL .
[LG-142] Compressed Recurrent Feedback in Tsetlin Machines: A Reproducible Boolean-FSM Study
链接: https://arxiv.org/abs/2609.06133
作者: Ankit Kumar,Utkarsh Raj,Rishad Shafik,Sudip Roy
类目: Machine Learning (cs.LG)
*备注: 8 pages, 4 figures, Accepted in ISTM 2026
Abstract:Sequential inference on small devices requires a model to retain useful history without repeatedly processing a long input record. A Recurrent Tsetlin Machine (RTM) provides this memory by returning Boolean clause outputs from one time step as inputs to the next. Direct feedback, however, grows with the clause bank and can make the recurrent input unnecessarily wide. This paper investigates a fixed-width alternative. We combine clause activations by exclusive-OR (XOR) folding, retain the folded bits at two time scales, and threshold them back to a binary state. The resulting design reduces 480 clause activations to 96 recurrent bits. We evaluate the method on a reproducible Boolean finite-state-machine benchmark with explicit transition rules, data splits, and random seeds. Across 144 runs, the compressed model obtains 61.47 \pm 6.74% and 62.94 \pm 9.92% accuracy on the two task families. Raw clause feedback changes these means by less than one percentage point, while increasing the recurrent width tenfold and measured host execution time by 4.38\times and 3.71\times . Gated neural models remain more accurate, and a no-feedback control retaining only short input history achieves comparable or slightly higher accuracy. On this benchmark, folding matches raw feedback within small empirical margins at a much narrower interface; these findings also underscore the critical necessity of no-feedback recurrence controls when benchmarking sequence models.
[LG-143] Sparse Incident-Cluster Learning for 12-hour Port Flood Pre-warning in Digital-Twin Analytics
链接: https://arxiv.org/abs/2609.06109
作者: Jie Zhang,Qiang Ni,David Windridge,Huan X. Nguyen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Port flood digital twins require analytics that warn operators before disruption, but official warning incidents are often few and adjacent observations are temporally dependent. Row-level classification can therefore overstate performance by placing windows from the same event in both model-development and evaluation data. We formulate 12-hour port flood pre-warning as an incident-cluster learning problem and evaluate a digital-twin analytics module using eight-point water-level histories, prediction-time contextual covariates, and interpretable short-window dynamics. The protocol combines fold-specific sparse feature selection, warning-cluster grouping, negative-label controls, 100-repeat random top-k controls, and alert-episode evaluation. Liverpool is the primary four-cluster case study, with harmonised Humber/Hull-proxy and Wessex South data used for protocol-transfer checks. Across the Liverpool folds, the top-10 ElasticNet model achieves mean F2 = 0.696, compared with 0.633 without top-k truncation and 0.681 for full-feature weighted XGBoost. It is the strongest ElasticNet variant, remains competitive with the nonlinear reference using only ten predictors, and exceeds the repeat-level 95th percentile of broad and same-family random subsets. Contextual covariates provide a strong prediction-time anchor, complemented by physically interpretable local dynamics. Historical replay converts risk scores into alert episodes and measures alert duration and false-episode burden. The result is an offline-evaluated analytics and validation module designed for integration into a port digital twin.
[LG-144] FANS: Federated Adaptive Network Search Learning for Heterogeneous Devices
链接: https://arxiv.org/abs/2609.06106
作者: Jiaxin Zhang,Xingwei Wang,Bo Yi,Liang Zhao,Alireza Furutanpey,Ziyi Chen,Qiang He,Keqin Li,Schahram Dustdar
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注:
Abstract:Heterogeneous Federated Learning (HFL) aims to train models across devices with diverse resource budgets while preserving data privacy. Existing HFL methods typically bind training to a small predefined menu of model configurations, which limits architectural coverage. To address this bottleneck, we introduce Federated Adaptive Network Search (FANS), a hypernetwork-based framework that learns a shared architecture space rather than a fixed set of client models. To optimize this shared space efficiently, we propose the Federated Parallel Scaling (FPS) algorithm, which jointly trains multiple sampled subnetworks in parallel with self-distillation so that larger sampled subnetworks can supervise smaller ones during local updates. We evaluate FANS on CIFAR-10, CIFAR-100, and MNLI using ResNet-18, DenseNet-121, and BERT-base, respectively. Across all benchmarks, FANS expands the feasible subnetwork pool by orders of magnitude (e.g., 4,680 candidates for ResNet-18 vs. 4 in existing methods) and improves the average accuracy-efficiency trade-off relative to representative HFL baselines. Device heterogeneity is emulated through resource tiers, and evaluation covers accuracy, parameter count, and MACs.
[LG-145] Connectome-to-Function: Conditional Generative Latent Representations for Reservoir Computing
链接: https://arxiv.org/abs/2609.06093
作者: Zhuolin Yu,Xingyu Liu,Yuanhao Jia,Yunhang Xiao,Hairuo Xue,Feihan Sun,Guozhang Chen
类目: Machine Learning (cs.LG)
*备注: 9 pages, 4 figures. Supplementary material included
Abstract:Connectomes, graph-level maps of neurons and their synaptic connections, provide a structural basis for understanding how brain circuits support function and computation. However, mapping connectome structure to computation remains difficult because these graphs are high-dimensional, sparse, and sensitive to local structural variation. Existing approaches often depend on hand-crafted structural descriptors or task-specific predictors, which limits their ability to represent connectomes in a form that is both generative and functionally meaningful. We propose a conditional generative latent framework that encodes connectome graphs into a compact structural space while using available node-level conditions to guide reconstruction and generation. From this space, the model can reconstruct observed connectivity with a mean edge-reconstruction AUC up to 0.910 and generate new candidate connectomes, enabling a unified analysis of graph structure and computational behavior. Using connectome-derived graphs as recurrent computational substrates, we found that the learned latent space captures functional variation across reservoir-computing experiments, with cross-validated R^2 values up to approximately 0.87. Interpretability analysis further revealed task-specific structural mechanisms: in our examples, memory performance is associated with reciprocal recurrent connectivity, whereas prediction and classification are more strongly associated with spectral properties of the recurrent network. These findings suggest an AI-for-science approach to linking neural connectivity to computation and provide a generative and interpretable basis for studying how distinct structural mechanisms shape computational capacity.
[LG-146] Learning to Price and Stock Under Contextual and Censored Demand
链接: https://arxiv.org/abs/2609.06083
作者: Zean Han,Zezhen Ding,Jiheng Zhang
类目: Machine Learning (cs.LG)
*备注: 9 pages, 3 figures
Abstract:To make optimal joint pricing and inventory control decisions is a critical challenge for modern retailers. In practice, retailers face changing market conditions where demands are influenced by various contextual factors, while simultaneously dealing with the difficulty of lost sales that obscure true demand information. However, existing approaches often fail to account for both contextual information and censored demand observations. We address this gap by presenting a framework where we model demand as a linear combination of basis functions with unknown coefficients, allowing for adaptive pricing and inventory decisions that respond to changing contexts. We propose an efficient algorithm to achieve regret bound \mathcalO(K\sqrtT\log T) under concave revenue conditions and \mathcalO(K^2/3T^2/3(\log T)^1/2) for the general case, with matching lower bounds confirming optimality. Extensive numerical experiments across diverse scenarios demonstrate our algorithm’s effectiveness.
[LG-147] Beyond Retraining-Free MoE Compression: A Cost-Normalized Study of Post-Compression Adjustment EMNLP2026
链接: https://arxiv.org/abs/2609.06076
作者: Sieun Hyeon,Jaeyoung Do
类目: Machine Learning (cs.LG)
*备注: Accepted to EMNLP 2026 (Main Conference)
Abstract:Retraining-free MoE compression reduces deployment memory by pruning or merging experts, but often treats the compressed checkpoint as the final artifact. We argue that this view is incomplete: compressed MoE checkpoints are better understood as compressed initializations that benefit from a tiny post-compression adjustment stage. Across two MoE LLM backbones, four pruning/merging methods, three expert-retention ratios, and 28 benchmarks, we compare LM fine-tuning and teacher-based KD under matched small-data budgets and measured GPU costs. Using only 3,000 C4 examples and a single epoch of adjustment, Full FT recovers 37.3% of the original-to-compressed performance gap on average. Moreover, LM fine-tuning is more cost-effective than standard token-level KD, and full-parameter adjustment gives the strongest cost–recovery trade-off among the tested scopes. These results suggest that retraining-free compression should be paired with small post-compression adjustment to recover a substantial portion of the performance lost during compression.
[LG-148] Data Quality Rule Generation with LLM s
链接: https://arxiv.org/abs/2609.06053
作者: Anna-Christina Glock,Thomas Hütter,Johannes Fürnkranz,Wolfram Wöß,Christine Dominka-Kiss,Lisa Ehrlinger
类目: Machine Learning (cs.LG)
*备注:
Abstract:The validation of data, such as customer and employee data, is an important task in many organizations. Errors in data can have severe consequences. For example, a wrong drug unit in a patient record can lead to life-threatening medication errors, and a missing street number in an address to failed deliveries. Companies often employ rule-based enterprise data quality (DQ) tools, which allow domain experts to specify rules to validate the data over time. While rule-based DQ tools are computationally efficient and provide explainable reports, maintaining a comprehensive rule set manually is challenging, as domain experts often overlook essential rules, especially in complex domains and large data volumes. Hence, closing these gaps remains an open problem in practice. In this paper, we address the challenge of automated DQ rule generation. For this, we formalize a generalizable generate-filter framework and introduce LeDQeR, an LLM-based DQ rule generation approach. First, a large language model (LLM) generates candidates rules from an observed dirty data tuple for a given rule-based DQ tool syntax. Second, we apply four filter techniques that ensure the (i) executability, (ii) correctness, and (iii) generalizability, and avoid (iv) redundancy of the generated rules. An extensive experimental evaluation suggests that LeDQeR is able to produce effective and compact rule sets for various datasets and error types.
[LG-149] FALCON-S: Fixed-wing ground-effect Aerodynamics Simulator and Flight Control Learning Suite
链接: https://arxiv.org/abs/2609.06046
作者: Matteo El Hariry,Pedro Lima,Andrej Orsula,Matthieu Geist,Miguel Olivares-Mendez
类目: Robotics (cs.RO); Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注: Index Terms: Robot Learning, Flight Control, Reinforcement Learning, Autonomous Navigation, Control and Dynamics, Modeling and Simulation, Wind In Ground Vehicles
Abstract:We present a modular, high-fidelity simulation framework for the development and benchmarking of flight control strategies in fixed-wing aerial robots operating near the ground. Unlike existing simulators that rely on simplified or hover-oriented dynamics, our framework models full 6DoF rigid-body physics, semi-empirical ground-effect aerodynamics, actuator dynamics, sensor noise, and environmental disturbances. This physical realism, combined with modular component design, enables systematic analysis of low-altitude flight behavior under realistic conditions. The simulator supports both CPU and GPU backends via Torch and NVIDIA Warp, enabling high-throughput parallel execution suitable for large-scale reinforcement learning training and optimal control rollouts. A unified interface accommodates a range of controllers (both RL and optical control algorithms) across tasks such as altitude regulation and trajectory tracking. Cross-validation with X-Plane and JSBSim is also supported to facilitate engineering integration and visual fidelity.
[LG-150] Minimizing the Effect of Sleep Deprivation in the Forward-Forward Algorithm ICPR2026
链接: https://arxiv.org/abs/2609.06042
作者: Joy Datta,Puja Saha,Rawhatur Rabbi,Nafiz Imtiaz Rafin,Swakkhar Shatabda,Md. Golam Rabiul Alam,Chad Mourning
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注: 14 pages, 6 figures, 7 tables. Accepted at the 28th International Conference on Pattern Recognition (ICPR 2026), Lyon, France, August 17-22, 2026. This is the accepted manuscript; the Version of Record is published by Springer in Lecture Notes in Computer Science and is available online at this https URL
Abstract:This paper addresses the challenge posed by sleep deprivation in the Forward-Forward algorithm, where separating the two passes in this algorithm and imbalancing the data processing in the passes is considered an imitation of the cognitive processes observed in humans suffering from sleep deprivation. Previous research has demonstrated that sleep deprivation in the Forward-Forward algorithm has a catastrophic effect on learning efficacy. To mitigate this issue, we explore several approaches; these include alternative activation, optimized loss function, and threshold tuning. To simulate periodic rest, we reduce the number of positive passes in alternating epochs, creating short break phases. We additionally investigate the potential of caffeine-induced stimulation to enhance performance during sleep-deprived conditions. Experimental evaluations conducted on the MNIST and Fashion-MNIST datasets demonstrate that these modifications improve accuracy under the context of sleep deprivation. For example, a 2%-62% accuracy gain is observed in a severe sleep deprivation setting (16 positive or awake periods and 1 negative or sleep period). The approaches also enhance the resilience of the algorithm and its alignment with the adaptive mechanisms of human cognition.
[LG-151] IXPLORE: Bounded Ideal Point Estimation with Grid-Based Uncertainty Quantification
链接: https://arxiv.org/abs/2609.06018
作者: Fynn Bachmann
类目: Machine Learning (cs.LG); Mathematical Software (cs.MS)
*备注:
Abstract:Ideal point estimation is widely used to analyze and visualize political data. However, selecting the corresponding spatial model involves various trade-offs: while model-based approaches such as Item Response Theory (IRT) are based on utility functions rather than optimized for predictive accuracy, most Machine Learning (ML) alternatives struggle to generalize beyond training data when embedding sparse test responses. We introduce IXPLORE, a bounded ideal point estimation algorithm that combines a predictive fit objective with a sparsity-aware likelihood function. On five benchmark datasets spanning surveys, roll calls, and deliberation, this approach surpasses model-based and ML-based algorithms on reconstruction and imputation error - especially for users with sparse responses. Furthermore, we show that non-linear feature transforms can further reduce the reconstruction error while remaining visually interpretable. To quantify uncertainty, IXPLORE applies grid-based posterior inference on a bounded 2D latent space. Available as a Python package on PyPI, IXPLORE offers a flexible framework for constructing bounded, interpretable political maps with fast inference and strong imputation performance.
[LG-152] Granular-Ball Quantum Clustering for Resource-Efficient and Robust Learning
链接: https://arxiv.org/abs/2609.06016
作者: Suzhen Yuan,Qilin Xie,Lifeng Shen,Shuyin Xia,Jermiah D.Deng,Guoying Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Quantum clustering aims to exploit quantum feature representations to uncover complex data structures beyond conventional Euclidean geometry. Yet this sample-level kernel construction requires O(n^2) quantum circuit executions for n data points, creating a major bottleneck under near-term quantum resource constraints. Prior solutions fail to resolve this efficiency-accuracy dilemma: classical granular-ball clustering reduces sample complexity but relies on Euclidean metrics that cannot capture quantum correlations, while existing quantum compression schemes prioritize efficiency over structural preservation, degrading performance on non-convex or noisy data. Here we propose Granular-Ball Quantum Clustering (GBQC), a framework that tightly couples granular-ball structural abstraction with quantum feature learning. GBQC first compresses raw data into compact, representative granular balls via a PCA-guided splitting strategy, reducing kernel evaluations by 80% compared to full-sample methods. A quantum cohesion mechanism then filters noisy granules in Hilbert space to improve clustering robustness. Extensive experiments on synthetic, noisy, overlapping, and real-world datasets demonstrate that GBQC consistently achieves superior clustering accuracy and robustness compared with representative classical and quantum clustering methods. Meanwhile, the proposed granular-ball compression significantly reduces quantum kernel evaluations and computational overhead, enabling quantum clustering experiments on larger datasets within parameterized quantum learning frameworks. These results suggest that granular-ball representations serve not only as a compression mechanism to reduce quantum computational costs but also as an effective structural abstraction mechanism that improves clustering quality by eliminating redundant and structurally ambiguous learning units.
[LG-153] Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model
链接: https://arxiv.org/abs/2609.06008
作者: Roberto Tacconelli
类目: Information Theory (cs.IT); Databases (cs.DB); Machine Learning (cs.LG)
*备注: 10 pages
Abstract:We present Cadence, an error-bounded lossy compressor for numeric time series pairing a 330M-parameter time-series foundation model (Google TimesFM-3) with an adaptive arithmetic coder, guaranteeing |\hatx_t-x_t|\le\tau on every sample. One negative result constrains the design space: for lossless coding a foundation model is worth nothing, because bits saved are logarithmic in predictor accuracy, \Delta b=\log_2(\mathrmMAE_old/\mathrmMAE_new) . So the 1.51\times advantage TimesFM-3 holds over a 32-tap linear predictor buys 0.60 bits of 20.28, a median gain of +0.03%. Error-bounded coding escapes this at one point: once a forecast lands inside the band the residual index is zero and the sample nearly free. Cadence contributes: (1) an adaptive range coder with context-modelled binarization, beating xz/zstd on real indices by 9.7% (15/15) and reversing a finding from a general-purpose back end; (2) a determinism result – predictions are not bit-identical across batch sizes, and no PyTorch configuration repairs this, forcing group size and execution device into the container format; and (3) domain localization on corpora postdating any plausible training cutoff. On 49 EIA-930 balancing-authority demand series (2026) Cadence gains 13.3% over the best of six classical predictors, and 28.3% on 50 MTA ridership series (2026): 21.4% median over 297 series-tolerance pairs, winning all 297. Against downsampling, what time-series databases deploy for retention, its guaranteed worst-case error is 28 – 56\times tighter at equal size. End-to-end, once the context bootstrap is paid for, gains run from 6.8% at six months of hourly data to 15.1% asymptotically. Attempting to falsify the domain claim on SDRBench, theory predicts failure and delivers: -0.8% median, 0 of 27 pairs gaining. Three further negative results and eight retracted claims are reported in full. Comments: 10 pages Subjects: Information Theory (cs.IT); Databases (cs.DB); Machine Learning (cs.LG) Cite as: arXiv:2609.06008 [cs.IT] (or arXiv:2609.06008v1 [cs.IT] for this version) https://doi.org/10.48550/arXiv.2609.06008 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-154] Machine Learning for Pre-Culture ESBL Risk Stratification to Guide Empiric Antibiotic Selection: A 12-Hospital Study of Enterobacteriaceae Cultures
链接: https://arxiv.org/abs/2609.05970
作者: Aravind V. Kuruvikkattil,Lalitha Pranathi Pulavarthy,Rashmita Kudamala,Saptarshi Purkayastha
类目: Machine Learning (cs.LG); Applications (stat.AP)
*备注: Accepted for presentation at American Medical Informatics Association (AMIA) Annual Symposium 2026
Abstract:Empiric antibiotic therapy for suspected ESBL-producing Enterobacteriaceae must be selected 48-72 hours before culture results, forcing clinicians to choose between undertreating resistant infections and overusing carbapenems that drive further resistance. We developed a cost-sensitive XGBoost model predicting an ESBL phenotype (resistance to ceftriaxone, ceftazidime, cefepime or piperacillin-tazobactam) at culture ordering using 45 pre-culture EHR features across 132,955 cultures from 72,217 patients at 12 hospitals (14.41% with the ESBL phenotype). Cultures were partitioned at the patient level. At 90% sensitivity, the model achieved 95.8% NPV, reducing post-test ESBL probability to 4.2%, a threshold that may support safe carbapenem-sparing in non-ICU settings, while sparing 307 of every 1,000 cultures an unnecessary broad-spectrum course at the cost of 14 missed ESBL cases per 1,000. SHAP analysis identified prior ESBL colonization as the dominant predictor, ahead of prior organism burden and neighborhood deprivation; removing deprivation features caused minimal performance loss ( \Delta\textAUROC = -0.020 ), enabling equitable bedside deployment. Discrimination was unchanged under a strict IDSA ESBL-E definition (AUROC 0.766), with specimen type added as a predictor (0.764) and without any class-imbalance correction (0.762), and ranged from 0.71 to 0.78 across organism strata.
[LG-155] On-the-go Forgetting without Explicit Unlearning via ERASE
链接: https://arxiv.org/abs/2609.05966
作者: Kushal Chakrabarti,Mayank Baranwal
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: this https URL
Abstract:Existing unlearning approaches typically rely on post hoc weight adaptation or distillation, leading to duplicated memory costs, degraded generalization, and limited scalability. In this work, we introduce ERASE, Erasure via Reconstructive Adversarial Signal Editing, a framework for on-the-go forgetting that suppresses the observable influence of private data without modifying model weights. ERASE leverages structured, class-conditioned input perturbations to induce selective forgetting during inference, eliminating the need for retraining, fine-tuning, or model copies. We rigorously characterize sufficient conditions when ERASE provably achieves functional forgetting of designated subclasses while preserving predictions across other subclasses within the same superclass. This analysis offers a principled foundation for inference-time forgetting under mild regularity assumptions. Across diverse architectures and benchmark datasets, ERASE maintains the best observed balance between forgetting efficacy, computational efficiency, and retention fidelity over recent unlearning-based methods. By reimagining data removal as forgetting without unlearning, our work establishes a scalable, regulation-aligned pathway for continual, privacy-conscious learning.
[LG-156] LoGIC: Budgeted Context Construction for Node-Level Graph In-Context Learning with Tabular Foundation Models
链接: https://arxiv.org/abs/2609.05955
作者: Mingqi Yang,Zidong Guo,Jihui Yang,Wenming Zuo
类目: Machine Learning (cs.LG)
*备注:
Abstract:Tabular foundation models have become powerful graph learners. Systems such as G2T-FM and GraphPFN encode each node as a feature row and make predictions through in-context learning (ICL), with labeled rows serving as the prompt. Current protocols employ the complete training table as context, causing attention to scale quadratically with the labeled pool and introducing preprocessing and memory bottlenecks. We investigate context construction for node-level graph ICL: which labeled nodes and auxiliary unlabeled nodes should constitute the prompt for specified queries. We formulate this allocation in terms of two resources: a labeled-context budget for predictive evidence and an unlabeled-halo budget for adapter message passing without using label capacity. We present LoGIC, which retrieves labeled nodes via structural, feature-based, and coverage channels, shares each context across the queries in a graph-local cluster, incorporates an unlabeled halo for adapter backbones, and chooses the channel and context budget without test labels. Across three backbone configurations drawn from two model families on GraphLand, budgeted contexts maintain locally runnable full-context performance, stay competitive with published large-dataset results, and markedly lower peak memory requirements compared with full-context and whole-graph inference. They further permit frozen graph ICL on million-node graphs without retraining. Our analysis identifies when retrieval channels work best and connects their behavior with graph properties.
[LG-157] QGB-WkNN: Quantum Granular-Ball Learning for Robust Classification
链接: https://arxiv.org/abs/2609.05952
作者: Suzhen Yuan,Dehang Chen,Lifeng Shen,Shuyin Xia,Jeremiah D.Deng
类目: Machine Learning (cs.LG)
*备注:
Abstract:Nearest-neighbor classification is widely used in machine learning, yet existing methods often suffer from low computational efficiency and limited robustness in noisy environments. To jointly address these challenges, this paper proposes an efficient and reliable weighted K -nearest neighbor classification framework based on quantum granular balls, termed QGB-W k NN. The proposed framework improves computational efficiency by integrating quantum-enhanced granular-ball representation with hierarchical nearest-neighbor search, while enhancing classification reliability through a purity-aware weighted decision mechanism. Specifically, quantum-kernel granular balls are constructed to reduce retrieval redundancy and strengthen nonlinear feature representation under limited quantum resources. A granular-ball purity-guided HNSW optimization strategy is developed to exploit structural reliability for hierarchical graph construction during neighbor retrieval, alleviating the local optimality issue caused by conventional random layering. Finally, a weighted voting mechanism jointly incorporating granular-ball similarity and purity is introduced to produce more reliable classification decisions in noisy environments. Extensive experiments on benchmark datasets demonstrate that QGB-W k NN achieves competitive classification accuracy while exhibiting favorable Pareto trade-offs between classification performance and computational cost. Moreover, the proposed framework consistently improves robustness under various noisy conditions, suggesting that reliability-aware quantum granular-ball learning provides a promising paradigm for efficient and robust nearest-neighbor classification.
[LG-158] Interpretable and Fair Generalized Additive Neural Networks via Multi-objective Learning
链接: https://arxiv.org/abs/2609.05946
作者: Ziming Wang,Changwu Huang,Ke Tang,Yew-Soon Ong,Xin Yao
类目: Machine Learning (cs.LG)
*备注: Published in Neural Networks
Abstract:Interpretability and fairness are two of the most emphasized dimensions in trustworthy artificial intelligence (AI). Various explainable AI methods have been introduced to improve interpretability. This paper focuses on neural network (NN)-based generalized additive models (GAMs), a class of self-interpretable models. While most existing research has prioritized improving the accuracy of NN-based GAMs, their interpretability remains largely underexplored. To address this gap, this paper introduces explicit quantitative metrics for evaluating the interpretability of NN-based GAMs, empirically examines their effectiveness, and explores strategies for improving interpretability within these models. In addition, the simultaneous and explicit optimization of both interpretability and fairness, along with their trade-offs and the underlying reasons, remains underexplored. To address this, we propose a multi-objective neural basis model (MONBM) framework based on multi-objective evolutionary learning to consider accuracy, interpretability, and fairness simultaneously. A partial retraining strategy is further developed to facilitate the practical application of evolutionary multi-objective optimization to deep model architectures. Based on MONBM, this paper reveals the complex relationships between these dimensions and the reasons behind these intricate relationships. This analysis demonstrates how multi-objective optimization can be combined with self-interpretable models to reveal relationships among trustworthiness objectives. In addition, MONBM obtains a set of models with different trade-offs between dimensions, and the competitiveness of the approach is validated by comparing it with state-of-the-art methods.
[LG-159] Rethinking the Evaluation of Efficiency Methods for Multi-Agent Systems
链接: https://arxiv.org/abs/2609.05933
作者: Jiamu Zhang,Lingxi Zhang,Pengjun Lu,Qiyue Zhang,Yu-Neng Chuang,Zhengchen Li,Shuai Xu,Vipin Chaudhary,Hanjie Chen
类目: Machine Learning (cs.LG)
*备注: The 2026 Conference on Empirical Methods in Natural Language Processing
Abstract:Efficiency is increasingly important for Large Language Model (LLM)-based multi-agent systems (MAS), as larger models and more agents introduce substantial execution costs. Recent methods aim to make MAS cheaper by pruning agents, removing communication edges, or searching for compact structures. However, we argue that existing evaluations may overestimate their true ability to improve MAS efficiency. Reported gains are often measured under method-specific prompts and starting topologies, making them difficult to attribute to the proposed structural changes. Moreover, many reported successes appear in non-MAS-demanding settings, where a single agent or a randomly pruned system can already preserve strong performance. To study these issues, we introduce a controlled and MAS-demanding diagnostic benchmark for representative MAS efficiency methods. We evaluate methods under a shared backbone model, agent registry, and runtime, across controlled variations in topology, scale, depth, and tool use. Our analysis shows that many reported gains are setup-dependent and may arise from structural collapse, disabled tool pathways, or starting systems where random pruning already preserves accuracy, rather than robust improvements in MAS efficiency.
[LG-160] A dictionary learning framework for graphs via filters and optimal transport
链接: https://arxiv.org/abs/2609.05919
作者: Jinchuan Liao,Dai Hai Nguyen
类目: Machine Learning (cs.LG)
*备注:
Abstract:We propose a graph dictionary learning (GDL) framework where each graph is represented as a zero-mean Gaussian distribution derived from its filtered Laplacian. Each observed graph is approximated by a barycenter over learned atom graphs, computed under the filter graph distance (fGOT), a graph comparison metric sensitive to global structural properties. The reconstruction error between the observed graph and its barycenter is measured by the surrogate fGOT (sfGOT) distance, a tractable approximation of fGOT that handles graphs without known node correspondence, and is minimized end-to-end via backpropagation. We further provide a novel interpretation of sfGOT through the lens of the Hilbert-Schmidt Independence Criterion, showing that minimizing the sfGOT distance between two graphs is equivalent to maximizing statistical dependence between the spectral embedding of their nodes. Experiments on benchmark datasets demonstrate competitive performance over existing GDL methods on graph clustering and classification tasks.
[LG-161] Polarity-Asymmetric Structural Calibration for Link Sign Prediction ICDM2026
链接: https://arxiv.org/abs/2609.05896
作者: Qiqi Gao,Wenzhuo Song,Xueyan Liu
类目: ocial and Information Networks (cs.SI); Machine Learning (cs.LG)
*备注: Accepted by IEEE ICDM 2026. 10 pages, 5 figures
Abstract:Link sign prediction (LSP) aims to infer the positive or negative polarity of unobserved links in signed networks. Signed Graph Neural Networks (SGNNs) usually rely on signed-graph structural priors, including structural balance and homophily-like similarity, to guide message passing and prediction. These priors describe population-level tendencies, not guarantees for individual target edges. Their failures are especially costly under severe sign imbalance, where errors on minority and locally conflicting relations are harder to detect and correct. We propose Polarity-Asymmetric Structural Calibration (PASC), a target-edge structural-prior calibration framework for signed link prediction. PASC constructs a structure-only prior representation, estimates a target-edge structural prior score, and compares this score with a local signed-context cue to derive a conflict residual. The residual calibrates signed attention aggregation, target-edge gated fusion, and regime-adaptive optimization. Experiments on five real-world signed network datasets show that PASC consistently achieves the best Macro-F1 among representative baselines, with competitive AUC, Binary-F1, and Micro-F1. Structural-shift experiments further suggest reduced dependence on dense-neighborhood and local-closure shortcuts. Source code is available at this https URL.
[LG-162] A First-Order Learning Algorithm for Online Resource Allocation with Constant Regret
链接: https://arxiv.org/abs/2609.05895
作者: Menglong Li,Jiawei Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:We study a finite-horizon online resource allocation problem with initial resource capacities proportional to the horizon. In each period, a request type is observed and one action is chosen from a finite menu. Each action earns a reward and consumes a vector of resources. The arrival types are independent and identically distributed, but their probabilities are unknown. We present a primal first-order learning policy that, in each period, performs one gradient ascent update of the action coordinates associated with the current request type. The policy achieves O(1) expected additive regret relative to the hindsight optimum, with a bound independent of the horizon T . It does not solve any linear program, and the regret bound does not require a nondegeneracy assumption on the fluid linear program.
[LG-163] One Rate Is Not Enough: Adaptive Anisotropic Learning Rates for LoRA Fine-Tuning
链接: https://arxiv.org/abs/2609.05885
作者: Huiyi Wang,Daijiao Liu,Lina Yao,Dong Gong
类目: Machine Learning (cs.LG)
*备注:
Abstract:Low-rank adaptation (LoRA) has become the standard for parameter-efficient fine-tuning of large language models. Most LoRA variants follow a uniform-LR convention, applying a single global learning rate across every rank-one component of every adapter. We show that this convention overlooks substantial within-module heterogeneity, where the rank-one components of a LoRA adapter update at highly uneven rates and low-velocity modules converge to concentrated singular spectra that underutilize the nominal rank budget. To address this, we propose an adaptive anisotropic learning-rate model that assigns each rank-one component its own effective learning rate, computed online from training-time signals and mean-normalized per module to preserve the global LR budget. AnLR-LoRA instantiates this model with two signals available during AdamW optimization, namely function-space velocity and Adam SNR, as a lightweight scheme with no extra trainable parameters. Across commonsense reasoning, natural language generation and visual instruction-tuning benchmarks, AnLR-LoRA consistently improves over LoRA while encouraging broader use of rank capacity, with gains that remain robust across a wide range of global learning rates and transfer cleanly to other LoRA variants.
[LG-164] CALM: Class-wise Agreement and Label-gated Disagreement Modulation for Decentralized Federated Learning
链接: https://arxiv.org/abs/2609.05884
作者: Yifan Ying,Qing Tian
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注:
Abstract:Conventional federated learning relies on parameter averaging, which forces clients to be doubly homogeneous: all must run an identical architecture, and accuracy degrades when local data are non-IID. Decentralized federated distillation sidesteps both: each client runs its peers’ model snapshots as teachers on its own local data and distills from their soft predictions, with no server, no public data, and no shared architecture. Under severe non-IID skew, however, the trustworthiness of the aggregated teacher target is a matter of degree, yet existing pipelines make hard, all-or-nothing decisions: outlier teachers are discarded by threshold, and whatever target survives is trusted in full. We propose CALM, which replaces every hard decision with a smooth trust gate at three levels: per class, teachers are weighted by agreement with the peer consensus; per sample, distillation is scaled by the teachers’ divergence from that target; and a label gate scales it by how strongly the target supports the sample’s true label. None of this adds communication or auxiliary data. On CIFAR-10, SVHN, OrganAMNIST, and Google Speech Commands with heterogeneous client architectures under Dirichlet label skew, CALM consistently outperforms uniform and hard-filtered distillation and matches or exceeds competing heterogeneous-FL methods.
[LG-165] Broken on Arrival: Silently Defective LLM Artifacts in Public Model Registries and How to Catch Them
链接: https://arxiv.org/abs/2609.05881
作者: Aditi Patodiya
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG)
*备注: Submitted to the IEEE International Conference on Big Data (IEEE BigData 2026). Code and data: this https URL
Abstract:Developers increasingly run large language models locally by pulling quantized GGUF artifacts from public registries, yet nothing in the distribution pipeline functionally tests these conversions before they reach users. We executed 327 quantized code-capable model artifacts: 305 from the official Ollama library, spanning 15 model lines at every eligible quantization level at or under 8 GB, and 22 from the most-downloaded community repositories on HuggingFace. Each ran a 15-task smoke suite calibrated so that healthy artifacts pass while a known-broken one fails; suspects then faced full 164-task evaluation, a second inference backend, an independent distributor’s conversion of the same model and quantization as referee, and, for community files, re-testing under the artifact’s own template. The official library carries five silently defective artifacts, a batch of four Qwen2.5-Coder-3B conversions and one phi3.5-mini conversion, that solve zero of 164 tasks and zero of the smoke suite on both backends while independent conversions of the same models work: 1.6% of official artifacts, 2 of 29 model-and-size conversion groups. The adjudication chain cleared small-model artifacts that a naive threshold would condemn as broken when they are merely collapsed by extreme quantization, and it exposed two older community conversions that degrade badly on CUDA yet pass on Metal: not defective files but backend-dependent failures, a third phenomenon no registry currently tests for. Two confirmed defects produce output whose surface statistics sit inside the healthy range, invisible to any low-noise heuristic short of execution. We release the audit dataset, the quantcheck acceptance-testing tool, and disclosure reports for every confirmed defect (this https URL), and argue that model registries need the acceptance gate that package registries already run.
[LG-166] A budget-dependent crossover between coverag e- and response-based training-set selection for machine-learned interatomic potentials
链接: https://arxiv.org/abs/2609.05877
作者: Jia Bi,Alin-Marin Elena
类目: Machine Learning (cs.LG)
*备注:
Abstract:Selecting compact training sets for machine-learned interatomic potentials requires deciding whether to preserve structural diversity or target configurations on which models disagree. The better choice can depend on how much data is retained, making a comparison at one training-set size insufficient. Here we link selection criteria to prediction accuracy through a budget-resolved comparison of retrained MACE models on GAP-20 Carbon and pooled revised MD17. Structural coverage is compared with a response-guided selector that targets disagreement between a coverage-trained model and a full-data reference. This retrospective response witness tests the value of model disagreement for compressing an already labelled pool. At 5%, coverage gives smaller absolute deviations from the full-data error than random sampling across four force endpoints in both datasets. The witness has larger deviations than coverage at 1% and 5%, but the ordering reverses at 20%. At 20%, witness-selected models also lower direct held-out force errors by 0.46–5.89% relative to coverage, with all eight paired training-seed intervals favouring the witness. Six errors fall below the full-data reference. Mean force-error reductions are 0.164–0.167~meV~ \textÅ^-1 , with larger gains for tail and masked endpoints. Complementary analyses show that learned similarity preserves the coverage ranking, while selecting by frozen-model error gives higher error than embedding coverage. These findings establish retained-data budget as a deciding variable in atomistic training-set selection and provide a direct test of when response-guided compression improves on structural coverage.
[LG-167] Budgeted Task-Aware Acquisition of Dynamic Networks
链接: https://arxiv.org/abs/2609.05862
作者: Zihe Zhou
类目: Machine Learning (cs.LG)
*备注:
Abstract:Learning on dynamic graphs is difficult when changes in the underlying network are only partially observed. Acquiring current graph information incurs observation and computational costs, making complete updates impractical under limited resources. This paper focuses on budgeted task-aware acquisition on dynamic networks, where a model needs to decide which stale graph information to refresh for a downstream task. We propose Scout, a lightweight framework that learns the task value of querying each node from the maintained graph and observation history. Our evaluation covers one synthetic and four real-world dynamic networks, two downstream tasks, nine acquisition baselines, and several query budgets. Scout achieves the highest mean downstream performance in 19 of the 21 benchmark-budget settings. Task-utility supervision also outperforms structural-change supervision in 13 of the 16 real-world settings. On the same dynamic network, task-matched acquisition improves link-prediction AUC by 0.012-0.016 and node-classification accuracy by 0.064-0.09 over task-mismatched acquisition. These results show that useful graph observations depend on the downstream task and that limited observation budgets can be allocated more effectively by learning directly from downstream utility.
[LG-168] SLA-Safe Energy Control for AI-Native NG-RAN Using Stability-Aware Constrained PPO
链接: https://arxiv.org/abs/2609.05861
作者: Dharmendra Kumar
类目: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注: 7 pages, 1 figure, 3 tables. Accepted for publication in the Proceedings of the 2026 IEEE PIMRC Workshops, Singapore
Abstract:One important AI-for-RAN use case is energy saving, in which radio resources and cell energy modes must be dynamically controlled without violating user quality-of-service (QoS) or service-level agreement (SLA) requirements. However, aggressive sleep-state or deactivation decisions may reduce energy consumption at the cost of throughput degradation, delay increase, SLA violations, and unstable mode switching, especially under time-varying and bursty traffic conditions. This paper proposes a stability-aware constrained reinforcement learning framework for SLA-safe energy control in 5G NG-RAN. The problem is formulated as a constrained Markov decision process in which an AI-native controller selects closed-loop energy-saving actions based on cell load, queue status, active-user information, current energy mode, and SLA-related indicators. The proposed framework uses constrained proximal policy optimization with adaptive Lagrangian penalties to account for throughput, delay, and SLA constraints. To improve operation under traffic distribution shift, the controller is trained using mixed nominal and stress traffic regimes, while a switching-stability penalty is introduced to reduce oscillatory transitions between active and low-power modes. Simulation results in a seven-cell NG-RAN environment show that the proposed controller reduces energy consumption by approximately 41.4% under nominal traffic, 10.5% under stress traffic, and 22.9% under unseen-stress traffic relative to the Always-On baseline. Under stress and unseen-stress traffic, the controller preserves zero SLA violation and zero throughput loss, indicating service-preserving operation under challenging conditions. The proposed method also reduces switching activity compared with basic threshold-based energy saving.
[LG-169] Beyond Arbitrary Geometry: Topology Generalization In neural PDE Operators
链接: https://arxiv.org/abs/2609.05860
作者: Peiyao Chen,Zhouyuan Xu,Jianguo Nie,Jiansheng Fan,Chen Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Neural operators that accept arbitrary meshes are often treated as geometry-general, but unseen domain topology changes both the invariant and decaying subspaces of a PDE operator. We use Hodge heat flow as a controlled lens on this distinction and introduce TopoBox-3D, where tunnels and cavities vary Betti support while the exact Hodge decomposition separates the harmonic kernel from the positive spectrum. Across six architectures, models that infer topology implicitly suffer excess matched degradation in 37 of 45 model–task topology-OOD cells, yet cases that change harmonic dimension are not more strongly penalized on average. The dominant difficulty is instead spectral: the initial Rayleigh quotient is the most stable predictor of error, and spectral broadening adds information for edge and face cochains. Most strikingly, controlled probes show that explicit incidence and harmonic coordinates do not yield the best kernel-identity accuracy; nevertheless, TNO ranks first in mixed-input nonharmonic accuracy on all six tasks with nontrivial harmonic support. Together, these results establish topology as a distinct generalization axis beyond arbitrary-geometry compatibility and show that its influence extends across the Hodge spectrum rather than remaining confined to the harmonic kernel. More broadly, they suggest that global, low-frequency structural priors may help organize predictions in the faster-decaying complementary component, offering a new perspective on how neural operators may generalize across topology as well as geometry.
[LG-170] Scaling Optimal Classification Trees via Adaptive Feature and Sample Reduction
链接: https://arxiv.org/abs/2609.05826
作者: Jiancheng Tu,Wenqi Fan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Dynamic programming for optimal classification trees becomes computationally expensive as the numbers of features and training samples increase. We develop a joint feature- and sample-space reduction framework based on STreeD. Weighted STreeD merges duplicate records created after projection onto a fixed candidate set into weighted representatives. This reduces sample-dependent computation without changing the fixed-candidate optimization problem. Adaptive STreeD repeatedly refines a bounded candidate set, retains features used by the incumbent tree, rebuilds the weighted representation, and solves the resulting reduced problems. Each certified Weighted STreeD solution is optimal for its current candidate set, while the outer feature search remains heuristic over the full feature space. Experiments on five data sets show that Weighted STreeD achieves speedups of up to 121.41 times over standard STreeD. Adaptive STreeD reduces runtime in matched comparisons at depths 2 to 4 and continues to return feasible trees at greater depths where full-feature methods are limited by time or memory. Under the same computational budget, its predictive performance remains comparable to the evaluated optimal classification tree baselines and is higher in some comparisons. These results show how joint feature- and sample-space reduction can scale dynamic-programming-based optimal-tree learning to more demanding instances.
[LG-171] Generalizing HVAC Control With Domain Randomized Reinforcement Learning
链接: https://arxiv.org/abs/2609.05822
作者: Pablo Boitel,Kun Zhang
类目: Machine Learning (cs.LG)
*备注: 4 pages, 5 figures. Short paper published in ACM Sustainability Week Companion '26
Abstract:Deploying advanced HVAC (Heating, Ventilation and Air Conditioning) controllers at scale remains difficult because performance often depends on accurate building models or per-site retuning. We propose NOMAD-RL (Neural Online Meta-Adaptation for Dynamics), a general-purpose Reinforcement Learning (RL) controller designed to transfer across heterogeneous thermal zones through a universal, non-invasive thermostat interface. The controller acts on temperature setpoints from zone measurements and forecasts, while a recurrent policy supports online adaptation under partial observability. Our main contribution is an adaptive domain randomization scheme based on physics-informed normalizing flows, which models correlated and multimodal distributions of thermal-zone parameters while maintaining physical plausibility and controllability. This produces a realistic and progressively adaptive training curriculum that improves transfer across buildings. We evaluate NOMAD-RL against a constant-setpoint PID controller, RL without domain randomization, and MPC in single- and multi-zone settings. NOMAD-RL consistently outperforms the PID and non-randomized RL baselines, and approaches the performance of a well-tuned MPC, especially in the more challenging multi-zone case. These results highlight the potential of adaptive, physics-informed domain randomization for robust and transferable HVAC control. Comments: 4 pages, 5 figures. Short paper published in ACM Sustainability Week Companion '26 Subjects: Machine Learning (cs.LG) Cite as: arXiv:2609.05822 [cs.LG] (or arXiv:2609.05822v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.05822 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: Proceedings of the 2026 ACM Sustainability Week (ACM Sustainability Week Companion '26), pp. 383-386, ACM, 2026 Related DOI: https://doi.org/10.1145/3765611.3815359 Focus to learn more DOI(s) linking to related resources
[LG-172] Online Learning with LLM Experts from Limited Feedback
链接: https://arxiv.org/abs/2609.05820
作者: Wang Wei,Soumyabrata Pal,Koyel Mukherjee,Franck Dernoncourt,Ryan A. Rossi,Branislav Kveton,Hoda Eldardiry
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 21 pages, 4 figures
Abstract:We study adaptive routing of prompts to large language model (LLM) experts to maximize response quality in an online setting with limited feedback. We formulate it as a bandit problem with K actions that represent experts and d features that encode prompts, over a horizon of T rounds. We propose algorithms that strategically select and observe rewards to minimize regret. In the full-information setting, we achieve a regret of \tildeO(d T / \sqrtm) , while in the bandit setting we achieve \tildeO(d T \sqrtK / m) , where m \ll T is a budget on feedback. Our experiments show that we efficiently learn high-quality routing strategies across diverse LLMs from limited feedback.
[LG-173] Nonlinear elliptic homogenization with the parametric Deep Ritz method
链接: https://arxiv.org/abs/2609.05778
作者: Conor Rowan
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注:
Abstract:Elliptic homogenization is used to determine coarse-grained properties of materials with features on small scales. When these small scale features have rapid, periodic fluctuations, the solution field corresponding to a homogenized constitutive relation closely resembles the true solution based on the heterogeneous material. This homogenized behavior of the material is computed from a cell problem, where a cell is defined to be one period of the fluctuating material. In the context of linear elliptic partial differential equations, the homogenized constitutive relation is defined simply by a constant coefficient tensor, but for nonlinear problems, the homogenized response depends on the macroscopic state and/or its gradient, thus requiring solutions to parametric cell problems. When computing a numerical solution with the homogenized constitutive relation, it is useful to have a differentiable representation of the solution to the cell problem, as derivatives of the homogenized constitutive relation are required in Newton iterations for the macroscopic state field. In this work, we use the Deep Ritz method to solve the parametric cell problems that arise from nonlinear homogenization. First, we exploit the variational structure of the cell problem, then we discretize the dependence of the cell response on both space and the macroscopic state with a neural network. Enforcing boundary conditions on the cell response strongly, we next use the parametric Deep Ritz method to simultaneously solve the cell problem over a range of macroscopic states. We show that this method is accurate, efficient, and offers a continuous and differentiable representation of the cell response over the macroscopic state and gradient. We then show that our parametric representation of the cell response significantly expedites macroscale solutions when compared to a traditional \textFE^2 scheme.
[LG-174] GraphNOSE: A Graph Transformer in Olfaction
链接: https://arxiv.org/abs/2609.05694
作者: Mrityunjay Sharma,Sarabeshwar Balaji,Valentina Parma,Ritesh Kumar
类目: Machine Learning (cs.LG)
*备注:
Abstract:Predicting olfactory qualities from molecular structure is an open problem in chemoinformatics. Although linear models can link molecular features to odor descriptors, they often fail when extrapolating to novel chemical scaffolds, extreme molecular weights, or complex odor mixtures. To address this, we introduce GraphNOSE, an open-source graph transformer framework that predicts multi-label odor descriptors from simplified molecular-input line-entry system (SMILES) strings for single molecules and binary mixtures. By integrating positional and structural encodings within a transformer-based graph architecture, GraphNOSE achieves strong performance with six times fewer parameters than standard graph neural network (GNN) baseline while consistently outperforming linear models, molecular language model embeddings, molecular fingerprints, and baseline GNNs by an average area under the ROC curve (AUROC) margin of 4.52% (p 0.01). GraphNOSE achieves an AUROC of 84% on out-of-distribution compounds (OODs). This exceeds the current state-of-the-art GNN for OOD in olfaction (Open-POM: 81%, p 0.001), and identifies conditions under which linear models empirically fail. Finally, we apply XAI (explainable AI) methods to identify which substructures and molecular features drive odor predictions, yielding insights consistent with chemical intuition and grounded in the model’s learned representations. Together, these results establish GraphNOSE as a scalable and interpretable architecture for olfactory prediction that generalizes to structurally distinct compounds underrepresented in current perceptual databases.
[LG-175] Connecting Score Matching Maximum Likelihood and Expectation-Maximization in Mixed Linear Regression
链接: https://arxiv.org/abs/2609.05688
作者: Zhankun Luo,Abolfazl Hashemi
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:We study variance-preserving diffusion of the response in mixed linear regression (MLR) with unknown mixing weights. Our analysis separates the statistical guarantees of score matching from the loss geometry and optimization signal at a fixed diffusion noise level. The KL divergence links the denoising score matching objective integrated over the diffusion path with the likelihood and a terminal discrepancy. Under mild regularity conditions and terminal schedule, the resulting estimator converges up to the ground truth parameters of MLR, and its scaled error converges to the Gaussian limit of the maximum-likelihood estimator. At a fixed scale of the diffusion noise level, we derive a decomposition linking the score matching loss to cross-entropy and Expectation-Maximization (EM) operators. This decomposition yields an EM-related low-noise gradient expansion with additional correction terms of latent variance. In the high-noise limit, we further characterize gradient descent on this limiting loss under isotropic covariance. Along fixed high signal-to-noise ratio rays, the score matching imbalance gradient and the latent-variance term tend to zero pointwise. Numerical experiments illustrate our theoretical findings and statistical guarantees.
[LG-176] Robustness of LLM -Generated SystemVerilog Assertions to Semantics-Preserving RTL Transformations
链接: https://arxiv.org/abs/2609.05658
作者: FNU Aditi
类目: Machine Learning (cs.LG)
*备注: 10 pages, 3 figures, 5 tables
Abstract:Large language models (LLMs) are increasingly being explored for automating SystemVerilog Assertion (SVA) generation, yet most evaluations report correctness on a single syntactic representation of an input. Such point accuracy does not reveal whether a model’s correct output is stable when the same RTL behavior is written differently. This paper presents a controlled metamorphic evaluation of LLM-based SVA generation under semantics-preserving RTL transformations. Starting from the VERT dataset, we construct a quality-filtered conditional-control pool and a stratified 40-program evaluation set containing 295 assignment behaviors. We evaluate two open code models, Qwen2.5-Coder-7B and DeepSeek-Coder-V2-Lite, with an identical evaluation prompt and greedy decoding. Three transformations are studied: operand reordering, deterministic identifier renaming, and redundant parenthesization. Beyond baseline and transformed accuracy, we measure conditional robustness, invariance failure, and any-flip rate, with 10,000-sample clustered bootstrap intervals at the RTL-program level. Across all six model-transformation conditions, 9.7%-27.0% of behaviors that were correct on the original RTL become incorrect after a semantics-preserving transformation. Aggregate accuracy can therefore hide substantial instability: under identifier renaming, DeepSeek-Coder-V2-Lite improves from 53.9% to 63.7% accuracy while 19.5% of its originally correct behaviors fail. Manual review of 30 sampled correct-to-wrong transitions identifies dropped path predicates, branch-polarity errors, Boolean-structure corruption, and output-contract violations. The results show that point accuracy alone is insufficient for characterizing LLM reliability in assertion generation and motivate robustness-aware evaluation for AI-assisted hardware verification.
[LG-177] Endogenous Exploration in Reinforcement Learning with Intrinsic Curiosity
链接: https://arxiv.org/abs/2609.05650
作者: Armando Vieira
类目: Machine Learning (cs.LG)
*备注: 25 pages, 5 figurees
Abstract:We propose a reinforcement learning framework in which exploration is driven by intrinsic curiosity, designed for scenarios where environments are non-stationary and rewards are sparse, delayed, uninformative, or absent. In our model, action selection is guided by a combination of external rewards and an epistemic motivation mechanism that biases the agent toward structured exploratory directions. The central hypothesis is that effective exploration emerges at intermediate levels of incoherence, while performance degrades under both overly rigid and overly disordered dynamics. To test this idea, we implement the framework on top of a Liquid State Machine (LSM) substrate and evaluate it on two standard benchmarks: the discrete-action LunarLanderv2 and the continuous-control BipedalWalkerv3. The proposed method achieves competitive performance on both tasks relative to established deep RL algorithms, including Proximal Policy Optimization (PPO) and Intrinsic Curiosity Module (ICM). We further show that the curiosity window is not recovered in Active Inference agents under the same analysis, suggesting that the proposed dynamics capture a distinct exploration regime
[LG-178] A Nuclear-Norm Lower Bound for Dithered Scalar Quantization of Matrix Products
链接: https://arxiv.org/abs/2609.05641
作者: Piyush Sao,Narasinga Miniskar,Pedro Valero-Lara,Keita Teranishi,Sudip Seal
类目: Information Theory (cs.IT); Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注: 17 pages, 2 figures. Code: this https URL
Abstract:We consider the problem of minimizing error in quantized matrix multiplication C=AB . Scalar quantization of the factors introduces rounding errors whose scale depends on the maximum absolute entries – the ranges – of their rows and columns. These ranges determine the quantization grid steps. To reduce the error, we optimize over product-preserving transformations that alter the factor ranges and grid steps without changing C . Specifically, we seek the smallest leading expected squared error over invertible inner changes of basis and orthogonal outer rotations. Under independent, zero-mean subtractive dither noise on an unbounded lattice, we prove the output-only bound E_\rm lead \ge (c_A+c_B)/K \Vert AB\Vert_^2 , where K is the inner dimension, c_A and c_B are normalized noise variances, and \Vert AB\Vert_ is the nuclear norm. The bound is tight: an SVD-aligned Hadamard construction attains the infimum whenever a Hadamard matrix of order K exists, including every power of two, while an SVD-aligned DCT construction is within a factor of two for every K . Without outer rotations, Gram-matrix balancing minimizes factorization energy, and finite-set flattening achieves the bound within C\log(K(m+n)) . For power-of-two K , conditional expectations deterministically select the Hadamard signs in O((m+n)K^2) exact-real operations. Synthetic experiments verify both constructions and illustrate the tradeoff between regularization and conditioning. These results characterize the full-gauge optimum and quantify the cost of preserving row and column indices.
[LG-179] HB-PVI: A Hierarchical Bayesian Personalization and Value-of-Information Framework for Complex Activity Recognition ALT
链接: https://arxiv.org/abs/2609.05582
作者: Hammed A. Olayinka
类目: Machine Learning (cs.LG)
*备注: Manuscript submitted to IEEE Journal of Biomedical and Health Informatics
Abstract:Personalization can improve activity-recognition performance, but participant-specific gains are heterogeneous, and every additional calibration label has an acquisition cost. This study presents HB-PVI, a hierarchical Bayesian personalization and value-of-information framework jointly modeling participant heterogeneity, the benefit and harm of four personalization mechanisms, and the economic value of an additional label, for the 47-participant MUSIC-CAR complex-activity cohort. A leakage-safe, leave-one-participant-out evaluation combines a sequential-Monte-Carlo participant-effect updater with a Student- t hierarchical gain model and a one-step expected-value-of-sample-information (EVSI) stopping rule. Adapter personalization produced small positive mean F1 gains, growing from 0.00099 at one label to 0.00198 at ten, while adapter-plus-head and prototype-residual personalization were negative on average. Under the primary practical-benefit threshold ( \Delta_\min=0.01 ) and cost setting, one-step EVSI was zero at every decision state, so the policy purchased no labels and retained population inference for all 47 participants, matching always-stop exactly (region-of-practical-equivalence probability =1 ). Relative to fixed ten-shot adapter personalization, this reduced labeling by 100% while keeping the posterior mean F1 loss at 0.00217 (95% credible interval, 0.00048 to 0.00389), with posterior probability 0.9992 of remaining below the 0.005 tolerance. HB-PVI was utility-optimal in 199 of 216 cost-threshold settings and in every setting at or above the primary label cost. These results argue for a population-first deployment policy whenever personalization gains are small relative to labeling, computation, and harm costs, and show that value-of-information reasoning, not raw predictive accuracy, should drive personalization decisions in health-sensing applications.
[LG-180] Multi-granularity Adaptive Hypergraph Representation Learning via Granular-ball
链接: https://arxiv.org/abs/2609.05574
作者: Sen Zhao,Yifan Guan,Jinyuan Ni,Gaojie Xu,Zhang Xu,Xiaoyu Lian,Yi Liu,Yi Wang,Wei Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Hypergraph representation learning aims to capture high-order information in graphs by constructing hyperedges that simultaneously connect multiple nodes. These hyperedges adapt to the graph’s topological features, facilitating the extraction of high-order relationships at multiple granularities. Most prior work relies on predefined definitions to generate hyperedges, overlooking the diversity in graph topological structures and the multi-granularity characteristics of hyperedges. As a result, this limits their ability to effectively and adaptively discover high-order relationships and efficiently process complex structural information. To address this limitation, we propose a novel framework called \underlineMulti-\underlineGranularity \underlineHypergraph \underlineRepresentation \underlineLearning (MGHRL). MGHRL introduces an Adaptive Granular Hypergraph Generation strategy, which generates hyperedges at multiple levels of granularity through the adaptive splitting of granular-ball, effectively capturing high-order relationships based on the graph’s topological structure. Additionally, we propose a Multi-Granularity Hypergraph Network with multiple sub-networks, capturing features from hyperedges at different granularities and integrating them via hierarchical reversible connections. Experimental results show that MGHRL significantly outperforms baseline models on benchmark datasets.
[LG-181] oward Sustainable Distributed LLM Inference: A Systems Synthesis and Research Agenda for an Energy- Carbon- and Cache-Aware llm -d Control Plane
链接: https://arxiv.org/abs/2609.05565
作者: Twinkll Sisodia
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注: 13 pages, 1 figure, 1 table. Systems synthesis and research agenda for sustainability-aware distributed LLM inference. No new experimental measurements are claimed; reported results are attributed to the cited work
Abstract:Large language model (LLM) sustainability is increasingly a serving-systems problem, not only a training problem. In production, energy and carbon impact depend on more than model size: workload shape, batching, key-value (KV) cache reuse, prefill/decode placement, model and accelerator choice, power state, geographic carbon intensity, and service-level objectives (SLOs) all matter. Recent systems papers study many of these factors separately. This paper connects those results and asks a practical engineering question: what do they imply when the decision point is a distributed inference control plane such as llm-d? The contribution here is synthesis, not a new set of benchmark results. Reported performance, energy, carbon, and cost improvements remain the results of the cited papers and systems. I group the literature into recurring design patterns and use those patterns to sketch a Sustainable Inference Control Plane (SICP) for llm-d. The proposed control plane would consider latency, energy, carbon, cache reuse, serving cost, and quality when routing and scaling, while keeping TTFT/TPOT SLOs as hard constraints. I also outline an evaluation framework based on SLO-satisfied goodput per joule and per gram CO2e, together with a reproducible experimental plan. The main observation from connecting the literature is that sustainable LLM inference is unlikely to come from one “green” model or one accelerator; it is more naturally treated as a control problem across model, phase, cache, hardware, replica, region, and time. Comments: 13 pages, 1 figure, 1 table. Systems synthesis and research agenda for sustainability-aware distributed LLM inference. No new experimental measurements are claimed; reported results are attributed to the cited work Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG) Cite as: arXiv:2609.05565 [cs.DC] (or arXiv:2609.05565v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2609.05565 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-182] Benchmarking Storag e Systems for Machine Learning Workloads Using NIO Bench
链接: https://arxiv.org/abs/2609.05418
作者: Jonathan W. Morris,Ionut Mistreanu,Connor Louie
类目: Performance (cs.PF); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注: 7 pages, 6 figures
Abstract:Machine learning training workloads place unique demands on storage systems, yet most existing benchmarks focus on computational throughput rather than file system I/O behavior. We present a benchmarking framework, Neural I/O Benchmark (NIO Bench), that characterizes storage access patterns across six diverse ML model architectures: Language Transformers, Vision Transformers, Diffusion Models, Spiking Neural Networks, Artificial Neural Networks, and Reinforcement Learning. Our framework employs a two-layer tracing approach combining Python-level I/O hooks for semantic phase context with Linux strace for complete syscall coverage including DataLoader worker subprocesses. We evaluate all six models on a Nautilus Kubernetes cluster with Ceph distributed file system. Our results reveal that I/O is heavily concentrated in data preparation, model loading, and model checkpointing. We also found that training is compute-bound rather than data-bound once data is staged, and that storage access follows an extreme power law where fewer than 10% of files account for over 90% of bytes transferred, and that read tail latency from cache misses on distributed storage is the primary storage bottleneck. These findings suggest that storage systems optimized for ML should prioritize aggressive data prefetching, page cache pinning, and efficient handling of bursty checkpoint writes.
[LG-183] From 80x to 385x: A Best-Matching-Unit Search at the L2 Roof Measured Against a Symmetrically Tuned Baseline FAST
链接: https://arxiv.org/abs/2609.05138
作者: Andrew James Amos
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注: 15 pages, 8 tables, 3 figures. Companion to arXiv:2608.24067 . Both implementations were tuned symmetrically; cuSPARSE became 2-3x faster in the process. Code, data and frozen results: doi: https://doi.org/10.5281/zenodo.22245712 (tag v2.0)
Abstract:Comparisons between GPU implementations are usually asymmetric: one side is tuned by its author, the other is run as found. I report a programme that tuned both a novel SOM algorithm (SparseBin) and the baseline algorithm it was being compared to (cuSPARSE). The best-matching-unit search that dominates self-organizing map training was tuned through four levers - tile size, tile-membership clustering, neuron-axis chunking and vectorised loads - reaching 5.6-10.1x per epoch over the previously published configuration at map sizes from 32x32 to 512x512, and lifting the margin over the CUDA implementation behind our earlier MEDLINE atlases from ~80x to ~385x. cuSPARSE, the implementation SparseBin is compared against, received every lever with an analogue on its side, and became 2-3x faster in the process. The tuned kernel pressed the L2 bandwidth roof at 77% of peak with every other unit at 40-65%, bounding any further lever at ~1.3x - a terminal result rather than a waypoint, and every untested lever was either capped by that bound by construction or measured null.
[LG-184] Silver Rate Is (Almost) Optimal for Gradient Descent Acceleration
链接: https://arxiv.org/abs/2609.09152
作者: Yuhan Ye,Kaizhao Liu
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注: 29 pages, 1 figure
Abstract:We study how far gradient descent (GD) can be accelerated by predetermined nonnegative stepsizes in smooth convex optimization. Writing p_\mathrmsil=\log_2(1+\sqrt2) , we prove an \Omega\left(n^-p_\mathrmsil-O(\sqrt\log\log n/\log n)\right) non-anytime lower bound. In the anytime setting, every infinite nonnegative schedule has infinitely many horizons with error \Omega\left(n^-\frac2p_\mathrmsil1+p_\mathrmsil-O(\sqrt\log\log n/\log n)\right) . Together with the silver-schedule upper bound [Altschuler and Parrilo, 2025] and the anytime upper bound [Zhang et al., 2025], our results determine the optimal polynomial convergence exponents in both settings.
[LG-185] Closed-Form of the Local Galactic Potential and Stellar Distribution Function from Gaia DR3
链接: https://arxiv.org/abs/2609.09011
作者: Indranil Das,Adam Kamoski,Dora Demiri,Brianna Isola,Hanieh Karimi,Dmitrii S. Zagorulia
类目: Astrophysics of Galaxies (astro-ph.GA); Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG)
*备注: The code can be distributed upon request
Abstract:The local dark matter density determines the strength of the signal expected in direct-detection experiments, yet published estimates from stellar motions disagree by more than their errors, and the most recent machine-learning analysis of Gaia data finds a local density consistent with zero. According to Jeans’ theorem, a distribution function built from integrals of motion satisfies the collisionless Boltzmann equation (CBE) trivially for any choice of potential, so a search that simultaneously fits the distribution function and the potential to the CBE identifies neither. Our pipeline instead estimates the distribution function in isolation, linearizing the equation in terms of accelerations and allowing for direct measurement of the local force field, and then fits closed forms to that field via symbolic regression. Throughout, we find that the usable information lies not in the CBE residual but in the stellar number counts, the observable most distorted by survey selection. Along the vertical profile, our recovered potential agrees with the classical self-gravitating isothermal disc.
[LG-186] ONE CYLinder: A Benchmark for Graph-Based Surrogate Modeling of Unsteady Bluff-Body Flows
链接: https://arxiv.org/abs/2609.08947
作者: Théodore Michel,Antoine Campos,Alban Dujardin,Henry Areiza,Philippe Meliga,Elie Hachem
类目: Fluid Dynamics (physics.flu-dyn); Machine Learning (cs.LG)
*备注: pre-print
Abstract:Graph-based surrogate models offer a promising route to accelerate computational fluid dynamics (CFD) simulations on unstructured meshes. However, their development is limited by the scarcity of benchmark datasets spanning multiple flow regimes and standardized protocols for long-horizon autoregressive prediction. We introduce ONECYL (ONE CYLinder), a new benchmark for unsteady flow past a circular cylinder across laminar, transitional, and high-Reynolds-number regimes. The benchmark comprises 450 high-fidelity Variational Multiscale finite-element simulations (270,000 flow snapshots) with randomized cylinder geometries, providing time-resolved velocity and pressure fields together with mesh connectivity, geometric descriptors, Reynolds numbers, and integrated aerodynamic quantities. Beyond the dataset, ONECYL establishes a unified evaluation framework combining full-field rollout errors, virtual probes, and drag and lift predictions to assess numerical accuracy and physical fidelity. To accompany the benchmark, we develop a Graph Transformer as a reference baseline predicting velocity and pressure fields autoregressively on unstructured meshes. Using ONECYL, we investigate geometric representations and physics-based regularization across the three Reynolds-number regimes. The results show that explicitly encoding the cylinder geometry through a level-set representation consistently improves long-horizon prediction accuracy and generalization to unseen geometries, while divergence-based regularization becomes increasingly beneficial as flow complexity increases. The ONECYL benchmark and its Graph Transformer baseline provide a reproducible framework for evaluating graph-based surrogate models and establish a foundation for future research on long-horizon prediction of unsteady bluff-body flows.
[LG-187] Optimal estimation for Functional Linear Regression with Noisy Discretized Data
链接: https://arxiv.org/abs/2609.08671
作者: Sixtine Sphabmixay
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 63 pages, 25 figures
Abstract:In this paper, we consider the scalar-on-function linear regression model under a realistic sampling scheme in which the functional covariates are observed on a regular grid and contaminated by additive noise. We propose a two-step estimation procedure: first, the underlying curves are reconstructed from the discrete noisy observations using a Fourier-based projection method; second, the slope function is estimated by a penalized least-squares criterion over finite-dimensional trigonometric spaces, with data-driven selection of the model dimension. We establish oracle-type inequalities for the prediction error, both with respect to the reconstructed curves and to the true latent curves. Under regularity assumptions on the slope function and polynomial decay of the eigenvalues of the covariate, we derive convergence rates for the prediction error and show that our estimator attains the minimax rate when the number of grid points is sufficiently large. Finally, the proposed method is illustrated on simulated data and on a real meteorological dataset.
[LG-188] Flexible Spectral-Normalized Neural Gaussian Process for Dynamic Aperture Prediction
链接: https://arxiv.org/abs/2609.08620
作者: Yousra El-Bachir,Frederik Van der Veken,Davide di Croce,Carlo Emilio Montanari,Massimo Giovannozzi,Ekaterina Krymova,Tatiana Pieloni
类目: Accelerator Physics (physics.acc-ph); Machine Learning (cs.LG)
*备注:
Abstract:We address the challenge of scalable uncertainty quantification in large-scale scientific applications, where complex state-of-the-art machine learning methods are often computationally infeasible. Our primary contribution is a simple yet effective empirical Bayes method for automatically tuning the hyperparameters of a flexible, heteroscedastic Spectral-normalized Neural Gaussian Process. This approach retains the expressiveness and uncertainty-awareness of semi-Bayesian neural models while significantly reducing the computational burden by integrating hyperparameter learning directly into the training loop. We demonstrate the practical impact of our method on the task of estimating the dynamic aperture in circular particle accelerators, a fundamental problem in high-energy physics colliders and storage rings, using simulation data from the case of the Large Hadron Collider at CERN. Traditional approaches to DA estimation require extensive particle-tracking simulations, which are prohibitively time-consuming and resource-intensive. Our results show that the proposed method achieves competitive predictive performance and well-calibrated uncertainty estimates at much lower computational cost than state-of-the-art approaches. We stress that, beyond this application, the proposed empirical Bayes framework offers a general solution for training heteroscedastic neural models in situations where manual hyperparameter tuning is impractical. Accordingly, we anticipate that this framework can be applied to other domains that encounter comparable computational limitations.
[LG-189] FedGenSC: Federated Generative Semantic Communication with Channel-Aware Adaptation
链接: https://arxiv.org/abs/2609.08593
作者: Rita Abou Fares,Razan Al Kakoun,Maher Nouiehed,Hadi Sarieddeen
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注: 6 pages, 3 figures, 4 tables. Accepted for presentation at IEEE GLOBECOM 2026, Macau. This is the author’s accepted version; the final published version will be available via IEEE Xplore
Abstract:Integrating generative adversarial networks (GANs) into federated semantic communication (SemCom) is a natural progression, as generative priors can recover semantic fidelity under channel distortion that discriminative decoders cannot. However, naive GAN federation introduces three failure modes that prior work has, to the best of our knowledge, neither identified nor resolved: discriminator aggregation instability under non-independent and identically distributed (non-IID) data, semantic drift caused by divergent local embedding spaces, and channel-agnostic generation that cannot adapt to heterogeneous link conditions. We propose federated generative semantic communication (FedGenSC), which mitigates all three by employing a global generator with local-only discriminators, providing cross-client semantic information through a semantic prototype bank, and conditioning generation on the instantaneous signal-to-noise ratio (SNR). Experiments on the Europarl dataset over Rayleigh fading channels (K=10 clients, Dirichlet \alpha=0.5) show that FedGenSC under non-IID data outperforms the FedDeepSC baseline across the tested SNR range, achieving up to a 58.2% relative improvement in bilingual evaluation understudy (BLEU)-1 at 18 dB. Ablation studies confirm the independent contribution of each component.
[LG-190] Non-Adaptive 1-Bit Mean Estimation: Minimax Rates and the Sample-Interval Tradeoff
链接: https://arxiv.org/abs/2609.08564
作者: Ivan Lau,Jonathan Scarlett
类目: Machine Learning (stat.ML); Information Theory (cs.IT); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:We study distributed one-dimensional mean estimation under a 1-bit communication constraint. Each agent observes one sample, drawn independently from an unknown distribution, and returns a single bit in response to a query Q: \mathbbR\to\0,1\ chosen by a central learner. The distribution has mean in [-\lambda,\lambda] and k -th central moment at most \sigma^k , for a fixed k1 . The order-optimal two-stage protocol of Lau and Scarlett uses responses from the first batch to choose the second-batch queries, motivating the question of whether this single round of interaction is necessary. We answer this negatively: for every k1 , a non-adaptive protocol attains the adaptive 1-bit minimax rate (and concurrent works reached the same conclusion via different strategies). We further determine the minimax sample complexity among non-adaptive 1-bit estimators when every one-set Q^-1(1) is restricted to a union of at most s intervals. Relative to unrestricted non-adaptive 1-bit querying, this constraint adds a term of order (\lambda\sigma/(s\varepsilon^2))\log(1/\delta) , giving the full tradeoff between sample complexity and interval complexity to within k -dependent constant factors. As a corollary, we identify, order-wise, the minimum interval budget needed to retain the unrestricted 1-bit minimax sample rate.
[LG-191] he Exact Time-Uniform Rate Frontier for Stochastic Gradient Descent on Smooth Convex Objectives
链接: https://arxiv.org/abs/2609.08537
作者: Ruijie Li,Kang Chen,Tianyu Wang
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 28 pages, including appendix
Abstract:We study the time-uniform convergence of the raw iterate of standard stochastic gradient descent (SGD) for unconstrained smooth convex objectives. We prove that, under standard noise assumptions, the time-uniform convergence rate gets arbitrarily close to \sqrt\log n / n but never reaches it. More specifically, we prove that for every positive, eventually nondecreasing sequence h satisfying h(n) = o(\sqrtn) , a bound of order h(n)/\sqrtn , holding simultaneously for all n with probability at least 1-\alpha and uniformly over the problem class, is achievable if and only if [ \sum_j = 1^\infty \frac1h(2^j)^2 \infty. ] The constructive sufficiency result follows from a dyadic horizon-free schedule together with an additive conditional-restart inequality. The necessity counterpart applies to every deterministic nonnegative schedule and holds even for a one-dimensional analytic smooth convex objective with Gaussian noise. Comments: 28 pages, including appendix Subjects: Optimization and Control (math.OC); Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2609.08537 [math.OC] (or arXiv:2609.08537v1 [math.OC] for this version) https://doi.org/10.48550/arXiv.2609.08537 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-192] Inclusive electron-nucleus cross section models from domain adaptation
链接: https://arxiv.org/abs/2609.08463
作者: Krzysztof M. Graczyk,Beata E. Kowal,Rwik Dharmapal Banerjee,Jose Luis Bonilla,Hemant Prasad,Jan T. Sobczyk
类目: High Energy Physics - Phenomenology (hep-ph); Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex); Nuclear Experiment (nucl-ex); Nuclear Theory (nucl-th)
*备注: 19 pages, 27 figures, 2 tables
Abstract:We apply transfer learning (TL) to construct data-driven models of inclusive electron-nucleus cross sections. Starting from an ensemble of deep neural networks pretrained on (^12)C data, we fine-tune the models separately for (^3)He, (^6)Li, (^16)O, (^27)Al, (^40)Ca, and (^56)Fe. The resulting models improve for all targets, marginally so for oxygen, where the carbon baseline is already adequate, although their predictive robustness depends on the amount, coverage, and precision of the available target data. We systematically study how model performance depends on the number of fine-tuned layers, on the fraction and selection of the training data, and on the overlap between the source and target kinematic domains. The layer-wise analysis shows that oxygen requires only shallow adaptation, whereas helium, calcium, and iron require substantially deeper fine-tuning. Lithium represents the least robust case because of its limited dataset, while aluminum demonstrates a strong sensitivity to a small subset of highly constraining measurements. For selected kinematic configurations outside the coverage of the carbon training data, the adapted models remain consistent with the measurements within their estimated uncertainties. Finally, we compare the resulting predictions with those of the phenomenological F1F2 model.
[LG-193] How to Make the Gradient Mapping Small for Constrained Stochastic Min-Max Problems and Beyond
链接: https://arxiv.org/abs/2609.08380
作者: Ahmet Alacaoglu
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:
Abstract:We study the stochastic first-order oracle complexity for constrained or regularized convex-concave min-max optimization and stochastic monotone variational inequalities. We focus on the case when suboptimality is measured in terms of the gradient mapping, also known as, forward-backward or natural residual, an optimality notion that generalizes the gradient norm for unconstrained problems. In this setting, under standard unbiased oracle access with now-standard variance assumptions, the best-known complexity for making the norm of the gradient mapping less than \varepsilon is \widetildeO(\varepsilon^-4) , compared to the near-optimal \widetildeO(\varepsilon^-2) that is established in the unconstrained case. We bridge this gap to improve the gradient mapping complexity for constrained convex-concave min-max problems to \widetildeO(\varepsilon^-2) . We then extend to prove the same complexity for problems without the bounded variance, by using the Blum-Gladyshev assumption.
[LG-194] Fixed-Dimensional Latent Flow for Generating Variable-Size 3D Molecules
链接: https://arxiv.org/abs/2609.08333
作者: Weichi Yao,Cameron Gruich,Bryan R. Goldsmith,Yixin Wang
类目: Chemical Physics (physics.chem-ph); Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
*备注:
Abstract:In molecular discovery, molecule size is coupled to composition, structure, and other target properties. Yet most 3D generators require molecule size to be specified before generation. Here, we introduce Equivariant-Free Transformer-Autoencoded Latent Flow Matching, a two-stage generative framework that relies entirely on a single fixed-dimensional molecule-level latent representation to generate variable-size molecules. The second-stage flow matching model samples this latent vector, and an autoregressive Transformer decoder then determines molecule size while generating atom types, coordinates, and chemically informative states. Canonical atom ordering and rigid-pose alignment enable standard Transformers without equivariant layers, while joint decoding of molecular geometry and an enriched chemical state enables reliable, deterministic, chemistry-guided graph recovery without requiring a learned dense pairwise bond decoder. The same fixed-dimensional latent supports unconditional and property-conditioned flow matching, while optional property supervision adds an internal ranking readout, with no separate predictor or reference calculations. On PCQM4Mv2, EF-TALFM achieves the highest fraction of molecules that are unique, training-set novel, pass sanitization and PoseBusters sanity checks, 89.4%, compared with 75.6% for UAE-3D and 69.8% for FlowMol. EF-TALFM also achieves higher measured computational throughput for training and sampling. Across ten target HOMO–LUMO gaps, internal ranking doubles the density functional theory (DFT)-verified hit rate within 0.1,\mathrmeV , while preserving 97% novelty among unique verified hits. These results demonstrate that fixed-dimensional molecule-level generation followed by symmetry-resolved autoregressive realization provides a practical architecture for open-ended and property-directed 3D molecular design.
[LG-195] Distribution-free inference on the number of changepoints
链接: https://arxiv.org/abs/2609.08234
作者: Rohan Hore,Aaditya Ramdas
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
*备注: 34 pages, 3 figures, 1 table
Abstract:Suppose we are given an ordered sequence of independent data whose distribution changes K times at unknown locations, for some unknown K \geq 0 . In this paper, we study the problem of performing distribution-free inference on K . First, we show an impossibility result: any distribution-free upper confidence bound on K must be trivial and uninformative. Then, using conformal p -values, and under only the assumption that the data segments induced by the changepoints are exchangeable (within themselves) and mutually independent, we construct a finite-sample valid lower confidence bound on K , which we call the Conformal LOwer bound on Changepoint Count (CLOCC). We show that CLOCC is the only feasible way to provide a lower bound on K under the stated assumptions, a property we refer to as its universality. We provide practical guidelines for choosing score functions that yield efficient and tight lower bounds. We evaluate CLOCC in several synthetic and real-data experiments, where it provides informative lower bounds on K , demonstrating its practical applicability.
[LG-196] Speed Limit for Information Acquisition in Stochastic Learning Dynamics
链接: https://arxiv.org/abs/2609.08219
作者: Shuta Kobayashi,Andreas Dechant
类目: atistical Mechanics (cond-mat.stat-mech); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 14 pages, 4 figures
Abstract:Neural networks acquire internal representations through learning. In this work, we formulate stochastic gradient descent (SGD) as a Markovian stochastic process and derive a Fisher-information flow speed limit that bounds the rate at which trainable parameters can acquire information about latent variables in the data-generating process. The resulting inequality decomposes the information flow into drift and noise contributions, thereby quantifying the roles of deterministic learning forces and SGD-induced fluctuations from an information-theoretic perspective. We verify the bound in analytically tractable basis-function linear regression, where the information budget predicted by the bound reproduces the ordering and characteristic time scales with which different latent variables are encoded in the learned parameters. These results establish Fisher-information speed limits as a quantitative framework for diagnosing when and how different aspects of the data-generating mechanism are acquired during stochastic learning.
[LG-197] Optimal Slice-Adaptive Tuning of Hybrid Slice Sampling
链接: https://arxiv.org/abs/2609.08172
作者: Trevor Campbell
类目: Computation (stat.CO); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 44 pages, 8 figures
Abstract:Slice sampling is a Markov chain Monte Carlo algorithm that draws its next state uniformly from a “slice”—a super-level set of the target density function—at each iteration, thereby providing automatic local adaptivity to the scale of the target. In practice the exact slice is not known, so general-purpose implementations use an approximate slice that is grown from a starting interval of length w0 , with a computational cost that depends on w . This work presents an analysis of the average per-iteration number of target density evaluations, as a function of w , of hybrid slice sampling with various slice-finding schemes for targets with contiguous slices. The paper uses the results of the analysis to develop automated, slice-adaptive tuning schemes along with suboptimality bounds and asymptotic convergence guarantees. Simulations demonstrate that the tuning schemes reliably yield near-optimal slice-adaptive tuning with essentially no dependence on the initial setting of w .
[LG-198] A Transformer-Based Delta Expression Encoder for Psilocybin Transcriptional Response: Architecture Representations and Biological Validation
链接: https://arxiv.org/abs/2609.08165
作者: Sai Jayakumar
类目: Genomics (q-bio.GN); Machine Learning (cs.LG)
*备注: 23 pages, 8 figures. Code: this https URL . Checkpoints: this https URL
Abstract:Understanding why individuals respond differently to psilocybin requires modeling the drug’s transcriptional perturbation signature at the cell-type level. I present a Transformer-based delta expression encoder that learns to classify differential gene expression status - upregulated, downregulated, or neutral - from single-nucleus RNA-sequencing data, without supervision from pathway annotations or prior biological knowledge. The model is trained on pseudobulk profiles from 623 examples spanning 18 cell types, 2 drug conditions, and 6 timepoints derived from the Liao et al. 2025 dataset, and achieves 69.4% weighted classification accuracy. Three principal findings are reported, alongside one direct test of a published hypothesis that returned a result inconsistent with that hypothesis. First, per-cell-type classification accuracy ranges from 28.3% (L2/3 IT, a primary HTR2A-expressing psilocybin target) to 99.6% (endothelial cells), consistent with known psilocybin response biology. Second, psilocybin-induced transcriptional downregulation is significantly more stereotyped across individuals than upregulation (Mann-Whitney U=18615.0, p0.0001), a novel finding with a cortical depth gradient across excitatory subtypes. Third, attention-guided gene co-regulation analysis recovers drug-specific modules without pathway supervision. Separately, a direct test of whether baseline HTR2A expression predicts drug-response separability across cell types found a significant negative correlation (Spearman r = -0.7088, p = 0.0021), the opposite of what a simple HTR2A-gating account would predict.
[LG-199] PocketVE: Stable and Property-Guided Structure-Based Drug Design with Variance-Exploding Diffusion
链接: https://arxiv.org/abs/2609.08101
作者: Peining Zhang,Jinbo Bi
类目: Biomolecules (q-bio.BM); Machine Learning (cs.LG)
*备注:
Abstract:Protein-conditioned 3D molecule generation is a central challenge in structure-based drug design, requiring a balance between pocket compatibility, molecular properties, and physical geometry. We propose \textbfPocketVE, a protein-pocket-conditioned variance-exploding (VE) diffusion framework that couples stable coordinate denoising with inference-time property guidance. Specifically, PocketVE combines an EDM-style training and sampling setup for 3D denoising, classifier-free guidance for multi-property steering without external property classifiers, and adaptive protein perturbation as a training-time pocket regularizer. Evaluated on CrossDocked2020 under the GenBench3D protocol, PocketVE improves Valid _3\textD from 58.6 to 80.6 and reduces strain energy from 457.4 to 127.9 relative to its TAGMol architectural baseline, while retaining competitive docking and molecular-property scores under moderate guidance. A guidance-scale study shows that moderate guidance gives a favorable balance between target-related objectives and geometric quality, whereas stronger guidance can degrade geometry and distributional fidelity. Pocket-permutation and PoseCheck diagnostics further support pocket-specific spatial compatibility with reduced steric conflicts. Overall, the results suggest that geometric stability and inference-time property guidance should be considered as coupled design objectives.
[LG-200] MI-PEFT: Mixture-of-Experts Integrated Parameter-Efficient Fine-Tuning Protein Language Models Improves Acidophilic Proteins Classification
链接: https://arxiv.org/abs/2609.08059
作者: Honghan Shen
类目: Quantitative Methods (q-bio.QM); Machine Learning (cs.LG)
*备注:
Abstract:Acidophilic proteins that remain stable and functional under highly acidic conditions, are important for industrial biocatalysis, acid-related bioprocessing, and the discovery of acid-stable enzymes. However, their identification relies heavily on time-consuming experimental screening methods. With the rapid growth of protein sequence databases, the need for computational identification methods that are both accurate and efficient has become stronger. The emergence of protein language models (PLMs) has significantly improved the sequence representation of downstream biological prediction tasks. This paper proposes MI-PEFT, a mixture-of-experts integrated parameter-efficient fine-tuning framework. Built on the ESM C-600M backbone, the framework incorporates LoRA-based PEFT methods and a DeepSeekMoE-based classification head to resolve the limitations of PEFT and significantly improve computational efficiency. Notably, this task is characterized by a significant class imbalance in the dataset, making high specificity particularly challenging. The experimental results demonstrate that MI-PEFT on PLMs, especially \textC^\text3\textA, serves as an efficient tool for identifying acidophilic proteins and a constrained pathway that helps resolve class-imbalance by preserving the pretrained representations.
[LG-201] Bayesian Matrix-Valued Graphs for Context-Dependent Multivariate Relationships
链接: https://arxiv.org/abs/2609.08055
作者: Papri Dey
类目: Methodology (stat.ME); Machine Learning (cs.LG)
*备注:
Abstract:Many scientific graphs attach several variables to each node, so a single scalar edge weight cannot describe direction-dependent interactions. We model each edge by a symmetric positive-definite (SPD) matrix and infer a posterior over matrix-valued graph geometries, which we call the Bayesian matrix-valued graph (BMVG). We ask how these interactions reconfigure across contexts: how large the change is and which multivariate directions strengthen or weaken. The geodesic distance induced by the affine-invariant Riemannian metric (AIRM) quantifies deformation magnitude and generalized eigenvalues resolve its signed this http URL fused graphical lasso, Bayesian multiple-GGM, and common principal components, BMVG is competitive on global precision recovery while retaining identifiable matrix-valued edge structure and accurately recovering edge-level deformation directions. In controlled known-truth experiments, it resolves structural change with increasing sample size, including orientation changes that leave ordinary eigenvalues unchanged. In one year of Bay Area weather data, the geometry of 12-hour change reconfigures spatial coupling about as much as whole seasons differ. In TCGA-BRCA, estrogen-receptor (ER)-associated reconfiguration concentrates on specific gene-module pairs and persists under graph-scaffold sparsification and removal of subgroup mean differences. These results establish posterior matrix-valued edge geometry as a unified framework for quantifying and interpreting context-dependent multivariate reconfiguration.
[LG-202] he Role of Uncertainty in Assessing the Fairness of Machine Learning Models
链接: https://arxiv.org/abs/2609.07959
作者: Francesca Panero,Ernst C. Wit,Marco Scutari
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:Machine learning models are widely used in clinical applications, social media, law enforcement and critical infrastructure. Verifying whether their outputs are biased against disadvantaged groups or individuals is crucial to ensuring they are fair and allowing their use in such settings. A rigorous risk assessment of possible fairness violations requires quantifying the uncertainty associated with selecting and estimating such models. Yet, this is rarely done in the literature, which focuses on identifying a single model with a suitable trade-off between predictive accuracy and fairness. In this paper, we move beyond point estimation and discuss frequentist and Bayesian approaches to uncertainty quantification for fair machine learning, with practical examples and implications for simulated and real data.
[LG-203] SGD in Multiclass Logistic Regression: Sequential Learning and Scaling Laws
链接: https://arxiv.org/abs/2609.07868
作者: Konstantinos Christopher Tsiolis,Denny Wu,Christos Thrampoulidis,Murat A. Erdogdu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:We study the training dynamics of multiclass logistic regression on high-dimensional Gaussian mixture models with a large number of classes and establish precise scaling laws governing the cross-entropy risk under gradient-based optimization. We show that learning proceeds sequentially across classes, from most to least frequent. When the class priors follow a power law distribution, the risk dynamics decompose into three phases: an initial plateau until the first class is learned, a power-law decay regime during which sequential learning occurs, and a final convergence regime. We then analyze how model capacity interacts with optimization under a fixed compute budget. When the effective dimension is restricted via projection onto leading principal components, the risk decomposes into a capacity term (a power law in the retained dimension) and an optimization term (a power law in training time). Optimizing this tradeoff yields a compute-optimal scaling law for logistic regression, with explicit prescriptions for model size and training time as functions of compute. These results extend theoretical scaling laws from linear regression to multiclass classification, while connecting to empirical scaling laws observed in large-scale neural networks. Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG) Cite as: arXiv:2609.07868 [stat.ML] (or arXiv:2609.07868v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2609.07868 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-204] Pre-Whitening and BCJR Posterior Distillation for Bi-LSTM Detection in Faster-than-Nyquist Signaling
链接: https://arxiv.org/abs/2609.07762
作者: Nurettin Safak,Osman Tokluoglu,Enver Cavus
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注: Accepted and presented at IDAP 2026. 5 pages, 13 figures
Abstract:Recurrent detectors such as bidirectional long short-term memory (Bi-LSTM) networks are low-complexity alternatives to the optimal Bahl-Cocke-Jelinek-Raviv (BCJR) detector for faster-than-Nyquist (FTN) signaling. Motivated by convolutional detectors that build the intersymbol interference (ISI) structure into their architecture, we ask whether processing nested ISI windows in separate recurrent branches improves the bit error rate (BER) of a Bi-LSTM. Across roughly 260 controlled trainings it does not: at a matched parameter budget and a matched readout, the multi-window architecture never significantly beats a plain Bi-LSTM. Nested windowing is an invertible rearrangement that adds no information, extra branches only add bottlenecks, and a distillation diagnostic shows the network is already near optimal for its window. The limitation is therefore the observation model, not the architecture. Keeping the architecture fixed, we pre-whiten the input, restoring the conditional independence that colored matched-filter noise violates, and distill the BCJR soft posterior into the network. With 3.4% more parameters this reaches 1.05 times the BCJR BER at a compression factor of 0.8 and 1.89 times at 0.7, improving to 1.47 times when the whitened window is widened. The 23.7% BER reduction at 0.8 requires an ill-conditioned ISI matrix but is not monotone in the conditioning, and it holds across five independent noise realizations and a symbol-level McNemar test.
[LG-205] Sub-6 GHz Over-the-Air AMC via Curriculum Fine-Tuned CNN-Transformers
链接: https://arxiv.org/abs/2609.07726
作者: Nurettin Safak,Muhammet Sefa Demirel,Alperen Marasli,Taha Eren Atmaca,Durdu Can Yerdeyatar,Ozgun Ersoy
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注: Accepted and presented at IDAP 2026. 5 pages, 8 figures
Abstract:Automatic modulation classification (AMC) models are frequently trained and validated on synthetic or channel-cabled data, leaving open the question of how they behave once path loss and antenna pointing error are introduced by a genuine free-space link. We report a curriculum fine-tuning study of a hybrid CNN-Transformer AMC model. The general-purpose, all-32-class dataset underlying the model was built entirely at 915 MHz, on a controlled, clock/PPS-synchronized MIMO-expansion-cable link (not spatial-multiplexing transmission); all subsequent free-space, real-hardware experimentation - the sequential fine-tuning curriculum, matched-distance evaluation, and every reported over-the-air accuracy figure - was carried out at 4 GHz, across five directional-antenna distances (25, 35, 50, 70, 75 cm) under fixed TX/RX gain. Three distances used near-ideal antenna alignment (~99%) and two used a deliberately introduced partial misalignment (~85%), fine-tuned last in the curriculum. We report matched-distance test accuracy (91.8-93.7% across all five 4 GHz conditions) and confusion-matrix analysis grounded in RF theory, and report honestly where our sequential fine-tuning order confounds cumulative link adaptation with antenna alignment, rather than overstating what the data can support.
[LG-206] SeisBench DAS: A machine learning framework for Distributed Acoustic Sensing
链接: https://arxiv.org/abs/2609.07558
作者: Jannes Münchmeyer,Han Xiao,Frederik Tilmann
类目: Geophysics (physics.geo-ph); Machine Learning (cs.LG)
*备注: 14 pages, 5 figures
Abstract:Fibre optic sensing, such as distributed acoustic sensing (DAS), has become a widespread technology for geophysical studies. To process the large-scale datasets produced by DAS, several machine learning methods have been proposed. However, without standardization of data and models, these methods lack comparability and interoperability. This introduces a gap between model developers and practitioners analyzing DAS data and inhibits adoption of deep learning for DAS. To address these limitations, here we present SeisBench DAS, an extension to the SeisBench library for machine learning in seismology. SeisBench DAS defines standard formats for DAS benchmark datasets, including standardised metadata and labels, and DAS models. It builds on the xdas framework for data ingestion and virtual array handling, and on PyTorch for reading and applying the machine learning models. Importantly, SeisBench provides an engine to efficiently apply deep learning models to diverse formats of DAS data, bridging the gap between model developers and practitioners. SeisBench DAS is designed as an open and extensible framework, allowing to easily incorporate future developments in deep learning for DAS.
[LG-207] Graph neural networks and the energetic cavity method for combinatorial optimization
链接: https://arxiv.org/abs/2609.07456
作者: Joe Bacchus George,George T. Cantwell
类目: Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (cs.LG)
*备注:
Abstract:We study the use of graph neural networks (GNNs) for finding approximate ground states of Ising models. Efficiently finding these ground states is of broad significance because many combinatorial optimization problems can be formulated as an Ising model with the appropriate choice of couplings and fields. Exactly solving these problems is hard but there are many good heuristic methods. A lineage of these heuristics build from mean-field approximations: one approach uses the leading eigenvector of an appropriately defined matrix, another is the min-sum algorithm, also known as the energetic cavity method. Without modification, GNNs perform worse than both of these methods. We consider small modifications to the GNN to incorporate these heuristics and find that this considerably improves performance. While the modified approach is competitive against other deep-learning approaches, we still find that simulated annealing is reliably at least as good as deep learning methods for the same computational cost.
[LG-208] Masking Radar Cognition under Adversarial Surveillance: A Distributional Privacy Framework
链接: https://arxiv.org/abs/2609.07428
作者: Sreedevi K,Nandhini K,Anup Aprem,Deepthi P P
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注:
Abstract:In this article, we propose an online electronic counter-countermeasure (ECCM) framework designed to conceal the strategic decision-making processes of a cognitive radar (CR) operating under adversarial surveillance. We model the CR under two distinct decision paradigms: a static constrained utility-maximizing behavior and a dynamic expected utility-maximizing behavior. The radar’s utility function is modeled via a von Mises–Fisher (vMF) distribution, with the distributional parameter constituting the private information to be protected from adversarial inference. We adopt a distribution privacy framework to conceal this private information and provide formal distribution privacy guarantees for cognition masking. In this work, we develop cognition-hiding algorithms for both static constrained utility maximization (WDPCH-SU), and dynamic expected utility maximization (WDPCH-DU). Through rigorous mathematical analysis, we show that both WDPCH-SU and WDPCH-DU satisfy \epsilon -distribution privacy ( \epsilon -DistP) against inference-based adversarial attacks and present the privacy–performance trade-off bounds, quantifying utility loss (in static setting) and expected utility deviation (in dynamic setting) as functions of \epsilon . Numerical results show that WDPCH-SU gives about 15% improvement in utility loss at maximum privacy compared to the existing methodology while WDPCH-DU achieves a greater reduction in adversarial Fisher information without requiring explicit Fisher information constraints, at a moderate, analytically bounded utility deviation. These results are highly promising in many 6G communication scenarios such as network slicing for automated driving and swarm UAV coordination, where it is essential to keep the resource allocation policy robust against privacy attacks.
[LG-209] Smoothed Picard Hamiltonian Monte Carlo
链接: https://arxiv.org/abs/2609.06906
作者: Fan Chen,Sinho Chewi,Jianfeng Lu,Matthew S Zhang
类目: atistics Theory (math.ST); Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注:
Abstract:We develop a new low-accuracy sampler, called \emphsmoothed Picard Hamiltonian Monte Carlo, which combines Gaussian smoothing, Picard iteration, and higher-order discretization. For a log-concave target \pi \propto \exp(-V) in dimension d satisfying 0 \prec \alpha I \preceq \nabla^2 V \preceq \beta I , with condition number \kappa := \beta/\alpha , smoothed Picard HMC returns a sample with \sqrt \alpha,W_2(\cdot,\pi) \le \varepsilon using \widetilde O(\kappa^2 + \kappa^7/6 d^1/6/\varepsilon^1/3) gradient queries. We also prove stronger W_q bounds, and then develop an algorithmic framework, the recursive warm start generator, to upgrade these W_q bounds to stronger divergence guarantees. This produces a warm start for the proximal bouncy particle sampler, introduced in a companion work, leading to a high-accuracy log-concave sampler with complexity \widetilde O((\kappa^7/6 d^1/6 + \kappa^1/2 d^1/4)\mathrmpolylog(1/\varepsilon)) .
[LG-210] Accelerated High-Accuracy Sampling from a Warm Start via the Proximal Bouncy Particle Sampler
链接: https://arxiv.org/abs/2609.06905
作者: Fan Chen,Sinho Chewi,Jianfeng Lu,Matthew S Zhang
类目: atistics Theory (math.ST); Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Probability (math.PR); Machine Learning (stat.ML)
*备注:
Abstract:We study the problem of sampling from \mu(\mathrmdx)\propto e^-V(x),\mathrmdx on \mathbbR^d , where V is \alpha -strongly convex and \beta -smooth, and write \kappa:=\beta/\alpha . We design and analyze the Proximal Bouncy Particle Sampler (Proximal BPS), a new sampler that combines ideas from the proximal sampler and the bouncy particle sampler. From a warm start initialization with O(1) Rényi divergence w.r.t. \mu , Proximal BPS returns a sample whose law is \varepsilon -close to \mu in total variation distance using \widetilde O(\sqrt\kappa,d^1/4 ,\mathrmpolylog(1/\varepsilon)) gradient queries in expectation.
[LG-211] Large Classification-Risk-Optional Label Acquisition
链接: https://arxiv.org/abs/2609.06873
作者: F. Setoudehtanzangi,Geoffrey J. McLachlan
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:We study how a limited labeling budget should be allocated to minimize multiclass zero-one classification risk. We consider parametric classification problems in which features are observed for all sampling units while class labels can be acquired selectively. By combining the Fisher information supplied by an acquired label with the local geometry of multiclass excess risk, we derive an acquisition criterion that minimizes the leading asymptotic coefficient of expected multiclass excess risk. The resulting rule values a label according to how strongly its information is aligned with parameter directions that perturb the active Bayes decision boundary, rather than according to posterior uncertainty or global parameter information alone. We characterize the oracle acquisition design, establish its threshold structure, and derive face-specific and cost-sensitive extensions. An analytic example shows that posterior uncertainty and classification value can produce different, and even reversed, acquisition rankings. We further develop a two-stage adaptive procedure that attains the oracle leading-risk criterion under regularity conditions and provide explicit results for Gaussian discriminant analysis. Three-class QDA experiments illustrate the resulting acquisition geometry, while an application to the six-class Statlog Landsat Satellite data shows that classification-risk acquisition can differ materially from both uncertainty-based acquisition and the complete-classification-information comparator. The adaptive classification-risk design attains lower mean error than this Fisher comparator across the labeling budgets considered, although it does not uniformly outperform entropy or margin sampling and differences among the targeted strategies become small as the labeling budget increases.
[LG-212] Likelihood-Based Unsupervised Anomaly Detection in CMS Dijet Events
链接: https://arxiv.org/abs/2609.06686
作者: Bhavishya Chebrolu(1),Hitesh Rasineni(1),Prajwal Aaryan Immadi(1) ((1) VIT-AP University, Amaravati, India, (2) Mohan Babu University, Tirupati, India)
类目: High Energy Physics - Experiment (hep-ex); Machine Learning (cs.LG)
*备注:
Abstract:We present an unsupervised search for anomalous dijet events in proton–proton collision data using neural spline flow density estimation. A normalizing flow model is trained on a high-dimensional feature space comprising jet, dijet, and event-level observables to learn the dominant Standard Model background directly from data, without assuming a specific signal hypothesis. Events assigned low likelihood under the learned density are identified as potential anomalous events. Using this approach on a CMS Open Data dijet sample, we investigate extreme events in the tail of the anomaly-score distribution and perform an extensive validation and robustness study. This study includes feature-level statistical comparisons, mass decorrelation tests, permutation-based null tests, and evaluations of training stability. The selected anomalous events exhibit notable departures from the background-only expectation, primarily in jet-substructure observables, while remaining stable under several known sources of bias in unsupervised learning. The identified anomalies are distributed across the kinematic phase space and do not exhibit a narrow structure in the dijet invariant-mass spectrum. Instead, they show correlated deviations across multiple observables, consistent with a multivariate difference in jet substructure and event topology rather than a localized resonance. While no claim of new physics is made, this study demonstrates that neural spline flow-based density estimation can be sensitive to rare, structured deviations in collider data and may provide a model-independent exploratory tool for searches for physics beyond the Standard Model at the LHC. Subjects: High Energy Physics - Experiment (hep-ex); Machine Learning (cs.LG) Cite as: arXiv:2609.06686 [hep-ex] (or arXiv:2609.06686v1 [hep-ex] for this version) https://doi.org/10.48550/arXiv.2609.06686 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-213] RoPE attention is an exact forward-pass gradient step with softmax intact
链接: https://arxiv.org/abs/2609.06685
作者: Julie Huang,Maggie Chlon,Leon Chlon
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:We derive an exact gradient-step representation of the RoPE-softmax forward pass. For every deterministic RoPE-softmax attention head with arbitrary affine projection weights, we construct a query-dependent effective matrix \Delta M_i satisfying y_i = \mu_i + u_i^\top \Delta M_i , where \mu_i is the uniform mean of the attended values and u_i is the augmented query input. The construction applies the classical exponential divided difference \rho = \phi_1 to retain the softmax exactly. Its positive coefficients give a unit gradient-step representation on a query-conditioned quadratic objective. The same function connects the RoPE generator to exact positional finite differences. We derive a tokenwise formula for the error of reusing one query’s matrix and prove that a nonconstant finite-cache head cannot admit a globally exact affine query readout. Reconstruction checks and frozen-reuse calibration on one pretrained Qwen2.5-0.5B layer verify the representation and quantify the correction required when one query’s matrix is reused.
[LG-214] Stochastic Nonconvex Bilevel Optimization: Improved Rates Without Rare-Visit Assumption
链接: https://arxiv.org/abs/2609.06580
作者: Daniel Cortild,Mathias Staudigl,Juan Peypouquet,Coralia Cartis
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:
Abstract:We investigate stochastic simple bilevel optimization with smooth and possibly nonconvex upper- and lower-level objectives. Existing stochastic extensions of dynamic barrier gradient descent (DBGD) either obtain fast convergence under an unverifiable trajectory-dependent ``rare-visit’’ assumption, or remove this assumption at a substantially higher oracle cost. We show that a simple denominator-only regularization of the DBGD multiplier eliminates the need for such an assumption while preserving fast convergence rates. Specifically, our method achieves (\varepsilon, \varepsilon) -stationarity in O(\varepsilon^-2) iterations using O(\varepsilon^-4) upper-level and O(\varepsilon^-7) lower-level stochastic gradients, which improves upon the best assumption-free complexities. We additionally derive anytime parameter schedules.
[LG-215] Beyond Worst-Case Coreset Bounds for k-Clustering via Determinantal Sampling
链接: https://arxiv.org/abs/2609.06394
作者: Diptarka Chakraborty,Satyaki Mukherjee,Gaurav Vallabhdas Revankar,Hoang-Son Tran
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Probability (math.PR)
*备注:
Abstract:Massive datasets in modern machine learning have made data reduction a central challenge, particularly for clustering tasks where memory and computational constraints demand compact yet faithful summaries. A standard approach is to construct an \textit \epsilon -coreset: a small weighted subset that approximately preserves the clustering cost for every plausible choice of centers. For the \textit (k,z) -clustering problem, existing worst-case bounds on coreset size are essentially tight, ruling out substantially smaller coresets in general. However, such worst-case instances are often unrepresentative of real-world data. In this work, we show that significantly smaller coresets are possible under mild and natural assumptions on the underlying data distribution. We introduce a new correlated sampling framework, called \textitdeterminantal sampling, based on a novel application of determinantal point processes. Using this framework, we obtain an efficiently constructible \varepsilon -coreset for (k,z) -clustering in \mathbb R^d whose dependence on 1/\varepsilon has exponent strictly smaller than 2 when d is fixed. This improves over the worst-case \varepsilon^-2 barrier under our beyond-worst-case assumptions. To the best of our knowledge, this is the first result that provably surpasses these lower bounds through beyond-worst-case assumptions. Finally, we validate our approach on synthetic and real-world benchmark datasets, where it consistently achieves smaller coresets than existing state-of-the-art methods, even without explicitly enforcing the assumptions used in the analysis.
[LG-216] Hierarchical Fourier Approximation for Variational Quantum Distribution Learning
链接: https://arxiv.org/abs/2609.06307
作者: Taha Hoseinpour Asli,Sajjad Hashemian,Ebrahim Ardeshir-Larijani
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: 18 pages, 3 figures
Abstract:We study variational quantum distribution learning through a hierarchy of Walsh–Fourier approximations on the Boolean cube. At each level, a selected set of target Fourier coefficients defines a spectral truncation, which is projected onto the probability simplex and used as the target of a quantum circuit Born machine. Parameters learned at one level initialize the next through a warm-start map. We prove an end-to-end expected learning guarantee where the approximation term is determined by the omitted Fourier mass, while a normalized unbiased estimator yields an explicit statistical bound for empirical truncations. We then instantiate the abstract discrepancy conditions for total variation distance and relate the resulting distributional error to quantum-state fidelity. The total-variation specialization incurs the explicit factor 2^n-1 under our normalized \ell_2 convention and is therefore informative only for sufficiently concentrated Fourier tails. The framework does not establish global trainability or eliminate barren plateaus; rather, it identifies the conditions under which low-to-high spectral training admits a approximation–estimation–optimization analysis.
[LG-217] Robust conditional dimension reduction for dissimilarity data
链接: https://arxiv.org/abs/2609.06284
作者: Xiao Ling,Anh Bui
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Optimization and Control (math.OC); Methodology (stat.ME)
*备注:
Abstract:Conditional dimension reduction (cDR) learns low-dimensional latent coordinates while accounting for observed covariates that represent known sources of variation in the data. Conditional Multidimensional Scaling (cMDS) is a cDR technique that works directly with dissimilarity data. Its standard squared-stress formulation, however, is sensitive to contaminated dissimilarity, since outliers can dominate the objective and distort the learned configuration. We proposed Robust Conditional Multidimensional Scaling (rcMDS) by replacing the squared-stress criterion with a Fair M-estimation objective. We developed a reweighted conditional SMACOF algorithm to optimize this objective. The proposed algorithm admits computationally tractable updates, and its stabilized objective values decrease monotonically and converge to a finite limit. Experiments on synthetic and real data show that the pro
[LG-218] Fast PAC Global Optimization via Restarted Langevin: Exploration Exploitation and Degenerate Cooling
链接: https://arxiv.org/abs/2609.06196
作者: Ioannis Kontoyiannis,Sean Meyn
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:
Abstract:We study the computational effort required for global optimization of a smooth, possibly nonconvex objective \Gamma:\mathbbR^d\to\mathbbR . An algorithm satisfies the (\varepsilon,\delta) -PAC performance requirement if its output \widehat X obeys \mathbbP\Gamma(\widehat X)-\Gamma^\star\varepsilon\leq\delta . Algorithm design and analysis are in continuous time. We compare classical simulated annealing and fixed-temperature Langevin diffusion with two approaches introduced and analyzed here: parallel-restart Langevin and a Langevin–gradient scheme using stochastic dynamics for global exploration and gradient flow for local exploitation. Let L=\log(1/\delta) and let E_* denote the dominant energy barrier. At logarithmic precision in the low-temperature regime, the first two approaches require simulation time exponential in L/\varepsilon . For parallel fixed-temperature Langevin, an appropriate number of independent trials gives C_3=L^1+o(1)/\varepsilon as \delta\downarrow0 , for each fixed \varepsilon0 . The most substantial improvement comes from separating exploration from exploitation. If \eta is the attraction margin of a target region containing the global minimizers, a sufficient low-temperature estimate for total simulation time in the best-state Langevin–gradient variant is C_4^©\approx N\exp\EL/(N\eta)\ +O(\log(1/\varepsilon)) , with EE_* . Thus global exploration is decoupled from the requested accuracy. Analysis beyond logarithmic precision reveals dimension-dependent prefactors, while experiments on the six-hump camel and Rastrigin objectives illustrate the benefits of warmer exploration and the usefulness of spectral information for understanding exploration time. Subjects: Optimization and Control (math.OC); Machine Learning (cs.LG) MSC classes: 90C26, 90C26 Cite as: arXiv:2609.06196 [math.OC] (or arXiv:2609.06196v1 [math.OC] for this version) https://doi.org/10.48550/arXiv.2609.06196 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-219] Recovering linear images of sparse signals from indirect observations
链接: https://arxiv.org/abs/2609.06182
作者: Anatoli Juditsky,Arkadi Nemirovski
类目: Machine Learning (stat.ML); Information Theory (cs.IT); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:In this paper, we develop and analyze techniques for recovering a linear image Bx of an unknown signal x from indirect noisy observation \omega=Ax+\xi . It is \em a priori known that x\in \cX , a given convex compact set, and that x is s -sparse—has at most s nonvanishing entries. The proposed estimates belong to a large family of recovery routines by \ell_1 -minimization. However, unlike the classical result describing performance of such estimates, we do not make any special (and hard to check) assumptions about the sensing matrix A such as nullspace or Restricted Isometry condition and the like. As a consequence, parameters of the estimates and the upper bounds on their risks are not available in a closed analytic form, but are delivered instead by efficient computation as solutions to explicit convex optimization problems.
[LG-220] Causal DAG Identification for Count Data via Poisson Thinning Structural Equation Models
链接: https://arxiv.org/abs/2609.06098
作者: Penggang Gao,Ming Cai,Hisayuki Hara
类目: Methodology (stat.ME); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 35 pages, 6 figures
Abstract:Count-valued variables arise in many scientific and applied settings, yet explicit structural models that allow full identification of causal DAGs from observational data remain limited. The Poisson branching structural causal model (PB-SCM) provides a count-valued analogue of linear structural equation models using binomial thinning and independent Poisson exogenous variables, but its causal DAG is generally only partially identifiable. Building on this framework, we propose the Poisson thinning structural equation model (PT-SEM), which replaces binomial thinning in PB-SCM with Poisson thinning and allows node-wise exogenous distributions from diverse count-distribution families. Under node-wise regularity conditions, we establish identifiability of the causal DAG, the thinning coefficients, and the node-wise exogenous distributions. The same identification analysis extends to binomial thinning, yielding full identifiability whenever every nonsink has non-Poisson exogenous noise. We further develop a structure learning algorithm that optimizes, via dynamic programming, a BIC score based on local likelihoods evaluated at plug-in moment estimates, and establish its consistency for DAG selection. Simulations demonstrate favorable performance in DAG recovery and thinning-coefficient estimation, and a real-data application illustrates the practical utility of PT-SEM. Comments: 35 pages, 6 figures Subjects: Methodology (stat.ME); Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2609.06098 [stat.ME] (or arXiv:2609.06098v1 [stat.ME] for this version) https://doi.org/10.48550/arXiv.2609.06098 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-221] Functional Attentive Interpretable Regression
链接: https://arxiv.org/abs/2609.05846
作者: Haixu Wang,Tianyu Guan,Jiguo Cao
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:In function-on-function regression, the coefficient surface \beta(s,t) may exhibit complex support structure—from localized patches to global patterns such as disconnected regions, bands, or rings—where effect similarity does not align with Euclidean proximity. Projection-based methods that rely on fixed basis expansions can obscure such structure, while direct smoothing approaches risk oversmoothing the surface and its boundaries. We propose Functional Attentive Interpretable Regression (FAIR), which represents \beta(s,t) directly through coordinate features and uses self-attention to learn effect-adaptive neighborhoods, enabling information sharing at both local and global scales. A scalar compression network maps these learned representations to the coefficient surface. Sparsity and smoothness penalties applied over these neighborhoods promote localized support with coherent boundaries. We establish a sieve equivalence to tensor-product spline spaces and derive convergence rates. Simulations and applications to oceanographic and hydrological data demonstrate that FAIR recovers support geometry more accurately than existing methods while achieving superior prediction, particularly under sparse sampling.
[LG-222] Physical policy gradient theorem for in situ stochastic-adjoint training
链接: https://arxiv.org/abs/2609.05808
作者: William Tuxbury,Zin Lin
类目: Optics (physics.optics); Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (cs.LG)
*备注: 9 pages, 2 figures
Abstract:In situ adjoint training extracts parameter gradients directly from measurement, but has so far been limited to reciprocal or restricted systems. Here, we introduce the physical counterpart of the policy gradient theorem: a stochastic-adjoint gradient estimator that lifts these constraints by trading reciprocity for nondegenerate diffusion. As validation, we train a nonlinear resonator network, whose own dynamics supply the policy, against antagonistic temporal modulations with gradients from measured stochastic trajectories alone, without finite differences or a separate adjoint experiment.
[LG-223] Diagonal Attenuation: A Finite-Sample Correction for PCA
链接: https://arxiv.org/abs/2609.05796
作者: Qiang Sun
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME)
*备注: 14 page main including references; 30 page supplement
Abstract:Principal component analysis (PCA) can rotate away from its population target when a covariance matrix is estimated from limited data. We introduce diagonal attenuation, which preserves sample cross-covariances while reducing coordinatewise sample variances. The method is revealed exactly by averaging a linear full-output reconstruction loss over random input masks; studying the correction directly extends it beyond the range attainable by masking. We isolate the part of the random coupling between retained and omitted population directions that is contributed by sample-variance errors, and show how attenuation can reduce the resulting rotation. Under balanced marginal variances, we derive an explicit expected-risk theorem, uniform over the attenuation path for all sufficiently large finite samples, and obtain the asymptotically risk-minimizing strength. For general covariances, we characterize when attenuation leaves the population PCA subspace unchanged and give a risk theorem that also accounts for changing eigengaps and the population cost when the target moves. Simulations track this tradeoff from exact preservation back to PCA. Across local image patches, speech spectra, and smartphone acceleration, both mask-derived and direct attenuation improve PCA under two fitting-sample budgets, and one of them has the largest mean gain among seven methods in every data–budget cell. The full path selects strengths beyond the mask-derived boundary on 63% – 95% of the subsamples.
[LG-224] ght Lower Bounds for State Tomography with Limited Entanglement
链接: https://arxiv.org/abs/2609.05718
作者: Ufuk Keskin,Jason Luo,Mahbod Majid,Matthew Radzihovsky
类目: Quantum Physics (quant-ph); Information Theory (cs.IT); Machine Learning (cs.LG)
*备注: 36 pages
Abstract:We study state tomography when each measurement acts on at most k fresh copies and no quantum memory is retained between blocks. We prove a lower bound matching the upper bound in [arXiv:2510.07788]. Thus the copy complexity of estimating an arbitrary d -dimensional state to trace distance \epsilon is, up to absolute constant factors, \max\d^3/(\sqrtk\epsilon^2),d^2/\epsilon^2\ for every k and all sufficiently small \epsilon . This removes the earlier restriction that k be small as a function of the accuracy. The lower bound applies to arbitrary measurements within each block and adaptive choices between blocks. The lower bound already applies in a small neighborhood of any state whose smallest eigenvalue is of order 1/d , even when the center is known. The main ingredient is a uniform Fisher information bound for one measurement block that depends only on the smallest eigenvalue of the state. The proof avoids the perturbative expansion responsible for the restriction in [arXiv:2402.16353]. Fano’s inequality for metric balls and a log-Sobolev comparison between mutual and Fisher information then reduce the adaptive protocol to this block bound [arXiv:1607.00550, arXiv:1902.08582]. Comments: 36 pages Subjects: Quantum Physics (quant-ph); Information Theory (cs.IT); Machine Learning (cs.LG) Cite as: arXiv:2609.05718 [quant-ph] (or arXiv:2609.05718v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2609.05718 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-225] Characterizing Privacy Risks of Quantum Machine Learning with Emergent Quantum-Native Access
链接: https://arxiv.org/abs/2609.05702
作者: Liou Tang,James Joshi,Ashish Kundu
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注:
Abstract:Quantum Machine Learning (QML) has shown rapid advances by utilizing quantum computing for machine learning tasks. Meanwhile, the privacy risks accompanying QML is also starting to be studied, which inherit privacy leakage channels from “classical” ML and also quantum-unique risks. Existing work on privacy-preserving QML largely focuses on a QML-as-a-service scenario, which generally assumes that the QML model owner provides only classical bit outputs to queries, while users (and adversaries) have only classical computing abilities. However, this view is increasingly challenged in a quantum-native world of quantum-capable users/adversaries, which may have access to both quantum computing abilities and access to quantum information output from service providers. In this paper, we aim to bridge this gap by examining membership inference attacks against QML models by demonstrating that increasing quantum access and quantum computing abilities provides provable theoretical privacy leakage and empirical adversarial gain. However, the probabilistic nature of QML introduces a gap between theoretical and empirical adversarial advantage. These results show that existing research on privacy leakage on QML models underestimate privacy leakage in an emergent quantum-native access regimes, which we hope to establish a first step into examining potential privacy leakages for QML in the quantum-native this http URL results show that existing research on privacy leakage in QML models underestimates privacy leakage in emergent quantum-native access regimes, and we hope to establish a first step in examining potential privacy leakages for QML in the quantum-native world. Subjects: Quantum Physics (quant-ph); Machine Learning (cs.LG) Cite as: arXiv:2609.05702 [quant-ph] (or arXiv:2609.05702v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2609.05702 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-226] Rollcast: Proper-Score Gated Rolling Anchors for Adaptive Probabilistic Time-Series Forecasting
链接: https://arxiv.org/abs/2609.05561
作者: Giancarlo Vercellino
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:Rollcast is a probabilistic forecasting method for univariate time series that combines a compact set of rolling statistical anchors rather than relying on a single global model. Rolling means, medians, extrema, regression endpoints, and quantiles define candidate forecast locations and a representation of the current state. A state-dependent softmax gate learns anchor probabilities by minimizing negative log predictive density, while residual distributions retrieved from similar historical states provide local uncertainty. Recursive simulation propagates the resulting mixture over multiple forecast horizons. The method is evaluated in a Monte Carlo study covering eight data-generating processes, including autoregressive, random-walk, local-trend, threshold, regime-switching, stochastic-volatility, heavy-tailed, and variance-break dynamics. Across 2,000 independent fitted series, Rollcast is compared with the true conditional predictive distribution generated by an oracle simulator. Overall empirical coverage is 86.2% for nominal 90% intervals and 91.5% for nominal 95% intervals. Predictive intervals are on average 13.6% wider than the oracle at the 90% level, while CRPS is 14.4% higher than oracle CRPS. Performance is closest to the oracle under autoregressive, threshold, stochastic-volatility, heavy-tailed, and variance-break dynamics, while local trends and regime switching are more challenging. The results indicate that Rollcast can construct competitive probabilistic forecasts from simple, interpretable local summaries, while also identifying limitations in calibration and recursive uncertainty propagation.
[LG-227] Physics-Informed Neural Networks for Depth-Averag ed Granular Avalanche Dynamics on Curved Topography
链接: https://arxiv.org/abs/2609.05542
作者: Pujan Pranavkumar Purohit,Pradyumn Singh Sikarwar,Vishal Sharma,Gaurav Bhutani
类目: oft Condensed Matter (cond-mat.soft); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG); Fluid Dynamics (physics.flu-dyn)
*备注: 16 pages, 5 figures
Abstract:Physics-informed neural networks (PINNs) provide a mesh-free framework for solving governing equations, but their application to granular avalanche dynamics over curved terrain remains largely unexplored. This study extends a depth-averaged PINN formulation based on the Savage-Hutter equations to an exponentially curved chute with spatially varying inclination and a strain-rate-dependent Mohr-Coulomb earth-pressure closure. The model is validated against measured front- and rear-edge trajectories from a laboratory granular-avalanche experiment, with selected observations withheld from training. A staged temporal curriculum proved essential for accurate prediction, reducing the held-out trajectory error by approximately two orders of magnitude compared with training over the full time domain from the outset. Sparse-data experiments further showed that observation placement was more influential than observation number within the configurations tested. Four observations bracketing the transition from acceleration to deceleration achieved nearly the same accuracy as the eight-observation reference configuration, whereas observations clustered at early or late times performed poorly. The results demonstrate the importance of both training strategy and informative data placement when applying PINNs to granular flows over curved topography.
[LG-228] Asymptotically-informed neural networks for Black-Scholes implied volatility computation
链接: https://arxiv.org/abs/2609.05491
作者: Samira Amiriyan,Youness Boutaib
类目: Computational Finance (q-fin.CP); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:The computation of Black-Scholes implied volatility is a fundamental task in quantitative finance, underpinning option valuation, model calibration and risk management. Although implied volatility is routinely used in practice, the inversion of the Black-Scholes pricing formula remains a challenging numerical problem, particularly in asymptotic regimes corresponding to extreme option prices, strikes or maturities, where the inverse map becomes highly sensitive to perturbations of the price. In this paper, we introduce a new family of asymptotically-informed neural-network architectures for implied-volatility computation. Exploiting the distinct behaviours of the Black-Scholes pricing function in different volatility regimes, we propose a family of architectures that learn a trainable partition of the price-log-moneyness domain through a system of gating functions and combines specialised local approximations of the implied-volatility function within each region. Extensive numerical experiments demonstrate that the proposed models consistently outperform standard feed-forward neural networks across a wide range of parameter domains, often by several orders of magnitude in relative accuracy while maintaining excellent generalisation properties. Furthermore, the neural-network outputs provide highly accurate initial guesses for a third-order Householder scheme, allowing near machine-precision implied-volatility computations after only two refinement iterations.
[LG-229] Condition aware learning enables robust prediction of oligonucleotide melting behavior across diverse chemistries and assay conditions
链接: https://arxiv.org/abs/2609.05454
作者: Danielle L. Ferreira,Lifeng Lin,Adam Aslam,Nicholas Chang,Rebekah G. Baig,Edgar Baculi,Zoey Cao,Melanie Senn
类目: Biomolecules (q-bio.BM); Machine Learning (cs.LG)
*备注:
Abstract:Oligonucleotide melting temperature is a fundamental determinant of nucleic acid hybridization and underpins the design of molecular diagnostics, polymerase chain reaction assays, and many other biotechnology applications. However, accurately predicting melting behavior remains difficult because it depends not only on sequence composition, but also on experimental conditions and chemical modifications commonly used in modern assay design. Existing thermodynamic models rely on fixed parameterizations that are often difficult to extend across diverse reaction environments and nucleotide chemistries. Here we show that a condition-aware nucleotide language model can accurately predict oligonucleotide melting behavior across diverse experimental conditions and both unmodified and chemically modified oligonucleotides. By combining contextual sequence representations with explicit information describing the reaction environment, the framework achieves sub-degree prediction accuracy and reduces prediction error for locked nucleic acid-modified oligonucleotides by up to 25% relative to nearest-neighbor thermodynamic approaches. The model also more accurately captures the thermal effects introduced by nucleotide modification and maintains strong performance on independent benchmark datasets spanning experimental conditions substantially different from those represented during training. Our results demonstrate that learned sequence representations can complement classical thermodynamic models by capturing context-dependent effects that are difficult to encode using fixed parameter tables alone. More broadly, this work provides a scalable framework for predicting oligonucleotide melting behavior across diverse chemistries and assay conditions, supporting more reliable molecular assay design.
[LG-230] ZetaDial: dialing net charge of protein binders at inference time for therapeutic developability
链接: https://arxiv.org/abs/2609.05451
作者: Mohammed Sameer Syed,Tamara Dinneen
类目: Biomolecules (q-bio.BM); Machine Learning (cs.LG)
*备注: 14 pages, 7 figures, 2 tables
Abstract:Net charge is a developability-relevant property of therapeutic binders, linked to viscosity, clearance, nonspecific interaction and aggregation, and antibody screens already use charge-related criteria. Yet inverse-folding pipelines expose no way to set it to a target value. ProteinMPNN and BindCraft offer amino-acid biases, weight choices and custom losses, but neither supplies a per-protein feedback loop that measures realised charge after sampling and corrects it to a setpoint. ZetaDial contributes a post-sampling, per-protein secant controller around fixed-backbone ProteinMPNN. On matched stochastic benchmarks the secant loop reduced mean absolute error relative to a fixed-slope loop on RCSB complexes (5.17 vs 6.46 charge units) and Cas13 monomers (5.57 vs 8.23). Relative to the optimised matched global bias, it cut RCSB error from 11.71 to 5.17 (cluster bootstrap p 0.001) and was statistically indistinguishable on Cas13 (5.47 vs 5.57). Across 800 eight-protein subsets, sensitivity heterogeneity was associated with calibration gain (Pearson r = 0.79); this is descriptive resampling, not a prospective decision rule. Foldability deteriorated as bias magnitude increased. In the full 52-complex seed-0 analysis, reference-based DockQ declined clearly at +/-3 but not at +/-1.5; a selected five-seed replication on eight complexes showed paired declines at every nonzero setting, but does not estimate the effect for all 52. In exploratory BindCraft sweeps, PD-L1 designs moved toward near-neutral charge at similar maximum interface pTM but with overlapping success-rate intervals; IL-7R-alpha responses were non-monotonic and RBD produced no strong designs. A fixed-backbone C-alpha-neighbour analysis found smaller same-sign charge-patch proxies near neutral charge, but this proxy is not a measured electrostatic surface or experimental developability endpoint.
[LG-231] Novel hybrid protein scaffold gap filling using weighted machine learning ensemble beam search and mass-constrained reranking
链接: https://arxiv.org/abs/2609.05436
作者: Tahmid Enam Shrestha,Md. Manzurul Hasan,Md. Rafiqul Islam
类目: Biomolecules (q-bio.BM); Machine Learning (cs.LG)
*备注:
Abstract:Protein scaffold gap filling is an important computational task in protein sequence reconstruction, where missing amino acid regions must be inferred from incomplete scaffold information. This study proposes a hybrid machine learning and mass constrained reranking framework for protein scaffold gap filling under known-gap-size and known-gapmass settings. Homologous protein sequences from MabCampath, P5A proteoform, and carbonic anhydrase 2 were used to generate masked 11-mer residue-level samples and fullgap evaluation cases. The residue prediction task was formulated as a 20-class amino acid classification problem using first-, middle-, and last-position masking. Multiple classical machine learning models were trained using raw encoded, row-average, and SVD-reduced features, and the strongest models were combined through a validation-accuracy-weighted ensemble. For known-size gap reconstruction, beam search was used to generate complete missing peptide sequences from residue-level probability estimates. For known-mass reconstruction, mass-constrained homologous candidate retrieval was combined with hybrid reranking based on mass validity, homologous frequency, context support, ensemble likelihood, mass error, and length penalty. The proposed framework achieved 95.41% residue-level validation accuracy, 87.50% known-size exact-match accuracy, and 100% top-5 recovery on seven CAH2 known-mass benchmark cases. These results indicate that the proposed framework can effectively reconstruct missing protein regions by integrating local sequence learning, homologous evidence, peptide mass constraints, and biochemical validation.
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