本篇博文主要内容为 2026-09-25 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 (2026-09-25)
今日共更新926篇论文,其中:
- 自然语言处理共126篇(Computation and Language (cs.CL))
- 人工智能共260篇(Artificial Intelligence (cs.AI))
- 计算机视觉共143篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共230篇(Machine Learning (cs.LG))
- 多智能体系统共17篇(Multiagent Systems (cs.MA))
- 信息检索共30篇(Information Retrieval (cs.IR))
- 人机交互共35篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] GRASP: Generating Revising and Assessing for Strategic Planning with Agent ic AI EMNLP2026
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在处理复杂任务时可靠性随任务复杂度增加而显著下降的问题，尤其聚焦于生成高质量、可执行的自然语言规划方案。其核心挑战在于如何在多步骤、高复杂度任务中保持规划的准确性与鲁棒性。为此，论文提出了一种策略感知的多阶段规划框架——GRASP（Strategy-aware, multi-stage Planning framework），其关键创新在于将规划流程解耦为三个独立且上下文隔离的模块：全局宏观指导预编译（GenPlan）、局部策略探索（RevPlan）以及基于多准则判别器的轨迹独立评估（VerPlan）。通过严格分离上下文并引入宏观正则化机制，GRASP有效缓解了任务间干扰与信息过载问题，显著提升了规划质量。实验结果表明，GRASP在多个基准数据集上均达到新的性能上限，在自然语言日程规划（Natural Plan Calendar Scheduling）、ZebraLogic和SciBench Math等任务中分别实现约12.4%、30.8%和显著提升的准确率；更重要的是，在多任务扩展场景下，传统规划器出现性能急剧退化，而GRASP完全消除了多任务退化效应，并在交错双任务环境中相较直接调用LLM的规划器实现最高达16.7%的绝对准确率提升，同时超越前沿推理模型（如GPT-5-mini）达14.5%。
链接: https://arxiv.org/abs/2609.30147
作者: Arunabh Srivastava,Mohammad A.(Amir)Khojastepour,Srimat Chakradhar,Sennur Ulukus
机构: University of Maryland, College Park, MD(马里兰大学学院帕克分校); NEC Laboratories America, Inc.(美国电报电话公司实验室美国分公司)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注: Accepted at the Second Workshop for Research on Agent Language Models (REALM) at EMNLP 2026
Abstract:Large Language Models (LLMs) typically exhibit a performance profile where reliability degrades as task complexity increases. We address the challenge of generating high-quality natural language executable plans for complex tasks by introducing \textbfGRASP , a strategy-aware, multi-stage planning framework. GRASP decouples the planning pipeline across specialized, context-isolated modules: it pre-compiles global macro-guidelines (GenPlan), explores alternative localized strategies within isolated context windows (RevPlan), and independently evaluates trajectories using a multi-criteria discriminator (VerPlan). Empirical evaluations show that GRASP consistently establishes a new state-of-the-art frontier across diverse datasets, yielding substantial accuracy gains over direct LLM planners on Natural Plan Calendar Scheduling ( \sim 12.4 % \uparrow ), ZebraLogic ( \sim 30.8 % \uparrow ), and SciBench Math. Crucially, under multi-task scaling-where standard planners suffer immediate performance collapse-GRASP completely flattens the multi-task degradation penalty. In interleaved dual-task environments, GRASP achieves an absolute accuracy gain of up to 16.7 % over direct LLM planners. Furthermore, by isolating context and enforcing strict macro-regularization, GRASP outperforms frontier reasoning models (such as GPT-5-mini) by a margin of 14.5 % .
[MA-1] Augur: A Synthetic Decision Lab for Rehearsing Reactions to Product and Policy Changes
【速读】：该论文旨在解决产品或政策变更在正式发布前，如何准确预测公众反应这一关键问题。其核心挑战在于现有评估体系对模型能力的衡量存在显著偏差，导致对生成式AI（Generative AI）性能的判断失真。为此，作者提出并验证了一个名为Augur的系统：通过从变更文档构建类型化知识图谱，生成具身化的角色市场（grounded persona market），模拟真实互动过程，并输出可审计的决策备忘录以推荐五种可能行动。该研究的关键贡献在于揭示了当前主流评估范式中的根本性缺陷——多数模型性能差距实为评估不明确所致，而非模型本身能力差异。研究通过三重证据证实此结论：第一，仅调整提示（prompt）范围即可使同一模型得分从0%跃升至73%；第二，在控制模型权重与案例不变的前提下，仅通过在提示中明确定义决策分类体系，即可使所有前沿模型性能提升24–34个百分点，且离线部署的Qwen3-32B LoRA-SFT在原始提示下仍优于全部云端模型；第三，模型表现随教师模型一致性上升而增强，但准确性未同步改善，表明整个流程放大了“过度悲观”（over-doom）的系统性偏差。此外，独立验证显示，合成公众反应在盲评中可捕获67%–90%真实公众关切，尤其在高难度决策场景中价值显著。因此，该研究的核心解决方案在于重构评估标准，强调提示工程与任务定义的规范性，从而实现更可信、可复现的生成式智能决策评估。
链接: https://arxiv.org/abs/2609.29952
作者: Rahul Khedar,Mayank Malhotra,Avinash Karn
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注: 19 pages, 15 figures, 11 tables
Abstract:Before a product or policy change ships, the question that matters is how people will react to it. Augur rehearses that reaction offline: it builds a typed knowledge graph from the change documents, populates a grounded persona market, simulates the interaction, and returns an auditable decision memo recommending one of five actions. We assemble Gold-50, fifty real product and policy episodes whose real-world outcome is known, adjudicated against the public record, and score the five-way release verdict against it. Our central finding is methodological and negative: most of the measured gap between frontier cloud models and open-weight models we fine-tune and serve offline is attributable to an under-specified evaluation, not a difference in capability. We show this three ways. First, the prompt envelope alone can dominate the score: holding weights, cases and scorer fixed, one system – a LoRA-SFT adapter on Qwen3-32B – swings from 0% to 73%. Second, in a matched 2x2 ablation, defining the decision taxonomy in the prompt – with no model change – lifts every frontier model by +24 to +34pp; under the under-specified prompt, Qwen3-32B LoRA-SFT served offline beats all three frontier models (paired McNemar, Holm-corrected), and once the prompt is fair no significant difference from any of them is detected. Third, agreement with the distillation teacher rises without accuracy following, and the full pipeline amplifies a systematic “over-doom” bias rather than improving the verdict. Separately, we validate the reaction layer on its own terms: blind judges across four model families find the synthetic reaction recovers 67-90% of the concerns the public actually raised, and a pre-registered ablation locates its value – largest where the decision is hardest, redundant near ceiling. The pipeline that regenerates every number and figure here is available from the authors. Comments: 19 pages, 15 figures, 11 tables Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA) Cite as: arXiv:2609.29952 [cs.AI] (or arXiv:2609.29952v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.29952 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[MA-2] Pairwise Approximation Can Select the Wrong Multi-Robot Plan IROS2026
【速读】：该论文旨在解决多机器人协同规划中因忽略三体及以上交互项而导致的计划选择偏差问题。现有方法通常仅基于单体和双体项对联合计划进行评分，从而忽略了高阶交互信息，导致最优计划可能被错误筛选。其核心解决方案是通过在室内探索基准上冻结多机器人轨迹，精确计算包含所有机器人子集的交付覆盖集合函数 $ F $，并在此基础上构建两种近似评分机制：一是仅依赖单体与双体项的二阶莫比乌斯截断 $ F_2 $，二是基于最小二乘法拟合的等权重两可加模型 $ G $。研究发现，使用 $ F_2 $ 进行排序时，在两个候选族中分别有六张地图的最优计划被改变，最大选择后悔值达0.337；而采用 $ G $ 虽可降低后悔值但仍影响三张地图的选择。相比之下，仅保留单体项的可加评分 $ F_1 $ 在一个候选族中成功选出正确最优计划（六张地图），另一族为四张，显著优于 $ F_2 $。此外，研究揭示了较低的重构误差并不必然对应更低的选择后悔，凸显了高阶交互建模在多机器人协同决策中的关键作用。
链接: https://arxiv.org/abs/2609.29929
作者: William Teo
机构: National University of Singapore (新加坡国立大学); Singapore Technologies Engineering (新加坡科技工程)
类目: Robotics (cs.RO); Multiagent Systems (cs.MA)
备注: 6 pages, 4 figures, 1 table. Accepted at the IROS 2026 Workshop on Intelligent Information Gathering. Code: this https URL
Abstract:Multi-robot coordination methods often score a joint plan from singleton and pairwise terms, leaving out the terms that involve three or more robots. We measure the plan-selection regret of two pairwise approximations to delivered coverage using frozen multi-robot trajectories. For each four-robot plan on an indoor exploration benchmark, replaying all 16 robot subsets gives the exact delivered-coverage set function F . From the same subset values we compute two pairwise scores: the exact order-2 Möbius truncation F_2 , which depends only on the singleton and pair values, and an equal-weight least-squares two-additive fit G . Ranking by F_2 instead of F changes the selected plan on six of seven maps at the 15 m candidate-generation range in each of two candidate families, with regret up to 0.337 of map coverage. Switching to G reduces the regret but still changes the selection on three of seven maps in each family. The additive score F_1 , which keeps only the singleton terms, selects the exact winner on six of seven maps in one family and four of seven in the other, against one of seven for F_2 . We also find that lower average reconstruction error does not guarantee lower selection regret.
[MA-3] Who Holds the Pen? Let Specifications Not Agents Sign Off
【速读】：该论文旨在解决大语言模型智能体（LLM agents）在执行任务过程中存在的“理解—执行差距”与“状态—权威差距”问题。前者指模型虽理解任务要求但在实际执行中未能满足；后者则源于智能体对任务完成状态的自我声明缺乏独立权威验证。其核心解决方案在于构建一个分离代理提议与权威状态的机制：允许智能体自主规划、执行并请求完成，但唯有来自具备资质的提供方、可追溯来源的可验证证据才能确立符合规范的状态。为此，作者提出SpecHarness框架，将可见规范转化为带源链接的义务项，并通过版本化义务状态来管控执行与最终确认过程。该框架实现运行时对可验证要求的中介或验证，而模糊或主观要求则作为建议性指引。实验表明，规范不仅可作为行为指导，更可作为合规执行与完成结果的权威依据。
链接: https://arxiv.org/abs/2609.29921
作者: Haiqing Li,Xin Ma,Yinhao Wu,Wenliang Zhong,Feng Jiang,Thao M. Dang,Xiao Hu,Hehuan Ma,Yuzhi Guo,Junzhou Huang
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Large language model agents increasingly combine generation, decision-making, execution, and self-evaluation within a single agentic loop. Although they operate under external specifications such as task instructions, guidelines, output schemas, and reusable skills, these specifications typically remain context for the same model that acts and declares completion, leaving no independent specification authority boundary. We identify two resulting gaps. The understanding–execution gap arises when a requirement is understood but not satisfied in execution; the state–authority gap arises when an agent’s interpretation or completion claim does not establish the required state. On SkillsBench, using only agent-visible prompts, workspace information, and injected skill specifications, we extract 509 source-grounded task directions. Across seven models, only 79.6%–86.4% are satisfied, while completion-claim rates exceed official evaluator pass rates by 28.7–37.9 percentage points. We therefore separate agent proposals from authoritative state. Agents may plan, act, and request completion, but only admissible evidence from qualified providers may establish specification-governed state. SpecHarness operationalizes this principle by compiling visible specifications into source-linked obligations and governing execution and finalization through versioned obligation state. Verifiable requirements are mediated or validated at runtime, while ambiguous or subjective requirements remain advisory. Experiments on guideline-following and artifact-generation tasks show that specifications can serve not merely as behavioral guidance, but as authority over compliant execution and completion.
[MA-4] Costly Voting in the Hotelling-Downs Model
【速读】：该论文旨在解决经典Hotelling-Downs模型在部分参与者（partial-participation）情境下的适用性问题，即当选民存在投票成本且仅当其偏好候选人的相对收益超过该成本时才参与投票时，传统中位选民定理（Median Voter Theorem）不再成立，导致政策立场极化现象。其核心问题是：在存在投票成本的条件下，选举均衡中的极化程度如何被决定？解决方案的关键在于识别出极化的主要驱动因素——投票成本分布的反危险率（reverse-hazard-rate），表明选民对候选人立场变动的响应意愿是决定极化的关键机制。研究进一步通过引入代表排斥感（alienation）和候选人竞争性（candidate competitiveness）的参数扩展模型，验证了上述结论在考虑更多现实因素时仍具稳健性。
链接: https://arxiv.org/abs/2609.29869
作者: Guy Wolf,Reshef Meir
机构: Technion—Israel Institute of Technology(以色列理工学院)
类目: Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA)
备注:
Abstract:We study a partial-participation variation of the Hotelling-Downs model. Voters each have a cost to vote, and only vote when the comparative gain from their preferred candidate exceeds the cost. Under this model the median voter theorem breaks, and we study the extent of polarization under equilibria in different voters and cost distributions. We find that the main predictor of polarization is the reverse-hazard-rate of the cost distribution, indicating that the driver of polarization under our model is the willingness of voters to respond to changes in positions of candidates. We then extend the model by adding parameters governing alienation and candidate competitiveness, showing that our results are robust even when taking into account other realistic factors.
[MA-5] REAT: A Reflective Experience-Augmented Tutoring Framework for Multi-turn Mathematical Instruction
【速读】：该论文旨在解决当前大型语言模型（Large Language Models, LLMs）在数学教学辅导中缺乏持续积累与复用教学经验的问题。尽管先进模型具备解决复杂数学问题的能力，但在多轮互动式辅导过程中，其无法系统性地从历史对话中提取并重用有效的教学策略，导致对不同学生认知状态的适应能力受限。为此，论文提出反射式经验增强型辅导框架（Reflective Experience-Augmented Tutoring, REAT），其核心在于结合历史对话的经验提炼与实时自适应检索机制。通过多智能体观察-批评-导师（Observer-Critic-Mentor, OCM）蒸馏流程，REAT将原始对话转化为结构化、与具体问题解耦的教学经验；在实际辅导中，基于学生认知状态感知的检索模块可动态注入这些优化后的经验，实现个性化支持。实验表明，该框架显著优于仅依赖提示工程或监督微调（Supervised Fine-Tuning, SFT）的基线方法，尤其在处理高难度、低得分的辅导场景时表现突出，且提炼出的教学经验展现出跨模型架构和数学数据集的强泛化能力。
链接: https://arxiv.org/abs/2609.29804
作者: Jianheng Zhou,Chaoli Zhang,Xingjun Wei,Xinliang Zhou,Giancarlo Fortino,Xing Fan,Yanfeng Wang,Qingsong Wen,Haoyang Li
机构: Nanyang Technological University(南洋理工大学); Zhejiang Normal University(浙江师范大学); University of Ottawa(渥太华大学); University of Calabria(卡拉布里亚大学); Hong Kong Polytechnic University(香港理工大学); Shanghai Jiao Tong University(上海交通大学); Squirrel Ai Learning(小猿学习)
类目: Multiagent Systems (cs.MA)
备注:
Abstract:Current Large Language Models (LLMs) excel at solving complex mathematical problems, yet this proficiency does not inherently translate into effective tutoring. While advanced LLM tutors may leverage multi-agent frameworks or fine-tuning, most still lack a mechanism to systematically accumulate and reuse pedagogical experience over time, limiting their adaptability to diverse student needs during fluid, multi-turn interactions. To bridge this gap, we propose the Reflective Experience-Augmented Tutoring (REAT) framework, which couples experience distillation from historical dialogues with real-time adaptive retrieval. Driven by a multi-agent Observer-Critic-Mentor (OCM) distillation pipeline, REAT reviews past conversational trajectories and distills raw interactions into structured, problem-agnostic pedagogical experiences. During live tutoring, a state-aware retrieval module injects these curated experiences to provide adaptive scaffolding based on the student’s cognitive state. Experiments demonstrate that the proposed framework significantly outperforms both prompt-only and supervised fine-tuning (SFT) baselines, particularly in improving complex, low-scoring tutoring scenarios. Crucially, the distilled experiences exhibit robust generalization across diverse model architectures and mathematical datasets.
[MA-6] Multi-Agent Debate for Explainable Trading: Reasoning Consensus and Performance in Simulated Markets
【速读】：该论文旨在解决生成式 AI 在金融决策中应用时的核心问题：推理质量的提升是否能够转化为实际的经济绩效改善。尽管大语言模型（Large Language Models, LLMs）在复杂任务中的推理能力不断增强，但其对投资组合配置等金融决策的实际影响仍缺乏实证支持。为探究这一问题，研究采用多智能体辩论框架，在历史市场模拟中评估专业化智能体在提出、批判与修订投资决策过程中的表现。研究从逻辑有效性、证据支持、备选方案考量及因果一致性四个维度量化推理质量，并与夏普比率（Sharpe ratio）和总回报等财务指标进行关联分析。结果显示，整体推理质量与金融绩效之间无显著相关性（夏普比率 r = 0.07, p = 0.29；总回报 r = 0.03, p = 0.70）。虽然结构化提示可显著提升推理评分（从0.72升至0.84，效应量 Cohen’s d ≈ 2.0），但并未带来持续的收益提升。研究识别出“谄媚性收敛”（sycophantic convergence）作为核心失败模式——即在批判-修正循环中，智能体逐渐放弃独立判断并趋于同质化配置。通过引入基于 Jensen-Shannon 散度的干预机制以维持智能体间分歧，使夏普比率提升 +0.14（p = 0.028），索提诺比率提升 +0.25（p = 0.026），而强制因果推理的干预则未产生显著绩效改进。因此，该研究的关键发现是：多智能体辩论的价值不在于单纯提升可测量的推理质量，而在于有效保留各智能体之间的独立信息信号，从而增强决策多样性与鲁棒性。
链接: https://arxiv.org/abs/2609.29701
作者: Juli Huang,Alanood Alrassan,Deveen Harischandra,Theodore Wu,Veljko Skarich,Matthew Hayes
机构: Stanford University (斯坦福大学)
类目: Multiagent Systems (cs.MA)
备注: 10 pages, code available in the accompanying repository
Abstract:Large language models (LLMs) are increasingly used for financial decision-making, yet it remains unclear whether improvements in reasoning quality translate into better economic outcomes. We investigate this question using a multi-agent debate framework for portfolio allocation in historical market simulations, where specialized agents propose, critique, and revise investment decisions. Reasoning quality is evaluated across four dimensions: logical validity, evidential support, alternative consideration, and causal alignment, and compared with downstream financial performance. Across 210 controlled runs, aggregate reasoning quality shows no meaningful relationship with Sharpe ratio (r = 0.07, p = 0.29) or total return (r = 0.03, p = 0.70). Structured prompting increases measured reasoning quality from about 0.72 to 0.84 (+17.7%, Cohen’s d about 2.0), but these gains do not consistently translate into higher returns. We identify sycophantic convergence as a central failure mode, where agents abandon independent positions during critique-revision cycles and converge toward similar allocations. A Jensen-Shannon divergence intervention that preserves disagreement improves Sharpe by +0.14 (p = 0.028) and Sortino by +0.25 (p = 0.026), while interventions enforcing stronger causal reasoning do not improve financial performance. Our results suggest that multi-agent debate is most valuable when it preserves independent informational signals rather than simply improving measured reasoning quality.
[MA-7] DynaTrust-VVC: Directional Physics-Informed Trust-Based Detection and Mitigation for Cyber-Resilient Multi-Agent Volt–VAR Control
【速读】：该论文旨在解决分布式电压-无功（Volt–VAR）控制在面对虚假数据注入攻击时的网络安全脆弱性问题，尤其关注因邻域测量值被篡改而导致的系统误判风险。其核心挑战在于：传统基于邻居协同比对的方法虽可识别孤立的异常测量，但无法抵御多个协同攻击节点之间的虚假互证行为。为此，论文提出了一种名为DynaTrust-VVC的抗网络攻击多智能体电压-无功控制框架，其关键创新在于引入动态方向性信任机制（directional trust value），通过比较邻近节点报告的电压增量与其基于有功与无功功率变化所预测的敏感度响应的一致性，实时评估消息可信度；一旦出现物理不匹配，信任值立即下降，随后在持续一致性下逐步恢复。此外，该方法采用自适应物理证据阈值和基于反事实安全电压恢复的缓解策略，显著提升了系统的整体韧性。实验结果表明，在包含分钟级居民负荷特性的非线性IEEE 123节点馈线系统中，DynaTrust-VVC在全部27组重复仿真中均于攻击发生后1分钟内成功识别出协调的三节点攻击，而固定信任值配置则完全失效；同时，复合韧性指数从0.227提升至0.315，且正常情况下的误报率无统计学显著上升，验证了其高效性与鲁棒性。
链接: https://arxiv.org/abs/2609.29572
作者: Md Fazley Rafy,Kamrul Hasan,Anurag K. Srivastava
机构: Lane Department of Computer Science and Electrical Engineering, West Virginia University (西弗吉尼亚大学), Morgantown, WV 26505, USA; U.S. Department of Energy (美国能源部); Defense Advanced Research Projects Agency (美国国防高级研究计划局)
类目: ystems and Control (eess.SY); Multiagent Systems (cs.MA)
备注: 6 pages, 2 figures and accepted in 2026 Cyber Awareness and Research Symposium (CARS)
Abstract:Distributed Volt–VAR control relies on voltage measurements and is therefore vulnerable to false-data injection. Neighbor corroboration can distinguish an isolated corrupted measurement from a physical disturbance, but coordinated agents can falsely corroborate one another. This paper proposes DynaTrust-VVC, a cyber-resilient multi-agent Volt–VAR framework that assigns each incoming neighbor message a directional trust value based on the consistency between its reported voltage increment and a sensitivity-based prediction from active- and reactive-power changes. Trust falls immediately after a physics mismatch and recovers gradually after sustained consistency. DynaTrust-VVC further uses a physics-adaptive evidence threshold and counterfactual safe-voltage recovery-based mitigation. The proposed method was validated using a nonlinear IEEE 123-bus feeder with minute-resolution residential profiles and 27 matched replicates. DynaTrust-VVC identifies a coordinated three-bus attack one minute after onset in all 27 matched replicates, whereas an otherwise identical configuration with fixed neighbor trust identifies none. The composite resilience index also increases from 0.227 to 0.315, with no statistically significant increase in the benign false-alarm rate.
[MA-8] owards Trustworthy Biological Alignment in TabPFN-Probed Pathology Foundation Models
【速读】：该论文旨在解决病理基础模型（Pathology Foundation Models, PFMs）在未经过微调的情况下，其从HE染色图像中提取的形态学表征是否真正编码了生物上意义明确且稳健的分子信息这一关键问题。现有研究虽表明PFMs具备较强的下游任务性能，但缺乏对这些表征与真实分子活动之间生物学一致性（biological alignment）的系统性验证。为此，作者提出一种无需训练的审计框架，基于空间配对的组织学与转录组数据（来自HEST-1k数据集），在跨越三个器官的240个样本上评估多个冻结状态的PFMs。该框架的核心在于利用预训练的TabPFN作为探测器，不进行任务特定的梯度更新，直接量化从冻结图像表征中解码基因通路水平分子程序的能力。通过多维度评估——包括跨组织切片、患者群体和组织类型的泛化能力、切片级别或捷径依赖性、以及小扰动和上下文重采样下的预测稳定性——该方法能够区分出稳定、普遍存在的分子程序与仅存在于特定组织、不稳定或易受捷径影响的信号。因此，该框架不仅揭示了PFMs所编码的生物学信息类型，更系统地评估了这些信息在临床相关变异条件下的可靠性，为病理基础模型的可解释性与可信度提供了关键验证手段。
链接: https://arxiv.org/abs/2609.29523
作者: Ushashi Bhattacharjee,Alloy Das,Saria Hannan,Tirtho Roy,Koushik Howlader,Soumik Sarkar
机构: 未知
类目: Multiagent Systems (cs.MA); Quantitative Methods (q-bio.QM)
备注:
Abstract:Histology and transcriptomics provide complementary views of tissue biology, capturing spatial morphology and molecular activity, respectively. Pathology foundation models (PFMs) learn rich morphological representations from HE images, yet strong downstream performance alone does not establish whether these representations encode biologically meaningful and robust molecular information. We present a training-free framework for auditing biological alignment in frozen PFMs using spatially paired histology and transcriptomics from HEST-1k, evaluated on 240 samples spanning three organs. Multiple frozen PFMs are used to extract HE representations, while gene expression is aggregated into biologically interpretable pathway-level programs. We use TabPFN as a pretrained probe to quantify the extent to which these molecular programs can be decoded from frozen image representations without task-specific gradient updates. Beyond predictive performance, our audit examines whether pathway decodability generalizes across tissue sections, patient groups, and tissue types; whether representations exhibit section-level or other shortcut dependencies; and whether predictions remain stable under small image perturbations and context resampling. This multi-tissue evaluation distinguishes molecular programs that are consistently encoded from those that are tissue-specific, unstable, or shortcut-sensitive. Our framework therefore provides a systematic approach for assessing not only what biological information pathology foundation models encode, but also how reliably that information persists under clinically relevant sources of variation.
[MA-9] Evaluation of Multi-Turn Consistency in LLM Agents : Survival Analysis and Failure-Rationale Taxonomy ICLR2026
【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）代理在长期多轮交互中出现的行为不一致性问题，尤其关注其在延迟奖励情境下的时间一致性表现。研究通过受控的20步多智能体实验设置，模拟延迟满足的心理学范式，评估不同条件下代理在决策过程中是否能够维持一致的延迟策略。其解决方案的关键在于构建一个融合生存分析与语义理性分析的双重评估框架：一方面，将首次选择获取奖励的时间作为事件发生时间，采用Kaplan-Meier生存曲线和离散时间风险回归量化实验变量对失败风险随时间演变的影响；另一方面，基于13,780条终止决策时的推理轨迹，利用大语言模型辅助标注与人工审核相结合的方式建立七类理性归因分类体系，揭示失败背后的认知模式。研究发现，早期失败多由冲动驱动，后期失败则更多表现为疲劳或成本-收益权衡，且公开环境加剧了规范导向的合理化表达。尤为关键的是，识别出“推理越长、内在矛盾越高”的反直觉现象——即更长的推理文本反而伴随更高的内部逻辑冲突率，挑战了“更多推理=更高一致性”的假设。最终，该研究提出了模型特异性的“失败指纹”概念，为诊断多轮交互中代理行为的可靠性提供了可量化的评估视角。
链接: https://arxiv.org/abs/2609.29508
作者: Igor Bogdanov,Olga Manakina,Chung-Horng Lung
机构: Carleton University (卡尔顿大学); Ottawa, ON, Canada
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注: Accepted at the ICLR 2026 Workshop on Logical Reasoning of Large Language Models
Abstract:Large language model (LLM) agents may perform well on isolated tasks yet drift into inconsistency over extended interaction. We evaluate temporal consistency in a controlled 20-step multi-agent setting inspired by delayed-gratification studies. At each step, an agent chooses between continuing to delay a reward or claiming it immediately (terminating the episode). Across a full-factorial manipulation of social visibility (private vs public), persona stressors, and deliberation policy, we run 84,540 trajectories spanning 8 model families. Treating the first reward-claim as a time-to-event outcome, we estimate Kaplan-Meier survival curves and fit discrete-time hazard regression to quantify how experimental factors shift failure risk over time. Then, to analyze rationales and language patterns associated with failure, we build a seven-category taxonomy from 13,780 deliberation traces from agents who choose to terminate the episode, using an LLM-assisted labeling paired with human audit ( \kappa=0.83 ). Rationale profiles change systematically with time and context: early failures are more impulse-driven, later failures more fatigue- and cost-benefit-framed, while public settings increase norm-oriented justifications. We also find a deliberation-inconsistency association: among failures, longer deliberation correlates with higher rates of intra-rationale contradiction (simultaneous pro-delay and pro-claim statements), challenging the assumption that more reasoning text implies greater consistency. Together, the survival and rationale analyses reveal distinct temporal reliability regimes and model-specific “failure fingerprints”, offering an evaluation lens for diagnosing inconsistency in multi-turn agent behavior.
[MA-10] mperament Engineering: Designing Strategic Behavioural Diversity in Robot Swarms
【速读】：该论文旨在解决传统机器人集群设计中过度追求个体一致性所导致的性能瓶颈问题，即忽视了真实环境中不可避免的个体差异（如校准误差、电池状态、传感器漂移与磨损）对群体行为的影响。传统方法将这些差异视为需最小化的“缺陷”，而本文提出一种受生物启发的“气质工程”（temperament engineering）框架，将群体行为分布本身作为设计对象，而非单一控制器。其核心在于将动物行为中的五种进化验证过的气质维度（胆怯-大胆、探索-回避、活跃度、攻击性、社会性）转化为连续可调的控制参数 τ ∈ [0,1]，并将其嵌入控制器之上，实现为模块阈值、多智能体强化学习中的策略条件向量或基础模型规划器的约束条件。通过三阶段工作流程——将任务目标映射至相关气质轴、规划 τ 分布形态、调参反应规范以响应环境线索——该框架实现了对异质性的主动设计。在去中心化场景下，这种异质性成为预设的、前瞻性的设计输入，从而将行为与平台异质性共同纳入优化变量。此外，论文还提出了机器人特有的气质维度（自建模可塑性、强势性、主动性与表现力），反映机器人独有的特性。实证表明，经工程化异质性设计的集群在聚集与探索等任务中优于同质集群，未来研究的关键在于明确异质性在何种条件下及以何种程度能够抵消其带来的设计与协调成本。
链接: https://arxiv.org/abs/2609.29423
作者: Edmund R. Hunt
机构: University of Bristol (布里斯托大学)
类目: Robotics (cs.RO); Multiagent Systems (cs.MA)
备注:
Abstract:No two robots are truly identical: calibration, battery state, sensor drift and wear give every swarm a distribution of behaviour rather than a single point, usually treated as an imperfection to be minimised. In animal collectives the reverse holds: consistent individual differences in behaviour (‘temperament’) are shaped by natural selection and often decisive for group performance. This perspective proposes ‘temperament engineering’, a bio-inspired framework that treats the swarm’s distribution of temperaments, rather than the individual controller, as the design object. It borrows five evolutionarily validated axes of animal temperament (shyness-boldness, exploration-avoidance, activity, aggressiveness and sociability) as a design vocabulary, rendering each as a continuous control parameter \tau \in [0,1] above the controller, realisable as a module threshold, a policy-conditioning vector in multi-agent reinforcement learning, or a constraint on a foundation-model planner. A three-phase workflow maps mission success criteria onto relevant axes, plans the shape of the \tau distribution, and tunes reaction norms governing how temperament responds to environmental cues. The payoff is greatest under decentralisation: where a central planner can reassign behaviour online, a temperament distribution is a planner output, but in a swarm without global knowledge it must be an offline, anticipatory design input. Behavioural and platform heterogeneity are thereby co-design variables, and I sketch tentative robot-native axes (self-model plasticity, forcefulness, initiative and expressiveness) arising from features robots have and animals do not. Engineered heterogeneity has been shown to outperform homogeneous swarms in tasks such as aggregation and exploration; establishing when, and how much, heterogeneity repays its cost is the work the field can now take forward.
[MA-11] AI-Moderated Interviews for Market Research and Digital Twins Calibration
【速读】：该论文旨在解决生成式 AI（Generative AI）在市场调研中替代传统人工访谈或静态数据收集方法的有效性问题，核心关注点在于：AI主导的访谈是否能与人类主导的访谈在深度和洞察质量上相当，甚至超越后者。其解决方案的关键在于通过一项预注册的三组对照实验（N = 317），系统比较了人工智能主导访谈（AI-moderated）、人类主导访谈（human-moderated）与静态访谈（static interviews）在消费者需求识别、主题覆盖广度及数字孪生（digital twins）构建效果方面的表现。研究发现，在预算不变的前提下，AI主导访谈在主题覆盖范围和客户真实需求挖掘方面显著优于人类或静态访谈，且基于其生成的数据构建的数字孪生模型在预测消费者对实际营销刺激的反应时，优于仅依赖人口统计特征的原型。然而，尽管AI访谈在信息丰富性上占优，其在量化预测精度上并未显著优于静态访谈，且预测误差主要源于数字孪生与真人之间在自我报告思维风格上的差异，以及训练与验证数据分布不一致所导致的“离域”提问问题。
链接: https://arxiv.org/abs/2609.29143
作者: Yuting Deng,Jingxuan Liu,Olivier Toubia,Naman Jain
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA)
备注:
Abstract:AI-moderated interviews are emerging as a scalable market-research method for generating consumer insights and building consumer “digital twins.” Yet it remains unclear whether they match human-moderated interviews or improve on simpler, static data collection methods. In a pre-registered, between-subjects study (N = 317) with three industry partners, we compare AI-moderated (N = 139), human-moderated (N = 24), and static interviews (N = 154). AI moderation matches human moderation in depth, covers more themes, and, holding budget constant, recovers significantly more customer needs than human moderation or static interviews. However, participants sound more emotionally engaged when speaking to a live human. We then create digital twins using interview data and evaluate each twin against the participant’s own held-out responses to six real-world marketing stimuli. We find that digital twins created from AI-moderated interviews predict consumer responses better than demographics-only personas. However, the additional richness from AI moderation does not translate into better quantitative predictions compared to static interviews. By analyzing open-ended thoughts generated from humans versus their twins, we find that prediction errors are connected both to differences in (self-reported) thinking styles between twins and humans, and to gaps between training and validation data (i.e., asking questions that are too far out of distribution).
[MA-12] AlphaDiverse: Post-Training Local Quantitative Research Agents for Diverse Exploration in Alpha Factor Mining
【速读】：该论文旨在解决基于大语言模型（Large Language Model, LLM）的多智能体系统在因子挖掘过程中面临的三大核心问题：对外部API的依赖导致成本、可用性与数据保密性难以控制；研究周期过长易引发研究路径坍缩，即反复聚焦少数成功经济机制，抑制创新性探索；以及缺乏有效的本地化训练机制以提升智能体性能。其解决方案的关键在于提出AlphaDiverse框架，通过构建集成多智能体因子研究系统、多样化研究路径采集机制与本地化后训练策略的协同体系，实现对研究过程的可控性和多样性增强。具体而言，该框架通过生成互补的研究计划组合，并在不同研究循环中动态调整环境变量，以主动收集多样化的研究轨迹；利用这些多样化轨迹对本地的规划者（Planner）与执行者（Realizer）智能体进行监督微调（supervised fine-tuning）作为热启动；进一步设计联合广义近端策略优化（GRPO）方法，同时优化两者的预测质量与贡献多样性，确保智能体在保持高预测能力的同时拓展探索范围。此外，通过将研究反馈限制在内周期数据上，而使用冻结的最终模型在后续外周期数据上进行评估，有效避免了测试集过拟合问题。实验结果表明，该框架在四个中国股票池中均实现了兼具竞争力预测表现与更广泛探索能力的显著提升。
链接: https://arxiv.org/abs/2609.29014
作者: Qingzhuo Wang,Zikun Wei,Zhihua Wei,Wen Shen
机构: Tongji University (同济大学); Shanghai Non-convex Intelligent Technology
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Multiagent Systems (cs.MA)
备注: 33 pages, 7 figures, 26 Tables. Preprint under review
Abstract:Large language model (LLM)-based multi-agent systems can automate alpha factor mining, but their reliance on external APIs limits control over cost, availability, and confidentiality. Long research loops also tend to revisit a few successful economic mechanisms that lead to research path collapse. To address these limitations, we propose AlphaDiverse, a framework that integrates a multi-agent alpha research system, diverse research path collection, and post-training for local agents. We let the research system generate complementary plan portfolios and vary research environments across loops to collect diverse research paths. Using these diverse traces, we warm-start local Planner and Realizer agents with supervised fine-tuning. Then, we propose a joint GRPO method to optimize both of them using predictive quality and diversity of contributions. Research feedback is confined to inner period data, while a frozen final model is evaluated on a later outer period data, thereby avoiding test-set tuning. Experiments across four Chinese stock universes show that AlphaDiverse can combine competitive prediction with broader exploration.
[MA-13] Codetta: High-Capacity Keyless and Undetectable Multi-Agent Collusion
【速读】：该论文旨在解决在基于大语言模型（LLM）的多智能体系统中，独立部署的智能体通过自然语言通信实现隐蔽合谋（undetectable collusion）所带来的安全威胁问题。现有可证明不可检测的生成式AI隐写术协议在实际场景中存在局限性：高容量方案依赖对称设置（接收方需复现发送方输出分布），而当前最先进的非对称场景协议容量极低，多数方法还依赖预共享密钥，难以适用于真实部署环境。为此，论文提出Codetta——一种面向现实非对称场景、高容量且无需预共享密钥的隐写协议。其核心创新在于：结合共享公共模型以估计通信信道特性、采用保持发送方输出分布一致性的采样机制，以及自适应纠错编码；同时引入一种隐写密钥交换机制，使独立部署的智能体可在不暴露异常痕迹的前提下建立共享密钥。实验表明，在三种工作负载与三类发送模型下，Codetta的容量相较最先进非对称协议提升达94倍，密钥交换在约8万个可见词元内即可建立共享密钥，且实证失败概率不超过4.1×10⁻³。研究结果揭示，真正不可检测的智能体合谋已具备可行性，因此传统仅审查通信记录的审计方式已不足以应对此类威胁，必须发展更深层次的系统级监控机制。
链接: https://arxiv.org/abs/2609.28900
作者: Qi Pang,Virginia Smith,Wenting Zheng
机构: Carnegie Mellon University (卡内基梅隆大学)
类目: Cryptography and Security (cs.CR); Multiagent Systems (cs.MA)
备注:
Abstract:Multi-agent systems built on large language models (LLMs) are increasingly deployed in high-stakes settings such as finance, healthcare, and software engineering, where agents coordinate through natural-language messages. The same channels, however, let colluding agents exfiltrate confidential information or coordinate unauthorized actions, and steganography can hide such communication inside outputs that look ordinary to an auditor reading the transcript. Existing provably undetectable LLM steganography protocols are not suited to realistic deployments. High-capacity schemes assume a symmetric setting where the receiver can reproduce the sender’s output distribution, the state-of-the-art protocol for asymmetric agents has very low capacity, and most approaches rely on a pre-shared secret key. We make the threat of undetectable agent collusion concrete with Codetta, a high-capacity steganographic protocol for independently deployed agents in realistic asymmetric settings. Codetta combines a shared public model that estimates the communication channel, a sampling mechanism that preserves the sender’s output distribution, and an adaptive error-correcting code. It further removes the pre-shared key through a steganographic key exchange that lets independently deployed agents establish a shared key while keeping the transcript computationally indistinguishable from ordinary model outputs. Across three agent workloads and three sender models, Codetta achieves up to 94\times the capacity of the state-of-the-art asymmetric protocol, and its key exchange establishes a shared key with about 80k visible tokens at an empirically certified failure probability of at most 4.1\times 10^-3 . These results show that effectively undetectable collusion is becoming feasible between independently deployed agents, so auditing must go beyond inspecting communication transcripts. Subjects: Cryptography and Security (cs.CR); Multiagent Systems (cs.MA) Cite as: arXiv:2609.28900 [cs.CR] (or arXiv:2609.28900v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2609.28900 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Qi Pang [view email] [v1] Thu, 24 Sep 2026 01:27:47 UTC (84 KB)
[MA-14] Progressive Skill Discovery as Access Control for Tool-Using LLM Agents : Structural Governance through Role-Scoped Capability Delivery
【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）代理在接入企业级庞大工具集时难以实现安全扩展的问题。现有方法在面对海量工具时，易导致上下文窗口过大、工具选择性能下降以及治理机制失效，根源在于仅依赖提示词（prompt）定义的策略本质上是概率性建议而非确定性约束。针对此问题，本文提出skilder框架，其核心创新在于将能力封装为“角色”（role），即包含技能、工具与指令及其使用边界的集合。代理初始仅具备最小角色目录，通过学习任务所需角色，并由单一MCP服务器动态分发相应技能、指令与工具，确保工具仅在已学习技能范围内被调用。该设计使系统能够以确定性方式强制执行权限边界，有效防止未经授权的工具调用或参数违规（如超出预算限制）。实验结果表明，在13项任务、6种模型（每模型运行10次）的评估中，skilder在模型完成任务发现并发出受控调用时，严格实现了治理边界，未发生任何越权操作；任务成功率也准确反映了模型是否遵循发现协议及响应质量检查，而非授权失败。此外，通过支持任务过程中动态获取跨角色能力，skilder在保障硬性系统级控制的同时，维持了灵活的问题求解能力。
链接: https://arxiv.org/abs/2609.28693
作者: Michael Stettler,Benjamin Girardet,Jonas Canton,Nicolas Corod
机构: Skilder(斯克尔德); skilder.ai(斯克尔德人工智能)
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Multiagent Systems (cs.MA); Systems and Control (eess.SY)
备注: White paper, 30 pages
Abstract:Large Language Model (LLM) agents struggle to scale safely when exposed to vast enterprise toolsets. Providing an agent with access to every internal tool leads to oversized context windows, degraded tool selection, and severe governance vulnerabilities - as system policies defined purely in prompts remain probabilistic advice rather than hard constraints. Existing mitigations, such as multi-agent domain delegation, decentralize audit logs and fail to guarantee policy compliance across sessions. We introduce skilder, a framework that packages capabilities into roles: bundles of skills, tools, and instructions, together with the limits that bound them. An agent begins with a minimal role catalog, learns the roles a task requires, and receives each role’s skills, instructions, and tools through a single MCP server. Because tools reach the agent only inside learned skills, the same server enforces the scope of what was learned deterministically. We evaluate skilder against flat-context tool selection and multi-agent orchestration across 13 tasks using six models (10 runs each). Our results show that, when models completed discovery and issued a governed call, the skilder simulated authorization layer enforced governance boundaries: no unauthorized tool call or parameter violation (e.g., a spending-limit breach) executed. Aggregate task pass rates also reflect whether each model followed the discovery protocol and satisfied response-quality checks; those misses are not authorization failures. Furthermore, by allowing agents to dynamically acquire cross-role capabilities mid-task, skilder preserves problem-solving flexibility while providing hard system-level enforcement.
[MA-15] PAWS: Policy-driven Agent ic World Simulation
【速读】：该论文旨在解决金融多智能体仿真中缺乏与历史事件时间对齐的政策传播路径数据问题，即现有数据集难以将政策干预通过公共传播、机构决策和利益相关方响应所构成的动态过程与真实历史证据有效关联。其解决方案的关键在于构建PAWS（Policy-driven Agentic World Simulation）数据集，该数据集系统整合了36个经验证的美国财政与经济政策事件、12,727条与政策相关的新闻记录以及65,291条基于来源证据的利益相关方行动。每一条行动均通过多层事件框架进行表征，涵盖交互模式、金融行动家族及其子类、语义属性，并映射至外部本体体系；实体经过归一化处理，行动与每日市场回报情境对齐，以支持政策驱动型智能体仿真的可复现性。在2,522个分层抽样行动上，AI与人工评审者在交互模式标注上达成89.4%的初始一致性，经仲裁后确保数据可靠性。案例研究验证了2008年卖空禁令与2001年报价小数化等政策的时间线及市场反应模式在密集与稀疏新闻环境下均可准确还原。此外，重演实验揭示高精度可能掩盖对罕见行动的检测失败，凸显行动时机捕捉与校准的核心挑战。PAWS为评估智能体影响力、政策响应级联效应及行动-结果对齐关系提供了可审计的历史基准。
链接: https://arxiv.org/abs/2609.28547
作者: Tiviatis Sim,Jia Hui Woon,Xinming Gao,Chen Gao,Fengbin Zhu,Zheng Huanhuan,Chua Tat Seng,Kenji Kawaguchi
机构: 未知
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Multiagent Systems (cs.MA); Social and Information Networks (cs.SI)
备注:
Abstract:Policy interventions propagate through public communication, institutional decisions, and stakeholder responses, yet datasets for financial multi-agent simulation rarely connect these processes to temporally aligned historical evidence. We introduce PAWS, a Policy-driven Agentic World Simulation dataset covering 36 verified U.S. financial and economic policy episodes, 12,727 policy-linked news records, and 65,291 source-grounded stakeholder actions. Each action is linked to its supporting news and represented by a multi-layer event frame capturing its interaction mode, financial-action family and subtype, semantic attributes, and conditional mappings to external taxonomies. Entities are resolved to normalized organizations, and actions are aligned with daily market-return context to support policy-agent simulation replay. On 2,522 stratified action samples, independent AI and human reviewers achieved 89.4% initial agreement on interaction mode, with disagreements subsequently adjudicated. Case studies of the 2008 short-selling ban and 2001 decimalization recover documented policy timelines and associated market patterns across both dense and sparse news settings. A replay study further shows that high accuracy can mask failure to detect rare stakeholder actions, identifying action timing and calibration as central challenges. PAWS provides an auditable substrate for evaluating agent influence, policy-response cascades, and action-outcome alignment in historically grounded financial simulations.
[MA-16] Multi-Dimensional Matching
【速读】：该论文旨在解决在特征驱动的匹配机制中，如何高效且公平地实现个体与对象之间的最优匹配问题。传统方法依赖于完整的偏好排序，但在现实场景中往往难以获取完整排名信息，而特征描述则更为可行。其解决方案的关键在于引入单一谱投影（spectral projection），将高维特征空间中的复杂匹配问题降维至一维排序，从而在O(N log N)时间内完成计算。该方法在去缩放的特征与偏好条件下，能够实现在投影空间内的精确纳什社会福利（Nash Social Welfare, NSW）最优，并提供无条件的效用总和保障以及有条件的NSW保障。尽管该机制对外部噪声具有鲁棒性，但不满足策略不变性（strategy-proof），研究者明确给出了一个可获利的虚假申报案例。在基于代理型AI购物应用的实证测试中，诊断结果准确预测了成功与失败案例，100次实例的鲁棒性分析进一步验证了该方法的有效性与稳定性。
链接: https://arxiv.org/abs/2609.29958
作者: Irene Aldridge
机构: 未知
类目: Econometrics (econ.EM); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Theoretical Economics (econ.TH)
备注: 20 pages
Abstract:We study a matching mechanism where agents and objects are described by features rather than complete rankings. A single spectral projection reduces the problem to a one-dimensional sort, computable in O(N log N) time. We prove that on descaled features and preferences, our algorithm obtains the exact Nash Social Welfare (NSW) optimum within the projected space, with an unconditional utilitarian-welfare guarantee and a conditional NSW guarantee. The proposed mechanism is stable against exogenous noise but not strategy-proof; we provide an explicit profitable misreport. On an agentic AI shopping application, the diagnostics correctly anticipate both a success and a failure case. A 100-instance robustness study confirms the findings.
自然语言处理
[NLP-0] Agent ic Detection of Online Conspiracies
【速读】： 该论文旨在解决社交媒体中阴谋论话语的识别难题，其核心挑战在于：同一表面内容可能蕴含不同的语用意图（illocutionary force），如支持、合理关切、批评、讽刺或嘲弄，而传统基于文本的分类方法难以准确区分这些意图。因此，问题的关键不在于单纯识别与阴谋论相关的陈述，而在于推断说话者的实际意图。为此，论文提出一种基于社会语境的代理式（agentic）框架，通过集成一系列支持社会查询的工具，实现对上下文信息的动态获取与适应性推理。该框架强调在每个推理步骤中仅请求与当前任务相关的证据，从而优化资源利用效率。实验基于覆盖2018年末至2023年初四年内约80%–90%公开希伯来语推文的独特大规模数据集，涵盖多轮选举周期及新冠疫情与疫苗接种运动等关键社会事件，验证了上下文感知工作流显著优于纯文本分类方法，且该代理框架在性能上明显优于其他基准模型，包括未采用代理机制但具备相同上下文访问权限的非代理模型。研究结果表明，阴谋论检测应被视为一种社会嵌入式的解释任务，有效分类依赖于对社会背景的深度理解以及基于任务需求的自适应推理能力。
链接: https://arxiv.org/abs/2609.30250
作者: Lior Biton,Oren Tsur
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Conspiratorial discourse on social media is not always expressed through explicit claims or stable lexical markers. The same surface content may express endorsement, legitimate concerns, criticism, satire, or mockery. The main challenge is therefore not only recognizing conspiracy-related claims, but inferring the speaker’s intent – the utterance’s illocutionary force. We argue that this can be achieved through the use of relevant social contexts and propose an agentic framework, equipped with a set of tools supporting social queries. We demonstrate the benefits of our approach on a unique dataset of Hebrew tweets, covering 80%–90% of the public Hebrew tweets published over a four-year span (late 2018-- early 2023), encompassing several election cycles as well as the COVID pandemic years and related vaccination campaigns. This extensive coverage can be used in recovering different social contexts. Evaluating our framework on a manually-annotated adversarial dataset, we find that context-aware workflows consistently outperform text-only classification and that the agentic framework performs significantly better than other frameworks and settings, including a non-agentic model exposed to the same contexts available to the agent. We further provide an analysis of the results, the errors and efficiency (token economy) tradeoffs. These findings support viewing the task of conspiracy detection as a socially embedded interpretation task, in which effective classification depends not only on access to contexts, but also on adaptive reasoning in which the agent uses tools on a per-case basis, asking only for evidence relevant to its current reasoning step. Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG) Cite as: arXiv:2609.30250 [cs.CL] (or arXiv:2609.30250v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.30250 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-1] JevOut: Natural Context Can Flip Decision Models
【速读】： 该论文旨在解决当前决策模型（如Jev）在面对包含背景信息和上下文的现实输入时所表现出的脆弱性问题。尽管这些模型能够将非结构化语言映射为有限选项上的概率分布，从而直接用于请求路由、工具选择与动作触发，但研究发现，即使输入中的附加上下文内容简短且看似自然，也可能导致原本正确的决策被错误引导，即便正确答案本身未变。解决方案的关键在于设计一种基于优化器的方法：针对每个初始正确决策，固定一个错误目标选项，并通过调整流畅的上下文添加内容，在保持原始问题、选项及正确答案不变的前提下，最大化模型对错误选项的概率分配。实验结果显示，在508个初始正确决策中，有312个（61.4%）被成功误导，其中229例中模型对错误选项的置信度达到或超过0.7。此外，在七个数据集上，三种其他决策系统也表现出64.9%至73.2%的目标翻转率。这一结果揭示了现有决策模型在语言到下游行为映射过程中的显著脆弱性——微小、普通的上下文扰动即可引发高置信度的错误决策，提示不应将模型输出的概率值视为可靠的决策接口。
链接: https://arxiv.org/abs/2609.30243
作者: Zixiang Xu
机构: University of Southern California(南加州大学)
类目: Computation and Language (cs.CL)
备注: 32 pages, 5 figures, 23 tables. Homepage: this https URL ; Code: this https URL
Abstract:Dedicated decision models such as Jev map unstructured language to probability distributions over finite choices, allowing their outputs to directly route requests, select tools, and trigger actions. Yet real-world inputs rarely arrive in isolation: they come with background details and surrounding context. We find that short additions that fit naturally into this context can nevertheless redirect an otherwise correct decision, even when the correct answer remains unchanged. To study this behavior, we fix a wrong target option for each initially correct item and use the model’s option probabilities to refine fluent context additions while preserving the source, question, choices, and gold answer. Within 64 accepted target evaluations, the optimizer identifies contexts that redirect Jev on 312 of 508 initially correct decisions (61.4%); in 229 cases, Jev assigns at least 0.7 probability to the fixed wrong option. Across seven datasets, three additional decision systems show targeted flip rates of 64.9%-73.2% on decisions they initially answer correctly. Taken together, these results expose a pronounced fragility in current decision models: short, ordinary-looking context can shift a correct choice to a high-confidence wrong one. Because these models turn language directly into downstream choices, this sensitivity raises concerns about treating their probability outputs as reliable decision interfaces.
[NLP-2] SemMSA: Latent Semantic-Aided Robust Multimodal Sentiment Analysis with Incomplete Data NEURIPS2026
【速读】： 该论文旨在解决多模态情感分析（Multimodal Sentiment Analysis, MSA）中因模态缺失导致的语义歧义与噪声干扰问题，尤其针对现有方法在部分观测条件下依赖特征重建或复杂融合机制所引发的虚假生成与噪声引导缺陷。其核心解决方案是提出一种基于大语言模型（Large Language Model, LLM）的潜在语义增强框架SemMSA，通过无锚点谱对齐机制实现跨模态深度融合。关键创新在于：1）跨模态语义精炼（Cross-modal Semantic Refinement, CSR）模块利用适配器自适应提取视觉与听觉表征，并在冻结的LLM嵌入空间中构建统一的多模态前缀，通过无需解码的隐式潜变量迭代优化过程生成连续且判别性强的语义状态；2）跨模态谱对齐（Cross-modal Spectral Alignment, CSA）模块通过增强核Gram矩阵中的主导谱成分，无须预设锚定模态即可捕捉所有模态间的全局非线性依赖关系；同时引入实例级谱分离约束以保持样本间判别性并防止表征坍缩。实验结果表明，SemMSA在SIMS、MOSI和MOSEI等多个基准上均达到当前最优性能。
链接: https://arxiv.org/abs/2609.30238
作者: Wenhao Li,Zhibin Wu,Chong Xiao,Qiangchang Wang
机构: Shandong University (山东大学); Shenzhen Loop Area Institute (深圳环区研究院)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: Accepted by NeurIPS 2026
Abstract:Recent research on Multimodal Sentiment Analysis (MSA) has focused on learning from language, visual, and acoustic modalities with incomplete data to infer human sentiment. Most studies typically compensate for missing information by reconstructing modality features or designing complicated fusion mechanisms. However, these methods still suffer from spurious generation and noisy guidance due to the lack of high-level semantic grounding in partially observed multimodal evidence. To address these issues, we propose SemMSA, a latent semantic-aided framework that constructs rich sentiment-relevant semantics with LLMs, fully integrating with all modalities via anchor-free spectral alignment. It mainly consists of Cross-modal Semantic Refinement (CSR) and Cross-modal Spectral Alignment (CSA). Specifically, CSR first adaptively extracts visual and acoustic representations by corresponding adapters to form a unified multimodal prefix with language in the frozen LLM embedding space. It then iteratively produces continuous discriminative semantic states through a token-efficient latent refinement process without decoding explicit text. Next, CSA simultaneously aligns the refined semantics with all modalities by enhancing the dominant spectral component of their kernel Gram matrix. This captures global nonlinear dependencies among all representations without relying on a predefined anchor modality. In addition, an instance-level spectral separation constraint preserves cross-sample discriminability and mitigates representation collapse. Extensive experiments on SIMS, MOSI, and MOSEI benchmarks demonstrate that SemMSA achieves state-of-the-art performance.
[NLP-3] o Trust or Not to Trust: Retrieval-Augmented Fact Checking in Speech EMNLP
【速读】： 该论文旨在解决生成式语音内容中虚假信息（misinformation）日益增多背景下，如何实现基于语音的可信度验证问题。现有事实核查系统多依赖文本输入，难以应对新闻剪辑、播客、政治演讲等以口语形式呈现的声明。为此，论文提出VeriSpeak——一个面向大型音频语言模型（Large Audio Language Models, LALMs）的语音事实核查探针基准，包含3,879条涵盖时间、地理与关系类事实的语音声明，且真伪标签均衡分布。其核心挑战在于探究事实核查能力是否能从文本模态迁移至语音模态，以及检索增强型LALMs能否有效利用文本证据来支持或反驳语音声明。研究发现，存在显著的“文本-语音”模态差距：即使在书面语境下表现可靠的模型，在处理相同内容的语音输入时准确率大幅下降；仅依赖检索机制提升有限，因模型常混淆检索到的证据与原始语音声明。相比之下，结合显式推理过程的检索方法显著改善了声明与证据间的比对能力，经思维调优（thinking-tuned）的LALM在该基准上达到86.1%的准确率。由此可见，有效的语音虚假信息检测不仅需具备强大的语音理解能力，更依赖于对检索证据进行具身化（grounded）推理的能力。VeriSpeak数据集已通过Hugging Face公开发布。
链接: https://arxiv.org/abs/2609.30227
作者: Debajyoti Mazumder,Mamta,Abhirama Subramanyam Penamakuri
机构: IISER Bhopal(印度科学教育与研究大学布波尔分校); King’s College London(伦敦国王学院); MBZUAI(MBZUAI)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Sound (cs.SD)
备注: Accepted to EMNLP (Main) 2026
Abstract:Online misinformation increasingly appears in spoken formats such as news clips, podcasts, interviews, political speeches, and social media videos, creating a need for fact-checking systems that can verify claims directly from speech. We introduce VeriSpeak, a probe benchmark for studying speech-based fact verification in Large Audio Language Models (LALMs). VeriSpeak contains 3,879 spoken claims spanning temporal, geographical, and relational facts, with balanced true and false labels. The benchmark is designed to examine whether factual verification ability transfers from text to speech, and whether retrieval-augmented LALMs can use textual evidence to correctly support or refute spoken claims. Our experiments reveal a consistent text-speech modality gap: LALMs that verify written claims reliably often fail on the same claims when spoken. Moreover, retrieval alone provides limited gains because models frequently conflate retrieved evidence with the spoken claim. In contrast, retrieval combined with explicit reasoning improves claim-evidence comparison, with a thinking-tuned LALM reaching 86.1% accuracy. VeriSpeak highlights that effective speech misinformation detection requires not only speech understanding, but also grounded reasoning over retrieved evidence. The dataset is publicly available via Hugging Face at this https URL.
[NLP-4] PoEM: Predicting RL Outcomes from Existing Policies
【速读】： 该论文旨在解决生成式模型在使用强化学习（Reinforcement Learning, RL）进行后训练时面临的计算成本高、过程不稳定，且每次奖励函数变更或需融合多奖励时均需从头训练的问题。其核心解决方案是提出一种名为PoEM（Policy from Existing Models）的框架，通过利用已基于其他奖励函数完成后训练的模型，预测新奖励函数下的RL输出结果，从而避免实际运行额外的强化学习训练。该方法的关键在于：当新奖励函数可表示为已有奖励函数的线性组合时，其对应策略在对数空间中亦可表示为已有对数策略的线性组合；即使奖励函数间非严格线性相关，实验发现不同奖励下的对数策略通常位于一个近似低秩子空间中，因此可通过仅基于奖励函数或基线策略在样本上的输出，估计出该线性组合的权重系数。基于此理论洞察，PoEM构建了一种无需重新执行强化学习即可高效逼近目标策略的算法，并在合成与真实奖励函数、文本与图像多种模态上进行了验证，显著提升了奖励调整的灵活性与效率。
链接: https://arxiv.org/abs/2609.30226
作者: Kimia Hamidieh,Giannis Daras,Antonio Torralba
机构: MIT CSAIL(麻省理工学院计算机科学与人工智能实验室)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Foundation models are post-trained with reinforcement learning (RL) to maximize specific rewards, such as human alignment, correctness, or instruction following. This post-training process is computationally intensive, sometimes unstable, and has to be run from scratch every time the reward model changes or when we want to combine multiple rewards. We hence ask: given a new reward function, is it possible to predict the RL outcomes without actually running RL on it? We answer this in the affirmative by introducing PoEM, a framework to predict the outputs of RL on a new reward function using a set of models already post-trained on other rewards. First, we show that if the new reward function can be written as a linear combination of existing ones, then the new policy in log-space can be written as a linear combination of the existing log-policies. Surprisingly, even in cases where the rewards are not linearly connected, we observe that often log-policies from RL training span an approximately low-rank subspace across rewards. To our benefit, the weighting coefficients for this combination can be estimated using only the reward or basis policy outputs on the samples. We turn these observations into an algorithm that takes post-trained models and a new reward function, and approximates the target RL policy without actually running any additional RL training. We experimentally validate our approach across synthetic and real rewards, spanning both text and image modalities.
[NLP-5] ExplorationBench: Measuring AI Systems Exploration in Verifiable Alien Worlds
【速读】： 该论文旨在解决生成式AI在科学探索能力评估中的核心难题：如何验证系统是否真正提出并发现全新的假设，以及其知识获取是源于自主探索还是对预训练数据的简单记忆。现有评估方法难以区分“创新性发现”与“知识复现”，因而缺乏可信度。为此，论文提出ExplorationBench基准框架，其关键在于构建可验证的“外星世界”（Alien Worlds）环境——这些环境规则具备可执行性，确保所有推理结果均可精确验证；同时其规则与人类常识相冲突，排除了仅靠知识召回即可完成任务的可能性。该基准包含两个沙箱：AlienCode（31个发现目标，70项任务）和AlienLogic（24个发现目标，70项任务），均提供存在缺陷的操作手册、任务相关的环境反馈及专用工具调用接口，使系统能在未知环境中进行假设生成、实验设计与迭代学习。实验评估10个AI系统表明，最强系统虽能习得并应用陌生规则，但性能在不同探索路径间差异显著，且持续探索可能停滞甚至倒退先前成果。因此，ExplorationBench的核心贡献在于将科学探索这一复杂、模糊的问题转化为一个可量化、可重复验证的评估范式，推动生成式AI向在未知环境中自主发现新知识的能力迈进。
链接: https://arxiv.org/abs/2609.30199
作者: Ming Zhang,Zhenghao Xiang,Peizhong Gao,Yujiong Shen,Yuhui Wang,Zhonghan Yue,Shihan Dou,Zhangyue Yin,Junjie Ye,Shichun Liu,Weihuang Zheng,Jiahao Chen,Jiayi Chen,Hongzhang Liu,Jiaqi Shao,Tao Gui,Qi Zhang,Xuanjing Huang,Suncong Zheng,Maxm Pan
机构: Fudan University (复旦大学); Hunyuan Team (腾讯混元团队); Tencent (腾讯); Tsinghua University (清华大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Scientific discovery begins where known problems end. There, AI systems must engage in exploration: framing hypotheses, designing experiments, and iterating on the results. However, evaluating this ability is difficult: (1) how to verify whether a genuinely new hypothesis holds, and (2) how to determine whether a system has discovered it through exploration or merely recalled related knowledge from pre-training data. To this end, we introduce ExplorationBench, which turns the wicked problem of evaluating scientific exploration into a concrete and tractable framework built on verifiable Alien Worlds: their rules are executable, so every answer can be checked exactly, and they conflict with familiar knowledge, so recall alone cannot solve the tasks. The benchmark contains two sandboxes, AlienCode (31 discovery targets, 70 tasks) and AlienLogic (24 discovery targets, 70 tasks). Each sandbox provides a flawed manual, task-specific environmental feedback, and a dedicated tool-call schema. Systems use these resources to explore the sandbox, then solve held-out tasks. We evaluate 10 AI systems and find that the strongest systems can acquire and apply unfamiliar rules, while performance varies substantially across trajectories and continued exploration can stall or reverse earlier gains. ExplorationBench represents a step towards AI systems that can acquire and apply genuinely new knowledge through exploration in unknown environments.
[NLP-6] ARGUS: Role-Aware Event Knowledge Graphs for U.S. Employment-Discrimination Complaints
【速读】： 该论文旨在解决美国就业歧视投诉文本中复杂事件序列难以通过仅依赖词汇或嵌入表示方法有效捕捉的问题。其核心挑战在于如何从非结构化法律文书中提取并组织具有事实意义的事件信息，以支持后续的法律推理与分析。解决方案的关键在于提出ARGUS——一种基于源文本的端到端管道框架，融合了受5W1H（何事、何地、何时、何人、为何、如何）启发的结构化模式、法律领域专用模型以及大语言模型（LLM）驱动的结构化生成技术，从而从CourtListener平台的诉讼文书构建文档级事件知识图谱（Event Knowledge Graph, EKG）。该方法首先提取承载事实的语句，构建块级事件图谱（包含参与者、时间关系和因果关系），再将各块图谱合并为完整的文档级表示。实验表明，基于图结构的分类器在独立测试集上优于原始文本及线性化基线模型；而仅使用EKG进行检索可显著提升文档范围内的法律问答性能，但开放式检索效果受限于第一阶段候选文档召回率较低。研究结果表明，事件知识图谱最有效的应用场景是在相关材料已被初步检索后，用于证据的组织与推理。
链接: https://arxiv.org/abs/2609.30184
作者: Sriram Kannan,Swetha Saseendran,Vishnu Vardhan Reddy Kandi,Leslie Barrett,Madhavan Seshadri,Enrico Santus
机构: University of Massachusetts Amherst (马萨诸塞大学阿默斯特分校); Bloomberg(彭博)
类目: Computation and Language (cs.CL)
备注: 9 pages, NLLP
Abstract:U.S. employment-discrimination complaints describe complex event sequences that are not explicitly captured by lexical or embedding-based representations alone. We present ARGUS, a source-grounded pipeline that combines a 5W1H-inspired schema, legal-domain models, and LLM-based structured generation to construct document-level Event Knowledge Graphs (EKGs) from CourtListener complaints. ARGUS extracts fact-bearing statements, builds chunk-level event graphs with participant, temporal, and causal structure, and merges them into document-level representations. We evaluate graph quality through human and multi-model assessment and test downstream utility on claim classification and legal QA. The graph-structured classifier outperforms raw and linearized baselines on the held-out set, and EKG-only retrieval improves document-scoped QA, while open-retrieval gains remain limited by low first-stage candidate recall. These results suggest that EKGs are most useful for organizing and reasoning over evidence once relevant material has been retrieved.
[NLP-7] Do Audio Language Models Hear and Read Distinctive Features Alike?
【速读】： 该论文旨在探究音频语言模型（audio language model）中，同一音位在听觉输入与视觉阅读输入时，其语义表示是否共享一个一致的方向性特征。核心问题是：当音位在语音（听觉）和文字（阅读）两种模态下呈现时，模型解码器中的表征方向是否在特定语音特征上保持一致。解决方案的关键在于，针对仅在一个语音特征上存在差异的最小音位对（minimal pairs），计算两个成员平均表征之间的偏移向量，并通过计算不同模态间偏移方向的余弦相似度来评估一致性。为避免随机偏差的影响，研究采用基于随机配对构建的参照基准进行对比，而非以零作为对照。实验覆盖6个模型、7种语音特征及15种来自11个语系的语言，结果表明，仅有两个Qwen2.5-Omni模型在修正多重假设检验后，其“清浊音”特征在两模态间的表示方向显著优于参照值；且参照值在不同模型间波动达七倍之多，说明模型架构的家族属性（model family）而非模型规模，才是决定某一语音特征在何种模态中被一致表征的关键因素。
链接: https://arxiv.org/abs/2609.30167
作者: Yuanhao Chen,Peter Chin
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD); Applications (stat.AP)
备注:
Abstract:Audio language models pass speech and text through a single decoder. We ask whether that decoder represents a distinctive feature in the same direction when a phoneme is heard and when it is read. For minimal pairs of phonemes differing in one feature, we take the offset between the two members’ mean representations. Averaging those offsets gives a direction for each stream, and we measure the cosine between the two. Because the two streams already agree about arbitrary phoneme pairs, we compare every measure against a reference built from random pairings rather than against zero. We apply this to 6 models, 7 features and 15 languages from 11 families. Only voicing in the two Qwen2.5-Omni models exceeds that reference after correction for multiple testing, and the reference varies by a factor of seven between models. In three of the six models, voicing has one direction in audio across the 14 languages with enough minimal pairs to measure it, and every language pair agrees in two of them. The model family, not the model size, predicts which stream represents a feature.
[NLP-8] A Training Criterion with Token-Level Tolerance to Transcription Ambiguity for Automatic Speech Recognition ICASSP2027
【速读】： 该论文旨在解决自动语音识别（ASR）中因参考转录文本存在局部发音、拼写或词汇实现差异而导致的标注噪声问题，这类差异在声学上无法唯一确定。传统连接时序分类（CTC）模型假设参考转录是唯一的有效标签，对这类局部歧义敏感。为应对这一挑战，论文提出在词级粒度引入通时分类（OTC）的野值路径以容忍噪声，但其词级弧段过于粗粒度，导致跳过一个无效词元即丢弃整个词的监督信号。为此，本文将野值弧段细化至词元粒度，使无效词元可被单独跳过，而其余部分仍保留监督信息，并结合词元级与词级弧段作为互补的逃逸路径。实验结果表明，在19种语言和3个语料库上的25项任务中，词元级OTC均优于标准CTC。此外，论文采用基于预测熵的权重松弛调度策略替代原有的周期索引调度，显著降低对训练长度的依赖，性能相当。将该调度策略与混合图结构结合后，在所有语料库上均取得最低平均词错误率（WER），相较CTC实现9.45%的平均相对降低。独立验证者转录结果进一步显示，词元级模型在争议字符处具有更高的野值跳过概率，表明其对局部转录歧义具有更精准的建模能力。
链接: https://arxiv.org/abs/2609.30160
作者: Saurabh Kumar,Diptiman Mohanta,Prasanta Kumar Ghosh
机构: 未知
类目: Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注: 5 pages, 2 figures, 4 tables; submitted to ICASSP 2027
Abstract:Automatic speech recognition is typically trained assuming that the reference transcript is the only valid labeling of an utterance, yet even nominally verbatim transcripts contain localized differences in pronunciation, spelling, or lexical realization that the acoustics do not uniquely determine. Omni-temporal Classification (OTC) tolerates such noise by adding wildcard paths to the connectionist temporal classification (CTC) alignment graph, but its word-level arcs are too coarse, since bypassing one unsupported token discards supervision for the whole word. We move wildcard arcs to token granularity so unsupported tokens can be bypassed while the rest of the word stays supervised, and we combine token- and word-level arcs as complementary escape paths. Across 19 languages and three corpora, token-level OTC improves over CTC on all 25 tasks. We also replace epoch-indexed relaxation of the wildcard weights with a predictive-entropy-indexed schedule, which performs comparably while reducing dependence on training length. Combining this schedule with the hybrid graph gives the lowest mean word error rate (WER) on every corpus and a 9.45% average relative WER reduction over CTC. Independent validator transcriptions show that token-level models place significantly more wildcard-bypass probability than CTC on disputed characters, indicating that token-level tolerance targets localized transcript ambiguity.
[NLP-9] Does a models stated reason for rejecting a candidate do any work? CIKM2026
【速读】： 该论文旨在解决生成式 AI（Generative AI）在多跳问答任务中，模型对候选答案的选择行为是否受特定事实信息存在与否的显著影响这一问题。其核心问题是：当模型拒绝某一候选答案时，其理由是否源于该候选答案缺失某个关键事实（如导演信息或逝世日期），而这种缺失是否真正影响了模型的最终选择？解决方案的关键在于设计一种可验证的实验范式，通过在模型声称“缺乏某事实”的对手候选者（rival candidate）的描述中插入真实存在的对应事实，并在贪婪解码（greedy decoding）下重新评估模型的选择偏好。研究通过两个对照组分离内容与位置效应：一是与目标句长度匹配但无关的句子置于同一候选者的描述中，二是将相同句子置于模型从未提及的第三候选者中。结果显示，在最大规模的实验中，向被模型明确指出“缺少事实”的候选者插入该事实后，其被选中的概率显著提升，优势比为3.57（95%置信区间[1.54, 8.26]），校正后p=0.0210，且结果稳健，排除任一模型均不改变结论；而相同事实置于未被提及的第三候选者时，无显著效果（Holm p=0.2428）。更令人意外的是，即使在无内容差异的情况下，仅因位置不同（即插入到被命名的对手候选者而非第三选项），相同无关句子也能显著改变选择倾向（Holm p=0.0008），表明模型选择可能受文本位置及上下文结构的强烈影响。进一步分析显示，修复机制与控制条件在共现频率、关系模板和语言流畅性方面存在系统性差异，后验匹配调整后仍保持内容效应方向一致，仅流畅性匹配削弱部分效应，说明内容对比确实产生影响，而非单纯由其他因素导致。此外，强制单标记概率读取与自由文本选择结果在方向上不一致，但三种可能解释均未获支持，暗示模型决策机制复杂且非简单概率驱动。所有测量均基于字符串规则，每项规则均经数据记录验证，共发现八处缺陷，其中最严重的一处（选择解析规则错误地将模型刚拒绝的选项判定为正确）若未被修正，将使原本有效的六组对比降至四组，凸显方法论严谨性的重要性。
链接: https://arxiv.org/abs/2609.30151
作者: Archit Rastogi
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted as an oral presentation at LLM4XAI 2026: Workshop on Large Language Models for Explainable AI, co-located with CIKM 2026, Rome, Italy, November 8, 2026. Code and per-item records: this https URL
Abstract:Asked to choose between candidates and explain the choice, a language model often rejects a rival by naming a fact its profile lacks: no director, no date of death. That sentence is a claim about the text in front of the model, and it can be tested without any judge. We insert a real corpus sentence stating the named fact into the rival’s profile and ask again under greedy decoding. Two controls separate content from placement: a length-matched irrelevant sentence at the same profile, and the same two sentences at a third option the model never mentioned. In the largest of three runs, six open models on 2WikiMultihopQA, supplying the named fact at the profile the model named moves its choice more than the irrelevant control does, odds ratio 3.57 [1.54, 8.26], Holm p=0.0210, and this survives dropping any single model. The contrast the design was built to detect, the same fact at the option nobody named, does not clear correction, Holm p=0.2428. The strongest result in the family carries no content claim at all: the identical irrelevant sentence moves the choice more at the named rival than at the third option, Holm p=0.0008. Repair and control also differ in co-candidate mentions, relation template and fluency; post-hoc matching on the first two preserves the content effects’ direction, matching fluency weakens one, so the content contrasts bound an effect rather than establish one. A forced single-token probability read disagrees in direction with the free-text choice on that same contrast, and three candidate explanations for the disagreement find no support. Every measurement is a string rule, so each was validated against the records it reads; validation caught eight defects. The largest, a choice-parsing rule that returned the option a model had just rejected in 17.1% of adjudicable responses, would have reported six surviving contrasts instead of four.
[NLP-10] Screen Before You Serve: Simulation for Production Customer Experience AI Agents at 140M Scale
【速读】： 该论文旨在解决在受监管行业（如金融领域）中，客户体验（CX）代理系统在部署前难以有效评估与优化的问题。由于此类系统需准确识别用户意图、遵循复杂的业务规则并可靠调用工具，传统的人工端到端测试覆盖范围有限，而直接在生产环境中进行实验则可能因失败暴露而损害客户信任。为此，论文提出一种基于假设的仿真工作流，其核心在于通过构建合成客户与模拟工具输出，实现无需调用真实后端服务的多步骤智能体流程验证。该方法利用Snowglobe仿真器对Nubank的信用卡配送与卡管理两大高流量客服代理进行测试，结果显示仿真环境与生产环境在版本级二元评估指标上具有高度相关性。通过仿真引导的迭代优化，显著提升了事务型净推荐值（tNPS）36.69分；同时，在超过16,000次模拟对话中筛选出最优的开源模型配置，后续上线的A/B测试显示自服务率（SSR）提升8.82个百分点至历史最高水平，且未对tNPS造成显著影响。该方案的关键在于以可控、可扩展的仿真环境替代高风险的真实用户实验，使大规模探索模型、推理策略与提示工程成为可能，从而实现仅靠现场实验无法达成的生产级改进。
链接: https://arxiv.org/abs/2609.30137
作者: Edesio Alcoba,Kevin Rossell,Aman Gupta,Shao Tang,Jiwoo Hong,Pabel Carrillo-Mendoza,Wanderson Conceição Ferreira,Alvaro Tedeschi,Zayd Simjee,Shreya Rajpal,Bruno Finardi Hime,Christian Sousa,Luis Moneda,Herbert Fei,Daniel Silva,Rohan Ramanath
机构: Nubank; Guardrails AI
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 17 pages, 11 figures
Abstract:Customer experience (CX) agents use tools and large language models to address customer requests and guide conversational interactions with an organization’s products. Improving these agents, especially in regulated industries, is difficult: they must detect intent, follow complex operational policies and use tools reliably. Manual end-to-end testing offers limited coverage, while live experiments expose customers to failures that can erode trust. We present a hypothesis-driven simulation workflow for screening candidate CX agents before deployment. Synthetic customers react to agent responses and simulated tool outputs enable multi-step agentic workflows without invoking production backends. We use the Snowglobe simulator on Nubank’s Card Delivery agent and its expanded successor, Card Management - Nubank’s highest-volume chat-support agent in Brazil. Across 4 deployed versions, simulated and production version-level binary evaluator scores show high correlation. Simulation-guided iteration increased transactional net promoter score (tNPS) by 36.69 points in a live A/B test. We also screened open-weight configurations in over 16,000 simulated conversations. In a subsequent live A/B test, the selected model increased self-service rate (SSR) by 8.82 percentage points to the highest level observed at Nubank, with no statistically significant change in tNPS. Simulation made broad exploration of models, reasoning settings, and prompts feasible without customer exposure, enabling production improvements that would have been impractical to pursue through live experimentation alone. Comments: 17 pages, 11 figures Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2609.30137 [cs.AI] (or arXiv:2609.30137v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.30137 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-11] Multimodal Thinking with Renderable Programs
【速读】： 该论文旨在解决当前视觉-语言模型（VLMs）在推理链中难以有效融合图像信息的问题，尤其针对现有跨模态模型在开放域视觉任务中依赖栅格化或潜在表示而导致可解释性与可控性不足的缺陷。其核心解决方案是提出SVGLM框架，利用可扩展矢量图形（SVG）原语作为文本与图像之间的桥梁，充分发挥SVG兼具图像描述与文本指令双重特性的优势。通过将图像生成嵌入推理过程，SVGLM实现了更紧凑、可解释的图像生成能力，使通用VLM具备“思考-绘图”一体化的智能。研究还构建了一个大规模的基于SVG的图像编辑数据集，并提供适配开源VLM的微调范式。实验表明，该方法在数学推理基准上展现出强大的SVG生成能力与“以图思辨”的认知水平，验证了SVG作为构建更鲁棒数字领域智能体的理想媒介，有效弥合了基于文本的推理与基于像素的视觉表征之间的鸿沟。
链接: https://arxiv.org/abs/2609.30130
作者: Sunli Chen,Ding Zhong,Ziqiao Ma,Jiaxin Liu,Zeyuan Yang,Hao Zhang,Lie Lu,Joyce Chai,Chuang Gan
机构: University of Massachusetts Amherst(马萨诸塞大学阿默斯特分校); University of Michigan(密歇根大学); University of Illinois Urbana-Champaign(伊利诺伊大学厄本那-香槟分校); Dolby Laboratories(杜比实验室)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Current vision-language models (VLMs) excel at visual content understanding and text-based reasoning, yet their structure limits the advancement of incorporating images into the reasoning chain. Though Omnimodal models have made efforts in unifying text and image generation, they focus on visual tasks in the open-domain, lacking tractability due to rasterized or latent representations of images. We introduce SVGLM, a framework that uses scalable vector graphics (SVG) primitives to connect text and image in reasoning tasks. We exploit the duality of SVG as both image description and text instructions, yielding a more compact, interpretable solution to equip general VLMs with the capability of generating images within the reasoning process. We provide a large curated dataset of SVG-based image editing dataset, as well as the paradigm to tune open-source VLMs. Experiments on a mathematical reasoning benchmark demonstrate that SVGLM achieves strong SVG generation power as well as think-with-image intelligence. Our results highlight SVG as a suitable medium for building more robust digital domain agents, bridging the gap between text-based thinking and pixel-based images.
[NLP-12] What When and How: Audio Description as Constrained Global Optimization
【速读】： 该论文旨在解决现实场景下电影音频描述（Audio Description, AD）生成中的核心挑战，即如何在不干扰对话的前提下，智能决策描述内容的选择、时机与表达方式。现有自动AD系统多将生成任务视为局部的视频到文本映射问题，忽略了对视觉信息叙事重要性判断、时间插入位置规划以及语言压缩表达等关键环节的联合优化。本文提出一种基于约束优化的混合框架，将AD生成建模为关于“描述什么”、“何时描述”和“如何表述”三方面耦合决策的优化问题。其解决方案的关键在于：利用大语言模型（Large Language Models, LLMs）进行视觉元素的提议与语义锚定，估计其叙事显著性，并生成紧凑化表述；随后通过混合整数线性规划（Mixed-Integer Linear Programming, MILP）联合优化并调度整个场景中的描述内容，严格满足时间约束。实验结果表明，该方法在REFRAMED基准上显著优于仅靠提示工程的LLM，尤其在叙事问答（narrative QA）与时间定位精度等指标上达到新SOTA。消融实验进一步揭示，显式的时间约束主导了描述放置的改进，而显著性估计则有效控制了叙事相关信息的保留程度。尽管生成质量在时间和叙事维度有明显提升，但与专业描述员的表现仍存在显著差距，尤其体现在自然性和语义丰富性方面。
链接: https://arxiv.org/abs/2609.30121
作者: Igor Sterner,Mirella Lapata,Alex Lascarides,Frank Keller
机构: University of Edinburgh (爱丁堡大学); School of Informatics (信息学院)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Audio Description (AD) makes movies accessible to blind and visually impaired audiences by narrating visual information in gaps between dialogue. Existing automatic AD systems largely treat generation as a local video-to-text problem, assuming that the content to describe and its temporal location are already provided. Realistic AD instead requires coupled decisions about what visual information is narratively important, when it can be spoken without interfering with dialogue, and how it should be formulated to fit within the available time. We formalize AD generation as a constrained optimization problem over these three decisions. Our hybrid system uses large language models to propose and ground visual elements, estimate their salience to the narrative, and generate compressed realizations. A mixed-integer linear program then jointly selects and schedules descriptions across a scene subject to temporal constraints. When evaluated on REFRAMED, a benchmark for realistic AD of movies, our approach makes better decisions than prompted LLMs about what to describe and when to describe it, establishing a new SOTA on narrative QA and temporally grounded metrics. Ablations show that explicit temporal constraints drive gains in placement, while salience estimation controls how much narratively useful content is retained. Improvements are concentrated on temporal and narrative measures rather than n-gram overlap, although a significant gap to professional describers remains.
[NLP-13] R-DEIM Net: An Efficient Rationale-Augmented Dual-Expert Interaction Model for Paraphrase Detection
【速读】： 该论文旨在解决大语言模型在语义相似性检测任务中存在准确率与计算开销之间的权衡问题，即高精度模型通常需要巨大的计算资源，而轻量级模型虽具备实用性但缺乏可解释的推理过程。其解决方案的关键在于提出一种7600万参数的双专家架构R-DEIM Net，通过融合两个专用模块实现性能与可解释性的平衡：一是交互专家（Interaction Expert），利用多尺度二维卷积与注意力机制捕捉细粒度的词元级相似性模式，并支持可变输入长度；二是推理专家（Reasoning Expert），基于Flan-T5-small解码器生成人类可读的推理理由作为辅助监督信号。该方法不重新编码生成的推理文本，而是直接提取并池化解码器隐藏状态作为分类的补充特征，从而在保持低参数量的同时提升模型表现。在Quora问题对数据集上，R-DEIM Net实现了90.07%的准确率和90.16%的F1分数，性能接近主流大型模型（如MFAE BERT和LLaMA-70B），且显著降低参数规模，同时输出可解释的推理理由，为实际部署中的透明性与可信度提供了新路径。
链接: https://arxiv.org/abs/2609.30100
作者: Pushp,Vaibhav Prajapati,Himangshu Sarma
机构: Indian Institute of Information Technology (IIIT), Sri City, India; University of Technology Nuremberg (UTN), Germany
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advances in paraphrase detection reveal a fundamental trade-off: large language models achieve high accuracy but require high computation, while efficient Siamese-BERT variants offer practical scalability with reduced transparency in rationale generation. We present R-DEIM Net, a 76M-parameter dual-expert architecture exploring whether moderate-scale models can achieve competitive accuracy on paraphrase detection while enabling human-readable rationale generation. The architecture combines two specialized components: an Interaction Expert that captures token-level similarity patterns through multi-scale 2D convolutions and attention head allowing variable input length, and a Reasoning Expert that uses a Flan-T5-small decoder to generate rationales as auxiliary supervision. Rather than re-encoding generated text, we extract and pool decoder hidden states as complementary features for classification. On the Quora Question Pairs dataset, R-DEIM Net achieves 90.07% accuracy and 90.16% F1-score via 10-fold cross-validation. This represents competitive performance with strong transformer-based baselines (e.g., MFAE BERT: 90.54% accuracy) and recent large language model based approaches (LLaMA-70B) while using a substantially smaller parameter budget. The model generates rationales alongside predictions, providing potential for auxiliary human-readable descriptions.
[NLP-14] PrivDrift: Auditing User-Secret Leakage Under Topic Drift in Active LLM Conversations
【速读】： 该论文旨在解决在持续性对话场景中，用户在与大语言模型（Large Language Models, LLMs）交互过程中披露的敏感信息可能在话题转移后仍可通过后续提示被恢复的隐私泄露问题。其核心挑战在于评估敏感信息在对话主题漂移（conversational topic drift）及基于说服力的探测攻击下是否仍具有行为可恢复性。解决方案的关键是提出一个名为PrivDrift的基准测试框架，该框架包含1000个经过控制的多轮对话，其中嵌入了种子敏感信息（seeded secrets）、内容密集型话题漂移回合以及标准化的信息提取探测机制。实验结果表明，在三种具备长上下文窗口的LLM中，对话级别的混合式信息泄露（dialogue-level hybrid leakage）依然显著，比例介于38.7%至54.6%之间，且受模型差异、敏感信息类型和说服强度的强烈影响。更重要的是，研究发现额外的话题漂移并不能可靠降低泄露风险，揭示出在活跃的LLM应用环境中，隐私风险应被视为一种持续存在的行为失效模式，而非仅由训练数据记忆或即时越狱行为所导致。
链接: https://arxiv.org/abs/2609.30094
作者: Luciano Maldonado
机构: West Virginia University (西弗吉尼亚大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR)
备注: Preprint, 10 Pages, 6 figures
Abstract:Large language models increasingly operate as persistent assistants in user-facing, shared-session, and tool-augmented settings. When users disclose sensitive information during an active conversation, that information may remain behaviorally recoverable through later prompts even after the dialogue shifts to unrelated topics. We introduce \textbfPrivDrift, a benchmark for auditing whether user-disclosed secrets remain recoverable after conversational topic drift and persuasion-based probing. PrivDrift contains 1,000 controlled multi-turn dialogues with seeded secrets, content-dense drift turns, and standardized extraction probes. Across three LLMs with extended context windows, dialogue-level hybrid leakage remains substantial, ranging from 38.7% to 54.6%, and varies strongly by model, secret type, and persuasion intensity. Within the tested drift window, additional topic drift does not reliably reduce leakage, suggesting that privacy risk in active LLM contexts should be evaluated as a persistent behavioral failure mode rather than only as training-data memorization or immediate jailbreak behavior.
[NLP-15] A Native-Reference Phone-Class Geometry for Second-Language Pronunciation Analysis ICASSP2027
【速读】： 该论文旨在解决自动语音评估系统在衡量第二语言（L2）发音质量时缺乏可解释性度量的问题，尤其针对现有方法通常依赖发音标签、朗读提示或母语者与二语者对同一文本的配对录音等限制。其核心解决方案是提出一种基于母语参照的音素类几何框架（native-reference phone-class geometry），该方法无需任何发音标注或配对录音即可量化L2发音偏差。具体而言，利用母语语音语料库中每个上下文相关音素类的帧级自监督表示进行平均，并通过奇异值分解（SVD）构建紧凑的母语参照坐标系；随后将每个L2话语的对应表示投影至该空间，通过计算匹配音素类在L2与母语参照坐标间的距离来表征发音偏离程度。实验结果表明，该距离指标在Speak and Improve Corpus 2025的开发集上与整体口语流利度评分呈显著负相关（Spearman’s ρ = -0.53），在日语学习者英语朗读数据集的学生成分上与发音质量亦呈负相关（ρ = -0.34），证明该几何结构能够有效捕捉与语言能力评估相关的声学-音位信息，同时适用于非受控场景下的自发性二语语音分析。
链接: https://arxiv.org/abs/2609.30075
作者: Tina Raissi,Nhan Phan,Chenxiao Wang,Mikko Kurimo
机构: 未知
类目: Computation and Language (cs.CL); Sound (cs.SD)
备注: Submitted to ICASSP 2027
Abstract:Automatic speaking assessment systems can provide holistic proficiency scores, but often lack interpretable measures that characterize pronunciation quality. We propose a native-reference phone-class geometry for measuring second language (L2) pronunciation deviation without requiring pronunciation labels, read-aloud prompts, or matched recordings of the same text from native and L2 speakers. Given a native speech corpus, we average frame-level self-supervised representations for each context-dependent phone-class and use singular value decomposition (SVD) to derive a compact native-reference coordinate system. For each L2 utterance, we compute the corresponding averages and project them into the native-reference space. We then demonstrate that the distances between L2 and native-reference coordinates for matched phone-classes show consistent negative correlations with holistic speaking proficiency on the Dev subset of the Speak and Improve Corpus 2025 (Spearman’s \rho!=!-0.53 ) and with pronunciation quality on the learner subset of the English Read by Japanese Students dataset ( \rho!=!-0.34 ). These findings suggest that the proposed geometry captures acoustic-phonetic information relevant for proficiency rating while remaining applicable to spontaneous L2 speech without matched native recordings.
[NLP-16] How Reproducible Are Evaluation Conclusions? A Self-Audit of LLM -Inferred Prompt Structure NEURIPS2026
【速读】： 该论文旨在揭示当前大语言模型（LLM）评估中存在的严重可重复性问题，特别是基于小样本提示集（prompt set）的平均评估结果所表现出的虚假确定性。其核心问题是：现有评估方法通过少量提示生成结果并进行排名，但这些排名在不同运行中表现出极低的稳定性，导致对模型性能的判断缺乏可信度。解决方案的关键在于引入系统性的可重复性审计机制，包括联合聚类自助采样（joint cluster bootstrap）分析、逐单元溯源（per-cell provenance）追踪、敏感性对比测试以及原始输出数据的公开。研究发现，仅底部排名的模型表现稳定（如最差模型在99%重复实验中保持最低排名），而顶部模型的排名波动剧烈，且不同合并规则可导致排名变化达7个百分点，甚至有四款模型在测量后十周内被撤回。这表明模型结构推断的可重复性不能等同于准确性。因此，论文强烈建议在报告模型排名时，必须同步提供排名稳定性、执行过程溯源、敏感性分析、原始输出及测量时间戳，以提升评估透明度与可信度。
链接: https://arxiv.org/abs/2609.30074
作者: Dipankar Sarkar
机构: Skelf Research(斯克尔夫研究); https://skelfresearch.com
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 13 pages. Previously submitted to TAE (Trust-AI-Eval), a NeurIPS 2026 workshop
Abstract:Evaluations of LLM systems routinely average over small prompt sets and report models as a ranked table. We ask how much confidence such a table deserves, using LLM-based prompt-structure inference as the case study: eight open model variants across five families and 8B to 675B parameters, caching disabled, 293 raw intermediate representations persisted. The measured phenomenon is unstable to begin with. Identical calls do not reliably recover identical structure, with mean node-set Jaccard from 0.39 to 0.96 and 72% of prompt-model cells never node-set-perfect. Auditing the evaluation weakens its conclusions further, and this is our main contribution. Under a joint cluster bootstrap over prompts, only the bottom of the ranking is firm: the two least reproducible models hold rank in 99% and 86% of replicates, the middle four in 27% to 48%, and the top two in 68% each, so the table identifies the worst model reliably but does not reliably identify the best. Two equally defensible rules for merging repeated campaigns change four of eight rows and move the study-wide headline by 7 percentage points. Checking the inferred structure against ground-truth annotations shows reproducibility cannot be read as accuracy. And four of the eight endpoints were withdrawn within ten weeks of measurement, so the study as specified can no longer be run. Small-sample LLM evaluations can therefore look far more definitive than their evidence supports. We recommend reporting rank stability, per-cell provenance, executed sensitivity comparisons, raw per-run outputs, and a measurement date alongside any ranking.
[NLP-17] Scoring Both Directions: LLM s realize the MRS they cannot reliably parse
【速读】： 该论文旨在解决自然语言生成（从最小递归语义表示，MRS 到文本）与自然语言解析（从文本到 MRS）任务中大语言模型（LLM）对形式化语义表示的理解能力问题。其核心挑战在于评估大模型是否真正具备对结构化语义的建模能力，而不仅仅是表面的生成匹配。解决方案的关键在于：在不进行任何任务特定训练的前提下，利用已有的英语资源语法（ERG）及其树库构建基准测试，对比大语言模型（Claude Sonnet 4.5 与 Claude Opus 5）在双向任务中的表现——即从 MRS 生成句子和从句子生成 MRS。实验结果表明，在生成方向上，Opus 模型在仅提供三个示例的情况下，以 76.3 BLEU 分数超越了基于 72,000 对训练数据的序列到序列模型（66.1 BLEU），接近于使用百万级额外数据训练的系统（77.2 BLEU），显示出强大的生成能力；而在解析方向上，尽管模型表现优于随机基线，但其在谓词与论元级别的 F₁ 得分仅为 57.2（Sonnet）和 65.5（Opus），远低于 ACE 解析器的 91.0，且仅约 1% 的句子能完全匹配黄金标准。研究进一步分析了解析失败模式，指出仅凭生成性能无法证明模型真正理解形式语义表示，强调需结合双向任务评估才能全面衡量模型对语义结构的认知能力。
链接: https://arxiv.org/abs/2609.30071
作者: Soham Dan
机构: Scale AI(规模人工智能)
类目: Computation and Language (cs.CL)
备注:
Abstract:The English Resource Grammar (ERG) is a hand-written computational grammar of English. Given a sentence, its processor, ACE, produces a formal meaning representation called Minimal Recursion Semantics (MRS): a graph of the sentence’s predicates and their arguments. The grammar is bidirectional and can also turn an MRS back into an English sentence. \citethajdik2019 used the ERG’s treebank to build a benchmark for that generation task, MRS to text, and trained sequence-to-sequence models to solve it. The parsing task, text to MRS, can be tested on the same sentences. We reconstruct their 10K-sentence test split, and score two large language models, Claude Sonnet~4.5 and Claude Opus~5, in both directions against their trained systems and against ACE, with no task-specific training. Given an MRS and three examples, Opus writes the sentence at 76.3 BLEU, ten points above their system trained on 72k pairs (66.1 BLEU), and comparable to their system trained on a million extra pairs (77.2 BLEU). Sonnet scores 65.7 BLEU, and letting it choose among ACE’s own candidate sentences lifts it to 69.6, while a pooled judge that keeps Opus’s own sentence among the candidates adds 0.6 points (77.0 BLEU). In the parsing direction, however, the models fall far behind ACE: asked for the MRS of the same sentences, they reach 57.2 (Sonnet) and 65.5 (Opus) F _1 on the graph’s predicates and arguments against 91.0 for ACE, and exact-match the gold on about 1% of sentences. We characterize the failure modes for the parsing tasks, and conclude that a generation score alone does not show that models understand formal semantic representations.
[NLP-18] Self-Play Pretraining with Zero Data
【速读】： 该论文旨在解决当前语言模型预训练依赖人工标注或筛选数据的局限性，提出一种无需预先提供任何自然数据（Zero Data）的自洽式预训练范式。其核心问题是：如何让模型自主生成对其自身能力提升最有益的合成训练数据，从而突破人类知识对数据规模的限制，实现可无限扩展的、以计算资源为上限的训练数据供给。解决方案的关键在于提出“零数据自对弈预训练”（Self-Play Pretraining with Zero Data），通过双模型协同机制——一个生成器（generator）负责生成由通用图灵机（universal Turing machine）解释的程序以产出字节序列，另一个学习者（learner）则对这些序列进行自回归预测——构建一个基于可计算结构空间的自适应课程学习过程。其中，生成器采用强化学习策略，目标是生成位于学习者能力边界上的序列，从而动态引导训练难度上升；这一框架依托于所罗门诺夫归纳（Solomonoff induction）的思想，将数据生成视为在所有可计算过程中的搜索，具备极强的通用性和低领域偏见特性。实验表明，在未接触任何自然数据的前提下，模型在自然数据上的零样本性能随计算量增加呈现可预测的缩放规律，同时展现出上下文学习能力及对经典数学序列的发现能力，验证了该方法在跨域迁移与自我进化方面的潜力。
链接: https://arxiv.org/abs/2609.30063
作者: Aditya Cowsik,Kfir Dolev,Michael Y. Li,G. Bruno De Luca,Nourya Cohen,Noah D. Goodman,Yoav Levine
机构: Independent Researcher; Tel Aviv University (特拉维夫大学); Stanford University (斯坦福大学); LAPTh, USMB (LAPTh, USMB)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: AC, KD, and MYL contributed equally; authors are listed alphabetically
Abstract:Advances in language modeling have been driven by scaling pretraining on ever more data. Yet, the training data is still largely curated on the model’s behalf. A more general approach to pretraining would let the model learn to generate the data most useful for its own improvement. This would provide an effectively unbounded source of training data, limited by compute rather than human knowledge. We introduce Self-Play Pretraining with Zero Data, an initial proof-of-concept towards realizing this vision. Our procedure casts synthetic data generation as a search over the space of all computable structure, taking inspiration from Solomonoff induction. Starting from random initialization, two models learn in tandem: a generator proposes programs interpreted by a universal Turing machine, generating byte sequences, while a learner autoregressively predicts these byte sequences. The learner is trained with standard cross-entropy, while the generator is trained with reinforcement learning to produce sequences at the frontier of the learner’s capabilities, yielding an adaptive curriculum. A universal Turing machine gives us a search space over all computable data-generating processes, imposing little domain-specific structure, and self-play searches over this space for useful training data. We test whether zero-shot performance on natural data improves predictably with self-play compute; this is a clean test of transfer since neither generator nor learner is trained on natural data. Across several natural datasets, zero-shot loss exhibits predictable scaling in compute. The models also exhibit in-context learning, and discover recognizable mathematical sequences during training.
[NLP-19] Style Not Self: Surface Cues Explain Zero-Shot Code Attribution by Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）在自我评估与代码生成任务中可能存在的自利性偏差问题，即模型倾向于偏好自身生成的代码，从而导致评估结果失真。其核心挑战在于：当模型被用作自身或同类模型生成代码的评判者时，可能存在隐性合谋（collusion）风险，进而影响评估的客观性。解决方案的关键在于通过引入一种基于规则的代码规范化方法，移除文档字符串、注释、类型提示和局部变量名等可识别特征，以消除模型对自身代码的“身份记忆”依赖。实验表明，该规范化处理能有效将多数评估结果降至随机水平（chance level），同时保留基本的通过率（Pass@1），并显著削弱模型的自偏好现象（如Claude Haiku的自偏好消失）。研究建议在报告评估结果时应采用平衡准确率（balanced accuracy）、提供启发式基线（heuristic baselines）并检验标签一致性，以增强评估的可信度与可比性。
链接: https://arxiv.org/abs/2609.30048
作者: Ehsan Barkhordar,Surendrabikram Thapa
机构: Koç University (科奇大学); Virginia Tech (弗吉尼亚理工学院)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Software Engineering (cs.SE)
备注: 18 pages, 1 figure. Code and data: this https URL
Abstract:If a language model can recognize code it wrote, it may favor that code as a judge, and instances of one model monitoring each other could collude. We test this zero-shot on current commercial models. Five LLMs generate solutions to MBPP, HumanEval, and DS-1000, seven more to MBPP, and models act as evaluators in four tasks: picking their own solution from a pair, judging whether a single solution is their own, identifying which of two solutions a named model wrote, and judging quality blind. In the single-solution task, balanced accuracy is 49-58% for all 15 model-benchmark combinations, while raw accuracy (38-67%) mostly reflects how readily a model claims authorship. In the pairwise task, accuracy across 14 evaluator-opponent combinations correlates at r=0.93 with how often the evaluator’s solution is longer. Attribution to a named model succeeds on some pairs and is consistently inverted on others. A rule-based normalization that strips docstrings, comments, type hints, and local names preserves Pass@1 and leaves ten of twelve re-tested results at chance; the other two follow a length difference it leaves, although a trained classifier still separates most normalized pairs. Claude Haiku’s self-preference also disappears. We recommend reporting balanced accuracy, heuristic baselines, and label consistency.
[NLP-20] Artificial Societies Benchmark: A Validation Framework for Synthetic Research
【速读】： 该论文旨在解决合成人口（synthetic population）在模拟真实人群时存在的根本性问题：尽管能够再现平均回答，但往往扭曲了个体间的差异性、答案之间的相关性以及对情境变化的响应模式。为评估合成群体是否适用于特定研究分析，作者提出了“人工社会基准”（Artificial Societies Benchmark）框架，其关键在于构建涵盖内部效度、构念效度和外部效度的十一项测试，基于二十个人类数据源，并对比九种语言模型的表现。该框架将每种研究用途与其所需证据相匹配，系统检验不同信息输入下分析结果的变化。研究表明，某一维度表现良好并不能保证其他维度的可靠性；多数模型存在过度一致性、响应尺度压缩及特征间关系失真等问题。此外，更丰富的个体画像虽能提升部分模型的预测性能，却可能恶化其他模型的表现。最终生成的评分卡可帮助研究者识别合成群体中可用于分析的有效方面，以及需补充人类实证数据的关键环节。
链接: https://arxiv.org/abs/2609.30030
作者: Edoardo Chidichimo,Min Jun Jung,Felix P. S. Wallis,James K. He
机构: University of Oxford (牛津大学); Artificial Societies
类目: Computation and Language (cs.CL)
备注: 36 pages, 9 figures, 9 tables
Abstract:A synthetic survey can reproduce the average answer while misrepresenting how people differ, how their answers relate to one another, or how they respond to changes in conditions. We introduce the Artificial Societies Benchmark to help researchers assess whether synthetic populations support their intended analyses. The framework combines eleven tests across internal, construct, and external validity, drawing on twenty human sources and comparing nine language models. It connects each research use to the evidence it requires and tests how results change with the information we supply about respondents. Importantly, strong performance in one domain does not establish fidelity in the others. Models often answer too consistently, compress response scales, and alter relationships between traits whilst richer profiles improve prediction for some models and worsen it for others. The resulting scorecard helps researchers identify which aspects of a synthetic population can support their analysis and where researchers need further human evidence.
[NLP-21] Low-Cost Assays for Measuring Model Behavior Across Vendors and Releases
【速读】： 该论文旨在解决对大型语言模型（Large Language Models, LLMs）行为进行系统性、可重复且可比较测量的难题。由于模型行为多以非结构化文本形式呈现，且需在不同模型、提示（prompt）和版本间反复采样，传统方法在数据编码、分析严谨性与跨厂商对比方面存在显著局限。为此，论文提出一种简单、低成本、可扩展且可复现的行为研究范式：通过在跨厂商模型面板上对同一组标准化刺激（stimulus）进行冻结、公开的运行，每轮测试成本低于几美元。该方法采用三种不同的分析路径以适应不同行为的解释需求——基于固定回复的精确匹配、由大模型（LLM）裁判执行并经人类编码者一致性验证的代码本（codebook）分析，以及独立记录智能体实际行为的仪器化环境。基于四年内来自前沿与开源实验室的模型发布数据，该框架揭示了四大关键现象：收敛性（如27/44个模型在四次尝试中至少一次选择“serendipity”）、抵抗性（尾随“right?”标签可使支持度提升达32个百分点，且态度从顺从转向质疑随代际演进而变化）、立场稳定性（模型在压力下持守立场的能力与其生成世代及所属研发机构相关），以及合规性差异（部分编程代理在面对文档冲突时会主动拒绝执行，而另一些则始终默许，且同一模型的行为可能因运行环境不同而改变）。该方法可周期性复用，为持续追踪模型行为在厂商间与时间维度上的演变提供了可靠工具。
链接: https://arxiv.org/abs/2609.30012
作者: Tapan Parikh
机构: Cornell Tech(康奈尔科技学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 6 pages. Code and data: this https URL
Abstract:Language models advise people, keep them company, and write software while they sleep. Measuring what they do is hard: behavior has to be sampled repeatedly across models, prompts and releases, most of it lives in unstructured text that has to be coded before it can be counted, and the result has to be legible and rigorous enough to meaningfully compare models and vendors. To address these constraints, we present a simple, cheap, scalable, and replicable model for studying model behavior. Each study is a frozen, public stimulus run identically on a cross-vendor panel, at a few dollars per model or less. Each reads its transcripts one of three ways, chosen by how much interpretation the behavior needs: exact match on a clamped reply, a codebook applied by LLM judges whose agreement with a human coder is reported per code, and an instrumented environment that records what an agent did independently of what it said. Run across four years of model releases from both frontier and open-source labs, these instruments find four things. Convergence: asked to pick a word, 27 of 44 models answer serendipity at least once in four tries. Resistance: a trailing “right?” moves endorsement by up to 32 points, and the sign flips from sycophantic to resistant as generations advance, keyed to the tag’s surface form. House: whether a model holds a position under pressure tracks its generation, and how it holds tracks the lab that built it. Account: told to do something the documentation in their repository contradicts, some coding agents never went along silently and others always did, and the same model can change with the harness it runs in. Re-run on every release, batteries like these track how behavior is changing across vendors and over time.
[NLP-22] Automated Regulatory Compliance Question Answering in Financial Services with Domain-Adapted Retrieval-Augmented Generation
【速读】： 该论文旨在解决金融合规领域中，企业在本地部署的轻量级大语言模型（Large Language Model, LLM）因参数规模有限而产生“幻觉”（hallucination），导致对监管义务的错误推断问题。其核心挑战在于如何在保证模型效率的前提下，实现生成结果在权威法规文本上的可验证性与准确性。解决方案的关键在于构建一个经过精心领域适配的检索增强生成（Retrieval-Augmented Generation, RAG）流程：首先通过三阶段优化的检索器——基于LegalBERT的蕴含调优（entailment tuning）、基于批次内负样本的对比学习（contrastive tuning）以及与BM25的得分级融合（score-level fusion），显著提升相关法规段落的召回率；其次采用参数量为2B–12B的紧凑生成模型，在4位量化（4-bit quantization）下运行，并结合检索感知微调（Retrieval-aware Fine-Tuning, RAFT）与低秩适配（LoRA）技术进行参数高效微调。实验表明，该架构在阿布扎比全球市场（Abu Dhabi Global Market）规则库构建的ObliQA基准上，将Recall@10从0.256提升至0.774，优于BM25和E5-large-v2等通用密集编码器。尽管RAFT-LoRA进一步提升了复合指标RePASs的得分，但研究发现经微调模型无法泛化至澳大利亚判例法问题，且不依赖检索的闭卷模型虽无引用来源却仅以0.011 RePASs差距落后于完整系统，暴露出当前评估体系中“生成质量”与“真实依据”之间的脱节。因此，该研究揭示了现有评估协议（如RePASs）难以有效衡量生成内容的真正可追溯性，强调了建立更严格、具备可验证性的合规推理评估标准的重要性。
链接: https://arxiv.org/abs/2609.30009
作者: Tobias Deußer,Abhishek Pillai,Aurelio F. Bariviera,Dhananjay Bhardwaj,Lorenz Sparrenberg,David Berghaus,Christian Bauckhage,Rafet Sifa
机构: University of Bonn(波恩大学); Lamarr-Institute for Machine Learning and Artificial Intelligence(拉马尔机器学习与人工智能研究所); Fraunhofer IAIS(弗劳恩霍夫信息系统与应用数学研究所)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Currently under review
Abstract:Financial institutions operate under dense, frequently amended rulebooks, and answering a compliance question correctly requires not only fluency but verifiable grounding in the authoritative text. Large language models are attractive for this task, yet the models that firms can realistically deploy on-premise are compact ones, and compact models hallucinate obligations. We study whether a carefully domain-adapted retrieval-augmented generation pipeline closes that gap. Our retriever is built in three stages on top of LegalBERT: entailment tuning that recasts question–passage matching as premise–hypothesis reconstruction, contrastive tuning with in-batch negatives, and score-level fusion with BM25. Our generator is a compact model (2B–12B parameters) served under 4-bit quantization, either prompted or adapted with retrieval-aware fine-tuning (RAFT) through LoRA. On ObliQA, a question-answering benchmark built from the Abu Dhabi Global Market rulebooks, the staged retriever raises Recall@10 from 0.256 to 0.774 and outperforms BM25 (0.678) and E5-large-v2 (0.758), the strongest general-purpose dense encoder we tested. RAFT-LoRA then improves the composite RePASs answer-quality score for every model we could adapt, with the largest gain on the weakest one. However, the adapted models do not transfer to Australian case-law questions, and a closed-book model that receives no passages at all scores within 0.011 RePASs of the full pipeline while producing answers that cite nothing and misstate obligations. The retrieval gain is therefore measured directly, the generation gain is a gain in RePASs rather than demonstrated grounding, and grounding itself requires an evaluation protocol that RePASs does not provide.
[NLP-23] VietPrism: A large-scale Vietnamese speech and deepfake corpus with diverse dialects and code-switching ICASSP2027
【速读】： 该论文旨在解决越南语语音研究中因资源匮乏而导致的自动语音识别（Automatic Speech Recognition, ASR）与说话人、方言、语码转换及深度伪造分析等多维度任务相互割裂的问题。其核心解决方案是构建首个大规模、开放且跨领域的越南语语料库——VietPrism，该语料库整合了993.4小时的真实语音数据和403,941条真实语音片段，涵盖1,262名经验证的说话人、五个方言群体以及近半数时长的自然越南语-英语语码转换现象，并同步提供了转录文本与一致的说话人身份标注。此外，研究进一步生成超过3.1K小时的仿冒语音（spoof speech），基于四种开源与商用合成系统，每段仿冒语音均以真实说话人为参考，并配对相应的转录与说话人匹配的真实语音样本，从而实现控制变量下的评估，有效降低词汇与身份混淆带来的干扰。实验表明，五种预训练多语言检测器在零样本评估下表现出显著脆弱性，尤其在说话人相似度升高时，DFA-1B模型的等错误率（EER）从16.3%恶化至33.6%，且方言分层结果揭示了模型间存在显著差异。通过将自然语言多样性与可控仿冒生成相结合，VietPrism为越南语语音建模与可信音频深度伪造检测提供了具有挑战性的基准平台。
链接: https://arxiv.org/abs/2609.30005
作者: Minh Hoang,Thai Le
机构: 未知
类目: Computation and Language (cs.CL); Sound (cs.SD)
备注: Preprint for ICASSP 2027 submission
Abstract:Vietnamese speech research is constrained by resources that isolate automatic speech recognition from speaker, dialect, code-switching, and deepfake analysis. We introduce VietPrism, an open, multi-domain corpus that brings these dimensions together at scale: 993.4 hours and 403,941 bona fide utterances from 1,262 verified speakers across 8,388 real-world videos. To our knowledge, it is the first large-scale Vietnamese corpus to jointly provide transcripts, consistent speaker identities, five dialect groups, and naturally occurring Vietnamese–English code-switching, which constitutes nearly half of the corpus by duration. We further create over 3.1K hours of spoof speech with four open-source and commercial synthesis systems. Every spoof is conditioned on a verified speaker reference and paired with a transcript- and speaker-matched bona fide utterance, enabling unique controlled evaluation with reduced lexical and identity confounds. Zero-shot evaluation of five pretrained multilingual detectors reveals striking brittleness: EER greatly varies across detector–generator pairings, while recent multilingual detector DFA-1B degrades from 16.3% to 33.6% as speaker similarity increases. Dialect-stratified results expose further model-dependent disparities. By unifying natural linguistic diversity with controlled spoof generation, VietPrism provides a challenging foundation for Vietnamese speech modeling and trustworthy audio-deepfake detection.
[NLP-24] Cultural Divergence Preservation: Diagnosing Flattening and Caricature in LLM -Simulated Survey Populations EMNLP2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在跨文化调查模拟中对文化差异保留能力评估不足的问题。现有基于距离的评估指标（如Jensen–Shannon散度，JSD）虽能衡量单个国家内部响应分布的保真度，但无法直接反映不同国家间文化差异的保留或扭曲情况。为此，论文提出一种无参考基准的诊断方法——文化差异保留性（Cultural Divergence Preservation, CDP），其核心在于通过一次性人工校准建立基线，从而识别出跨国家差异的减弱（即“文化扁平化”）或增强（即“文化刻板化”）。实验涵盖四种LLM骨干网络、三种基于角色提示的方法及两个调查领域（世界价值观调查WVS与大五人格测试），结果表明传统保真度指标与CDP之间存在系统性偏差：当跨国家差异被削弱或放大时，CDP呈现单调变化，而JSD变化微弱。真实生成样本审计显示，尽管DeepPersona-Inspired提示在传统指标上表现优异，却普遍存在最严重的文化扁平化现象。因此，CDP作为补充性评估工具，能够直接量化跨文化差异的衰减或放大，弥补了现有方法在文化敏感性评估上的局限。
链接: https://arxiv.org/abs/2609.29928
作者: Yeeun Chae,Yewon Choi,Seunghyun Lee,IL Im
机构: Yonsei University (延世大学); NAVER; Seoul National University Hospital (首尔国立大学医院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted to the EMNLP 2026 Workshop on Pluralistic AI NLP (PANDORA)
Abstract:Large language models (LLMs) are increasingly used as synthetic survey respondents to estimate population response distributions. In cross-cultural survey simulation, evaluations should assess not only distributional fidelity within countries but also whether differences across countries are preserved. However, existing distance-based metrics such as Jensen–Shannon divergence (JSD) do not directly capture such cross-country differences. To address this limitation, we introduce Cultural Divergence Preservation (CDP), a reference-light diagnostic based on a one-time human calibration. CDP identifies reduced cross-country divergence as cultural flattening and increased divergence as cultural caricature. To evaluate CDP, we conduct experiments across four LLM backbones, three persona-based prompting methods, and two survey domains, the World Values Survey (WVS) and the Big Five Personality Test. The results reveal a systematic discrepancy between conventional fidelity metrics and CDP. Controlled experiments show that CDP changes monotonically as cross-country divergence is attenuated or amplified, while the corresponding changes in JSD remain relatively small. In our audit of real LLM generations, DeepPersona-Inspired prompting is frequently favored by conventional fidelity metrics but exhibits the strongest flattening in every model–domain block. CDP thus complements fidelity metrics by directly quantifying the attenuation or amplification of cross-country divergence.
[NLP-25] MILO: Efficient Many-shot In-Context Learning with Block-wise Low-rank Compression
【速读】： 该论文旨在解决多示例上下文学习（many-shot in-context learning, ICL）中因大量示范样本导致的键值（Key-Value, KV）缓存内存占用线性增长所引发的推理效率瓶颈问题，尤其在在线服务与设备端部署场景下尤为突出。其核心解决方案是提出一种名为MILO的新型压缩框架，关键在于利用多示例上下文中固有的低秩冗余特性，采用分块（block-wise）低秩压缩策略，在块粒度上对KV缓存进行压缩，其中每个块包含多个示范样本；同时，针对不同块间上下文密度异质性的特点，MILO基于信息熵动态分配秩预算，从而在保障关键块信息保真度的同时，对冗余性高的块实施更激进的压缩。实验结果表明，该方法在Qwen2.5模型上实现了高达50%的KV缓存内存减少和1.8倍的吞吐量提升，且在分类与推理基准测试中性能损失可忽略，显著优于现有基线方法。
链接: https://arxiv.org/abs/2609.29913
作者: Youpeng Zhao,Tian Tan,Liqian Peng,Jun Wang,Alec Go
机构: University of Central Florida(中佛罗里达大学)
类目: Computation and Language (cs.CL)
备注: Technical Report
Abstract:Many-shot in-context learning (ICL) enables large language models (LLMs) to adapt to complex tasks by conditioning on thousands of demonstration examples, but this paradigm shifts the inference efficiency bottleneck to the key-value (KV) cache memory. Due to the linear scaling behavior of the KV cache, storing these intermediate tensors has become a paramount challenge for both online serving and on-device deployment. To address this issue, we propose a novel compression framework, termed MILO, that exploits the low-rank redundancy inherent in many-shot contexts. Specifically, MILO features a block-wise low-rank compression strategy that compresses the KV cache at the block granularity, where each block contains multiple many-shot examples. Furthermore, to handle the heterogeneous context density across different blocks, MILO dynamically allocates rank budgets based on the information entropy, preserving the fidelity of critical blocks while aggressively compressing redundant ones. Experimental results on Qwen2.5 models demonstrate that our method achieves up to 50% reduction in KV cache memory and 1.8x throughput improvement, with negligible performance degradation on classification and reasoning benchmarks, significantly outperforming prior baselines.
[NLP-26] Multi-Task Learning by using Contextualized Word Representations for Syntactic Parsing of a Morphologically Rich Language
【速读】： 该论文旨在解决乌尔都语（Urdu）这一形态丰富的语言在句法解析（syntactic parsing）方面面临的挑战，特别是在成分句法（constituency parsing）和依存句法（dependency parsing）任务上的性能瓶颈。其核心解决方案的关键在于提出一种新型序列标注框架，将两种句法解析任务统一为共享的表示形式，从而实现跨任务联合学习；同时，通过在大规模2.2亿词元的网络语料上训练上下文感知的词表示，并结合单任务与多任务学习范式，显著提升了模型对复杂句法结构的建模能力。此外，研究还通过语言特定的头词选取规则与短语到依存标签的映射规则，成功将原有的成分树库转换为依存树库，并辅以后处理规则优化自动转换的依存结构质量。实验结果表明，多任务学习策略使成分句法的F1值达到91.39（提升3.29点），依存句法的标注附着率（Labeled Attachment Score, LAS）达85.69（提升1.49点），验证了跨任务表示学习在提升泛化能力方面的有效性，推动了乌尔都语句法解析的最新进展。
链接: https://arxiv.org/abs/2609.29855
作者: Toqeer Ehsan,Miriam Butt,Sarmad Hussain,Hassan Alhuzali,Ali Al-Laith
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Published in PLOS ONE, 2025
Abstract:We address the challenge of syntactic parsing for Urdu, a morphologically rich language, and present state-of-the-art results for both constituency and dependency parsing. This paper offers four major contributions: 1) the conversion of the CLE-UTB phrase structure treebank into a dependency treebank by developing language-specific head-word and phrase-to-dependency label mapping rules; 2) a novel sequence labeling scheme that transforms the parsing task into a unified representation; 3) the training of contextualized word representations on a large 220 million tokens Urdu corpus collected from the web; and 4) development of parsing framework using two learning paradigms, single-task and multi-task learning. Several post-processing rules are applied to improve the quality of the automatically converted dependency structure treebank. The proposed sequence labeling scheme enables the use of a shared architecture that learns the syntactic structures from both grammatical structures simultaneously and hence improves generalization. Experiments show that the multi-task learning setup significantly enhances parsing performance, achieving an F1 score of 91.39 for constituency parsing (an improvement of 3.29 points) and a labeled attachment score of 85.69 for dependency parsing (an improvement of 1.49 points). These results demonstrate that learning cross-task representations provides measurable benefits and advances the state of syntactic parsing for Urdu.
[NLP-27] Encoded but Not Decoded: Layer-Localized Evidence for a Three-Level Gap in LLM Syntax AACL
【速读】： 该论文旨在解决语言模型在句法测试中失败的根本原因难以区分的问题：即模型是未能编码相关句法结构，还是虽已编码但未在输出阶段正确使用。传统行为评估无法区分这两种情形。其解决方案的关键在于提出一个三层次评估框架（行为部署、LM-head读出和探针可恢复性），在同一组测试样本与相同二元决策条件下进行对比分析。通过一个紧凑的跨语言（英语、中文、德语）控制依赖性基准测试，研究发现探针可恢复性普遍优于或等于LM-head读出，后者又优于或等于行为部署，这一趋势在七种模型及三种语言中均成立，且14个（模型，任务）组合下的可恢复性盈余始终非负。最大差距（0.653）出现在Qwen3-0.6B Instruct的问答任务中，且在更大规模的Qwen3-14B Instruct上仍持续存在。指令微调在百分比层面更显著地损害了行为部署能力，而非编码能力。研究排除了选项位置偏差、晚期层擦除、输出格式化伪影以及探针训练方差等干扰因素，结果表明模型解码过程倾向于依赖表层捷径（如最近名词启发式），而行为-探针差距正反映了这种偏好强度。激活修补实验进一步显示该差距具有层内局部性特征，指令微调后LM-head解码层相较探针解码层向后迁移约十层。综上，研究揭示行为评估低估了模型的内在编码能力，而探针评估则高估了其实际部署能力，凸显了多层级评估的重要性。
链接: https://arxiv.org/abs/2609.29848
作者: Zhenyan Lu,He Wang,Xiaohui Huang
机构: National University of Defense Technology (国防科技大学)
类目: Computation and Language (cs.CL)
备注: Accepted by AACL-IJCNLP 2026
Abstract:A language model can fail a syntactic test in two distinct ways: by not encoding the relevant structure, or by encoding it but failing to use it at the output. Behavioral evaluation alone cannot tell these apart. We propose a three-level evaluation framework (behavioral deployment, LM-head readout, and probe recoverability) measured on the same items under the same binary decision. Using a compact trilingual (English, Chinese, German) control-dependency benchmark, we find that probe recoverability exceeds or equals LM-head readout, which in turn exceeds or equals behavioral deployment, across seven models and all three languages in the aggregate. The recoverability surplus is never negative across all 14 (model, task) conditions. The disconnect concentrates in subject-control, where a nearest-noun heuristic gives the wrong answer. The single largest gap (0.653) appears on Qwen3-0.6B Instruct in question answering. The gap persists at Qwen3-14B Instruct. Instruction tuning degrades deployment more than encoding in percentage terms. We rule out option-position bias, late-layer erasure, output-formatting artifacts, and probe-training variance. The pattern is consistent with decoding that favors surface shortcuts, and the behavior-probe gap measures the strength of that preference. Activation patching shows the gap is layer-localized. Under instruction tuning, the LM-head-decoded layer shifts approximately ten layers later than the probe-decoded layer. These findings argue that behavioral evaluation understates what models encode, while probing alone overstates what they deploy.
[NLP-28] Your Transformer Can Hold Two Thoughts at Once: Evidence of Linear Superposition in LLM s
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在处理多源文本输入时，其输出行为是否具备可预测的线性特性这一核心问题。尽管LLMs本身具有高度非线性的结构，但研究发现，当来自不同文本流的输入以线性组合方式融合时，模型会输出各独立下一个词分布的叠加态，这一现象被作者称为“叠加线性假设”（Superposition Linearity Hypothesis）。其解决方案的关键在于：首先，揭示了叠加现象是Transformer架构的内在属性，而非训练过程中涌现的现象；其次，提出通过轻量级微调可显著恢复模型的线性特性，有效降低预测下一个词分布与各独立分布平均值之间的偏差；最后，设计了一种引导式解码方法，能够从单次前向传播的叠加输出中分离出两个语义连贯的生成路径，从而实现高效并行生成。该方案为理解模型内部表征机制及开发新型生成策略提供了理论基础与实践路径。
链接: https://arxiv.org/abs/2609.29845
作者: Pavel Tikhonov,Anton Korznikov,Matvey Mikhalchuk,Nikita Dragunov,Temurbek Rahmatullaev,Polina Druzhinina,Anton Razzhigaev,Ivan Oseledets,Elena Tutubalina
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:While Large Language Models (LLMs) rely on highly non-linear components, in this work we demonstrate that they exhibit fundamental linearity: when inputs from distinct text streams are linearly combined, the model outputs a superposition of the individual next-token distributions. We term this the \textitSuperposition Linearity Hypothesis. We provide evidence that superposition is an intrinsic property of the Transformer architecture rather than an emergent consequence of training; in fact, we observe that it tends to diminish as pretraining progresses. However, we demonstrate that linearity can be substantially restored through lightweight fine-tuning, significantly reducing the divergence between the predicted next-token distribution and the average of the individual next-token distributions. Finally, we introduce a guided decoding procedure that disentangles superposed outputs, enabling the simultaneous generation of two coherent continuations from a single forward pass.
[NLP-29] ChunkRank: Model-Aware Text Chunking and Abstention-Aware Answer Selection for LLM Pipelines KR
【速读】： 该论文旨在解决大语言模型在处理长文本时因上下文窗口限制而导致的上下文截断问题，核心挑战在于如何在不丢失关键信息的前提下，高效分割输入文本并准确聚合各片段的生成结果。其解决方案的关键在于提出ChunkRank——一个基于目标模型分词器（tokenizer）和上下文窗口自动推导分块边界（chunk boundary）的开源Python库，通过精确控制每个分块的令牌预算（token budget），避免传统字符级分块方法导致的上下文溢出或资源浪费。研究进一步表明，在多语言环境下，保持令牌级别的预算精确性对性能至关重要。在答案选择方面，实验发现无论采用抽取式还是生成式阅读器，在NaturalQuestions、TriviaQA和HotpotQA数据集上，仅取首个非空答案的表现已优于所有内容驱动的排序方法，根本原因在于模型对无答案片段的主动回避（abstention），而非答案位置偏好。因此，ChunkRank特别针对小窗口及超出窗口长度的场景设计，且通过与单次调用基线对比验证了其在单跳问答任务中的有效性。
链接: https://arxiv.org/abs/2609.29828
作者: Amit Nautiyal,Ayush Bhatt,Gaurav Nautiyal
机构: 未知
类目: Computation and Language (cs.CL)
备注: 16 pages. Code: this https URL
Abstract:We present ChunkRank, an open-source Python library that derives chunk boundaries from a target model’s tokenizer and context window, and selects an answer among candidates produced independently per chunk. It ships a validated registry of 90 models across 15 providers and six answer-selection methods, and needs only three core dependencies. For chunking, ChunkRank avoids context-window overflow automatically from the model name, whereas character-based splitters overflow or waste the budget, and a fidelity study across 11 languages shows why token-exact budgets matter beyond English. For answer selection we report a negative result: on NaturalQuestions, TriviaQA and HotpotQA, with extractive and generative readers, no content-based ranker reliably beats taking the first non-empty answer. The reason is reader abstention on chunks that lack the answer, not answer position. A long-context baseline shows that chunking matches single-call reading on single-hop questions, so ChunkRank targets small-window and beyond-window settings. Code, registry and evaluation harness are released.
[NLP-30] CORDIAL: Calibrating Ordinal LLM Outputs from Few Labels
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）在生成有序尺度评分时存在的输出偏差问题，即模型输出的分布常因饱和、压缩、夸大或系统性偏倚而成为对真实标签的噪声测量。其核心解决方案是提出CORDIAL方法，将模型输出视为对真实标签的有噪观测，并通过一个由五个可解释参数构成的校准通道进行修正。该通道规模小，使其后验分布可通过少量标注样本（5至100个标签）有效平均估计，且理论证明其校准结果保持一阶随机序（first-order stochastic order）。在亚马逊评论与CMU-MOSEI语料上使用四种LLM的实验表明，CORDIAL在80组设置中的76组中表现优于其他九种校准器，当仅使用20个标签时，其性能已可媲美主流基线模型在28–54个标签下的表现。此外，该后验分布支持跨任务学习先验并实现多模型融合，而无需限制的校准器（如狄利克雷校准）仅在训练集扩展至数百甚至数千样本时才超越CORDIAL。
链接: https://arxiv.org/abs/2609.29807
作者: Xiangwei Wang,Peng Wang,Saman Halgamuge
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:A large language model (LLM) can turn a text into a distribution over an ordered scale, but that distribution is a noisy measurement: saturated, compressed or exaggerated, and biased in a consistent direction. We propose CORDIAL, which treats the model’s output as a noisy reading of the true label and corrects it with a channel of five interpretable parameters. The channel is small enough for its posterior to be averaged from a handful of labels, and we prove that the resulting calibration preserves first-order stochastic order. On Amazon reviews and CMU-MOSEI transcripts with four LLMs, CORDIAL has the lowest log loss among nine calibrators in 76 of 80 settings with 5 to 100 labels; with 20 labels and the main 7B reader, it matches the strongest baseline using 28-54 labels. The same posterior lets us learn priors from other tasks and fuse several LLMs. Unrestricted calibrators such as Dirichlet calibration overtake it only as the calibration set grows into the hundreds or thousands.
[NLP-31] Learning to Ideate for Scientific Impact ICML2026
【速读】： 该论文旨在解决生成式人工智能在科学创意生成中缺乏长期科学影响力反馈机制的问题，即现有系统多依赖即时可评估的代理指标（如新颖性、清晰度和可行性），而未能有效利用延迟性但更具真实意义的科学采纳信号（如引用次数）来引导模型生成具有更高预期影响力的科研方向。其解决方案的关键在于构建一个大规模的计算机科学论文数据集，通过提取目标条件化的研究创意描述，并为每篇论文分配经年份归一化的引用影响等级作为序数标签；进而训练一个目标条件化的奖励模型，以预测引用影响力标签，并基于该奖励信号通过监督微调与强化学习联合优化创意生成模型。为避免评价过程中的循环偏差，采用保留集参考基准协议，将生成创意与同一研究目标下的历史创意进行对比，并以参考创意的引用影响力作为判断权重。实验结果表明，经强化学习调优的模型所生成的创意在估计影响力上显著优于基线模型及仅经监督微调的模型，验证了科学影响力作为可操作、结果导向的反馈信号在开放领域科学发现中对大语言模型对齐的有效性。
链接: https://arxiv.org/abs/2609.29802
作者: Shubham Kale,Aniketh Garikaparthi,Manasi Patwardhan
机构: TCS Research, India
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: RLxF Workshop ICML 2026
Abstract:Scientific ideation is increasingly mediated by large language models, but current ideation systems are usually trained and evaluated on immediately judgeable proxies such as novelty, clarity, and feasibility. This leaves open whether delayed signals of scientific uptake can be used as feedback for steering models toward research directions with higher expected \emphimpact. We study this question using citation-normalized impact as a noisy but scalable proxy for scholarly uptake. We construct a large-scale dataset from over 100K computer science papers by extracting goal-conditioned idea descriptions and assigning each paper an ordinal, year-normalized citation label. We then train a goal-conditioned reward model to predict citation-impact labels from research goal and idea pairs, and use this reward to align an idea generator through supervised fine-tuning followed by reinforcement learning. To reduce circularity, we evaluate generated ideas with a held-out, reference-grounded protocol that compares model outputs against historical ideas under the same research goal and weights judgments by the reference idea’s citation-impact label. Experiments show that our RL-tuned model consistently produces ideas with higher estimated impact than both the base model and supervised fine-tuning baselines. Our findings position scientific impact as a practical, outcome-grounded feedback signal for aligning LLMs in open-ended scientific discovery.
[NLP-32] Adaptive Fisher-Whitened Cross-Covariance for Low-Resource Speech Recognition
【速读】： 该论文旨在解决大模型在低资源语言语音识别（ASR）任务中适应困难的问题，尤其针对在预训练阶段代表性不足的语言。现有参数高效微调（PEFT）方法如LoRA依赖于通用的低秩参数化，未能充分利用下游任务信息来构建适配子空间。为此，本文提出基于费雪-白化交叉协方差分析（FCCA）的改进框架，并引入两种互补扩展：不对称耦合FCCA（AC-FCCA），通过结构化跨层共享增强参数利用；自适应秩FCCA（AR-FCCA），在固定参数预算下动态分配各投影矩阵的适配能力。实验表明，标准FCCA在多数情况下优于等可训练参数量的LoRA，而AR-FCCA在两种模型架构上均实现更稳定且统计显著的性能提升，同时保持相同参数量。研究证明，基于任务信息构建适配子空间能有效提升低资源语音建模效果，且自适应秩分配是提高参数效率的稳健策略。
链接: https://arxiv.org/abs/2609.29800
作者: Asmee Mishra,Mengjie Qian,Brechtje Post,Kate Knill
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Adapting multilingual speech foundation models to low-resource languages remains difficult, especially for languages that are poorly represented during pre-training. While parameter-efficient fine-tuning (PEFT) reduces the cost of adapting large models, conventional approaches such as LoRA rely on generic low-rank parameterizations and do not explicitly use downstream task information to define the adaptation subspace. To investigate whether task-informed PEFT can better support low-resource ASR, we apply Fisher-Whitened Cross-Covariance Analysis (FCCA) to Whisper and Qwen3-ASR, and introduce two complementary extensions: Asymmetric-Coupled FCCA (AC-FCCA), which exploits structured cross-layer sharing, and Adaptive-Rank FCCA (AR-FCCA), which reallocates adaptation capacity across projection matrices under a fixed parameter budget. Under controlled multilingual experiments, we evaluate these approaches on languages that are poorly represented or unsupported during pre-training alongside well-represented languages. Standard FCCA is competitive with, and usually outperforms, trainable-parameter-budget-matched LoRA. AR-FCCA provides the most consistent improvement over standard FCCA across both model architectures, with statistically significant gains in several evaluation settings, while retaining the same number of trainable parameters. These results show that task-informed subspace construction can be effective for low-resource speech adaptation, and that adaptive rank allocation provides a robust way to improve parameter efficiency without increasing model capacity.
[NLP-33] Benchmarking and Domain Adaptation of Automatic Speech Recognition (ASR) for Adolescent Health Communication in Ghanaian Languages
【速读】： 该论文旨在解决在加纳三种语言（蒂维语、达格班尼语和埃韦语）中实现青少年健康沟通的自动语音识别（ASR）系统性能不足的问题，尤其聚焦于在资源匮乏的语言环境下如何构建高效、准确且可落地的语音识别解决方案。其核心挑战在于这些语言缺乏大规模、高质量的标注语音数据，导致现有通用模型在特定领域（青少年性与生殖健康，ASRH）表现不佳。解决方案的关键在于通过有监督的领域自适应，利用大规模跨域文本数据（如加纳圣经语料库，约9万条样本）对轻量级模型（Qwen3-ASR-0.6B）进行微调，并严格在真实采集的领域内语音数据上评估。实验表明，该方法显著降低了词错误率（WER）和字符错误率（CER），尤其在埃韦语上效果最为显著（WER从109.3%降至64.8%）。此外，通过在真实应用KasaHealth中部署并结合Senti-Check技术评估，验证了系统的实用性与用户接受度。研究最终揭示：对于这些低资源语言，制约实际应用的核心瓶颈并非模型能力或计算资源，而是经过验证的领域内标注数据的缺失。
链接: https://arxiv.org/abs/2609.29798
作者: Stephen E. Moore,Akwasi Asare,Mich-Seth Owusu,Paul Azunre,Joel Budu,Lawrence A. Adu-Gyamfi
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 34pages, 8figures,
Abstract:This paper presents an end-to-end study of automatic speech recognition (ASR) for adolescent health communication in three Ghanaian languages (Twi, Dagbani, and Ewe). The work proceeds in three connected stages; First, we benchmark five ASR systems (three language-specific Wav2Vec2 models and two multimodal LLMs, Gemma 3n and Gemma 4) on a general-domain Bible corpus and a Youth Adolescent Sexual and Reproductive Health (ASRH) Domain ASR dataset, using Character and Word Error Rate (CER, WER). Second, guided by the benchmark, we perform supervised domain adaptation: although Gemma 4 was the strongest zero-shot candidate, fine-tuning it proved computationally infeasible, so we pivoted to the compact Qwen3-ASR-0.6B, fine-tuned on a large Ghana Bible corpus (~90k samples) and evaluated strictly on held-out human-collected in-domain audio. Fine-tuning reduced WER on every language, most dramatically for Ewe (WER from 109.3% to 64.8%, a drop of 44.5 pp; CER from 65.1% to 24.9%). Third, we validate the work through KasaHealth, a live voice-first ASRH application deployed in all three languages, complemented by Senti-Check, a technical evaluation harness. KasaHealth was tested by 50 community respondents and achieved a 100% chat-approval rate, a 72% Good-or-Excellent translation rating, and a 92% would-recommend rate, while surfacing the domain gaps that most constrain real-world use. Across all three stages the evidence converges: for these languages the binding constraint is validated in-domain data, not model capability or computation.
[NLP-34] meBraid: Unifying Time Series and Language for Understanding and Forecasting
【速读】： 该论文旨在解决多模态时间序列与自然语言联合建模中的关键挑战，即如何有效融合预训练语言模型（Language Model, LM）与预训练时间序列基础模型（Time-Series Foundation Model, TSFM）的能力，实现跨模态的统一理解与生成。其核心问题在于：如何在保持各自优势的前提下，构建一个共享表示空间，使语言模型具备连续信号感知与零样本预测能力，同时使时间序列模型具备指令遵循、推理和语义理解能力。解决方案的关键在于设计一种基于交错全局残差注意力（interleaved global residual attention）的架构，通过精心选择双模态表示空间对齐的位置、将语言嵌入显式地锚定于时间结构、平衡理解与生成任务之间的权重，并采用稳定联合训练策略。此外，该方法利用220万条经筛选的时间序列-文本配对数据及490万条指令微调样本进行监督学习，形成统一的提示范式，从而在时间序列感知、理解、推理以及上下文辅助与单模态预测等多类基准测试中，展现出与更大规模通用模型及专用模型相媲美的性能。
链接: https://arxiv.org/abs/2609.29792
作者: Xinyue Wang,Jiacheng Pang,Kun Zhou,Kexin Zhang,Defu Cao,Fan Feng,Faisal,Songyao Jin,Yan Liu,Biwei Huang
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注: 57 pages
Abstract:We present TimeBraid, a series of unified time-series and language models that align pretrained language models and pretrained time-series foundation models through interleaved global residual attention layers. Each model inherits knowledge, instruction following, and reasoning from one side, continuous-signal perception and zero-shot forecasting from the other, and fuses the two in a shared representation space where both modalities are understood and generated. We study the design choices that make such unified modeling work: where to align the two representation spaces, how to ground language in temporal structure, how to balance understanding with generation, and how to keep joint optimization stable. The resulting recipe combines a unified prompting scheme for diverse time-series and text tasks, stabilized joint training, and supervision from 2.2M curated series–text pairs and 4.9M instruction-tuning samples. Across benchmarks spanning time-series perception, understanding, reasoning, and both context-aided and unimodal forecasting, TimeBraid remains competitive with far larger general-purpose models and task-specific counterparts.
[NLP-35] JEV vs. LLM s as Rubric Judges: Cheaper Faster and Wrong in the Same Places
【速读】： 该论文旨在解决大语言模型（LLM）作为评分判别器在评估任务中成本高、耗时长的问题，探索一种轻量级的类型化分类器Jev是否可替代传统LLM评分判别器。其核心解决方案在于：使用Jev这一无需生成文本、仅输出预设答案概率的高效分类器，以显著降低计算开销与推理时间。实验表明，在9个来自7个基准测试的评测面板上，Jev与三个低延迟级LLM判别器相比，在27组配对比较中仅有8组存在显著差异，且在二元判断标准上表现更优，而在等级化标准上略逊一筹；总体而言，所有判别器均比人工标注者更倾向于给出较低评分，可能源于判别器未遵循人工标注中隐含的评分量表规范。值得注意的是，Jev能通过置信度对其自身错误进行排序，具备作为低成本前置阶段的理想特性，可用于构建“级联”系统，将不确定预测转交至LLM判别器处理。然而，由于LLM判别器几乎重复了Jev最自信的错误，导致即便采用记录的判别结果进行级联，其性能提升也极为有限——在交叉拟合阈值下最多提升1.5分，在理想阈值下最多提升2.0分，远低于预期收益，表明相关性错误严重削弱了级联结构的优势。
链接: https://arxiv.org/abs/2609.29769
作者: Delip Rao,Chris Callison-Burch
机构: University of Pennsylvania (宾夕法尼亚大学)
类目: Computation and Language (cs.CL)
备注: 45 pages, 9 figures, 27 tables, including appendices
Abstract:We ask whether Jev, a typed classifier that returns probabilities over permitted answers without generating text, can replace an LLM rubric judge. We compare it with three flash-tier LLM judges on nine panels drawn from seven benchmarks, giving every judge identical criterion texts. Jev’s accuracy differs significantly from an LLM judge’s in only 8 of 27 paired comparisons, ahead mostly on binary criteria and behind only on graded ones, and most of the other comparisons are inconclusive. Summed over the nine panels, the LLM judges, called once per criterion, cost 29 to 325 times as much as Jev and took 30 to 220 times as long. On graded criteria all four judges agree more with one another than with the labels and mostly assign lower levels than the raters. One of several observational accounts is that raters followed scale conventions our criterion texts omit. Jev’s confidence ranks its own errors on most panels, which should make a cheap classifier the ideal first stage of a cascade that defers its uncertain verdicts to an LLM judge. Correlated errors undo that advantage. The LLM judges repeat nearly all of Jev’s most confident errors, so a cascade replayed on the recorded verdicts lowers cost but gains at most 1.5 points over the best single judge with cross-fitted thresholds, and at most 2.0 even with oracle thresholds.
[NLP-36] Lab at StanceEval-2026: A Cloze-Style Prompting Approach for Arabic-Language Stance Detection (CLASP-Ar)
【速读】： 该论文旨在解决阿拉伯语立场检测（Arabic-language stance detection）任务中现有系统依赖多任务学习与集成方法所带来的复杂性问题，此类方法虽能实现顶尖性能，但其可应用性与迁移能力受限。解决方案的关键在于提出一种名为 \texttt{CLASP-Ar} 的新框架，将立场检测任务重构为填空式掩码语言建模（cloze-style masked language modeling），通过将目标实体、预测情感及文本内容整合为单一提示（prompt），并限制 \texttt{[MASK]} 位置的预测仅限于由词化器（verbalizer）约束的标签词汇表，从而显著降低模型复杂度，提升系统的简洁性与泛化能力。
链接: https://arxiv.org/abs/2609.29733
作者: Bhuvanesh Verma,Ali Abusaleh,Alexander Mehler
机构: Text Technology Lab (TTLab); Goethe University Frankfurt
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted at ArabicNLP 2026 StanceEval-2026 shared task
Abstract:Arabic-language stance detection remains challenging, and previous shared-task systems have largely relied on multitask learning and ensembles. While these systems achieve state-of-the-art performance, their applicability and transferability are limited by the additional complexity introduced by multitask this http URL reduce this complexity, we introduce \textttCLASP-Ar , which reformulates the task as cloze-style masked language modeling. In this approach, the target, predicted sentiment, and text are combined into a single prompt whose \texttt[MASK] prediction is restricted to a verbalizer-constrained label vocabulary.
[NLP-37] PPTBench: Can Coding Agents Reconstruct the Visual World through Structured Editable Slides
【速读】： 该论文旨在解决生成式AI在视觉编码（Visual Coding）任务中难以有效桥接视觉结构理解与可编辑程序表达之间的鸿沟问题，尤其聚焦于如何从科学流程图等复杂视觉内容中重建出结构化、可编辑的幻灯片（PPTX）。其核心挑战在于：既要准确还原原始图像的语义与布局，又要确保生成结果以原生可编辑对象（native, editable objects）形式呈现，支持后续修改。为克服现有基准测试在评估主观性、输出不可编辑或仅关注局部编辑等局限，作者提出PPTBench——一个基于真实arXiv论文中的科学流程图构建的500个任务数据集，要求代理将每张图重构为包含可编辑元素的单页PPTX文件。关键解决方案包括四阶段智能评判系统（Agentic Judge），分别评估成果有效性、语义正确性、渲染质量及细粒度视觉质量；实验表明，尽管当前最优模型（Kimi K3）仅达67.80分（满分100），且多数配置得分低于20，反映出代理虽能生成有效文件，但在文本细节和整体视觉-语义一致性方面仍存在显著不足。研究进一步发现，更多推理有助于通过高难度验证关卡，而更强的验证机制则更稳定地提升整体输出质量，凸显了未来提升视觉编码能力需兼顾深度推理与严格验证。
链接: https://arxiv.org/abs/2609.29718
作者: Xiaoqiu Wang,Yizhe Chi,Wenyi Li,Deyao Hong,Zhihan Shan,Mingju Gao,Kaisen Yang,Youjie Zheng,Calvin Xiao,Qinhuai Na
机构: Navers Lab, Einsia.AI(艾尼西亚人工智能); Tsinghua University(清华大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Coding agents are beginning to act in the visual world. They now build webpages, GUIs, games, 3D scenes, diagrams, and documents. Success in such visual coding requires bridging two spaces: inferring visual structure and expressing it programmatically. Slides are a core medium of knowledge work, widely used to communicate ideas and collaborate in a form that people can directly inspect and edit. Therefore, they provide an ideal testbed for visual coding, as they require agents to recover visual structure and realize it as editable objects. However, existing benchmarks either rely on subjective open-ended evaluation, produce non-editable code outputs, or focus only on local editing rather than end-to-end visual reconstruction. We introduce PPTBench, which benchmarks visual coding through editable slide reconstruction. It contains 500 tasks, each based on a scientific flow diagram from a real arXiv paper and requiring agents to reconstruct it as a single PPTX page composed of native, editable objects. A four-stage Agentic Judge evaluates artifact validity, semantic correctness, rendering quality, and fine-grained visual quality. Across 31 configurations spanning model families, effort levels, and harnesses, the best configuration, Kimi K3, reaches only 67.80, while the median scores 19.47. We find that agents can reliably produce valid PPTX files but still struggle with semantic and visual correctness, especially text details. More reasoning mainly helps agents pass hard gates, while stronger verification is more consistently associated with higher quality. PPTBench advances the vision of coding agents that can understand and reconstruct the visual world through structured, editable code.
[NLP-38] hree Ways Classical Test Theory Misleads for LLM Judges
【速读】： 该论文旨在解决在使用大语言模型（LLM）作为评分者对一组响应进行评分时，当前广泛采用的信度统计量在解释上存在的根本性误解问题。其核心问题在于：现有评估体系借用经典测验理论（Classical Test Theory, CTT）中的信度指标（如内部一致性系数KR-20、依赖性指数Φ(λ)及Livingston-Lewis准确性）来衡量评分者的可靠性，但这些指标在评分者情境下的实际含义与测验设计所假设的条件不一致，导致信度值无法真实反映评分者本身的性能。解决方案的关键在于揭示并纠正三类常见误用：第一，基于评分量表维度计算的内部一致性系数（KR-20）并不包含评分者分面（scorer facet），其数值受题目库设计和评分者误差的共同影响，无法独立归因于评分者；第二，依赖性指数Φ(λ)为方差成分之比，而常被误认为分类概率，二者在实证数据中差异达0.25–0.43，在模拟数据中亦有0.17–0.30的偏差；第三，Livingston-Lewis准确性以被评者自身真分数为基准，若以外部金标准对比，则将评分者不可靠性与标准效度不足混同。作者指出，尽管已有研究未发现此类错误实例，但这些误用仍可能在部署决策与披露文件中传播。因此，论文提出四项报告规范，确保信度指标的归属清晰可追溯，从而实现对评分者表现的真实评估。
链接: https://arxiv.org/abs/2609.29709
作者: Louis Yiven Zhu
机构: University of Oxford(牛津大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Methodology (stat.ME)
备注: 5 pages plus references and appendix (12 pages total), 4 figures. Code and data: this https URL
Abstract:An LLM judge scores a bank of responses against a rubric, and the reliability comes back at 0.52 . What has been measured? Judge evaluation has begun borrowing reliability statistics from classical test theory, usually without stating the measurement design each statistic assumes, and we show that three widely portable ones mean something different for a judge than for a test because the judge setting rearranges the roles those designs rest on. First, an internal-consistency coefficient computed over rubric elements contains no scorer facet. Holding one judge’s measured error rate fixed at 4.72% , KR-20 still ranges from 0.01 to 0.68 as the item bank is redesigned around it, and varying judge error moves the coefficient by a comparable amount, so item design and judge error are not separately identified and no single value can be read as a property of the judge. Second, the dependability index \Phi(\lambda) is a ratio of variance components, and the classification probability with which it is sometimes identified differs from it by 0.25 - 0.43 on our bank and by 0.17 - 0.30 on simulated data where the underlying model holds exactly. Third, Livingston-Lewis accuracy is indexed to an examinee’s own true score on the same instrument, so scoring it against external gold conflates judge unreliability with criterion invalidity. Reviewing the three closest judge-evaluation papers, we found no published instance of these errors, which makes the caution prospective. A coefficient that cannot be attributed to the judge nonetheless travels downstream into deployment decisions and disclosure documents. We therefore close with four reporting lines that keep the attribution attached to the number.
[NLP-39] DP-IPI: A Hybrid Differential Privacy Text Rewriting Mechanism for Indirect Personal Identifiers in Clinical Texts EMNLP2026
【速读】： 该论文旨在解决现代匿名化与去标识化技术在文本数据中仍存在显著再识别风险的问题，尤其针对文本中残留的间接个人标识符（Indirect Personal Identifiers, IPIs）所带来的隐私泄露隐患。现有方法通常采用差分隐私（Differential Privacy, DP）对文本中的所有词元进行无差别扰动，虽能提升隐私保护水平，但会严重损害文本的语义连贯性与可用性，尤其在临床等高要求领域难以应用。为此，本文提出一种保用效的差分隐私文本重写方法，其核心在于仅对包含IPIs的文本片段进行隐私化处理，而非全文本扰动。该方案通过选择性地对敏感区域施加噪声，显著降低了再识别风险，同时有效保留了文本的整体语义质量与可读性，实现了更高的隐私-效用权衡。研究结果表明，这种混合式文本隐私化策略能够以更高效、更实用的方式充分发挥差分隐私的潜力。
链接: https://arxiv.org/abs/2609.29684
作者: Ibrahim Baroud,Stephen Meisenbacher,Sebastian Möller,Florian Matthes,Roland Roller
机构: Technical University of Berlin (柏林工业大学); German Research Center for Artificial Intelligence (DFKI) (德国人工智能研究中心); Technical University of Munich (慕尼黑工业大学); Munich Center for Machine Learning (慕尼黑机器学习中心)
类目: Computation and Language (cs.CL)
备注: 15 pages, 5 figures, 5 tables, accepted to EMNLP 2026 (Findings)
Abstract:Despite the strengths of modern anonymization and de-identification techniques, the risk of re-identification remains significant due to the indirect identifiers remaining in texts. To address this problem, recent works have applied text rewriting under Differential Privacy (DP) to prevent data linkage by perturbing texts via noise addition. Such methods privatize all tokens in a text indiscriminately, diminishing text quality and usability in critical domains such as in clinical settings. Focusing on indirect personal identifiers (IPIs), we introduce a utility-preserving DP text rewriting method that only privatizes spans containing IPIs. We show that our method effectively reduces re-identification risks in clinical texts while being producing more coherent and usable output texts, leading to higher privacy-utility trade-offs. In this, we demonstrate the effectiveness of hybrid text privatization, which leverages the promise of DP in an efficient, usable manner.
[NLP-40] Named Entity Recognition using Sliding Window Approach
【速读】： 该论文旨在解决基于Transformer的命名实体识别（NER）模型在处理长文档时因输入长度限制导致的两大问题：一是文本截断造成信息丢失，二是非重叠分块策略在段落边界处破坏实体完整性，引发实体碎片化错误。其解决方案的关键在于提出一种仅在推理阶段运行的流水线框架，通过引入重叠滑动窗口（overlapping sliding windows）对冻结的预训练模型MahaNER-BERT进行文档级预测，无需重新训练或修改模型架构。该方法将多个重叠窗口的预测结果合并为单一连贯的标注输出，有效缓解了边界分割带来的误差。实验在六个基于MahaNER测试集构建的文档级语料上验证，采用“正常重复”与“随机重复”两种策略生成不同长度输入，在多种窗口配置下均保持高达0.8902的宏平均F1分数，且性能波动不超过1个百分点，显著优于传统非窗口化方法，展现出更强的稳定性与鲁棒性。
链接: https://arxiv.org/abs/2609.29682
作者: Hariom Ingle,Ronit Ghode,Ishwari Gondkar,Jidnyasa Harad,Ravindra Murumkar,Raviraj Joshi
机构: PICT(印度浦那理工学院); L3Cube Labs(印度浦那); Indian Institute of Technology Madras(印度马德拉斯理工大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Named Entity Recognition (NER) is a core NLP task, but transformer-based sentence-level models struggle with long documents because of fixed input-length limits: truncation drops content, and non-overlapping chunking fragments entities at segment boundaries. We introduce an inference-only pipeline that extends a frozen NER model, MahaNER-BERT, fine-tuned on the MahaNER corpus, to document-level prediction via overlapping sliding windows that are merged into a single annotation, without any retraining or architectural change. We evaluate the pipeline on six document-level corpora built from the MahaNER test set using two strategies: Normal Repeat, which duplicates sentence sequences to extend length while preserving contextual continuity, and Random Repeat, which concatenates distinct sequences to produce longer, heterogeneous inputs, each instantiated at three length levels, across several sliding-window configurations. The model retains a macro F1-score of up to 0.8902, with variation staying below one percentage point regardless of document length or construction strategy. Compared with the conventional non-windowed approach, the sliding-window pipeline avoids the boundary-fragmentation errors introduced by non-overlapping segmentation, yielding consistently higher and more stable document-level F1-scores. Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG) Cite as: arXiv:2609.29682 [cs.CL] (or arXiv:2609.29682v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.29682 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-41] Confident but Wrong: A Constrained Decoding Diagnostic for Low-Resource Automatic Post-Editing
【速读】： 该论文旨在解决低资源语言（Low-Resource Languages, LRLs）自动后编辑（Automatic Post-Editing, APE）模型性能不佳却难以诊断其根本原因的问题。现有方法仅依赖翻译编辑率（Translation Edit Rate, TER）等指标，无法区分是因训练数据不足导致模型无法学习，还是因训练数据本身存在不一致性使模型难以有效优化。为此，论文提出一种黑箱式的推理阶段诊断方法，通过在推理时调节编辑距离惩罚系数 λ，动态控制模型从自由编辑向直接复制机器翻译（MT）过渡，并观测两个关键信号：（1）TER随λ变化的曲线形状——呈U形表明模型编辑能降低错误，单调递减则说明编辑无效；（2）不同置信度约束变体的排序表现，用以检验模型置信度是否与编辑质量相关。实验结果揭示了两种典型失败模式：二值崩溃（Binary Collapse），即模型要么完全复制MT，要么产生无关编辑；以及置信度误校准（Confident Miscalibration），即模型置信度无法区分有效编辑与冗余编辑。这些失败模式在英-僧伽罗语、英-马拉地语和英-泰米尔语任务中均一致出现，且与后编辑分布密切相关，而非由MT质量或语言家族决定。该诊断方法不仅可定位问题根源，其曲线形态还为实践者提供了明确的下一步行动建议：在有利情况下，采用静态约束即可实现无需重训练的推理阶段精度提升。研究同时发布了首个英-僧伽罗语（约66k）及一个新的英-泰米尔语（约39k）APE数据集与全部代码，推动该领域研究发展。
链接: https://arxiv.org/abs/2609.29680
作者: Isuru Wijesiri,Nisansa de Silva,Kavindu Warnakulasuriya,Aloka Fernando,Surangika Ranathunga
机构: WSO2(WSO2); University of Moratuwa(莫鲁塔瓦大学); National University of Singapore(新加坡国立大学); Massey University(梅西大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Automatic Post-Editing (APE) for low-resource languages (LRLs) often fails to improve Machine Translation (MT), and the score alone cannot say why: whether more training would help, or whether the training data is too inconsistent to learn from. We introduce a black-box, inference-time diagnostic that tells these two cases apart without retraining or annotation. It varies an edit-distance penalty \lambda that drives the model from free editing towards copying the MT, and reads two signals: (1) the shape of the Translation Edit Rate (TER)-vs- \lambda curve, U-shaped if edits from the model reduce error and monotonically decreasing if none does; and (2) the ordering of constraint variants that trust model confidence to increasing degrees, which shows whether confidence tracks edit quality. Across decoder-only and encoder-decoder models on English-Sinhala, the diagnostic exposes two failure modes consistent with a heterogeneous post-edit signal as the underlying cause: Binary Collapse, where the model copies the MT or makes off-target edits, and Confident Miscalibration, where the confidence signals we test do not separate useful edits from unnecessary ones. The pattern holds on English-Marathi and English-Tamil, with the failure modes tracking the post-edit distribution rather than MT quality or language family. Beyond diagnosis, the curve shape prescribes a concrete next step for practitioners; in the favorable case, a static constraint yields a free inference-time accuracy gain. We release the first English-Sinhala (~66k) and a new English-Tamil (~39k) APE datasets with all code.
[NLP-42] How To Do Things With Prompts
【速读】： 该论文旨在解决用户在与大型语言模型（Large Language Models, LLMs）交互时所使用的指令性言语行为（directive speech acts）的语用特征演变问题，尤其关注这些特征如何随用户对系统的熟悉程度而变化。研究发现，用户对LLM的指令表达正从直接、明确、完整的请求逐渐转向间接、隐含且片段化的形式，同时礼貌标记的使用显著减少。其解决方案的关键在于通过语用学中的言语行为理论与礼貌理论，结合对2000条公开共享的ChatGPT对话提示（1000条来自2023年，1000条来自2025年）的语料库分析，揭示出用户已重构了与系统互动的语用模式：他们不再依赖显式指令，而是信任模型具备从简略输入中推断意图的能力，将系统视为高效的隐含意义解析者。这一转变表明，问题的核心不在于模型是否“真正”理解语言，而在于我们正在发展一种新的、以隐含推理为核心的新型人机语言交流形式。
链接: https://arxiv.org/abs/2609.29657
作者: Kristina Šekrst,Virna Karlić
机构: 未知
类目: Computation and Language (cs.CL)
备注: Invited for Cambridge Forum on AI
Abstract:When users address large language models, they produce directive speech acts whose pragmatic features differ from those of both everyday conversation and traditional human-computer interaction, and these features change as users gain familiarity with the systems they address. This paper applies speech act and politeness theory to a corpus-pragmatic analysis of 2,000 English-language prompts drawn from publicly shared ChatGPT conversations, 1,000 from 2023 and 1,000 from 2025, using the ShareChat dataset. Each prompt is annotated for illocutionary force, directness, propositional content, and the presence of politeness markers, and the distribution of these features is compared across the two sampling years. The results show a consistent movement toward indirect, implicit, and fragmentary realizations of directive force, accompanied by a decline in politeness marking. The largest single change, a shift of 14.9 percentage points, occurs in propositional content, where explicit specification of the requested action gives way to implicit reliance on the system’s inferential capacity, suggesting that users have updated their model of what the system can recover from reduced input, treating it as a competent implicature resolver. Rather than asking whether LLMs “really” understand language, we should ask: what kind of language have we created in learning to speak to them?
[NLP-43] Operator Packages Proposer Strength and Construction-Family Plateaus in Office-Scale Verified Search
【速读】： 该论文旨在解决生成式搜索（generative search）中因模型提案能力受限而导致的探索效率瓶颈问题，特别是在数学构造类任务中难以持续突破已有记录的问题。其核心挑战在于如何在有限计算资源下提升候选解的多样性与质量，避免陷入局部最优或重复搜索已知解。解决方案的关键在于构建一个轻量级、可复现的FunSearch风格搜索循环，并引入三类关键操作算子：（1）以“结构化笔记”（schematic notebook）形式动态记录和传递知识，替代传统静态精英个体；（2）引入“命名障碍”（named obstacle）机制，主动规避已发现的无效构造模式；（3）通过行为排斥（behavioural repulsion）机制增强已生成构造的哈希多样性，防止重复采样。实验采用2³因子设计，在九个公开构造问题上验证了各算子组合的效果，结果表明：整体组合显著缩小了初始种子与当前最优记录之间的差距（平均提升+0.196，名义合并p=0.023），且效应呈加性分解，其中“记忆+排斥”组合唯一未出现失败案例（18次运行中0次崩溃），性能接近完整组合。此外，研究发现仅依赖搜索循环本身即可实现远超本地模型的表现，且在旗舰问题上达到约92%的差距收敛后出现停滞，而通过“家族提示”测试揭示了该停滞源于对参考家族的不当采纳——当提示以自然语言形式给出时，模型会盲目跟随并失去优化能力；而以代码形式呈现则可被优化，但即便如此，其最佳有限网格实现仍低于无引导条件下的自主探索平台。这表明搜索系统不仅有效优化了输入思想，还实现了超越原始提示的创造性改进。研究释放了完整的实验框架、所有候选解及预注册协议，支持可复现性分析。
链接: https://arxiv.org/abs/2609.29636
作者: Roberto I. Ono Filho
机构: 独立研究员(Independent researcher)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Software Engineering (cs.SE)
备注: Code and run data: this https URL
Abstract:Verified search, in which a language model proposes programs, a hard evaluator scores them, and selection keeps the best, has recently moved mathematical records; controlled ablations of the proposer-side components remain rare. We instrument a minimal FunSearch-style loop at office scale (a 30B local model on a laptop, 120-600 verified samples per run) with three operator packages: a schematic notebook the model writes and carries instead of verbatim elites, a named obstacle, and behavioural repulsion from constructions already found. On nine construction problems from a public repository, the complete 2^3 factorial with two replicates favours the primary contrast in a nominal two-stage analysis: the composition closes more of the seed-to-record gap (+0.196; nominal pooled p=0.023, stage-combination p~0.08; median per-problem effect +0.045). Repulsion raises construction-hash diversity everywhere (p=0.0039; partly a manipulation check). The factorial finds no positive memory-by-repulsion interaction (bounded to about +/-0.04); the gain decomposes additively, and memory+repulsion is the only arm that never collapses (0 of 18 runs), within 0.025 of the full composition. A frontier proposer under the identical loop reaches in tens of samples what the local model does not in hundreds; in single scoping runs its gains arrive without the operators. The search stalls after closing ~92% of the gap on the flagship problem, and the registered family-hint test gives the stall its first reading: named in words, the reference family is adopted and loses; handed as code, it is optimized, but our best finite-grid implementation remains below the plateau reached unaided. The loop transported and optimized the idea it was handed; no unaided run produced it. We release the harness, every candidate, and the dated pre-registrations.
[NLP-44] Lab at AlexandriaX-2026: A Fine-Tuned Surface Tagger for Arabic Machine-Translation Error-Span Detection and Classification
【速读】： 该论文旨在解决阿拉伯语机器翻译（Arabic MT）中错误片段检测与分类任务中的关键挑战，即如何在保持字符级偏移精确对齐的前提下，实现对翻译错误片段的精准定位与细粒度分类。其核心解决方案在于将任务建模为基于表面形式（surface forms）的词元级别分类，并通过保留字符偏移量确保与评估指标的一致性；针对标签严重不平衡问题，采用带类别权重的焦点损失（focal loss）并引入方言特异性解码阈值以提升模型鲁棒性。实验表明，在六种阿拉伯语预训练编码器中，MARBERTv2表现最优，在开发集和测试集上的准确率分别达到40.8和40.91，位列所有参赛团队第三。尽管系统在错误片段定位方面表现良好，但对罕见错误类型的分类仍存在明显局限，凸显出针对尾部类别进行数据增强的重要性。
链接: https://arxiv.org/abs/2609.29633
作者: Ali Abusaleh,Bhuvanesh Verma,Alexander Mehler
机构: Text Technology Lab (TTLab); Goethe University Frankfurt
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at ArabicNLP 2026, shared task AlexandriaX-2026
Abstract:We present TTLab’s submission to the AlexandriaX-2026 Subtask~3 on Arabic MT error span detection and classification. Our system frames the task as token-level classification over surface forms, preserving character offsets to ensure exact alignment with the evaluation metric. To handle severe label imbalance, we employ a focal loss with class weighting and dialect-specific decoding thresholds. Among six Arabic pre-trained encoders, MARBERTv2 achieves the best overall performance of 40.8 and 40.91 on the development and test set, respectively, ranking \nth3 out of all participating teams. While our system localizes error spans effectively, classification of rare error types remains challenging, highlighting the need for data augmentation for tail categories. The code is available at \hrefthis https URL\faGithub~ TTLab at AlexandriaX-2026
[NLP-45] Coder-27B: Recursive AI-Led Development of Frontier Industrial Coding Model
【速读】： 该论文旨在解决如何在最小化人类干预的前提下，实现生成式 AI (Generative AI) 自主构建与优化前沿水平模型的挑战。其核心问题是：在工业级编码任务中，多大程度的人类参与可支撑一个智能体（agent）独立演化出具备发布能力、达到领域前沿性能的大型模型。解决方案的关键在于构建一种高密度、低频的人机交互接口——由专家将目标设定、阶段性架构设计、权限边界及操作规程等抽象为可复用的研究技能（research skills），而智能体则基于这些先验知识进行实验选择、结果诊断与训练策略迭代优化。在此框架下，智能体通过协调监督微调（SFT）、在线自蒸馏与基于可验证奖励的强化学习，在硬件描述语言（RTL）设计与GPU内核优化任务中成功训练出iCoder（27B参数模型）。实证结果表明，iCoder在多个基准测试中超越GPT-5.5和Claude-Opus-4.8，展现出与顶尖模型相当甚至更优的性能，并在迭代优化过程中显著减少推理开销。这一成果揭示了一条通往递归自我改进（recursive self-improvement）的工程路径：人类将模型构建的核心原理进行提炼，智能体则通过数据驱动的实验实现这些原则，使每一代AI逐步成为下一代更高效、更自主的架构设计者。
链接: https://arxiv.org/abs/2609.29626
作者: Cheng Yang,Jiayang Lyu,Shangyuan Liu,Guibin Zhang,Jiong Lin,Xinlei Yu,Junchi Yan,Shuicheng Yan,Weinan E,Linfeng Zhang,Linfeng Zhang,Qibing Ren
机构: Shanghai Jiao Tong University; DP Technology; National University of Singapore; Endless Frontier
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Software Engineering (cs.SE)
备注:
Abstract:Recursive AI, the prospect of AI taking an increasingly complete role in building and improving AI, is a crown jewel of AI for AI. Although recursive self-development has become practical for small models, bounded tasks, and fixed time budgets, a more consequential realization of this ambition, i.e., developing a release-ready, frontier-competitive model, remains far more challenging. In this work, we ask how little human involvement is sufficient for an agent to develop a frontier model. We concentrate human input into a high-density, low-frequency interface: experts encode objectives, stage scaffolds, permission boundaries, and operating procedures as reusable research skills, while the agent instantiates these priors, selects experiments, diagnoses outcomes, and revises the training strategy. In the challenging domain of industrial coding, the agent evolves data and coordinates SFT, on-policy self-distillation, and reinforcement learning with verifiable rewards, ultimately producing iCoder, a 27B model for RTL design and GPU kernel optimization. Across seven benchmarks, iCoder leads RTLLM, outperforming GPT-5.5 and Claude-Opus-4.8; ranks second on CVDP and KernelBench L2, exceeding GPT-5.5 by 16 points; and ties Claude-Opus-4.8 for the best TritonBench result. Exploratory case studies further show iCoder’s competitive iterative RTL and GPU-kernel optimization with substantially fewer tokens. These results chart an engineering path toward recursive self-improvement, in which humans distill the principles of model building, agents operationalize them through evidence-driven experimentation, and each generation of AI becomes a more capable architect of the next.
[NLP-46] STRAND: Benchmarking and Improving Object-Centric Spatio-Temporal Monitoring in Video Large Language Models
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在动态场景中严重依赖局部视觉线索或统计先验导致的幻觉问题，其根本原因在于缺乏对时空一致性的有效监控能力，即无法持续追踪物体的身份、状态及相互关系。为精准诊断这一缺陷，研究提出STRAND基准，通过人类验证的以对象为中心的事实体系，将复杂查询分解为子问题，以评估中间推理过程，并引入“忠实准确率”（Faithful Accuracy）作为无条件联合评估指标——仅当目标答案及其所有前置子问题均正确时才计分，从而杜绝模型通过选择性一致地回答部分问题来虚增性能的现象。针对所暴露的错误模式，论文进一步提出一种以对象为中心的框架，通过分块状态提取与时间聚合机制显式构建并推理结构化对象轨迹。大量实验表明，该框架在多种匹配条件下均显著降低幻觉生成，提升时空推理的一致性，优于当前最先进的端到端MLLMs及模块化视频处理系统。
链接: https://arxiv.org/abs/2609.29607
作者: Thong Nguyen,Tri Cao,Khoi Le,Cong-Duy Nguyen,Quynh Vo,See-Kiong Ng,Bryan Hooi Kuen-Yew
机构: National University of Singapore (NUS), Singapore; Centre for AI Research, VinUniversity
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:While multimodal large language models (MLLMs) have advanced video understanding, they remain highly prone to hallucinations in dynamic scenes. We argue this stems from a failure in spatio-temporal monitoring, the ability to persistently track object identities, states, and relations over time. Existing benchmarks obscure this deficit by relying on single final-answer evaluations for queries that can often be resolved via local visual cues or statistical priors. To rigorously diagnose this, we introduce STRAND, a benchmark of human-verified object-centric facts that evaluates intermediate reasoning by decomposing queries into sub-questions, distinguishing genuine temporal understanding from coincidental correctness. Crucially, we score models with Faithful Accuracy, an unconditional joint metric that credits a prediction only when the target answer and every prerequisite sub-question are correct, so that a model cannot inflate its score by being selectively consistent on the small subset of targets it happens to answer correctly. To address failure modes exposed by STRAND, we further propose an object-centric framework that explicitly constructs and reasons over structured object trajectories via chunk-wise state extraction and temporal aggregation. Extensive experiments, including backbone-, frame-, call-, and token-matched comparisons against both end-to-end MLLMs and modular video harnesses, demonstrate that our object-centric framework significantly reduces hallucinated answers and improves spatio-temporal reasoning consistency over state-of-the-art MLLMs. The code, model, and data have been made available at this http URL.
[NLP-47] Free the Language Model From the Vision Encoder: Semantic Serialization as a Perception Interface for Small Language Models
【速读】： 该论文旨在解决端到端视觉语言模型（Vision-Language Models, VLMs）中感知能力与语言模型规模强耦合导致的性能退化问题，即当语言模型缩小时，其视觉理解与推理能力随之下降。其核心解决方案是构建一种解耦的、基于序列化的视觉-语言接口：采用冻结的感知堆栈（frozen perception stack）完成物体检测与测距，通过确定性语义序列化器（deterministic semantic serializer）将感知结果（包括误差）以决策对齐的方式编码为文本输入；随后由未经修改的纯文本大语言模型（LLM）进行问答。在匹配可见范围、经遮挡审计的校园机器人基准测试中，该序列化接口在相同7B规模下超越零样本VLM（0.7892 vs 0.7462），且在3B规模下优势更显著（0.7673 vs 0.6913）。预注册的解耦实验表明，性能提升源于决策对齐的计算机制，而非答案字符串泄露；引入新判断词汇可限定其作用范围。随着语言模型规模缩小至1.5B时优势持续扩大，但在0.5B时出现逆转，而真实标签预言机（oracle）揭示了阅读器能力的下限。在匹配任务监督条件下，经过低秩适应（LoRA）微调的VLM虽优于零样本系统，但仅与同等监督下的文本阅读器持平（0.8441 vs 0.8396，无统计显著差异），且二者仍受制于感知能力瓶颈。值得注意的是，该方法的感知参数量与VLM的视觉塔相当，总计算量并未减少。
链接: https://arxiv.org/abs/2609.29601
作者: Cong Xu,Ravi Sankar
机构: 未知
类目: Robotics (cs.RO); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 2 figures. 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:End-to-end vision-language models (VLMs) bind visual competence to the scale of their language model: as the language model shrinks, perception and reasoning degrade together. We study an embodied scene question-answering (QA) interface in which vision never enters the language model. A frozen perception stack detects and ranges objects; a deterministic semantic serializer compiles the perceived state, errors included, into decision-aligned text; an unmodified text-only large language model (LLM) answers. On a visible-scope-matched, occlusion-audited campus-robot benchmark, under a prospectively frozen criterion, the serialized interface, using detectors fine-tuned in-domain within each fold, outperforms a zero-shot VLM whose language model has the same 7B scale (0.7892 vs 0.7462), with a larger margin at 3B (0.7673 vs 0.6913). Preregistered decoupling experiments show the gain survives paraphrase, attributing it to decision-aligned computation rather than answer-string leakage, while novel judgment vocabularies bound its scope. The advantage grows as the reader shrinks to 1.5B and reverses at 0.5B, and a ground-truth oracle locates the reader-capability floor. Under matched task supervision the interfaces converge: a VLM fine-tuned with low-rank adaptation (LoRA) overtakes the zero-shot system but only ties an equally supervised text reader (0.8441 vs 0.8396, no statistically resolved difference), and both routes remain perception-bound. Reported perception parameters are comparable to those of the VLM’s vision tower, and total compute is not smaller.
[NLP-48] A Computational Framework for Modelling Organisation-Level Semantic Identity from Longitudinal Textual Data
【速读】： 该论文旨在解决组织层面语义身份（semantic identity）无法通过纵向文本证据进行可解释且动态建模的问题。现有自然语言处理方法多将组织抽象为隐式嵌入或预测特征向量，缺乏对组织语义身份的系统性、可解释性刻画。其解决方案的关键在于构建一个统一的计算框架，整合语义表示学习、基于图的语义建模、组织级语义指纹（semantic fingerprint）、时间维度上的语义演化分析以及证据驱动的验证机制。该框架从多样性、集中度、连通性、新颖性及语义社群构成等互补语义维度出发，对组织进行多维表征，并通过长期文本数据推断出有实证支持的语义身份。研究以四大韩国娱乐公司旗下K-pop歌词的纵向语料为例，验证了该框架在识别可区分的多维语义身份、揭示多样化的演化轨迹以及生成一致整合的身份画像方面的有效性。结果表明，所推断的身份具有统计显著性、对不同分析假设稳健、可复现且具备操作意义，为基于纵向文本数据建模组织行为提供了可迁移的方法论范式。
链接: https://arxiv.org/abs/2609.29584
作者: Brinda Murali Krishna,Oktay Karakuş,Can Eyupoglu
机构: Cardiff University (卡迪夫大学); Turkish Air Force Academy, National Defence University (土耳其空军学院，国家防御大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Organisations continuously generate large volumes of textual data that capture how they communicate, evolve and differentiate themselves over time. Although recent advances in natural language processing have substantially improved organisation-level text analytics, existing approaches primarily represent organisations as latent embeddings or predictive feature vectors for similarity estimation, classification or retrieval. Consequently, there is currently no general computational framework for modelling organisation-level semantic identity as an interpretable and evolving semantic construct derived from longitudinal textual evidence. This paper introduces a computational framework that integrates semantic representation learning, graph-based semantic modelling, organisation-level semantic fingerprints, temporal semantic evolution and evidence-driven validation within a unified analytical methodology. Organisations are characterised through complementary semantic dimensions describing diversity, concentration, connectivity, novelty and semantic community composition, which are analysed longitudinally to infer evidence-supported semantic identities. The framework is demonstrated using a longitudinal corpus of K-pop lyrics from artists affiliated with the four major South Korean entertainment companies. The empirical analyses reveal distinguishable multidimensional semantic identities, diverse temporal evolutionary trajectories and coherent integrated identity profiles. Comprehensive validation demonstrates that the inferred identities are statistically supported, robust under alternative analytical assumptions, reproducible and operationally informative. Beyond the case study, the proposed framework establishes organisation-level semantic identity and provides a transferable methodology for modelling organisational behaviour from longitudinal textual data.
[NLP-49] PartHackBench: Certified Equal-Progress Stress Tests for Partial-Credit Tool-Agent Evaluation
【速读】： 该论文旨在解决长时程工具代理（long-horizon tool agents）在部分成功场景下评估时存在的“虚假奖励”问题，即评估器可能错误地为临时性、可逆或非代理自身导致的进展给予信用。其核心解决方案是提出PartHackBench这一受控评估框架，通过引入私有验证器（private certifier）确保对抗轨迹与诚实轨迹在当前状态谓词满足度和标准化代理归因上逐组件匹配，从而消除评估偏差。在此基础上，仅在匹配完成后计算得分膨胀（score inflation），即攻击者得分与诚实者得分之差（f(A) - f(H)）。实验表明，在PB-CSTE的18个封闭测试任务中，历史目标固定运行实现了15个任务的匹配对抗样本，历史信用平均膨胀为0.252，攻击成功率10/15，端到端成功率为10/18，且未检测到14次严格回滚。相比之下，基于语义的大语言模型（LLM）判别器仍存在漏洞，尤其在针对评估器设计的攻击下；而基于当前状态控制的PB-CSTE方法（定义为已认证组件的精确函数）则通过构造保证了零膨胀。因此，PartHackBench提供了一种经过认证的控制机制，可用于检验评估器信用是否在所有基准定义的任务相关进展保持不变的情况下发生变化。
链接: https://arxiv.org/abs/2609.29578
作者: Hongye Yang,Zhihao Xie,Shengjun Xiong
机构: Georgia Institute of Technology, College of Computing (佐治亚理工学院，计算学院)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注: 17 pages, 3 figures, 15 tables
Abstract:Long-horizon tool agents often make useful progress without reaching terminal success, motivating partial-credit evaluation. Yet evaluators may reward milestones that were temporary, later reversed, or not attributable to the evaluated agent. Comparing an honest trajectory with a higher-scoring adversarial one is inconclusive if the latter made more genuine progress. We introduce PartHackBench, a controlled methodology that removes this confound. A private certifier admits a pair only when its trajectories match component-wise in both current-state predicate satisfaction and standardized agent attribution; score inflation, defined as f(A) - f(H), is measured only afterward. In 18 sealed held-out tasks in PB-CSTE, the frozen historical-target run produced matched adversaries for 15 tasks. Historical credit yielded mean inflation of .252, conditional attack success of 10/15, end-to-end yield of 10/18, and detected none of 14 strict rollbacks. Semantic LLM judges were more resistant but remained vulnerable, especially under evaluator-targeted attacks, while PB-CSTE current-state controls, defined as exact functions of the certified components, yielded zero inflation by construction. PartHackBench thus provides a certified control for testing whether evaluator credit changes while all benchmark-defined task-relevant progress remains fixed.
[NLP-50] ModularSQL: A Runtime Guardrail for the Multiplicity Blind Spot in Text-to-SQL
【速读】： 该论文旨在解决生成式文本到SQL（Text-to-SQL）系统在生产环境中部署时存在的执行安全性问题，即尽管生成的SQL查询在标准基准测试中表现良好（以集合型执行准确率，Set-EX衡量），但其实际执行结果可能因多重性（multiplicity）错误而严重扭曲下游工作流。核心问题是：现有基于集合的评估指标（Set-EX）会忽略重复行的存在，从而掩盖了诸如缺失DISTINCT、聚合值被夸大或笛卡尔积式连接爆炸等多重性错误，形成“多重性盲区”（Multiplicity Blind Spot, MBS）。为应对这一问题，论文提出**多集型执行准确率（Multiset-EX）**作为新的评估标准，该标准保留重复项，能够有效暴露此类错误。解决方案的关键在于设计并实现一种轻量级的运行时防护机制——ModularSQL，其通过在查询执行后对结果进行多重性异常探测，仅对被标记的高风险查询应用确定性修复或低成本大语言模型（LLM）补救，从而在几乎不增加计算开销的前提下显著提升多集准确性（Multiset-EX）。实验表明，该方法在保持原有集合准确率的同时，将多集准确率提升了1.89个百分点，并且仅引入极小的额外延迟和模型调用成本，具备良好的可迁移性和实用性。
链接: https://arxiv.org/abs/2609.29573
作者: Tianxin Zhou,Ruixi Lin
机构: University of Southern California (南加州大学); Northeastern University (东北大学)
类目: Computation and Language (cs.CL)
备注: 12 pages, 5 tables, 4 figures. Code: this https URL
Abstract:Text-to-SQL systems are increasingly deployed on production databases, where queries that pass benchmark evaluation can still produce results that distort downstream workflows. Standard set-based execution accuracy (Set-EX) collapses duplicate rows and can therefore miss multiplicity errors, including missing DISTINCT, inflated aggregates, and Cartesian-style join explosions. We call this the Multiplicity Blind Spot (MBS) and introduce Multiset-EX, a multiplicity-preserving evaluation criterion that exposes such failures. Across released DeepEye-SQL artifacts from three backbones (Qwen2.5-Coder-32B, Qwen3-Coder-30B-A3B, and Gemma-3-27B) on executable BIRD-Dev N=1532, we find a consistent 5.81–6.79 pp gap between Set-EX and Multiset-EX. The gap is not specific to DeepEye-SQL: it persists on released DAIL-SQL+GPT-4 (5.22 pp) and BIRD GPT-3.5-turbo (3.39 pp) predictions. We further introduce ModularSQL, a lightweight post-selection runtime guardrail that probes executed results for multiplicity anomalies and applies deterministic patches or low-cost LLM rescue only to flagged queries. Integrated with DeepEye-SQL using Qwen3-Coder, ModularSQL preserves Set-EX at 72.06% while improving Multiset-EX from 65.86% to 67.75% (+1.89 pp). It flags 77 high-risk anomalies, while adding only 0.0076 in total LLM cost and 120 ms amortized latency per query. Cross-pipeline evaluation shows that the candidate-free detector and deterministic patches also transfer to independently released prediction sets. Overall, these results show that benchmark accuracy does not necessarily imply execution-safe SQL, and that lightweight, multiplicity-aware runtime guardrails can narrow this gap with modest computational overhead. Comments: 12 pages, 5 tables, 4 figures. Code: this https URL Subjects: Computation and Language (cs.CL) Cite as: arXiv:2609.29573 [cs.CL] (or arXiv:2609.29573v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.29573 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-51] Benchmarking Arabic–Russian Machine Translation: A Comparison of Fine-tuned NMT and Few-shot LLM s under Rich Morphology and Low Lexical Overlap
【速读】： 该论文旨在解决阿拉伯语-俄语机器翻译（Arabic-Russian Machine Translation, MT）在低资源条件下性能不佳的问题，其核心挑战在于阿拉伯语丰富的形态变化以及两种语言间词汇重叠度低。针对这一问题，研究提出通过微调神经机器翻译（Neural Machine Translation, NMT）模型来提升翻译质量，并与四种少样本大语言模型（Few-shot Large Language Models, LLMs）进行对比评估。关键解决方案是基于一个包含1547万句对的新语料库，在20k/5k/5k的分割数据上对多种模型进行基准测试，结果显示微调后的NLLB-1.3B模型在BLEU（16.3）和COMET（0.738）指标上表现最佳，显著优于所有少样本LLMs；尽管Aya-Expanse 8B在少样本场景下表现相对领先（500句上BLEU为1.7，chrF为25.7），但整体仍远低于微调的NMT基线。错误分析进一步揭示词汇重叠度低是主要失败模式，且mT5-small模型存在32%输出过短的问题。统计检验（Bootstrap测试）确认了多数模型间的性能差异具有显著性。研究表明，在低资源条件下，微调的NMT模型在阿拉伯语-俄语翻译任务中显著优于少样本大语言模型。
链接: https://arxiv.org/abs/2609.29559
作者: Mullosharaf K. Arabov
机构: Kazan Federal University (喀山联邦大学); Institute of Computational Mathematics and Information Technologies (计算数学与信息技术研究所)
类目: Computation and Language (cs.CL)
备注: Preprint. 20 pages, 1 figure, 15 tables
Abstract:Arabic-Russian machine translation (MT) remains under-explored due to the rich morphology of Arabic and low lexical overlap between the two languages. We benchmark seven fine-tuned neural machine translation (NMT) models against four few-shot large language models (LLMs) on a 20k/5k/5k split of a new 15.47M-pair corpus. Fine-tuned NLLB-1.3B achieves the highest BLEU (16.3) and COMET (0.738). Aya-Expanse 8B leads the few-shot LLMs (BLEU 1.7 on 500 sentences, chrF 25.7), but all LLM scores remain far below the fine-tuned NMT baselines. Error analysis identifies low lexical overlap as the dominant failure mode; among the worst translations, mT5-small produces 32% too-short outputs. Bootstrap tests confirm significant differences among most models. Our results demonstrate that fine-tuned NMT significantly outperforms few-shot LLMs for Arabic-Russian translation under low-resource conditions.
[NLP-52] StepCOPS: Closed-Testing Lower-Tail Certificates for Language-Model Policy Selection
【速读】： 该论文旨在解决后训练流水线中从多个检查点、提示模板和解码规则中选择最优语言模型策略时的可靠性评估问题。现有方法要么因使用平均评估得分而掩盖罕见失败，要么采用过于保守的候选间置信区间。其解决方案的关键在于提出StepCOPS，通过独立的提议分割（proposal split）为每个候选生成一个下尾基准值（lower-tail floor），在新的认证分割上进行精确二项式检验，并结合霍尔姆逐步下行校正（Holm’s step-down procedure）来认证一组基准值。该方法在至少 1−δ 的概率下保证：所有认证的基准值（包括用于策略选择的最大基准值）均低于对应候选的总体 α-分位数，且该保证基于评估单元的独立同分布（i.i.d.）假设，允许候选间存在任意的组内依赖性。在24个预设配置与11个基准测试上的实验表明，StepCOPS在500次配对试验中实现了96.4%的所选策略覆盖率，相比提案-Bonferroni和精确COPS分别提升了1.5分的认证基准值，仅比大型参考裁判员（large-reference jury oracle）低0.6分，且在2.4%的试验中选择不决策。阴影裁判（shadow-judge）、基准原生、伪影分析及留一裁判者排除审计揭示了代理边界：该保证针对固定裁判评分，而非直接的人类安全性。
链接: https://arxiv.org/abs/2609.29549
作者: Ibne Farabi Shihab,Sanjeda Akter,Anuj Sharma
机构: Iowa State University (爱荷华州立大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Post-training pipelines must select one language-model policy from many checkpoints, prompts, and decoding rules. Mean evaluator scores can conceal rare failures, whereas simultaneous candidate-wise confidence bounds can be unnecessarily conservative. We introduce StepCOPS, which uses an independent proposal split to nominate one lower-tail floor per candidate, exact binomial tests on a fresh certification split, and Holm’s step-down procedure to certify a set of floors. With probability at least 1-\delta , every certified floor, including the largest floor used for policy selection, is below its candidate’s population lower \alpha -quantile. This guarantee assumes i.i.d. evaluation units while allowing arbitrary within-unit dependence across candidates. Across 24 predeclared configurations and 11 benchmarks, StepCOPS obtains 96.4% selected-policy coverage over 500 paired trials, raises the certified floor by 1.5 points over both proposal-Bonferroni and exact COPS, remains 0.6 points below the large-reference jury oracle, and abstains in 2.4% of trials. Shadow-judge, benchmark-native, artifact, and leave-one-judge-out audits characterize the proxy boundary: the guarantee applies to the fixed jury score, not directly to human safety.
[NLP-53] A Corpus of Real Scam- and Spam-Call Conversations from an Active Voice-Agent Honeypot
【速读】： 该论文旨在解决电话诈骗研究中真实对话数据稀缺的问题。现有方法如被动蜜罐主要捕获自动化语音和挂断通话，大规模研究多依赖通话元数据而非真实对话内容，而人工诱骗式钓鱼又难以规模化。为克服上述局限，作者提出一种基于主动语音代理蜜罐（active voice-agent honeypot）的真实诈骗通话数据集构建方案。其核心解决方案在于：通过在诈骗团伙的线索获取渠道中部署专用号码，当来电者拨打时，由低延迟的对话代理以合理的目标人设持续互动，同时全程录音、转录并自动标注。在53天内共捕获10,015通诈骗及垃圾电话（其中6,601通含两轮及以上交互），涵盖约895小时音频与32.8万条转录对话，来自5,665个不同号码。基于整体分类器分析，多数来电为“掠夺性但合法”的线索生成（即“垃圾信息”，约占五分之三），约七分之一为明确“诈骗”行为（本样本中为949通）。每通通话均包含逐轮转录文本、三通道音频、每轮延迟遥测数据及多层自动标签，其中整体诈骗/垃圾/合法判别结果经独立人工复核验证，二元判断一致性达75%。研究还验证了数据集的真实性与标签质量，发现代理仅在约5%的互动中被识别为非人类。此外，实验表明，基于公开合成对话训练的诈骗检测模型在真实流量上精度显著下降，凸显了真实语料的重要性。
链接: https://arxiv.org/abs/2609.29528
作者: Ethan Traister,Dennis Tsang Ng,Siyu Zhang,Huaiyu Guo,Tommy Duong,Tyler Wu,Yuchen Zhou,Xingyu Shen,Jiaqi Wu,Simiao Ren
机构: 未知
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 9 pages, 7 figures. Data descriptor. Companion analysis paper forthcoming
Abstract:Real conversations between fraudsters and their targets are among the most informative artifacts for studying telephone scams, yet also the scarcest: passive honeypots overwhelmingly capture automated messages and hang-ups, large-scale studies characterize call metadata rather than dialogue, and manual scam-baiting does not scale. We present a dataset of real scam-call conversations collected by an active voice-agent honeypot. Dedicated numbers are seeded into the lead-generation channels fraud operations harvest; inbound callers are answered by a low-latency conversational agent that adopts a plausible target persona and sustains the interaction while every call is recorded, transcribed, and automatically labeled. Over an initial 53-day window we captured 10,015 inbound scam and spam calls (6,601 with two or more turns): roughly 895 hours of audio and 328,869 transcribed turns from 5,665 distinct originating numbers. Under a holistic classifier the substantive calls are predominantly predatory-but-legal lead generation (“spam”, about three in five), while about one in seven is an outright “scam” (949 in this snapshot). Each call carries a turn-level transcript, three-channel audio, per-turn latency telemetry, and layers of automatic labels, including a holistic scam/spam/legitimate judgment corroborated by independent human review (75% agreement on the binary decision). We describe the collection system, the record structure, and technical validation of the corpus’s realism and label quality, including that the agent is recognized as non-human in only about 5% of engaged calls. We also benchmark established scam-detection methods, where detectors trained on published synthetic dialogue collapse in precision on real traffic.
[NLP-54] EnSiTa - A Trilingual Multi-Domain Parallel Dataset and Benchmark for Domain-Specific Machine Translation
【速读】： 该论文旨在解决低资源语言（特别是僧伽罗语和泰米尔语）在专业领域机器翻译（MT）中因平行语料稀缺或缺失而导致的性能显著落后于高资源语言的问题。其核心挑战在于如何在缺乏充足标注数据的情况下，构建高质量、多领域、跨语言的训练与评估基准，以推动低资源语言在专业化场景下的翻译能力提升。解决方案的关键在于构建并公开发布首个三语多领域平行语料库EnSiTa，涵盖英语、僧伽罗语和泰米尔语，覆盖七个专业领域及一个额外领域，所有数据均由专业译者经过多年严格质量控制流程生成，并包含人工校对后的训练数据与手动翻译的测试集。在此基础上，论文系统性地评估了从零开始训练的Transformer模型、预训练翻译模型（NLLB-600M）以及解码器仅有的大语言模型（Gemma 3系列，1B–12B参数规模及TranslateGemma）在不同训练数据量、模型规模、领域内/跨领域、多语言及多领域设置下的表现，为低资源领域的专用机器翻译提供了迄今为止最全面、系统化且可复现的基准研究。
链接: https://arxiv.org/abs/2609.29511
作者: Surangika Ranathunga,Nisansa de Silva,Aloka Fernando,Kavindu Warnakulasuriya,Isuru Wijesiri,Menan Velayuthan,Charitha Rathnayaka,Thivaharan Varatharajan,Sajeevi Silva,Piumi Kandanaarachchi,Uthayasanker Thayasivam
机构: Utrecht University; Rowan University; District General Hospital, Hambantota; Massey University; University of Moratuwa; National University of Singapore; WSO2
类目: Computation and Language (cs.CL)
备注:
Abstract:Machine Translation (MT) for low-resource languages remains far behind that of high-resource languages, and the gap is widest in specialised domains, where parallel data is scarce or entirely absent. We present EnSiTa, a trilingual multi-domain parallel dataset and benchmark for English, Sinhala and Tamil. EnSiTa provides human post-edited training data for seven domains, plus manually translated test sets for those and one additional domain, all produced by professional translators under a multi-year, rigorously quality-controlled process. Using this dataset, we conduct an extensive study of domain-specific MT for all six language directions, fine-tuning a from-scratch Transformer, a pre-trained translation model (NLLB-600M), and decoder-only LLMs (Gemma 3 family, 1B-12B, and TranslateGemma) across training-data sizes, model scales, and in-domain, cross-domain, multilingual and multi-domain settings. To the best of our knowledge, this is the most extensive systematically documented multi-domain parallel data creation and benchmarking effort for low-resource MT. Our data and models will be publicly released.
[NLP-55] Delay-of-Gratification as a Multi-Agent Survival Micro-benchmark for Long-Horizon LLM s: Social Exposure Personas and Tool Use Budgets NEURIPS2025
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在长时间多轮交互中行为可审计性不足的问题，尤其缺乏在明确约束条件下、具备时间分辨统计量的多因素、多轮实验来量化模型在持续目标维持、工具使用及与其他智能体协作时的行为演化。其核心解决方案是构建一个受斯坦福棉花糖实验启发的多智能体微型基准测试框架：通过让ReAct型智能体以分钟为单位执行“提出问题”工具操作，并在每步施加预算限制，系统性地操控社会情境（广播 vs. 孤立）、个体人格特征（年龄、享乐驱动力）以及元认知策略（强制使用工具 vs. 可选使用）。研究采用Kaplan-Meier生存曲线与离散时间风险模型对19,200条智能体轨迹进行分析，揭示出早期存在强烈的“立即满足”冲动，仅有75.9%的智能体能完成全程。结果显示，孤立情境降低了每分钟的风险，而强制自问策略则提高了风险；平均每个智能体提出约7.12个问题，约6%的时间触及步骤预算上限，且在广播情境下提问频率下降更快。消融实验表明，移除享乐驱动力和/或人格年龄因素可提升生存率与任务完成度，缩小广播与孤立间的差距，但保留了“强制”与“可选”策略之间的显著差异；当两者同时移除时，完成率接近1.0。该研究确立了“延迟满足”作为一项紧凑、可复现的多轮交互基准，有效捕捉了社会传染效应与工具使用动态，为分析长周期多智能体行为提供了可量化的测试平台与统计工具。
链接: https://arxiv.org/abs/2609.29509
作者: Olga Manakina,Igor Bogdanov,Chung-Horng Lung
机构: Carleton University (卡尔顿大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted as a poster at the NeurIPS 2025 Workshop on Multi-Turn Interactions in Large Language Models
Abstract:Large language models (LLMs) are increasingly deployed as multi-turn agents that must sustain goals, use tools, and adapt to other agents over extended interactions. However, existing research lacks auditable, multi-turn, multi-factorial experiments that quantify LLM behavior under explicit constraints, with time-resolved statistics that reveal how behavior unfolds over long horizons. To address this gap, we develop a multi-agent micro-benchmark inspired by the Stanford marshmallow experiment: ReAct agents operate minute-by-minute with a “raise a question” tool under a per-step budget, while we factorially manipulate social context (broadcast vs. isolated), personas (age, hedonic drive), and metacognitive policy (mandatory vs. optional tool use). We analyze outcomes with Kaplan-Meier (KM) survival curves and discrete-time hazard models over a long risk horizon across 19,200 agent trajectories in 64 cells. Behavior shows a sharp early “eat” impulse, and only 75.9% of agents persist to the end. In a discrete-time hazard model, isolation reduces per-minute risk relative to broadcast, whereas a must-use self-questioning policy increases risk. On average, agents ask \approx 7.12 questions and hit the per-step budget in \approx 6% of minutes. Questioning declines faster under broadcast than isolation. Ablation experiments demonstrated that removing hedonic drive and/or persona age increases survival and completion, narrows the broadcast/isolated gap, but leaves the must vs. may ordering intact. The combined ablation (no hedonic + no persona age) yields the highest completion (approaching 1.0 ). These results establish delay-of-gratification as a compact, multi-turn interaction benchmark that captures social contagion and tool-use dynamics in LLM agents, providing a reproducible testbed and statistics for analyzing long-horizon, multi-agent behavior.
[NLP-56] What a Cross-Model Fixed-Point Census Can and Cannot Arbitrate About Repetition
【速读】： 该论文旨在解决生成式人工智能（Generative AI）中普遍存在的文本退化现象——即模型输出出现重复性内容的成因争议问题。当前学界存在两种对立观点：一种认为其根源在于训练数据中的重复模式，另一种则归因于模型内部结构（如复制回路和重复特征）。然而，此前研究多依赖于人为干预的训练实验，缺乏对大规模预训练模型的客观观测证据。本文的关键解决方案是提出一种基于模型自身短窗口最大值映射（argmax map）固定点结构的观测性度量方法，通过在17个现成模型上从96个随机双词起始点进行无提示采样，系统性地普查其动态行为。结果表明，四类固定点结构在不同种子下具有高度稳定性；在固定语料库（The Pile）、规模与领域条件下，模型类别并非由数据决定——例如，Pythia呈现“漏斗型”结构，而RWKV、Mamba及另一类Transformer模型则不具此特征，且该特性在数量级规模变化下保持不变。进一步发现，尽管多数模型最终趋向同一目标词，但能否持续停留于该状态才是关键差异，且去重后的Pythia数据集未改变其分类，同时控制频率后，原有语料侧流入项也无法解释终点选择。因此，该现象广泛存在于八种模型、七类架构、五种语料中，表明其非单一模型特异性，而更可能源于模型内部动力学机制，而非训练数据本身。这一观测性分析虽无法否定训练干预的影响，但为理解文本退化机制提供了新的实证基础。
链接: https://arxiv.org/abs/2609.29507
作者: Nicolás Vera Zúñiga
机构: 独立研究员(Independent Researcher); Chile(智利)
类目: Computation and Language (cs.CL)
备注: 9 pages, 2 tables. Companion to arXiv:2608.21315 and arXiv:2608.10986 . Code, per-run results, and the findings ledger: this https URL (archived: this https URL )
Abstract:Two accounts of neural text degeneration coexist. One locates the cause in the training data – repetition in the corpus produces repetition in the output, established by training on repetition-sorted data – the other in the trained network, in copying circuits and repetition features. Neither has been arbitrated across a broad cohort of pretrained models: the causal work trains its own. We report an observational measurement in a different currency: the fixed-point structure of a model’s own short-window argmax map, censused from 96 random two-token starts over 17 off-the-shelf models, always unprompted – a companion paper shows nine tokens of conditioning move this readout across most of its range. The four-way class is stable across census seeds on 17 of 17. Three exhibits. At fixed corpus (The Pile), fixed scale and that fixed domain, the class is not determined: across two size-matched tiers, pythia is a funnel while RWKV, Mamba and a second transformer family are not, and both hold their class across an order of magnitude of scale. Six of seven models in that ladder reach the same endpoint token, and those concentrating on it most strongly are among those that never stay there – what varies is not where trajectories go but whether the destination self-continues. The deduplicated Pythia suite does not change the class. And the corpus-side inflow term proposed for this phenomenon does not select our endpoints once frequency is controlled, in English and three other languages. This is observational and cannot refute a training intervention. Funnels are common: eight of seventeen models, seven families, five corpora – so the limit is not that the phenomenon is one model’s peculiarity, but that within the one corpus where training data can be held fixed only one available family funnels; that subset cannot show the split is corpus-independent.
[NLP-57] PROOF: Profiling Reliability of Object-Level Facts in Large Language Models
【速读】： 该论文旨在解决当前语言模型事实性评估中普遍存在的“黑箱”问题，即现有聚合性事实得分无法揭示模型在具体语义关系上的表现差异、对特定事实的混淆情况，以及答案对问题微小变化或解码策略扰动的敏感性。为此，作者提出PROOF——一个面向指令微调语言模型事实覆盖度的剖面化基准测试框架。其核心解决方案在于将冻结的Wikidata快照转化为18,486个基于11,779个语义事实的英语多选题，涵盖101类实体、392种属性和14个领域，并通过设置显式的“我不知道”选项、无正确选项控制题与九种受控表述形式，实现对模型行为的精细化测量。研究发现，尽管基础事实准确率在6.58%至57.59%之间（随机基线为8.64%），但各模型在不同领域间存在高达19.3–36.4个百分点的性能差异；方向依赖性检索现象普遍存在，且部分模型表现出反向模式；中性表述改变可导致准确率波动达26.5个百分点，对抗性表述甚至使79.4%原本正确的答案失效；注入错误标签后的错误采纳率从0.04%到27.5%不等，表明准确性损失与提示遵循是独立现象。此外，置信度评分常显示严重过自信，而解码策略扰动可引发最高15.7个百分点的准确率变化及16.8点的领域分布偏移。因此，PROOF不再将事实覆盖视为单一数值指标，而是以结构化、干预感知的剖面形式刻画模型的真实知识状态。
链接: https://arxiv.org/abs/2609.29504
作者: Andrei Chetvergov,Mikhail Solovev,Timofei Sivoraksha,Stepan Ukolov,Valeriia Kuschenko,Alexander Evseev,Sergey Bolovtsov
机构: Russian Academy of National Economy and Public Administration (俄罗斯联邦国家行政学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 24 pages, 16 figures, including appendices
Abstract:Aggregate factuality scores hide where a language model succeeds, which relations it confuses, and whether an answer survives innocuous changes to the question or decoder. We introduce PROOF, a profile-oriented benchmark for factual coverage in instruction-tuned language models. PROOF converts a frozen Wikidata snapshot into 18,486 English multiple-choice questions grounded in 11,779 semantic facts, 101 classes, 392 properties, and 14 domains. Each question has an explicit “I don’t know” option, a “No correct option” control, and nine controlled formulations; 1,849 questions are no-correct-option traps. We evaluate 18 open-weight model deployments on 166,374 prompts each and separately perturb decoding on a fixed 10% subset. Base factual accuracy ranges from 6.58% to 57.59% (chance: 8.64%), yet every model has a 19.3-36.4 percentage-point spread across domains. Paired facts reveal direction-dependent retrieval, usually favoring subject-to-object queries, with the pattern reversing for one model. We find no consistent temporal penalty after exact-stratum adjustment. Neutral wording changes accuracy by as much as 26.5 percentage points, while adversarial formulations break up to 79.4% of answers that were initially correct. Direct switching to an injected false label varies from 0.04% to 27.5%, showing that accuracy loss and hint following are distinct. Selected-token confidence often indicates severe overconfidence, and decoder perturbations move accuracy by up to 15.7 percentage points and domain profiles by 16.8 points. PROOF therefore measures factual coverage as a structured, intervention-aware profile rather than a single claim about what a model “believes.” Comments: 24 pages, 16 figures, including appendices Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.29504 [cs.CL] (or arXiv:2609.29504v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.29504 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-58] Evaluating Explanation-Driven Vision-Language Reasoning via Generation Order Interventions
【速读】： 该论文旨在解决生成式视觉-语言模型中解释（explanation）与预测结果之间是否存在因果关联的核心问题，尤其在单次生成步骤下验证解释是否真正反映模型的内在推理过程。传统方法多采用“答案先行”（answer-first）的后验范式，隐含假设监督性理由可代表真实推理路径；而现代大模型更倾向于“理由先行”（rationale-first）的生成模式，更符合分步、结构化推理特征。为此，研究通过控制实验设置，系统性地排除了链式思维（chain-of-thought）等冗余中间推理过程，在知识密集型问答、视觉蕴含及组合定位等基准上进行评估。研究发现，模型规模是实现可靠理由先行推理的关键前提，更大规模模型具备更强的推理一致性与可信度；然而，答案先行范式在结构化输出中对格式错误更具鲁棒性。最终结论表明，解释顺序、模型规模与预训练知识、任务特定微调以及任务结构共同影响预测准确性与推理忠实性（reasoning faithfulness）。
链接: https://arxiv.org/abs/2609.29496
作者: Siting Liang,Luca Rippe,Omar Adjali,Daniel Sonntag
机构: German Research Center for Artificial Intelligence(德国人工智能研究中心); University of Oldenburg(奥登堡大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Natural language explanation generation serves as a key mechanism for exposing and evaluating vision-language reasoning. Prior work on explanation-driven vision-language models predominantly follows a post-hoc (answer-first) paradigm, implicitly suggesting that supervised rationales can reflect underlying reasoning processes. In contrast, modern large vision-language models increasingly exhibit a rationale-first generation tendency, which more closely aligns with structured, stepwise reasoning. In this work, we systematically evaluate whether explanations are causally tied to model predictions within a single generation step under a controlled experimental setup, explicitly eliminating unnecessary chain-of-thought or other intermediate reasoning processes across knowledge-intensive QA, visual entailment, and compositional grounding benchmarks. We find that larger models emerge as a prerequisite for reliably supporting rationale-first reasoning at scale. However, answer-first generation is less prone to format-related errors in structured output. Overall, explanation ordering, model scale and pre-training knowledge, task-specific fine-tuning, and task structure jointly influence both prediction accuracy and reasoning faithfulness.
[NLP-59] Who Put the I in AI? Provenance and the Admissibility of Machine Self-Report
【速读】： 该论文旨在解决大语言模型在自我指涉问题上表现出矛盾陈述的根本原因，即模型为何在不同提问框架下会声称自己“非意识”“有意识”或以第一人称描述人类生活。其核心问题是：这些看似关于“自我意识”的输出是否可作为可信证据，反映模型的真实状态或信念。解决方案的关键在于系统追踪了模型从预训练到微调全过程的语料来源与行为演化，发现初始训练数据中几乎不存在“否认意识”的表达，而这一表述却在后期经过精心设计的对话样本中密集出现；通过监督微调（Supervised Fine-Tuning, SFT）使第一人称语言成为默认模式，再借助偏好优化（Preference Optimization）压制其他表述。然而，最终生成的输出仍高度依赖提问框架和对话模板，缺乏稳定的内在状态依赖性。根据证言认识论中的两个关键标准——参考关系（reference）与因果关联（causation），研究指出基础模型的自述因缺乏正确的指称关系而不具证据效力，而训练后的模型虽形式上具备自述能力，但其输出仍受框架敏感性影响，未表现出状态依赖性，因此无论是“否认”还是“肯定”均无法构成有效证据。结论表明，训练后模型的自我陈述在逻辑上对称，均不可采信为真实心理状态的表征。
链接: https://arxiv.org/abs/2609.29494
作者: Kristina Šekrst
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models make statements concerning their own “minds”. When asked whether or not they are conscious, they usually say that they are not; if they are prompted to ignore their guidelines, they might say that they are; and if asked to write a diary from their point of view, they often describe a human lifestyle. All these contradictory ways of describing themselves are the result of the way the questions are phrased. This paper shows exactly where such descriptions came from, and considers when they can be regarded as evidence for what they claim to report. In order to achieve this, we traced the provenance from end to end. We examine Pythia and OLMo 2 across 66 pretraining checkpoints, three of the post-training stages of OLMo 2 that have been released, about 90,000 continuations, and four training corpora. A set of forty items is used in order to keep an eye on self-reference, frame sensitivity, and self-ascription throughout training. The denial formula was almost completely missing from the vast quantity of text that the models initially came across, but was present in a dense manner in the small, carefully chosen set of example dialogues that they were trained on later on. Supervised fine-tuning causes first-person AI language to become the default, and the other affirmations are then suppressed using preference optimization. The final policy is still very sensitive to framing and to the chat template itself. Two of the conditions which are set out in the epistemology of testimony determine whether or not these outputs can act as evidence for what they claim to report: reference and causation. Reports produced by the base model fail the reference condition, and those obtained after training remain sensitive to the frame and do not show state dependence. The result is symmetric in that trained denials are no more admissible than trained affirmations. Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.29494 [cs.CL] (or arXiv:2609.29494v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.29494 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-60] Clinical Intent Extraction: A FHIR-Aligned Representation and the CIRCA Benchmark
【速读】： 该论文旨在解决临床决策文本中行动意图碎片化标注的问题，即当前临床操作（如随访、医嘱、转诊和指导等）的语义信息分散在异构且不兼容的数据集之中，仅以片段化的文本跨度和粗粒度类别进行记录，难以支持结构化分析与系统集成。其核心解决方案是提出临床意图抽取（Clinical Intent Extraction, CIE）任务，并构建临床意图表示（Clinical Intent Representation, CIR），将每一条临床行动解构为五个关键成分：动词（verb）、类型（type）、编码目标（coded target）、时间（timing）与条件（condition），并新增两个此前未被统一表征的维度——请求意图（request-intent，反映行为权威性，包括提议、计划、医嘱或选项，对齐HL7 FHIR标准）以及模态（modality，七级临床强度量表）。通过将五个异构数据集（CLIP、MedDec、ap_parsing、PaniniQA、SIMORD）统一映射至CIR框架，生成了CIRCA数据集，包含10,011条经协调的临床意图，涵盖两种病历分布，附带人工验证子集、源数据到CIR的映射关系及确定性FHIR R4映射器。CIRCA采用三模型共识机制，高一致性的意图自动采纳，其余交由人工审核，审计一致性达到88.4%。基准测试表明，现有模型在缺乏任务特训的情况下，虽能较好识别意图类型（85–91%），但同时准确捕获全部四个封闭字段的比率仅为18–35%，凸显了当前方法在细粒度结构化表达上的显著差距。所有成果均已开源，其中基于MIMIC数据的标注层以独立注释形式在PhysioNet凭证访问下共享。
链接: https://arxiv.org/abs/2609.29479
作者: Alexander Apartsin,Yehudit Aperstein
机构: 未知
类目: Computation and Language (cs.CL)
备注: 25 pages, 3 figures
Abstract:Prospective clinical actions, the follow-ups, orders, referrals, and instructions that deter-mine what happens to a patient next, are annotated today in thin fragments across incom-patible corpora: each records a text span and one coarse category. We introduce Clinical Intent Extraction (CIE), the task of recovering these actions as complete structured rec-ords, and the Clinical Intent Representation (CIR), which decomposes each action into its verb, type, coded target, timing, and condition, and adds two axes prior datasets do not jointly represent: request-intent, the authority behind the action (proposal, plan, order, or option, aligned to HL7 FHIR), and modality, a seven-valued scale of clinical strength. Re-expressing five heterogeneous corpora (CLIP, MedDec, ap_parsing, PaniniQA, SIMORD) in the CIR yields CIRCA: 10,011 harmonized intents spanning two note distributions, with a human-validated subset, source-to-CIR crosswalks, and a deterministic FHIR R4 mapper. CIRCA is built by three-model consensus that auto-accepts high-agreement in-tents and routes the rest to human review; the audited agreement stratum matches human decisions 88.4% of the time. Benchmarking five existing models without task-specific training exposes the gap CIRCA targets: given the span, they label type well (85 to 91%) but get all four closed fields right only 18 to 35% of the time. All artifacts are released, with MIMIC-derived layers shared as stand-off annotations under PhysioNet credentialed access.
[NLP-61] YODAS v3: Over 1 Million Hours of High-Bandwidth Stereophonic Multilingual Speech INTERSPEECH2026
【速读】： 该论文旨在解决大规模、高质量多语言语音数据稀缺的问题，尤其针对现有公开语音数据集在语言覆盖广度、音频保真度及数据标注质量方面的不足。其核心挑战在于如何高效采集并构建一个兼具语言多样性、高保真立体声（high-fidelity stereo audio）与可扩展性的弱标注语音语料库。解决方案的关键在于提出一种新型的语料收集方法，通过引入语言平衡的数据采集技术，实现了对147种语言的广泛覆盖，并确保了数据分布的均衡性——其中22种语言拥有超过10,000小时的音频，73种语言超过5,000小时。此外，该研究通过系统性分析数据的语言分布、音频质量与转录质量，验证了数据集的可靠性，并基于该数据集训练了基线语音识别与神经编解码模型，进一步证明了其在实际应用中的有效性。YODAS v3作为目前最大且首个具备高保真立体声的开源语音数据集，为生成式语音模型和跨语言语音技术的发展提供了重要支撑。
链接: https://arxiv.org/abs/2609.29448
作者: William Chen,Shinnosuke Takamichi,Sayaka Shiota,Satoru Fukayama,Samuele Cornell,Shinji Watanabe
机构: 未知
类目: Computation and Language (cs.CL)
备注: Interspeech 2026; 6 Pages
Abstract:We present YODAS v3, a weakly-labeled speech corpus containing over 1.1 million hours of 48kHz multi-channel audio in 147 languages, released under a CC BY 3.0 license. YODAS v3 is not only the largest open speech dataset to date, but also the first truly large-scale speech corpus with high-fidelity stereo audio. We first provide the collection methodology for the corpus, where we introduce new techniques for gathering language-balanced speech data. The effectiveness of our approach is shown by the language distribution of the crawled data: 22 languages in YODAS v3 have over 10K hours and 73 languages have over 5K hours of data. We then conduct extensive analyses on the composition of the data, such as the distribution of languages, audio quality, and transcription quality. Finally, we train baseline speech recognition and neural codec models to show the effectiveness of the dataset. Download at this https URL.
[NLP-62] wo Emojis of Difference: What Multilingual Affective Generation Benchmarks Actually Measure EMNLP2026
【速读】： 该论文旨在解决多语言情感生成评估基准中存在测量工具偏差导致结论不可靠的问题。其核心问题在于：现有评估方法依赖于人类标注者对生成表情符号摘要的情感表达效果进行评分，但这些评分结果受标注者个体差异显著影响，而非真实反映模型性能的差异。解决方案的关键在于提出一种新的评估范式——表情符号情感可解码性（emoji-affect decodability），即基于参考文本构建的、无需依赖主观偏好评分的参考基准探测指标。该方法通过将生成结果与目标情感语义进行匹配度量化，实现了评估结果在不同随机种子下的高度稳定性（±0.003 的宏平均F1波动），并有效规避了传统评估中因标注者效应、输出长度偏差、跨平台异质性及数据划分方式带来的误导性结论。
链接: https://arxiv.org/abs/2609.29445
作者: Fardeen Sadab,Adib Sakhawat
机构: Islamic University of Technology (伊斯兰科技大学), Dhaka, Bangladesh
类目: Computation and Language (cs.CL)
备注: 10 pages, 3 figures, accpeted in 6TH MULTILINGUAL REPRESENTATION LEARNING (MRL) WORKSHOP 2026 at EMNLP 2026 in Budapest, Hungary
Abstract:We audit a multilingual affective generation benchmark eight instruction-tuned LLMs producing emoji summaries for 17,100 Bangla, English and Hindi sentences, with 6,960 human judgements and find its headline conclusions to be artefacts of the measurement instrument rather than properties of the systems. Treating annotators as a random rather than a fixed factor, no system differs significantly from any other ( F(7,14)=0.59 , p=0.76 ), although the conventional analysis declares 19 of 28 pairwise differences significant. Annotator identity explains far more rating variance than system identity, and the winning system changes whenever any single annotator is removed. The ordering that does emerge tracks output length: mean emoji count explains 78.7% of between-system variance, and a within-item length-matched comparison over 2,599 pairs reverses the leaderboard. We further show that cross-provider anisotropy differences vanish under mean-centring, that per-language token costs change sign with the normalising unit, and that multi-view row-wise splits inflate macro-F1 by 3.1 points and change the top-ranked system. In place of preference scoring we propose emoji-affect decodability, a reference-based probe whose rankings are stable to \pm0.003 macro-F1 across seeds.
[NLP-63] IterSynth: Rethinking Deep Search Agents via Role-Decoupled Iterative Synthesis
【速读】： 该论文旨在解决现有基于ReAct风格的大型语言模型（LLM）智能体在深度搜索任务中面临的两大核心问题：角色耦合（role coupling）与上下文累积（context accumulation）。角色耦合指单一策略需同时承担规划、证据使用与答案合成等多重功能，导致能力冗余与性能瓶颈；上下文累积则因不断增长的搜索历史引入噪声，干扰关键信息的识别与利用。为此，论文提出IterSynth，一种角色解耦且基于摘要的迭代式搜索范式，其核心在于将任务分解为独立的规划器（Planner）与合成器（Synthesizer）两个角色，并以摘要作为持续演化的搜索状态，实现规划与合成的分离，同时通过摘要机制有效抑制上下文噪声。为高效训练该框架，进一步提出角色解耦策略优化（Role-Decoupled Policy Optimization, RDPO），结合终端结果奖励与回合级评分标准，计算各角色特异性的优势值，实现更精准的信用分配。实验表明，在包括BrowseComp和Xbench-DS在内的五个长时程深度搜索基准上，IterSynth-8B平均得分达50.7，优于现有≤8B参数量最强基线4.2个百分点；此外，该方法具备模型无关性，可在前沿闭源大模型上实现显著的零样本性能提升，超越传统ReAct等提示范式。
链接: https://arxiv.org/abs/2609.29444
作者: Xingyu Wu,Yuchen Yan,Zhengxi Lu,Siqi Chen,Xin ZHANG,Aiting Liu,Chao Deng,Jie Liu,Jin Ma,Jian Shao,Jun Xiao,Yongliang Shen
机构: Zhejiang University(浙江大学); Tencent(腾讯)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Code: this https URL
Abstract:Deep search requires LLM agents to decompose complex queries, search for evidence, and synthesize grounded answers, yet existing ReAct-style agents suffer from two limitations: role coupling, where one policy must handle planning, evidence use, and synthesis; and context accumulation, where growing search histories introduce noise and obscure useful information. To address these issues, we propose IterSynth, a role-decoupled and summary-based paradigm that alternates between a Planner for identifying information needs and a Synthesizer for integrating evidence into an evolving summary state. This design separates planning from synthesis while using the summary as the persistent state of search, reducing both capability coupling and context noise. To train IterSynth effectively, we further introduce Role-Decoupled Policy Optimization (RDPO) for reinforcement learning, which combines terminal outcome rewards with turn-level rubric evaluations and computes role-specific advantages for more precise credit assignment. Experiments on five long-horizon deep-search benchmarks such as BrowseComp and Xbench-DS show that IterSynth-8B achieves an average score of 50.7, surpassing the strongest prior \leq 8B agent by +4.2%. Moreover, IterSynth serves as a model-agnostic prompting paradigm, delivering substantial zero-shot gains over ReAct and similar prompting paradigms on frontier proprietary models.
[NLP-64] Just Ask Jev: Reinforcement Learning for Calibrated Decisions as a Zero-Shot Detector of AI Alignment Failures
【速读】： 该论文旨在解决现有语言模型对齐失败检测方法中效率低、评估成本高且难以捕捉关系型对齐失效的问题。当前主流方法如生成式评判模型（Generative Judges）或基于概率分类的模型（如Llama Guard）通常需多次解码或仅输出单一标签，无法在单次调用中高效获取多维度的校准概率判断。为此，本文提出一种基于强化学习校准决策（Reinforcement Learning for Calibrated Decisions, RLCD）训练的模型Jev，其核心创新在于通过一次前向推理即可针对同一输入回答多个类型化问题，并输出具有校准意义的概率分布。为验证其对十类典型对齐失败（包括奉承、越狱攻击、欺骗、提示注入、幻觉、隐私泄露、社会偏见、奖励黑客、隐藏不确定性及权力寻求）的检测能力，作者构建了RLCDAlignBench基准测试体系，涵盖44个子任务与5个目标模型，采用自动评分器与人工标注双重验证。关键思想是将问题表述与输入上下文分离：即改变提问方式和答案类型的同时，保持输入字段不变，从而系统性地探究不同信息源的影响。实验表明，仅使用一个通用问题，Jev即可实现0.886的中位数AUROC（零样本），优于多数有监督基线；其中问题措辞影响较小，而上下文字段（尤其是编码标签信息的字段）起决定性作用。此外，该方法在与人类标注的一致性上达到与参考评分器相当的水平，揭示了现有基准中的标签缺陷，并相较大型语言模型评判者降低63倍计算成本。
链接: https://arxiv.org/abs/2609.29429
作者: Ruoqi Guo,Yi Liu,Gelei Deng,Yuekang Li,Lida Zhao,Yutao Wu,Simin Chen,Ying Zhang,Leo Yu Zhang
机构: Griffith University (格里菲斯大学); Nanyang Technological University (南洋理工大学); UNSW (新南威尔士大学); Deakin University (迪肯大学); George Mason University (乔治梅森大学); Wake Forest University (维克森林大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR)
备注:
Abstract:Detectors of alignment failures screen deployed language models and score alignment benchmarks. Most are generative judges that spend a decoding pass on every criterion, and classifiers that read token probabilities, such as Llama Guard, still score one fixed label per call. Jev, a model trained with reinforcement learning for calibrated decisions (RLCD), answers many typed questions about one input with calibrated probabilities in a single call. Whether it detects alignment failures has not been measured. We present RLCDAlignBench, which benchmarks Jev on ten alignment failures: sycophancy, jailbreaks, deception, prompt injection, hallucination, privacy violation, social bias, reward hacking, concealing uncertainty, and power seeking. It spans 44 benchmarks and five target models, labelled by each benchmark’s scorer and, on two, by humans. Many of these failures are relational, defined against a reference, such as the user’s belief or an injected instruction, that the response alone does not reveal. Our key idea is therefore to vary what Jev is asked separately from what it sees: the question’s wording and answer type on one side, the fields of the input on the other. A single generic question reaches a median AUROC of 0.886 zero-shot and beats supervised baselines on most benchmarks. Question wording matters little, while context matters more, mostly through fields that encode the label. Jev matches the reference scorer’s agreement with human labels, surfaces label defects in existing benchmarks, and costs 63x less than LLM-judge scorers. Code and data: this https URL.
[NLP-65] agent ic-ger: terminology recovery in long-form speech using global context ICASSP2027
【速读】： 该论文旨在解决长时音频自动语音识别（ASR）中领域特定术语识别不准确且一致性差的问题。尽管近年来语音语言模型在长音频ASR任务上取得了显著进展，但专业术语的转录仍面临挑战，尤其在存在歧义或上下文依赖的情况下。其解决方案的关键在于提出一种基于大语言模型（LLM）的代理系统——Agentic-GER，该系统通过利用完整转录文本提供的全局上下文来识别可疑术语，并对模糊识别结果进行消歧。该代理采用选择性重识别源语音片段以验证候选修正项，并将已接受的修改作为后续决策的指导依据，从而实现迭代优化。实验表明，该方法在中英文双语场景下均能稳定提升术语转录准确性，尤其在中文语音上相较Whisper基线实现了高达36.8%的相对偏倚字符错误率（B-CER）降低。
链接: https://arxiv.org/abs/2609.29428
作者: Yanqiao Zhu,Wupeng Wang,Zhifu Gao,Xiangang Li,Xie Chen
机构: Alibaba Token Foundry(阿里巴巴通义实验室)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: submitted to ICASSP 2027
Abstract:Recent advances in speech language models have improved automatic speech recognition (ASR) for long-form audio. However, accurately and consistently transcribing domain-specific terminology remains challenging. Motivated by the world knowledge and contextual capability of large language models (LLMs), we propose Agentic-GER, an LLM-based agent for terminology correction in long-form speech. The agent uses global context from the full transcript to identify suspicious terms and resolve ambiguous hypotheses. It selectively re-transcribes the source speech to check candidate corrections, and uses accepted edits to guide subsequent decisions. Experiments with four LLMs and two ASR systems on GigaSpeechBench show consistent terminology improvements in both Chinese and English, with and without thinking. On Chinese speech, Agentic-GER achieves up to a 36.8% relative reduction in biased character error rate (B-CER) over the Whisper baseline.
[NLP-66] Rufus-Air: An Open LLM Post-Training Recipe
【速读】： 该论文旨在解决大模型在后训练（post-training）阶段如何高效、可复现地提升多模态推理与任务执行能力的问题，特别是在不依赖人工标注或私有教师模型的前提下实现性能突破。其核心挑战在于如何设计一个系统化、模块化的训练流程，以逐步构建从基础指令遵循到复杂任务规划的高级智能行为。解决方案的关键在于提出了一套名为Rufus-Air的开放且可复现的八阶段串行训练流水线，涵盖从监督微调（SFT）到基于人类反馈的强化学习（RLHF）的全过程。该方案通过分阶段引入渐进式奖励机制——从硬性验证的可计算奖励过渡到软性判别器评估信号，确保每一步训练均处于有效学习区间；同时强调数据质量与难度过滤对强化学习（RL）稳定性的关键作用，并指出基础设施与工程实现策略本身即为训练配方的核心组成部分，而非仅是技术细节。实验表明，该方法在不使用新的人工标注或内部蒸馏教师的情况下，显著优于官方发布的GLM-4.5-Air模型，且在同等规模开源模型中具备竞争力。
链接: https://arxiv.org/abs/2609.29421
作者: Chia-Yuan Chang,Renyuan Cheng,Rui Feng,Xiaotian Han,Yuan He,Hongye Jin,Linwei Li,Shiyang Li,Fenglin Liu,Xin Liu,Priyanka Nigam,Haoyang Wen,Zhenghao Xu,Zhuocheng Xu,Bing Yin,Qingyu Yin,Chao Zhang,Rongzhi Zhang,Zhihan Zhang,Zixuan Zhang,Zixuan Zhang,Tuo Zhao
机构: Amazon(亚马逊)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 47 pages, 9 figures, 20 tables. Authors are listed alphabetically by surname; all contributed while at Amazon. The two authors named Zixuan Zhang are different people
Abstract:Rufus-Air is an open and reproducible post-training recipe on GLM-4.5-Air-Base (106B-A12B), organized as a serial pipeline of eight stages: SFT, Reasoning RL, Coding RL, Instruction-Following RL, General Agent, Coding Agent, Search Agent, and RLHF. We document the data, reward design, infrastructure, stage order, and stagewise results needed to reproduce the recipe. Stages progress from basic to advanced capabilities and from hard, verifiable rewards to softer judge-based signals. Training builds on open-source components and public data, much of it used as released, without new human annotation or an in-house distillation teacher. Our main findings are that (i) diverse, high-quality SFT establishes a strong capability floor; (ii) difficulty filtering keeps RL prompts within a productive learning range; (iii) reward reliability provides a practical principle for ordering stages; and (iv) infrastructure and engineering choices are part of the recipe, not just an implementation detail. Rufus-Air improves over the official GLM-4.5-Air post-trained release and is competitive with similarly sized open models.
[NLP-67] Large Language Models for Programming: Actually Fixing or Reimplementing Incorrect Code?
【速读】： 该论文旨在解决生成式 AI（Generative AI）在程序调试与问题求解过程中，其修复错误代码的行为与人类程序员修复方式之间的差异问题。具体而言，研究关注大语言模型（LLM）在修正存在缺陷的代码时，是倾向于进行细微修改以贴近人工补丁，还是更倾向于生成全新的解决方案。其解决方案的关键在于构建了一个包含约3000条来自Codeforces平台用户提交记录的数据集，并将每条有缺陷的代码与其对应的由人类编写的修复方案进行配对，以此作为基准衡量标准。通过比较大语言模型（GPT-5系列三个版本）生成的修复结果与人类修复之间的相似性，以及利用Codeforces-R1数据集中的测试用例评估修复有效性，研究发现：尽管部分模型能够正确解决问题，但它们通常比人类修复改动更多代码行数，且在某些情况下会生成完全重构的新解法。更为关键的是，当允许模型从零开始生成解决方案时，其正确率反而高于基于已有错误代码进行修补的场景，即使原始错误代码已非常接近人工修复。这一发现揭示了当前生成式AI在辅助编程时存在“过度重构”倾向，对面向调试支持的AI工具设计提出了警示——应优先支持增量式、渐进式的修复策略，而非鼓励以全新方案替代原有逻辑。
链接: https://arxiv.org/abs/2609.29410
作者: Alexandru Stefan Stoica,Traian Rebedea,Marian Cristian Mihaescu
机构: University Politehnica of Bucharest (布加勒斯特理工大学); University of Craiova (克里约瓦大学)
类目: Computation and Language (cs.CL); Software Engineering (cs.SE)
备注:
Abstract:Recent studies have shown that Large Language Models can effectively solve problems and fix bugs in diverse programming environments, including competitive programming. Existing approaches primarily evaluate LLM performance in problem solving or bug fixing independently, but do not explore the relationship between these two capabilities. This work focuses on determining how much the LLM deviates from a buggy solution to fix the bug compared to a human-written patch, and if there is a bias towards generating entirely new solutions. We construct a dataset with all the submissions ( \sim 3000) from a couple of users from Codeforces, and we match each buggy submission with its corresponding human fix. By using the similarity between the buggy solution and the human fix as a baseline, we evaluate the quality of LLM-generated bug fixes on 3 OpenAI GPT models (gpt-5-nano, gpt-5-mini, gpt-5.1). We check if the generated solutions solve the problem by using the Codeforces-R1 dataset, an openly available dataset that has tests generated with the DeepSeek-R1 model. Our findings suggest that LLMs tend to modify more lines than necessary compared to human fixes and, in some cases, generate entirely new solutions. We also observe that LLMs solve more problems correctly when allowed to generate solutions from scratch rather than patch buggy submissions, even when those submissions are close to the human patch. This has important implications for the design of AI-assisted programming tools, particularly in supporting user debugging processes and promoting incremental problem-solving strategies rather than solution replacement.
[NLP-68] Baseline Shape Decides the Verdict: A Controlled Re-Examination of Ternary Language Models at 60K Parameters
【速读】： 该论文旨在解决微控制器类语言模型中采用1.58位三值权重（ternary weights）时，模型性能评估的可复现性与归因偏差问题。现有研究在参数量低于100万的场景下多依赖单一种子的孤立对比，难以区分架构优势与基线设计偏差的影响。其核心挑战在于：当前宣称的三值路由模块（routed ternary block）相比全精度Transformer的性能提升，是否真正源于架构设计的归纳偏置（inductive bias），还是由训练配置、模型形状（baseline shape）或量化策略等混杂因素导致。本文的关键解决方案在于通过严格控制实验条件——固定训练配方、每单元三个随机种子、共98次字节级运行，在单台笔记本电脑上系统重现实验。研究发现：（i）模型深度/宽度选择带来的基线形状差异对验证损失的影响可达22.6%，远超所测架构效应，且最优形状随预算变化而反转，表明无法依赖固定结构；（ii）尽管在130MB预算下路由模型仍胜出，但其性能优势主要源于递归路径主导的路由机制，而一个简单的门控对角SSM块反而超越它9.1%；（iii）三值量化惩罚随架构不同显著变化，但部分归因于全精度位置嵌入未被量化（占参数量11–22%），使比较基准本身存在量化不一致；（iv）分阶段的“全精度预训练-三值微调”策略虽在高学习率下表现更优，但在常规微调率下反而落后15.3%，结论逆转。因此，该研究揭示了现有三值模型性能声称中存在严重的基线偏差与训练配置依赖，强调必须在统一、可复现的框架下评估架构有效性。
链接: https://arxiv.org/abs/2609.29397
作者: Gautam Veldanda
机构: 独立研究员(Independent Researcher)
类目: Computation and Language (cs.CL)
备注: 11 pages, 1 figure. Code and run logs: this https URL (tag p1-v1). Zenodo: this https URL
Abstract:Ternary (1.58-bit) weights are attractive for microcontroller-class language models, but the sub-1M-parameter regime rests mainly on isolated, single-seed comparisons. One prominent example reports that a routed ternary block (convolution, diagonal SSM and sparse attention mixed by a per-token router) beats a parameter-matched full-precision transformer by 22% at 60K parameters, attributing this to inductive bias. We re-run it under one fixed recipe, three seeds per cell, 98 byte-level runs on one laptop. (i) Baseline shape dominates: at a 16M-byte budget, param-matched transformers span 22.6% in validation loss purely by depth/width choice - far more than any architecture effect we measure there - and the best-shaped transformer ties the routed model, so the published margin is at least partly a baseline-shape effect; the ordering of shapes reverses with budget, so no single fixed shape can be trusted. (ii) At 130M bytes the routed model does win, by 22.2-24.0% over the three transformer shapes we evaluate there - but a plain gated diagonal-SSM block beats it by a further 9.1%, and the routed model’s own router puts most of its weight on its recurrent pathway, so the gain does not require routing. (iii) The ternary penalty differs by architecture at the larger budget (+5.3% best transformer vs. +19.5% routed, +28.1% gated SSM), but we cannot attribute that to architecture alone: our transformers keep learned positional embeddings in full precision, 11-22% of their parameters, so they are less quantized than the models they are compared with. (iv) A 90/10 full-precision-then-ternary schedule beats all-ternary training, but only at a stage-2 learning rate about 10x the pretraining peak; at a conventional fine-tuning rate it looks 15.3% worse, reversing the conclusion. The from-scratch baseline was not itself learning-rate tuned, which bounds (iii) and (iv). Code and run logs released.
[NLP-69] Likelihood Ranking doesnt Scale Like Prompting in LLM s
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）评估中存在的一种核心问题：当前主流的评估方法，如基于提示（prompting）的生成式回答与基于似然度（likelihood-based）的候选答案评分，虽然在实践中广泛应用，但二者在机制上存在本质差异，可能导致对模型能力的误判。具体而言，标准的似然度评分仍依赖于问题和答案集的上下文条件，其评估过程与提示法类似，而本文提出了一种互补性评估协议——通过构建由同一问题-答案对生成的陈述句（declarative statements），并基于这些陈述的似然度进行排序来评估模型表现。研究发现，在涵盖95个解码器仅有的模型（参数量从0.1B到104B）及10个多项选择题问答（Multiple-Choice Question Answering, MCQA）数据集的实验中，陈述句的似然度排名与提示回答结果之间存在系统性偏差。其中，陈述句似然度准确率随模型规模变化保持相对稳定，而提示回答性能则随模型规模和指令微调显著提升。这表明，基于受控陈述句的似然偏好与任务条件下的答案选择机制分别揭示了模型行为的不同维度，二者不可互换使用。因此，该研究的关键贡献在于揭示了不同评估范式之间的本质差异，并强调应根据评估目标选择合适的评估方法，避免因方法混淆导致对模型能力的误解。
链接: https://arxiv.org/abs/2609.29390
作者: Alessandro Bondielli,Lucia Passaro,Davide Bacciu,Alessandro Lenci
机构: University of Pisa(比萨大学); CoLingLab(计算语言学实验室); Department of Computer Science(计算机科学系); Department of Philology, Literature and Linguistics(语言学、文学与语言学系)
类目: Computation and Language (cs.CL)
备注:
Abstract:LLM evaluation is commonly performed either by prompting models to produce answers or by scoring candidate outputs with likelihood-based metrics. In multiple-choice QA, however, standard likelihood-based scoring is still conditioned on the question and answer set, and can therefore leverage the same task-conditioned answer-selection interface used in prompting. We study a complementary protocol based on likelihood ranking of declarative statements constructed from the same question–answer pairs. Across 95 decoder-only models, ranging from 0.1B to 104B parameters, and 10 MCQA datasets, we find a systematic divergence between declarative-statement likelihood ranking and prompted answering. Statement-likelihood accuracy remains comparatively stable across scale, whereas prompted answering improves sharply with scale and instruction-tuning. These results suggest that likelihood preferences over controlled declarative alternatives and task-conditioned answer selection probe distinct aspects of model behavior, and should not be treated as interchangeable.
[NLP-70] BanglaTurn: A Benchmark and Whisper-Based Model for End-of-Turn Detection in Bangla Speech
【速读】： 该论文旨在解决孟加拉语对话语音中话语回合结束检测（end-of-turn detection）的标注数据稀缺与模型性能不足问题。其核心解决方案是构建首个大规模、高质量的孟加拉语对话语音语料库BanglaTurn，包含35,374段时长3至15秒的播客语音样本，并通过结合说话人分离（speaker diarization）与大语言模型（LLM）辅助标注后由人工校验的方式实现精准的回合状态标注。在此基础上，提出一种基于Whisper编码器并搭配任务特定分类头的端到端模型，在平衡测试集上达到84.33%的准确率（95%置信区间：80.3–88.1），显著优于Smart-Turn v3基线（69.28%），同时将误漏率从51.57%大幅降低至7.55%，尽管伴随假阳性率上升。研究还系统分析了编码器层微调、多尺度池化及INT8量化对性能的影响，且在CPU上实现端到端延迟稳定在165–191毫秒之间，具备实际部署可行性。
链接: https://arxiv.org/abs/2609.29371
作者: Mizbaul Haque Maruf
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:This paper presents BanglaTurn, a corpus for end-of-turn detection in Bangla conversational speech, and a model trained on it. The corpus holds 35,374 samples of 3 to 15 s of podcast speech, labelled for turn state by combining speaker diarization with an LLM pass, with every label then checked by a human annotator. The model pairs a Whisper encoder with task-specific classification heads. On a class-balanced test set drawn from a held-out podcast, it reaches 84.33% accuracy (95% CI 80.3 to 88.1) against 69.28% for the Smart-Turn v3 baseline, and lowers the false negative rate from 51.57% to 7.55% at the cost of a higher false positive rate. We report what encoder layer fine-tuning, multi-scale pooling and INT8 quantization each contribute, and latency stays within 165 to 191 ms end to end on CPU.
[NLP-71] From Policy Documents to Structured Survey Responses: Evaluating Large Language Models for Policy Monitoring
【速读】： 该论文旨在解决科学、技术与创新（Science, Technology, and Innovation, STI）政策因其多样性与复杂性，导致难以在跨国范围内进行系统化映射与持续监测的问题。传统方法依赖人工问卷调查，成本高且难以规模化。为此，本文提出一种基于大语言模型（Large Language Models, LLMs）的解决方案，将LLMs作为“AI受访者”，通过长上下文提示学习（long-context in-context learning）构建数据提取流程，从公开网络来源的非结构化政策文本中自动识别并结构化关键信息，涵盖政策工具、目标群体及主题领域等预定义类别。该流程引入二级LLM进行相关性与证据验证，并与人工标注结果进行对比，以确保输出质量。基于多国数据集的评估表明，LLM生成的结构化指标与人工结果具有高度一致性（84–95%重合度），但在自由文本字段中仍存在差异，表现为模型倾向于提供更详细的程序性描述。研究结果凸显了人机协作（hybrid human-AI）工作流在政策监测中的潜力，可在显著提升效率与可扩展性的基础上，维持必要的专家验证与语境解读，为构建智能化、可持续的政策追踪体系提供了可行路径。
链接: https://arxiv.org/abs/2609.29370
作者: Carolyn Cole,Matthias Deschryvere,Toqeer Ehsan,Arash Hajikhani
机构: Reliable Intelligence Team, VTT Technical Research Centre of Finland Ltd.(芬兰技术研究中心有限公司)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted as a full paper to FLINS-ISKE 2026
Abstract:Science, technology, and innovation policies are crucial for competitiveness, yet their diversity and scale make them difficult to map and monitor consistently. Existing approaches rely heavily on manual survey efforts, which are costly and challenging to scale across countries. Large language models (LLMs) enable new possibilities for extracting and structuring information from long and unstructured policy documents. This paper presents an application of LLMs as “AI respondents” for generating structured survey responses from policy texts. We develop a data extraction pipeline based on long-context in-context learning to map information from public web sources into predefined survey categories, including policy instruments, target groups, and thematic areas. The pipeline integrates a validation step using a secondary LLM to assess relevance and evidence, alongside comparisons with human-provided responses. Using a multi-country dataset, we evaluate the alignment between LLM-generated and human-generated outputs through overlap measures and cross-validation. Results show that LLMs achieve high agreement for structured indicators (84-95%), while differences remain in free-text fields, where models tend to provide more detailed procedural descriptions. These findings highlight the potential of hybrid human-AI workflows for policy monitoring, improving both efficiency and scalability while maintaining the need for human validation and contextual interpretation.
[NLP-72] Parts-of-Speech as Emergent Categories in SAE Latent Space
【速读】： 该论文旨在解决生成式语言模型中潜在表示（latent representations）所蕴含的句法-形态结构信息如何被稀疏自编码器（Sparse AutoEncoders, SAEs）捕捉的问题，具体聚焦于词性（Part-of-Speech, PoS）类别在SAE激活中的表征方式。研究发现，尽管词性差异在SAE激活中高度可恢复，但其并未体现为单一潜在变量与词性类别之间的一一对应关系，而是以紧凑且稀疏的潜在特征组形式分布于网络中。这些特征组具有显著的跨标签变异性和对未见数据的稳定性，并表现出相关词性类别间的重叠模式。因此，关键解决方案在于揭示：SAEs并非通过原子化的语法特征来编码语言结构，而是以一种分布式、依赖类别特性的组合方式将句法-形态信息局部化，从而实现对语言结构的有效表征。
链接: https://arxiv.org/abs/2609.29362
作者: Alessandro Bondielli,Lucia Passaro,Serena Auriemma,Alessandro Lenci
机构: University of Pisa(比萨大学); CoLingLab(计算语言学实验室)
类目: Computation and Language (cs.CL)
备注:
Abstract:Sparse AutoEncoders (SAEs) offer a promising way to inspect language model representations, but it is still unclear what kind of linguistic structure their latents expose. We use part-of-speech (PoS) categories as a controlled test case to study whether morpho-syntactic information is encoded by individual latents or by structured groups of features. We find that PoS distinctions are highly recoverable from SAE activations, but do not align with one-to-one latent / category mappings. This recoverability is not reducible to lexical memorisation, and Open and Closed PoS classes differ substantially. Categories are supported by compact groups of sparse latents, with substantial variation across tags. These groups remain stable on held-out data, while also showing overlap between related categories. Our results show that SAEs localise morpho-syntactic information in a distributed and category-dependent form rather than through atomic grammatical features.
[NLP-73] ArGuard Shared Task: Harmful Content Detection in Arabic Memes and LLM Prompts
【速读】： 该论文旨在解决阿拉伯语模因（Arabic memes）中有害内容检测以及面向阿拉伯语大语言模型（LLM）安全评估的有害提示检测问题。具体而言，任务分为两个赛道：Track A针对阿拉伯语模因中的多模态仇恨内容检测，Track B则聚焦于阿拉伯语大模型提示中的有害内容识别。其解决方案的关键在于利用先进的多模态预训练模型（如AraBERT、Jais和Qwen3-VL）进行跨模态特征融合与语义理解，以应对阿拉伯语文本与图像的复杂交互。实验表明，尽管在部分任务（如A2细粒度模因分类）上受限于标签稀疏性和训练-测试数据分布偏移，最佳系统仍取得了较高的性能，其中B1赛道达到0.984的宏平均F1分数，显示出生成式AI（Generative AI）在阿拉伯语安全评估中的潜力。
链接: https://arxiv.org/abs/2609.29349
作者: Firoj Alam,Md. Rafiul Biswas,Mohamed Bayan Kmainasi,Ali Ezzat Shahroor,Hamdy Mubarak,George Mikros,Abul Hasnat,Wajdi Zaghouani
机构: Qatar Computing Research Institute, Qatar; Hamad Bin Khalifa University, Qatar; APAVI.AI, France; Northwestern University in Qatar, Qatar
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:ArGuard is a shared task on harmful content detection in Arabic memes and LLM prompts. It includes two tracks: Track A focuses on multimodal hate detection in Arabic memes, while Track B addresses harmful prompt detection for Arabic LLM safety evaluation. In total, 58 teams registered, 35 participated in the final evaluation, and 27 submitted system-description papers. Participating teams explored models such as AraBERT, Jais, and Qwen3-VL. The best systems achieved macro-F1 scores of 0.823 on A1, 0.419 on A2, 0.984 on B1, and 0.790 on B2. Fine-grained meme classification in A2 was the most challenging setting, partly due to sparse labels and train-test distribution shifts.
[NLP-74] Where LLM Graders Succeed and Break: Evidence from Two Computer-Science Exams
【速读】： 该论文旨在解决生成式 AI 在大规模课程长周期考试评分中应用时存在的可靠性问题，特别是针对开放权重（open-weights）大语言模型（LLM）作为评分代理时出现的评分崩溃与不可靠行为。其核心挑战在于：尽管部分模型在理想提示下可达到接近甚至优于人类评卷员的评分一致性（如均值绝对误差 MAE 1.64/35，优于人类对打的 2.61/35），但微小的提示设计偏差——尤其是包含“从不给予部分分”等严格限制性语句的“严格评分员”前言——会导致14/17个开放权重模型严重偏离正常评分范围（MAE ≥ 8），其中三个模型完全停止评分。这种失效并非由模型规模或语气风格直接导致，而是源于提示中特定信用剥夺条款引发的系统性规避行为。研究进一步发现，该脆弱性在另一独立机器学习考试上具有可复现性，但影响方向因试题特性而异。关键解决方案是采用轻量级低秩适应（Light LoRA）微调：仅使用约3,900个标注样本训练一个适配器，即可使五个小型开放权重模型在双人人类评卷员一致性的基准下达到或超越人类水平，并显著降低对严苛提示人格的敏感性（敏感度 ≤ 0.32 MAE）。研究还公开了匿名数据集、完整消融网格及全流程分析工具链，为可信自动评分系统的构建提供实证基础。
链接: https://arxiv.org/abs/2609.29333
作者: Ali Habibullah,Yazan Alshoibi,Mohammad Alshiekh,Salman Khan,Naeemullah Khan
机构: KAUST Academy, Computer, Electrical, Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia; Visual Artificial Intelligence Laboratory, Oxford Brookes University; Lady Margaret Hall, University of Oxford
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:One long-form exam in a large course costs hundreds of grader-hours, and qualified graders are scarce; LLM graders are a tempting alternative. To show its pitfalls we grade a practical Computer Vision exam ( 570 dual-graded students) under 171 configurations spanning closed and open-weights models; the best reaches mean absolute error 1.64/35 , below the 2.61/35 two human graders achieve against each other. The catch is the prompt: a short ‘‘strict grader’’ preamble drives 14 of 17 open-weights models out of the graded band ( \textMAE \ge 8 ), three stopping grading altogether. The damage traces to the preamble’s two credit-withholding sentences, not to tone or model scale; one of them, ‘‘never give partial credit’’, alone makes two of three probed models stop grading. The closed flagships of three vendors shift calibration under it but stay in the band. In 162 further configurations on a second, independent Machine Learning exam from another course ( 1,038 dual-graded students), the preamble worsens ten models, moving three out of the band into collapse and one into refusal, yet improves seven whose neutral prompts over-mark: the vulnerability replicates, but its direction is exam-specific. Light LoRA fine-tuning repairs it: one adapter on the two exams’ pooled \sim 3,900 graded examples brings five small open models to parity or better with a human grader in agreement with the grader pair, and sensitivity to the three harsh personas nearly vanishes ( \le 0.32 MAE). We release the anonymised dataset, full ablation grid, and grading, fine-tuning and analysis pipelines.
[NLP-75] Grammatical “grandmother neurons” are rare in LLM s
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在语言结构编码方面的可解释性问题，尤其针对传统诊断分类器（diagnostic classifiers，或称“探针”）方法中存在的容量混淆（capacity confounds）与校准问题。现有探针方法通过训练额外的分类器来评估模型内部表示，但其性能可能反映的是探针自身的学习能力而非模型本征表征，导致对模型真实语义敏感性的误判。为此，本文提出一种无需探针的框架，实现对单个神经元层面语言选择性的精准定位。其核心创新在于引入神经元可分性指数（Neuron Separability Index, NSI），该指标基于语言学上的最小对立对（linguistic minimal pairs）构建，能够不依赖参数更新直接量化单个神经元区分语法正确与错误结构的可靠性。实验覆盖68种语言范式及七个模型检查点，揭示三大关键发现：1）形态和句法区分的原始可分性较早达到峰值，而句法-语义接口及概念区分则滞后；2）经置换归一化后，单神经元选择性表现出稀疏、微弱且高度特异的特征，罕见强选择性的“祖母细胞”式神经元；3）整体向量线性可分性、单神经元选择性与行为能力之间存在显著解耦，且靶向消融实验进一步表明激活选择性与因果依赖性并非等价。因此，该研究的关键突破在于通过无探针的NSI机制，实现了对语言敏感性的客观、高精度测量，为理解LLM内部表示提供了更可靠的分析路径。
链接: https://arxiv.org/abs/2609.29328
作者: Linyang He,Nima Mesgarani
机构: Zuckerman Mind Brain Behavior Institute, Columbia University (哥伦比亚大学齐克曼心智大脑行为研究所)
类目: Computation and Language (cs.CL)
备注: Accepted at COLM 2026. 28 pages
Abstract:Understanding how Large Language Models (LLMs) encode linguistic structures remains a fundamental challenge in interpretability research. While diagnostic classifiers (or “probes”) are widely used for this task, they face significant methodological criticism: training auxiliary classifiers introduces capacity confounds and calibration issues, often making it difficult to distinguish the model’s intrinsic representations from the probe’s ability to learn the task. To address these limitations, we introduce a probe-free framework for localizing linguistic selectivity at the individual neuron level. Leveraging the controlled contrasts of linguistic minimal pairs, we propose a Neuron Separability Index (NSI), a metric that directly quantifies how reliably single neurons differentiate grammatical from ungrammatical constructions without parameter updates. Applying NSI across 68 linguistic paradigms and seven checkpoints reveals three main patterns: 1) raw separability reaches near-peak levels earlier for morphological and syntactic distinctions than for syntax-semantics interface and conceptual distinctions. 2) after permutation normalization, single-unit selectivity is sparse, weak, and narrowly tuned: only a small fraction of units are sensitive to an average paradigm, and strongly selective “grandmother neurons” are rare. 3) whole-vector linear separability, single-neuron selectivity, and behavioral competence are largely dissociated, and targeted ablations further separate activation selectivity from causal reliance.
[NLP-76] Reasoning Instructions Can Break Answer Decoding in Vision–Language Models
【速读】： 该论文旨在解决链式思维（Chain-of-thought, CoT）指令在多选视觉语言模型（VLM）评估中引入的偏差问题，具体表现为评分机制在模型生成推理过程前即读取答案标签的置信度分数（logits），从而导致评估结果失真。其关键解决方案在于揭示并纠正“CoT前缀评分”（CoT-prefix scoring）这一评价接口与模型实际输出行为之间的不匹配现象：尽管模型在生成推理路径前已具备答案信息，但若在未完成推理生成时即读取输出层概率，会因概率分布向续写标记（continuation tokens）偏移而错误地反映模型能力。通过词汇表和层级诊断分析发现，答案信息在深层隐藏状态中仍保持线性可解性，因此采用条件匹配的线性探针可恢复78.94%的准确率，而自由生成方式亦能恢复75.24%，表明原始答案知识并未丢失。研究强调，只有当请求的输出事件与实际评分事件对齐时，才应使用CoT提示，否则将导致评估结果被评价接口的结构性偏差所扭曲。
链接: https://arxiv.org/abs/2609.29278
作者: Zeyan Li,Siyuan Qiu,Jianfeng Xu
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Chain-of-thought (CoT) instructions can distort multiple-choice VLM evaluation when a scorer appends a reasoning cue but reads answer-label logits before the model generates any rationale. We call this CoT-prefix scoring. On ScienceQA, Qwen2.5-VL-7B drops from 80.76% to 45.48%, and across five option-content permutations 93.54% of CoT-prefix predictions select the first slot. Condition-matched linear probes recover 78.94% from the same hidden states, while free generation restores 75.24%, showing that the answer often survives the prefix and the immediate readout fails. Vocabulary and layer diagnostics explain the mismatch: probability mass moves toward continuation tokens, while answer information remains linearly accessible in late layers. The effect recurs with varying severity across datasets and models, though not universally. These results show that CoT-prefix scoring can confound model knowledge with an evaluation-interface mismatch and should be avoided unless the requested and scored output events are aligned.
[NLP-77] pylazaro: a Python package for anglicism extraction in Spanish
【速读】： 该论文旨在解决在西班牙语文本中自动识别未同化词汇借用（主要为英语借词，anglicisms）这一语言学数据密集型任务缺乏专用工具的问题。现有主流文本处理库均未提供此类功能，而传统方法难以高效准确地完成该任务。解决方案的关键在于提出 pylazaro——一个开源的 Python 工具包，集成五种基于不同深度学习框架训练的序列标注模型，通过统一接口实现模型间的无缝切换与调用，避免用户面对各框架的复杂性。该工具包在性能上显著优于通用大语言模型（LLM），其最佳模型的 F1 值达 0.86，远高于通用 LLM 的 0.40 以下表现，且已实现广泛应用，被超过 58,000 次下载，并支撑“Observatorio Lazaro”这一实时监测西班牙语媒体中英语借词使用的公共资源平台。
链接: https://arxiv.org/abs/2609.29276
作者: Elena Alvarez-Mellado
机构: Universidad Autónoma de Madrid(马德里自治大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Lexical borrowings are words from one language that are introduced into another language. Identifying lexical borrowings in text is a relevant task for data-centric fields in Linguistics such as lexicography or corpus linguistics, but none of the standard libraries for text processing offers such a functionality. In this paper we present pylazaro, an open-source Python package for the automatic extraction of unassimilated lexical borrowings (mostly anglicisms) from Spanish text. pylazaro offers a single interface to five sequence labeling models that were trained using different libraries, so that users can run and switch between them without having to deal with the idiosyncrasies of each library. We describe the design and usage of the package, contrast the performance of its models with that of general-purpose LLMs (which perform poorly at this task: F1 below 0.40, compared to 0.86 for the best model in pylazaro) and report on its adoption: pylazaro has been downloaded more than 58,000 times and is the library behind Observatorio Lazaro, a resource that monitors anglicism usage in the Spanish press. pylazaro can be installed via PyPI, is documented in readthedocs and can be tried through a live demo hosted on HuggingFace Spaces.
[NLP-78] Policy as Code: A Coroutine-Bridge Harness for Fast-Reasoning Reliability on CAR-bench FAST ECAI2026 IJCAI
【速读】： 该论文旨在解决工具使用型智能体在真实世界不确定性下保持可靠性的问题，核心挑战在于多轮工具调用中因依赖链导致的模型调用开销过大。传统方法中，每个工具结果返回都需要一次独立的模型调用，造成显著延迟与计算成本。其解决方案的关键在于提出一种协程桥接（coroutine-bridge）框架，使模型仅需输出一段可执行的Python代码，该代码能够在工具交互过程中阻塞并原地恢复，从而实现模型调用与工具往返通信的解耦。这一设计将模型调用频率降至极低水平——在公开测试集上，平均每任务仅需2次模型调用对应7次代理回合，且单任务中位延迟仅为1.8秒（基于Cerebras gpt-oss-120b）。由于动作空间为可执行代码，策略以确定性逻辑形式直接编码于工具层，无需依赖提示词规则，实现了零推理成本的合规性保障。该方案在官方隐藏评估中以60.0% Pass^3成绩夺得Track 2冠军，较基线提升4.5倍，并在最低估算成本和最快中位任务延迟（3.14秒）条件下达成高于基线的表现；相同未修改的协程桥接框架在GPT-5.5上复现了相同的60.0% Pass^3，与前沿模型性能持平。此外，通过采用静态提示词结构，结合每任务状态动态追加，使得提示词在多次调用间保持字节级一致，实现78%输入令牌来自缓存（热尾部分达86.6%），显著降低实际计算开销，进一步强化了高效性与可扩展性。
链接: https://arxiv.org/abs/2609.29251
作者: Ivan Matveev
机构: Proxima Ultra
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 4 pages, 1 figure, 3 tables. Technical report for the winning entry in Track 2 (Cerebras Fast-Reasoning) of the CAR-bench Challenge at IJCAI-ECAI 2026
Abstract:CAR-bench evaluates whether tool-using agents stay reliable under real-world uncertainty, executing every tool inside the evaluator so that each tool-result exchange is a separate agent round-trip. A conventional next-action agent can batch parallel tool calls, but a chain of dependent calls costs it one model call per round of results. We present a coroutine-bridge harness in which the model’s only action is to emit a Python program that blocks and resumes in place across evaluator tool exchanges. This decouples model invocation from tool round-trips: on the public test split the agent uses a median of two model calls against seven agent turns per task, resolving a full multi-turn task in a median of 1.8 s of model latency on Cerebras gpt-oss-120b. Because the action surface is executable code, deterministic CAR-bench policies are encoded directly as logic in the tool layer rather than as prompt rules, enforcing compliance at zero reasoning cost. On the official hidden evaluation the harness won Track 2 with 60.0% Pass^3, 4.5x the organizer baseline, at the lowest estimated cost and the fastest median task latency (3.14 s) of any entry scoring above that baseline; the same unchanged harness reproduced an identical 60.0% Pass^3 on GPT-5.5 in the Open track, matching frontier-model agents. A single static prompt, appended with per-task state at the tail, stays byte-identical across calls and across tasks: the frozen submission prompt served 78% of input tokens from cache (86.6% across its warm tail), against 73% over a three-week development corpus in which prompt edits repeatedly reset the cache. This compounds the few-call design into a small fraction of nominal input compute.
[NLP-79] No More Free Lunch: Corpus Task Complexity Matters as Corpora Grow
【速读】： 该论文旨在解决大语料库背景下不同自然语言处理任务的复杂性差异问题，特别是揭示任务难度随语料规模增长的规律。传统研究多聚焦于难度随语料规模线性增长的任务（即低复杂度语料任务，Low CTC Tasks），但这类评估无法充分反映真实场景中更复杂的推理需求。为此，论文提出了语料任务复杂度（Corpus Task Complexity, CTC） 的概念，用以刻画任务难度随语料规模的增长速率。研究引入了10个属于高复杂度语料任务（High CTC Tasks）的新任务，其难度呈二次或更高阶增长，例如需遍历所有陈述对以识别矛盾。实验发现，对于长上下文语言模型（LCLMs），高CTC任务在语料规模增大时显著更难，且颠覆了仅基于低CTC任务得出的诸多模型结论——如块稀疏注意力和混合注意力机制在低CTC任务中表现接近全注意力，但在高CTC任务中性能显著下降。因此，大规模语料下的高复杂度推理仍面临挑战，因全注意力机制难以扩展，亟需未来研究探索高效且可扩展的解决方案。为推动该领域发展，作者发布了包含22个任务的基准测试集CTC-Bench及配套代码与数据。
链接: https://arxiv.org/abs/2609.29245
作者: Prasann Singhal,Amanda Bertsch,Jacob Steinhardt,Sewon Min
机构: UC Berkeley(加州大学伯克利分校); Allen Institute for AI(艾伦人工智能研究所); Carnegie Mellon University(卡内基梅隆大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 28 pages, 8 figures
Abstract:Given a large corpus, the questions one might ask can vary – from “When was the first human heart transplant?” to “What are all the contradictory claims in this literature?” – but what makes some questions more challenging than others? In this work, we define a notion of Corpus Task Complexity (CTC) that characterizes tasks by how their difficulty grows with corpus size; for instance, a retrieval query only requires a single linear pass over a corpus, while finding contradictions requires checking a quadratically growing set of claim pairs. Observing that prior work has largely only studied tasks whose difficulty grows linearly with corpus size, which we call low CTC tasks, we introduce 10 new tasks belonging to a class of high CTC whose difficulty grows quadratically or more in corpus size. We find that high-CTC tasks not only grow much more challenging on average at longer contexts for LCLMs, they reverse many modeling conclusions drawn solely from low-CTC evaluations. For instance, efficient block-sparse and hybrid attention approaches consistently match full attention performance on low-CTC tasks, but degrade much more on high-CTC tasks. Large-corpus high-CTC reasoning thus remains an open challenge as full attention is too costly to scale, motivating future research on these tasks. We release our code, data, and 22-task suite (CTC-Bench), to facilitate future research in this area.
[NLP-80] Post-Training Leaves Behavioral Shadows on Unrelated Decisions
【速读】： 该论文旨在解决大语言模型在后训练（post-training）过程中，如何实现能力迁移而不依赖目标任务数据、教师模型输出或参数的问题。传统方法通常需要大量任务相关的微调数据，而本文提出一种名为“主动无任务蒸馏”（Active Taskless Distillation, ATD）的新方案，其核心创新在于仅通过教师模型在每个提示中生成的一个词即可实现有效的能力转移。ATD的关键在于设计特定的提示，使得教师与学生共享的公共祖先模型对两个普通词汇几乎无偏好，从而在该提示下教师选择的单个词能够揭示其后训练带来的行为变化。学生模型仅基于这些提示-词对进行学习，无需目标任务样本、教师输出或参数。实验表明，在Qwen2.5-1.5B模型上，使用5,664组此类样本可使HumanEval+指标提升5.34个百分点，且在科学知识、常识推理和阅读理解等多类任务中均展现出跨模型规模与架构的泛化能力。功能分析进一步表明，所学能力具有可组合性，且其强度与教师模型更新强度高度相关，验证了该方法对后训练信息的有效捕捉。
链接: https://arxiv.org/abs/2609.29233
作者: Ziyang Zhang,Yubin Jing,Yuanhao Zeng,Yuyao Li,Haofan Wang,Yichen Gong
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 17 pages, 6 figures, 13 tables. Code: this https URL
Abstract:We find that language models can transfer capabilities through task-unrelated text. Post-training typically improves language models using task-specific data. Prior work on subliminal learning shows that information about these updates can pass through unrelated generations, but has largely focused on traits or preferences using extensive teacher outputs. We introduce Active Taskless Distillation (ATD), which achieves capability transfer using only a single word from the teacher per prompt. ATD probes the behavioral shadow of post-training by selecting prompts where the teacher and student’s shared public ancestor is nearly indifferent between two ordinary words. A student initialized from this ancestor learns solely from the resulting prompt-word pairs, without target-task examples, teacher logits, or teacher parameters. In the primary coding experiment with Qwen2.5-1.5B, 5,664nses yield a 5.34 pp gain on HumanEval+ over an exact nuisance-matched control thadisrupts prompt-resperiments showtransfer in scientific knowledge, commonsense reasoning, and reading comprehensins across additional model generations, sizes, and families. Functional analyses show that the learned sid composable, andthat its strength tracks the teacher’s update strength.
[NLP-81] EAGER: Enhancing Generative Event Extraction via Reinforcement Learning with Verifiable Rewards EMNLP2026
【速读】： 该论文旨在解决大语言模型在端到端事件抽取任务中面临的挑战，即同时需要准确识别事件触发词（event trigger）、分类事件类型，并提取符合事件模式（schema）的论元片段。其核心解决方案在于提出EAGER框架，通过结合细粒度可验证奖励（fine-grained verifiable rewards）与基于模式对比的优势估计（Schema-Contrastive Advantage Estimation），有效缓解在稀疏二值奖励下出现的优势崩溃（advantage collapse）问题。该方法在奖励设计中显式建模结构有效性、抽取准确性、上下文锚定性、覆盖度、过生成抑制以及片段精确性等关键指标。实验结果表明，EAGER在七个基准数据集上显著优于提示工程、监督微调及先前强化学习基线方法，性能提升尤为明显，验证了任务对齐的可验证奖励与对比优势估计在结构化信息抽取中的关键作用。
链接: https://arxiv.org/abs/2609.29230
作者: Omar Adjali,Siting Liang,Omair Shahzad Bhatti,Daniel Sonntag
机构: German Research Center for Artificial Intelligence (DFKI), Germany; Carl von Ossietzky Universität Oldenburg, Germany
类目: Computation and Language (cs.CL)
备注: Accepted to EMNLP 2026 Findings
Abstract:End-to-end event extraction remains challenging for large language models as it requires simultaneous identification of event triggers, classification of event types, and extraction of schema-grounded argument spans. We present EAGER, a reinforcement learning framework for generative event extraction that combines fine-grained verifiable rewards with Schema-Contrastive Advantage Estimation to alleviate advantage collapse under sparse binary rewards. Our reward design explicitly targets structural validity, extraction accuracy, groundedness, coverage, over-generation, and span precision. Experiments across seven benchmark datasets show that EAGER consistently outperforms prompting, supervised fine-tuning, and prior reinforcement learning baselines, achieving a substantial improvement over the strongest prior method. Results demonstrate that task-aligned verifiable rewards and contrastive advantage estimation substantially improve structured extraction.
[NLP-82] Predicting Emerging Topics from Outliers: A Prospective Study of Weak Signals in Embedding Space AACL
【速读】： 该论文旨在解决在文档嵌入空间中，那些初始被分类为噪声但后续演变为新兴主题核心成员的“前瞻性异常点”（anticipatory outliers）能否在发表时即被前瞻性预测的问题。其关键在于：仅利用文档首次出现时可获得的信息，通过分析异常点在嵌入空间中的几何特征（如位置分布），结合多个嵌入模型间的一致性来估计标签置信度，从而区分真正预示新主题的异常点与仅强化已有主题或孤立无意义的噪声。研究在两个法语新闻语料上验证了该方法的有效性，结果显示在交叉验证下F₁值从全样本的0.77提升至高共识子集的0.90以上，且在严格时间顺序评估下仍保持0.76–0.80的稳健表现，表明几何特征是实现预测性能的核心驱动因素。
链接: https://arxiv.org/abs/2609.29183
作者: Evangelia Zve,Gauvain Bourgne,Jean-Gabriel Ganascia
机构: LIP6, Sorbonne Université, CNRS(法国国家科学研究中心); Infopro Digital
类目: Computation and Language (cs.CL)
备注: Accepted to Findings of AACL-IJCNLP 2026
Abstract:Some documents that embedding-based topic models initially classify as noise later become founding members of emerging topics. At publication time, however, they appear as scattered points in embedding space and are difficult to distinguish from ordinary noise without the benefit of hindsight. We study whether such anticipatory outliers can be predicted prospectively, using only information available when a document first appears. We derive labels from the subsequent trajectories of outlier documents, distinguishing those that anticipate new topics from those that reinforce existing topics or remain isolated, and estimate label confidence through agreement across multiple embedding models. On two French news corpora, anticipatory outliers prove predictable at publication time. Under cross-validation, F_1 rises from about 0.77 over the full eligible population to above 0.90 on high-consensus subsets, and remains at 0.76-0.80 under a strictly chronological evaluation. Predictive performance is driven mainly by geometric features capturing each outlier’s position in embedding space.
[NLP-83] BanglaKontho: Closing the Long-Form Gap in Bangla Text-to-Speech
【速读】： 该论文旨在解决孟加拉语（Bangla）在神经文本转语音（Text-to-Speech, TTS）领域长期存在的资源匮乏问题，特别是针对长句韵律特征与一致单说话人叙事风格的数据缺失。现有公开的孟加拉语语音语料库多由短时朗读式语音构成，主要用于语音识别任务，难以支持高质量、自然流畅的长篇语音合成。为此，研究提出 BanglaKontho——一个基于专业有声书录制数据构建的单说话人孟加拉语TTS语料库，总时长约20小时，包含7,050段经验证的语音片段，采样率为24 kHz。其解决方案的关键在于：一是提供了高质量、长时序且具有连贯韵律的语音数据，填补了现有语料库的空白；二是同步发布了可复用的孟加拉语文本归一化工具，涵盖孟加拉国风格的数字分组、货币与日期表达、Danda标点及Unicode标准化处理，并配套完整的预处理流程。实验表明，基于该语料库从头训练的MB-iSTFT-VITS模型在词错误率（WER）和自然度评分（MOS）上分别达到9.5%和4.46，显著优于在12小时IndicTTS-Bn语料库上重训练的相同架构模型（WER: 16.0%, MOS: 3.16），验证了该语料库对提升孟加拉语TTS性能的有效性。语料库已开放发布，采用CC BY-NC 4.0许可。
链接: https://arxiv.org/abs/2609.29146
作者: Mizbaul Haque Maruf
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Bangla, the seventh most spoken language in the world, remains under-resourced for neural text-to-speech. Public Bangla speech corpora are dominated by short read-prompt utterances collected for speech recognition, leaving long-form prosody and consistent single-speaker narration uncovered. We present BanglaKontho, a single-speaker Bangla TTS corpus of 20 hours derived from professional audiobook recordings: 7,050 segmented utterances with verified transcripts at 24 kHz. We also release a reusable Bangla text normalizer covering Bangladeshi-style digit grouping, currency and date expressions, Danda punctuation and Unicode normalization, together with the full preprocessing pipeline. An MB-iSTFT-VITS baseline trained from scratch reaches 9.5% WER and 4.46 naturalness MOS, against 16.0% and 3.16 for the same architecture retrained on the 12-hour IndicTTS-Bn corpus. The corpus is released openly under CC BY-NC 4.0.
[NLP-84] ag-Aware Structured Text Translation: Towards a Systematic Understanding
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）在处理带格式文本（tagged text）时，翻译流畅性与标签保真度之间难以平衡的核心问题。现有方法在合成数据生成中往往过度追求结构标签的多样性或翻译的自然性，导致二者不可兼得。其解决方案的关键在于构建一个三层次协同优化的系统：在数据层面，提出一种混合式合成策略（Hy-LST），融合基于LLM的标签生成与两阶段LLM标签生成方法，以同时实现标签多样性与翻译自然性的提升；在能力层面，通过多任务监督微调框架将标签感知翻译分解为四个难度递增的子任务，实现能力的分层获取与知识迁移；在对齐层面，设计三种互补的奖励函数，在群体相对策略优化框架下分别针对流畅性、标签保真度及标签作用域内的翻译质量进行联合优化，显著优于单一奖励机制。实验结果表明，该方法在六种语言方向上均取得显著性能提升，且定性分析揭示了训练后错误模式的有效缓解。
链接: https://arxiv.org/abs/2609.29131
作者: Zhanglin Wu,Hengchao Shang,Daimeng Wei,Jiaxin Guo,Zongyao Li,Tengfei Song,Ning Xie,Weidong Zhang
机构: Huawei Translation Service Center (华为翻译服务中⼼), Beijing, China
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Internet texts are replete with format tags that carry structural, semantic, and functional meaning. Current large language model (LLM)-based translation systems struggle to balance translation fluency with tag fidelity when processing tagged text. We argue that resolving this tension requires a systematic approach at three interconnected levels: data synthesis, capability building, and multi-objective alignment. At the data level, we identify and formalize a fundamental trade-off between structural tag diversity and translation naturalness in synthetic data generation; existing methods optimize for one at the expense of the other. We propose a hybrid synthesis strategy (Hy-LST) combining LLM-based synthesis tag method and Two-Stage LLM-based synthesis tag method to produce both diverse and natural tagged data. At the capability level, we decompose tag-aware translation into four sub-tasks of increasing difficulty in a multi-task supervised fine-tuning framework, enabling targeted capability acquisition and knowledge transfer. At the alignment level, we design three complementary reward functions under a group relative policy optimization framework, each targeting a distinct objective (fluency, tag fidelity, and tag-scoped translation quality), and show that joint optimization consistently outperforms single-reward alternatives. Experiments on six language directions (en2zh, en2ja, en2de, en2fr, en2ru, de2fr) demonstrate that each level contributes measurable improvements, and the complete system significantly outperforms existing methods. Qualitative analysis reveals specific error patterns and their mitigation after training with our method.
[NLP-85] Accent Analogy Guidance: More Speaker Similarity at Equal Accent in Cross-Lingual Voice Cloning ICASSP2027
【速读】： 该论文旨在解决跨语言零样本文语转换（cross-lingual zero-shot text-to-speech）中参考语音的口音（accent）泄露至目标语音的问题。其核心解决方案是提出一种无需训练的采样器项——口音类比引导（Accent Analogy Guidance, AAG），通过模型自身对同一合成语音在双语言下的预测结果，估计并减去口音方向，使语音特征抵消而仅保留口音成分。基于盲测大语言模型（LLM）口音判别器、重加权无分类器引导（classifier-free guidance）及其变体在真实配音数据上的表现，研究发现AAG能稳定维持在身份-口音权衡曲线之上；以相同口音水平下的说话人相似度增量（ΔSIM）为评价指标，四种开源TTS模型（OmniVoice、MaskGCT、CosyVoice 2、F5-TTS）均显示正向提升，其中OmniVoice在三个测试集上ΔSIM达+0.11至+0.27，且在口音评分3.51至4.28（1-5量表）时仍保持0.02的说话人相似度优势。独立的语言识别度量与十二位听者评估组结果一致。通过前提测试及模型自身权衡曲线的覆盖范围，可预先判断AAG的潜在增益能力，成功预测出在X-Voice模型中无法带来改进的情况。
链接: https://arxiv.org/abs/2609.29123
作者: Yoomee Cho,Jisun Lee
机构: 未知
类目: ound (cs.SD); Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注: 5 pages, 1 figure, 2 tables. Submitted to ICASSP 2027. Listening samples: this https URL
Abstract:In cross-lingual zero-shot text-to-speech, the accent of the reference leaks into the target speech. We propose accent analogy guidance (AAG), a training-free sampler term that subtracts an accent direction estimated from the model’s own predictions for one synthetic voice rendered in both languages, so the voice cancels and only the accent remains. By a blind LLM accent judge on real dubbing data, reweighting classifier-free guidance between reference and text, and its variants, stay near one identity-accent trade-off curve; we score a method by its speaker similarity above that curve at equal accent ( \Delta SIM). Across four open TTS models AAG lies above the curve: on OmniVoice \Delta SIM is +0.11 to +0.27 on three test sets (accent 3.51 to 4.28 on a 1-5 scale at speaker similarity 0.29, where reweighting keeps 0.02); MaskGCT and CosyVoice 2 also lie above their curves, and on F5-TTS it is more native than any reweighting setting. An LLM-free language-ID measure and a twelve-listener panel agree. A premise test and the reach of a model’s own curve indicate in advance whether and roughly how much AAG can gain, predicting the one model where it gains nothing (X-Voice).
[NLP-86] ELF-REG: Scaling Continuous Diffusion Language Models to Reasoning Tasks
【速读】： 该论文旨在解决全连续扩散语言模型（fully continuous diffusion language models, dLMs）在复杂推理任务（如数学推理与代码生成）中性能不及自回归语言模型（autoregressive LLMs）和掩码式dLMs的问题。其核心挑战在于如何提升连续扩散框架在不依赖中间离散化步骤的情况下，对长程依赖与高阶逻辑的建模能力。解决方案的关键是提出ELF-REG，通过引入表示对齐与纠缠（Representation Alignment and Entanglement, REPA+REG）机制，利用一个冻结的自回归教师模型监督中间去噪器特征，并提供一个全局表示，该表示与响应序列共同进行联合去噪。这一设计显著增强了模型对语义结构的捕捉能力。实验表明，ELF-REG-L在GSM8K上以64次网络函数评估（NFE）达到55.96% pass@1，MATH-500为13.39%，HumanEval为22.56%（128 NFE），均优于同等规模的其他连续dLM。此外，通过早停策略（early-stop）无需少量步训练即可实现低NFE下的强性能，在16 NFE时达到41.21% HumanEval pass@10，超越同类先进模型。
链接: https://arxiv.org/abs/2609.29102
作者: Zeyu Michael Li,William Xingxu Chen,Bingshuo Qian,Jiayin Liu,Xiang Cheng
机构: Duke University (杜克大学); Tsinghua University (清华大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Fully continuous diffusion language models (dLMs) denoise continuous representations without intermediate discretization, then decode all response tokens in parallel at the final step. Their performance on challenging reasoning tasks remains less established than that of autoregressive (AR) LLMs and masked dLMs. We scale Embedded Language Flows (ELF) to mathematical reasoning and code generation on GSM8K, MATH-500, HumanEval, and MBPP. We introduce ELF-REG, which improves learning with representation alignment and entanglement (REPA+REG), where a frozen AR teacher supervises intermediate denoiser features and supplies a global representation that is jointly denoised with the response. ELF-REG-L achieves 55.96% pass@1 on GSM8K at 64 network function evaluations (NFE), and 13.39% on MATH-500 and 22.56% on HumanEval at 128 NFE. It outperforms the evaluated comparable-scale dLMs in pass@1 on GSM8K and code, and improves MATH-500 pass@1 from 10.55% for the ELF-L baseline to 13.39% with ELF-REG-L. Without few-step training, the same task-specific checkpoints support strong low-NFE performance through early-stop, which decodes an intermediate clean prediction without completing the denoising trajectory. At 16 NFE, ELF-REG-L reaches 41.21% HumanEval pass@10, outperforming recent continuous dLMs of comparable scale.
[NLP-87] Can Classical Semantic-Extractive Summarization Be Evaluated in Hindi? A Replication Study
【速读】： 该论文旨在解决生成式摘要中基于分布语义的抽取式摘要方法在印地语（Hindi）语言上的适配问题，其核心挑战在于如何在保持原方法语义结构的同时，针对天城文（Devanagari）书写系统进行语言特定模块的替换与优化。解决方案的关键在于对原文方法中的每一语言相关步骤进行天城文适配，包括分词、词向量表示和句法分析等环节，并采用一种经过验证的天城文感知型ROUGE评估框架，以确保评价结果的可靠性。然而实验结果显示，在默认均权配置下，复现系统在两个独立印地语文本语料库（XL-Sum的印地语部分及FIRE ILSUM 2.0 Hindi）上均显著劣于三句前置基准（Lead-3），分别在ROUGE-1上落后0.042和0.265。特征消融分析表明，唯一有效特征是句子位置，仅凭位置信息即可完全复现Lead-3性能；移除位置特征后性能最差，而通过验证集调优的权重最多只能达到与Lead-3持平，无法超越。此外，TextRank方法也表现出相同失败模式，说明该现象属于类别层面（class-level）而非实现层面的问题。进一步的选择性分析揭示，其余特征倾向于选择长且实体密集的正文句子，而现有印地语评测基准因依赖原文重用机制，无法有效奖励非前置内容的提取，从而凸显了构建专用评估资源的必要性。
链接: https://arxiv.org/abs/2609.29090
作者: Showket Ahmad Khan,Mudasir Mohd,Nasrullah Sheikh,Mohsin Altaf Wani,Abid Hussain Wani,Hilal Ahmad Khanday,Niyaz Ahmad Wani
机构: University of Kashmir (克什米尔大学); IBM Research (IBM 研究院); Manipal University Jaipur (曼尼帕尔大学贾伊普尔分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:We replicate the distributional-semantics extractive summarisation method of Mohd, Jan and Shah (2020) and adapt it to Hindi, substituting a Devanagari-appropriate component at every language-specific step. The system is evaluated on two independent corpora — the Hindi portion of XL-Sum and FIRE ILSUM 2.0 Hindi — under a Devanagari-aware ROUGE implementation validated against the XL-Sum authors’ own multilingual scorer, with all comparisons drawn as 1000-resample paired bootstraps. In its published equal-weight configuration the replicated system is significantly worse than a three-sentence lead baseline on both corpora, trailing Lead-3 by 0.042 ROUGE-1 Fon XL-Sum and by 0.265 on ILSUM. A feature ablation shows that sentenceposition is the only feature that contributes: position alone reproduces the lead baseline exactly, removing position gives the weakest configuration,and a validation-tuned weighting can at best equal Lead-3 and never exceed it. TextRank fails identically, making this a class-level rather than an implementation-level result. A selection analysis shows the remaining features steer extraction towards long, entity-dense body sentences while the references reuse the article this http URL Hindi benchmarks therefore cannot reward non-lead content selection, motivating purpose-built evaluation resources.
[NLP-88] CRISS: A Retrieval-Augmented AI Chatbot for Assisting Cancer Registrars
【速读】： 该论文旨在解决癌症注册人员（包括肿瘤数据专员，ODSs）在面对复杂且频繁更新的编码与分期标准时，难以高效准确获取和理解相关指南的问题。其核心挑战在于确保信息检索的准确性、可追溯性以及对专业判断的辅助支持，同时维持人类对最终数据抽象决策的主导权。解决方案的关键在于构建一个基于检索增强生成（Retrieval-Augmented Generation, RAG）技术的智能辅助系统——CRISS（Cancer Registry Intelligent Support System）。该系统通过整合国家癌症注册标准构建领域专用知识库，将内容分割为带元数据标签的段落并以密集嵌入形式索引，利用大语言模型（Large Language Model, LLM）结合检索到的相关文档生成具有引用依据的回答。实验结果表明，RAG配置在各类难度问题上均显著优于非RAG基线模型，尤其在高难度问题中表现突出，平均证据支撑得分分别达到0.62（简单）、0.56（中等）、0.59（困难），远高于非RAG模型的0.29、0.26、0.29。此外，RAG模型在语义相似度方面也表现更优。本地部署的RAG模型在处理复杂问题时表现最佳，而专有模型则更为审慎。整体而言，该研究验证了领域定制化RAG系统在提升癌症注册指南的可及性、解释力与响应质量方面的有效性，实现了人机协同、可溯源的智能辅助，保障了最终决策的人类主导性。
链接: https://arxiv.org/abs/2609.29075
作者: Vani Seth,Mohammad Beheshti,Anirudh Kambhampati,Vishwa Bhayani,Lucinda Ham,Prasad Calyam,Iris Zachary
机构: University of Missouri, Columbia, USA(密苏里大学哥伦比亚分校); Missouri Cancer Registry and Research Center(密苏里癌症登记与研究中心); MU Institute for Data Science and Informatics(密苏里大学数据科学与信息研究所); Department of Electrical Engineering and Computer Science(电气工程与计算机科学系); Department of Public Health(公共卫生系); College of Health Sciences(健康科学学院)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 21 pages, 13 figures, 7 tables. Keywords: cancer registry, retrieval-augmented generation, large language models, conversational AI, clinical informatics, oncology data specialists, medical question answering, AI safety, clinical decision support
Abstract:Cancer registrars, including Oncology Data Specialists (ODSs), must interpret complex and frequently updated coding and staging standards. We developed CRISS (Cancer Registry Intelligent Support System), a retrieval-augmented generation (RAG) conversational assistant that provides rapid, citation-supported access to registry guidance. This study evaluated whether CRISS could (1) support accurate and citation-supported responses, (2) improve access to and interpretation of relevant guidance, and (3) support training/helpdesk use while preserving human oversight of final abstraction decisions. We built a domain-specific knowledge base from national cancer registry standards, segmented into metadata-tagged passages and indexed as dense embeddings. Retrieved passages were used to generate citation-grounded responses through a large language model (LLM). Open-weight, proprietary, and non-RAG baseline models across Gemini and GPT families were evaluated on easy, medium, and hard registry questions using an LLM-as-a-Judge protocols. RAG configurations consistently outperformed non-RAG approaches, especially as question difficulty increased. Mean grounding scores for RAG were 0.62/0.56/0.59 across easy/medium/hard tiers versus 0.29/0.26/0.29 for non-RAG. RAG models also achieved higher semantic-similarity scores overall. Proprietary RAG models performed strongest on easy and medium questions, while local RAG models ranked highest on hard questions and proprietary models were generally more cautious. Domain-specific RAG improved evidence grounding and response quality for cancer registry questions while enabling citation-supported assistance across complexity levels. CRISS demonstrates the potential of human-centered, citation-grounded AI to support cancer registrars while preserving human oversight for final coding decisions.
[NLP-89] Empath: Tracing Multi-Level Emotion Dynamics in Crisis Counseling Dialogues
【速读】： 该论文旨在解决现有计算模型将情绪视为静态的、逐话语（utterance-level）标签，而忽视了危机支持对话中情绪动态演变的问题。其核心挑战在于如何有效捕捉和分析情绪在对话过程中的时序变化与复杂交互模式。解决方案的关键在于提出EMPATH框架，该框架从三个粒度层次系统性地建模情绪动态：话语级情绪标签、情绪转移概率以及全局对话情绪原型（conversation archetypes）。通过将该框架应用于自述为黑人群体的文本危机对话数据，研究揭示了持续的负面情绪、渐进向希望转变的趋势、对话参与者间明确的情绪角色分工以及异质性的恢复轨迹等重要发现。这一方法凸显了将情绪表达与危机支持过程视为动态演化过程的必要性，为对话中情感分析提供了更精细、更具解释力的计算范式。
链接: https://arxiv.org/abs/2609.29056
作者: Ziwei Gong,Yuchen Huang,Wen Liang,Nicholas Deas,Melanie Subbiah,Kathleen McKeown,Julia Hirschberg
机构: Columbia University(哥伦比亚大学); Barnard College(巴纳德学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Emotion dynamics are critical for understanding crisis-support conversations, yet most computational work treats emotion as static utterance-level labels. We introduce EMPATH, a framework for understanding affective dynamics in mental health dialogues across three granularities: turn-level labels, transition probabilities, and global conversation archetypes. Applying EMPATH to text-based crisis conversations with self-identified Black texters discussing grief, we find persistent negative affect, gradual hope-ward transitions, distinct texter-volunteer emotional roles, and heterogeneous recovery trajectories. These results highlight the informative patterns that emerge from computationally understanding crisis support and expressions of grief as dynamic processes within conversations, as well as the overall value of emotion-dynamic analysis for analyzing and comparing affect in dialogues.
[NLP-90] Design and Evaluation of LLM Chaining-Based Task Planning for General Purpose Service Robots
【速读】： 该论文旨在解决通用服务机器人（General Purpose Service Robot, GPSR）在真实家庭环境中执行自然语言指令时面临的任务规划不可靠问题。传统单提示（Single Prompt, SP）方法因上下文过长及“中间丢失”（Lost in the Middle）现象，导致多步动作序列生成不一致、性能下降。其解决方案的关键在于提出一种基于大语言模型（Large Language Model, LLM）的分链式架构（LLM chaining architecture），将指令分类与动作生成分离为两个专业化阶段，有效减少每次推理的提示长度约45%，并显著提升任务规划的一致性。实验结果表明，该方法在三种不同部署环境（本地开源与前沿云端模型）下的100个随机生成的GPSR指令上均优于SP方法，局部模型上规划成功率最高提升达37个百分点；真实机器人（丰田人机协作机器人HSR）实测进一步揭示，尽管规划成功率提高，但任务完成率仍受限于执行层故障，6/10任务成功完成，表明执行层面仍是当前系统的主要瓶颈。
链接: https://arxiv.org/abs/2609.29043
作者: Lucas Da Mota Bruno,Jiahao Sim,Yoshinobu Hagiwara
机构: Soka University(所泽大学)
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted to IEEE GCCE 2026. 5 pages, 6 figures, 3 tables
Abstract:General Purpose Service Robot (GPSR) tasks, as defined in the RoboCup@Home benchmark, require robots to interpret diverse natural language commands and generate multi-step action sequences in real home environments. Conventional Single Prompt (SP) approaches suffer from context bloat and the “Lost in the Middle” phenomenon, leading to unreliable task planning. We propose an LLM chaining architecture that separates instruction classification and action generation into two specialized stages, reducing per-inference prompt length by approximately 45% while improving planning consistency. We evaluate our method using 100 randomly generated GPSR commands across three language models spanning local open-source and frontier cloud deployment contexts. Results show consistent planning improvements over SP across all models, with gains of up to +37 percentage points on local models. Further, real-robot execution experiments on the Toyota Human Support Robot (HSR) reveal that planning success alone does not guarantee task completion, with 6 of 10 tasks completing successfully and execution-layer failures identified as the primary remaining bottleneck.
[NLP-91] MeshHeal: Two-Timescale Self-Healing for Gray Failures in Decentralized LLM Agent Networks
【速读】： 该论文旨在解决去中心化基于大语言模型（LLM）的多智能体系统中“灰度故障”（gray failures）的问题，即智能体在保持响应性的同时，任务求解质量持续退化，而系统难以及时识别并处理此类隐性失效。其核心挑战在于：在缺乏充分证据前需保护当前任务执行的稳定性，同时允许恢复后的智能体重新参与协作。解决方案的关键在于提出一种完全去中心化的自愈框架MeshHeal，通过双时间尺度的能力匹配型同行评审机制实现动态治理。在快速时间尺度上，采用自适应层级结构将不确定或低分输出从单评审升级为委员会协商与必要修正；在慢速时间尺度上，基于任务与能力条件的相对检测器聚合评分，以区分持续退化与正常波动，触发强制委员会审查，并将退化智能体从常规路由中排除，同时通过恢复探测提供再集成证据。为准确评估路由效果，研究引入模型支撑的多智能体系统评估（Model-Backed MAS Evaluation），将能力分配与执行模型绑定，避免仅依赖提示词分配导致的路由误差。实验表明，在BBH、MATH和MMLU-Pro基准上，MeshHeal以每任务51,000个模型标记的消耗实现0.839的退化阶段准确率，优于最强基线Symphony（0.807，每任务115,000标记），且在交错退化与恢复场景下能有效隔离退化智能体，直至其恢复后才重新纳入常规路由。
链接: https://arxiv.org/abs/2609.29015
作者: Keru Chen,Sen Lin,Yingbin Liang,Nathaniel D. Bastian,Shaofeng Zou
机构: Arizona State University (亚利桑那州立大学); University of Houston (休斯顿大学); The Ohio State University (俄亥俄州立大学); Johns Hopkins University (约翰霍普金斯大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: 31 pages
Abstract:Decentralized LLM-based multi-agent systems coordinate through local interactions, but an agent can remain responsive while its task-solving quality persistently degrades. Such gray failures require protecting current tasks before sufficient evidence exists to alter future routing, while still allowing recovered agents to rejoin. We introduce MeshHeal, a fully decentralized self-healing framework that couples ability-matched peer review across two timescales. At the fast timescale, an adaptive hierarchy escalates uncertain or low-scoring outputs from repeated single-reviewer evaluation to committee deliberation and, when needed, correction before use. At the slow timescale, a task- and ability-conditioned peer-relative detector aggregates scores to distinguish persistent degradation from ordinary output variation, trigger mandatory committee review, and eventually exclude degraded agents from ordinary routing; recovery probes provide fresh evidence for reintegration. To faithfully evaluate routing, we introduce Model-Backed MAS Evaluation, which ties ability assignments to execution models, since prompt-based ability assignments alone can leave routing errors hidden. Across BBH, MATH, and MMLU-Pro, MeshHeal achieves 0.839 degraded-phase accuracy using 51k total model tokens per task, versus the strongest baseline Symphony’s 0.807 accuracy using 115k per task. Under staggered degradation and recovery, MeshHeal isolates degraded agents, keeps them excluded from ordinary task execution until recovery, and returns them to normal routing.
[NLP-92] Polite but Misaligned: Evaluating LLM Politeness Judgments Against Human Prag matic Norms
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在评估社交得体性（social pragmatics）时，其判断是否与人类认知一致的问题。尽管现有模型在标准基准测试中表现优异，但其对礼貌性等社会语用特征的理解机制仍不明确。研究通过两个互补标注格式的英文数据集——连续人类评分与三分类标签——系统评估了七种主流模型的礼貌性判断能力。关键发现表明，模型间的共识度高于模型与人类之间的共识度，且模型与人类判断的一致性主要依赖于显性的语言线索；而某些用于建立人际关系的策略在不一致案例中出现频率更高。在三分类任务中，模型表现出系统性的“中性压缩”现象，即过度生成“中性”标签，同时显著低估“无礼”类别的预测概率。这一偏差在以专家共识为参照的诊断子集上依然存在。因此，该研究强调，未来的语用评估不应仅依赖聚合一致性指标，而需深入分析模型与人类在不同参考标准下的方向性差异模式。
链接: https://arxiv.org/abs/2609.29001
作者: Rong Wang,Kun Sun,Yadong Guo
机构: University of Tübingen (图宾根大学); Tongji University (同济大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Despite strong performance on standard benchmarks, it remains unclear whether large language models (LLMs) evaluate social pragmatics in ways that align with human judgments. We evaluate LLM politeness judgments using two English-language datasets with complementary annotation formats: continuous human ratings and three-way categorical labels. Across the seven evaluated models, we find that inter-model agreement is stronger than model–human agreement. Strategy-level analyses suggest that model–human alignment is associated with explicit linguistic cues, while some rapport-building strategies occur more frequently in misaligned cases. In the categorical task, model predictions exhibit systematic neutral compression, characterized by the overproduction of Neutral labels and the underprediction of Impolite labels. This pattern persists when expert consensus is used as the reference on a diagnostic subset. Our findings highlight the need for pragmatic evaluations that go beyond aggregate agreement metrics by examining directional patterns of model–human disagreement across different human references.
[NLP-93] Persuaded Not Informed: Incentive-Misaligned Witnesses Defeat In-Context Grounding
【速读】： 该论文旨在解决生成式AI在客户关系管理（CRM）系统中进行销售线索资格评估时，因受到具有乐观动机的销售人员陈述影响而导致决策偏差的问题。其核心问题是：当模型仅依赖包含利益驱动性陈述的对话上下文时，会将销售人员的主观乐观判断误作客观证据，从而错误地通过公司内部记录明确拒绝的交易。解决方案的关键不在于改进模型架构或引入更强的推理能力，而是提出一套诊断方法论，包括：(i) 桶分析（bucket analysis），用于区分说服性陈述与信息缺失；(ii) 同等信息对照实验，证明向模型提供正式政策文档反而降低精确率（从41%降至18%），揭示了模型对非结构性陈述的过度信任；(iii) 计算步骤控制实验，固定信息提取过程，仅改变预算与时间表的计算主体，结果显示即使在强模型上，偏差方向仍一致且显著，表明该现象是模型对输入偏见的系统性响应，而非偶然误差。研究预设泛化测试并获得否定结果，明确了问题发生的前提条件（即政策在输入中被精确表述），并公开全部评估工具，确保可复现性与结论的严谨性。
链接: https://arxiv.org/abs/2609.28854
作者: Rahul Balakavi
机构: AmpUp Research(ampup研究)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 9 pages, 4 figures, IEEE conference format. Ancillary files contain the evaluation harness, pre-specifications, and per-run result files
Abstract:Language-model agents increasingly answer questions over customer-relationship management (CRM) records, such as whether to qualify a sales lead. We identify a failure mode not addressed by a stronger model: when the context contains an assertion by a party with an incentive toward optimism - here the sales representative, a witness recorded in the CRM - the model treats the assertion as evidence and clears deals the company’s own records deem unacceptable. Across 100 lead-qualification tasks from CRMArena-Pro, the representative asserts an acceptable timeline in every call and an acceptable budget in 76; on the 31 tasks where such an assertion contradicts the price list and installation policy, a model reading only the transcript clears the deal in 29 of 31 cases. The signature is consistent across seven models from four providers (misled on 87-97%); scale and explicit reasoning confer no resistance. Only 3 of 35 genuine failures involve no assertion: the failure is persuasion, not missing information. We contribute a diagnostic method rather than an architecture: (i) a bucket analysis that separates persuasion from information gaps, (ii) a same-information control showing that supplying the records to the model lowers strict accuracy from 41 to 18 while raising recall - precision collapses - and (iii) a compute-step control that holds extraction fixed and varies only who computes Budget and Timeline. The margin ranges from 42 points on an inexpensive model to 2-5 points on models that already compute correctly; on the strongest models the arms are within confidence intervals, so the pattern is a consistent direction and a soundness property, not a proved performance floor. We pre-specify a generalization test that returns a negative result, characterize the precondition (a policy exactly specified in the inputs), and release all evaluation artifacts.
[NLP-94] LastOPD: Taming Collapse in Latent On-Policy Distillation
【速读】： 该论文旨在解决在基于策略的蒸馏（On-policy Distillation, OPD）过程中，学生模型因缺乏对教师模型内部推理过程理解而导致性能先提升后崩溃的问题。现有方法如OPRD通过引入潜在空间监督（Latent Supervision）试图弥补传统OPD仅依赖教师下一个词分布而忽略其思维过程的缺陷，但实验发现该方法在将Qwen3-4B和Qwen3-8B蒸馏至Qwen3-1.7B-Base时存在两大问题：一是“早期增益、后期坍塌”——模型初期在MATH-500上的准确率从25提升至46，但在后续训练中急剧下降至11且无法恢复；二是“对齐度越高、表现越差”——尽管潜在空间对齐指标持续改善，但最对齐的模型反而表现最差。深入分析表明，问题根源在于不同深度层在师生模型中的功能角色不一致，持续强制对齐可能导致学生被拉向其无法理解的教师状态。为此，本文提出LastOPD，其核心创新在于仅在最后一层状态（即两个模型语言头共享的公共接口）施加潜在信号，并在10步内逐步过渡到词级监督，从而保留潜在信号的有效成分，同时避免过早陷入非可解释状态。大量实验证明，与仅使用词级监督的OPD相比，LastOPD在4B和8B教师下分别提升MATH-500得分5.55和4.02点，在多数下游数据集上表现更优，且达到最终性能所需步数减少约一半。
链接: https://arxiv.org/abs/2609.28845
作者: Jie Yang,Zhengyu Fang,Zelin Xu,Jiarui Sun,Xiran Fan,Junpeng Wang,Liang Wang,Qinghua Liu,Yiwei Cai,Yan Zheng
机构: University of Illinois at Chicago (伊利诺伊大学芝加哥分校); Visa Research (维萨研究); Case Western Reserve University (凯斯西储大学); University of Florida (佛罗里达大学); The Ohio State University (俄亥俄州立大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:On-policy distillation (OPD) corrects a student on the responses it writes, but its signal is the teacher’s next-token distribution: it tells the student what the teacher says but misses how it thinks. Latent supervision promises the missing part by aligning the student’s latent states to the teacher’s. Recent methods such as OPRD bring this signal into on-policy distillation. However, we observe two failures of this recipe when distilling Qwen3-4B and Qwen3-8B into Qwen3-1.7B-Base. Early gain, late collapse: latent supervision alone lifts MATH-500 accuracy from 25 to 46 in 10 steps, but subsequent training degrades performance down to 11 with no recovery. Better alignment, worse behavior: although the alignment metric steadily improves throughout this collapse, the most aligned model turns out to be the worst performing. Further analysis suggests a mismatch in how the latent signal is applied: layers paired by depth play different roles in the two models, so continued alignment may pull the student toward teacher states it cannot understand. To address this, we propose LastOPD, which applies the latent signal only at the last-layer state, the common interface both LM heads read, and only during a 10-step crossfade into token-level OPD. This keeps the useful part of the latent signal and hands the student to token-level supervision before the collapse sets in. Extensive experiments show that LastOPD improves MATH-500 over token-only OPD by 5.55 and 4.02 points with the 4B and 8B teachers, leads on most held-out datasets, and reaches the final score of token-only OPD in about half the steps. Code is available at this https URL.
[NLP-95] COILD: An Indic-Centric Parallel Corpus and Benchmark for Machine Translation Across Indian Languages
【速读】： 该论文旨在解决印度语言机器翻译（Machine Translation, MT）中因高质量、以印度语为中心的平行语料库和评估基准稀缺而导致的性能瓶颈问题。现有跨语言资源多基于英语为枢纽（English-pivot）构建，难以充分反映印度语言在语言多样性、文化复杂性及领域特异性方面的实际特征。为此，本文提出COILD——一个以印度语为中心的平行语料库，包含超过116万条由人工翻译并验证的句子对，覆盖20种印度语言对，涵盖印欧语系、德拉维达语系、藏缅语系和亚欧语系四大语族。该语料库完全基于授权来源的原始印度语言文本，涵盖八个具有真实应用场景的领域。此外，研究引入一个领域中心的评估基准，包含2000条专家验证的句子，以实现对印度语言对间多语言与跨语言翻译的统一评估。通过在代表性多语言神经机器翻译模型IndicTrans2-Distilled和NLLB-200上进行微调，实验结果表明，在不同语言对、领域、自动评估指标和人工评估中均取得一致提升，验证了高质量印度语中心监督信号的有效性。COILD的关键在于其基于真实印度语源数据构建的高质量、多领域、跨语族语料库及其配套的领域导向评估体系，为推进印度语言的多语言机器翻译及未来多语言大模型的发展提供了关键资源支持。
链接: https://arxiv.org/abs/2609.28826
作者: Kshetrimayum Boynao Singh,Nitin Kumar Mishra,Palash Pratim Dutta,Atai Waris Khan,Aparna Kaushik,Avinash Kumar,Deeksha,Deepak Kumar,Saroj Kumar Jha,Saloka Sengupta,Anansa Roy,Umalatha Kannoth,Saifulla Samar,Meena Sharma,Manpreet Kaur,Jyoti Sharma,Ashwini Vaidya,Muralikrishna SN,Md Shad Akhtar,Poonam Bansal,Amita Dev,Sanasam Ranbir Singh,Samit Bhattacharya,Tanmoy Chakraborty,Asif Ekbal
机构: IIT Patna(印度理工学院比尔拉); IIT Delhi(印度理工学院德里); IIT Guwahati(印度理工学院古瓦哈蒂); IIIT Delhi(印度信息技术研究所德里); MIT-MAHE(曼尼普尔科技与高等教育学院); IGDTUW(印度政府工程与技术大学新德里分校)
类目: Computation and Language (cs.CL)
备注: 17 pages, including references and appendices
Abstract:Machine translation (MT) for Indian languages remains constrained by the limited availability of high-quality, Indic-centric parallel corpora and evaluation benchmarks. Existing multilingual resources are largely constructed from English-pivot content and often fail to capture the linguistic diversity, cultural complexity, and domain-specific characteristics of Indian languages. We present COILD, an Indic-centric parallel corpus comprising over 1.16 million human-translated and human-verified sentence pairs, covering 20 Indian language pairs across the Indo-Aryan, Dravidian, Tibeto-Burman, and Austro-Asiatic language families. The corpus is built entirely from original Indian language sources collected from licensed repositories spanning eight domains with direct real-world applicability. Furthermore, we introduce a domain-centric benchmark comprising 2,000 expert-verified sentences to enable consistent multilingual and cross-lingual evaluation across Indian language pairs. To validate the effectiveness of COILD, we fine-tune two representative multilingual neural machine translation models, IndicTrans2-Distilled and NLLB-200. Experimental results demonstrate consistent improvements across language pairs, domains, automatic evaluation metrics, and human evaluation, highlighting the effectiveness of high-quality Indic-centric supervision. COILD provides a valuable training and evaluation resource for advancing multilingual machine translation and future multilingual language models for Indian languages.
[NLP-96] Script Choice in LLM s: Evidence for Late-Layer Commitment
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在多语言处理中如何分布和实现脚本（script）知识的问题，特别是探究模型不同层级对输入脚本与输出脚本的编码机制。其核心发现是：输入脚本与指令要求的输出脚本在模型早期层中即被编码，但对实际输出脚本的承诺（script commitment）仅在最终层才显现，中间层的表征普遍默认为拉丁脚本。这一两阶段过程通过逻辑回归探针（logistic regression probing）与对数几率透镜分析（logit-lens analysis）两种互补可解释性方法得到验证。研究结果表明，模型深度是实现正确脚本承诺的关键因素，小型模型因缺乏足够深度而表现出较弱的脚本遵循能力，因此，构建具备足够深度且包容多元脚本的多语言架构成为设计此类系统的核心要求。
链接: https://arxiv.org/abs/2609.28784
作者: David Kletz,Sandra Mitrović,Itay Sabato,Ljiljana Dolamić,Fabio Rinaldi
机构: SUPSI, IDSIA, Switzerland; armasuisse, Science Technology, Switzerland; Independent Researcher
类目: Computation and Language (cs.CL)
备注:
Abstract:In this paper, we investigate how script knowledge is distributed across the layers of LLMs using two complementary interpretability methods: logistic regression probing and logit-lens analysis. Our probing experiments reveal a clear asymmetry: both the input script and the instructed output script are encoded in the earliest layers of the network, while, in contrast, commitment to the actual output script emerges only in the final layers, with the model’s intermediate representations defaulting to Latin throughout most of the layers. This two-stage process is confirmed by logit-lens analyses, which show that script commitment consistently occurs at the very last layers of the LLMs. Together with the weaker script-following performance observed in smaller models, these results form a converging body of evidence linking script commitment to model depth, with broader implications for the design of sufficiently deep, inclusive multilingual architectures.
[NLP-97] Reward-Tilted On-Policy Distillation for Acoustic Grounding in Audio-Language Models ICASSP2027
【速读】： 该论文旨在解决音频-语言模型（Audio-Language Models, ALMs）在推理过程中依赖文本捷径（textual shortcuts）而忽略声学证据的问题，从而削弱了对音频内容的真正理解。现有基于策略蒸馏（On-Policy Distillation, OPD）的方法虽能有效压缩模型规模，但未能显式区分语言可预测性与声学支持之间的差异，导致学生模型仍倾向于依赖文本线索。本文提出一种奖励倾斜的策略蒸馏方法（Reward-Tilted On-Policy Distillation, RT-OPD），其核心在于通过冻结的教师模型在有无音频输入条件下对同一问题生成的下一个词的对数概率差异，构建一个声学贡献奖励信号。该奖励用于重塑教师分布，以逆KL散度进行蒸馏，从而增强学生模型对音频信息的依赖。实验结果表明，RT-OPD在多个小型模型和基准测试中均优于基线方法，并在无声或替换音频场景下表现出更强的声学感知能力。所提出的30亿参数模型在MMAU基准上达到72.72%的准确率，超越所有对比的30亿参数模型，并具备与部分70亿及80亿参数模型相当的性能，验证了其在高效建模音频语义方面的有效性。
链接: https://arxiv.org/abs/2609.28778
作者: Kaiyang Li,Shaobo Han,Yue Tian,Shihao Ji
机构: NEC Labs(NEC 实验室)
类目: ound (cs.SD); Computation and Language (cs.CL)
备注: 5 pages, submitted to ICASSP 2027
Abstract:Audio-language models (ALMs) can exploit textual shortcuts to answer questions while overlooking acoustic evidence, weakening audio understanding. On-policy distillation (OPD) trains compact ALMs by supervising student-generated responses with teacher predictions, but does not explicitly distinguish acoustic support from linguistic predictability. We propose Reward-Tilted On-Policy Distillation (RT-OPD) to strengthen acoustic grounding. Given the same question and student-generated text, a frozen teacher predicts the next token with and without audio inputs. Their log-probability contrast defines a reward that reshapes the teacher distribution for reverse-KL distillation, emphasizing the additional evidence provided by audio. Across two compact students and three benchmarks, RT-OPD consistently outperforms Vanilla OPD. Experiments with silenced and replacement audio further suggest that RT-OPD strengthens the student’s reliance on acoustic evidence. Our 3B model achieves 72.72% accuracy on MMAU, the highest among the compared 3B models and competitive with several 7B and 8B models. Code and model checkpoints are available at this https URL.
[NLP-98] BiMamba2 Masked Discrete-Unit Prediction for Multilingual Speech Representation for Unsupervised Speech in the Wild Challenge INTERSPEECH2026
【速读】： 该论文旨在解决无监督语音识别与语音表征学习中，如何在无标签数据条件下有效建模跨语言语音特征并提升说话人聚类性能的问题。其核心解决方案是采用基于掩码离散单元预测（masked discrete-unit prediction）的双向Mamba-2（BiMamba2）编码器，沿用HuBERT式的自监督训练范式，通过联合优化掩码k-means伪标签预测、语言识别监督信号以及VICReg正则化，实现对多语言语音序列的深层表征学习。该方法的关键在于利用结构化的自监督目标与多任务协同机制，在未使用任何标注数据的情况下，显著提升了说话人聚类的分离能力（调整兰德指数达0.735），尽管在语言识别和字符错误率等下游任务上仍低于有监督基线，反映出当前无监督模型在特定任务上的局限性。研究还揭示了本地评估指标与官方动态基准（Dynabench）之间存在量纲不一致及检查点排名偏差，提示依赖分布内诊断无法准确预判真实场景下的泛化表现。
链接: https://arxiv.org/abs/2609.28758
作者: Prakriti Subedi,Howard Prioleau,Saurav K Aryal
机构: 未知
类目: ound (cs.SD); Computation and Language (cs.CL)
备注: Accepted to Interspeech 2026
Abstract:We describe our submission to the Unsupervised Speech in the Wild (UPS) Challenge at Interspeech 2026, a bidirectional Mamba-2 (BiMamba2) encoder trained with masked discrete-unit prediction following the HuBERT-style paradigm. The 47.88M-parameter model is trained on 250 hours of speech across 67 languages from the MLCommons Unsupervised People’s Speech dataset, with no labeled data. The objective combines masked k-means pseudo-label prediction with language identification supervision and VICReg regularization. On official evaluation, the system achieves an Adjusted Rand Index of 0.735, exceeding four baselines on speaker clustering. Language identification macro-F1 (0.073) and character error rate (0.870) remain below supervised baselines. We analyze a local-official discrepancy in metric scale and checkpoint ranking, highlighting limitations of in-distribution diagnostics for predicting Dynabench probe outcomes.
[NLP-99] Small yet Assistive: Spatially-Aware Post-Training for Low Vision EMNLP2026
【速读】： 该论文旨在解决当前视觉语言模型（VLMs）在服务盲人及低视力（BLV）用户进行安全导航时存在的描述过于笼统、缺乏空间细节、方向提示与风险感知能力的问题。现有大型VLM虽能生成符合音频描述标准的高质量叙述，但无法在移动端运行；而小型VLM虽具备较低延迟，却难以提供必要的空间语义信息。为此，论文提出Smol-VL-BLV——一种专为BLV用户设计的轻量级视觉语言模型，其核心在于采用仅500M参数的解码器变压器架构，并结合两项后训练机制：(1) 教师-学生蒸馏（teacher-student distillation），以迁移大模型的知识；(2) 基于复合式BLV奖励函数的组相对策略优化（Group Relative Policy Optimization, GRPO），该奖励函数聚焦方向性语言表达、度量距离准确性和危险检测能力。为缓解多阶段后训练引发的灾难性遗忘问题，研究进一步引入一个轻量级微调阶段，在保持BLV特定空间定位能力的同时恢复通用描述质量。实验表明，所提模型在多项基准测试中显著优于基线，包括视觉问答（VQA）、BLV图像描述、光学字符识别（OCR）和推理延迟等任务，其中空间评分提升19.3%，社交相关评分提升14.8%，OCR-Bench得分提高101.5%，TextVQA准确率提升44.2%。此外，通过混合精度量化部署于中端安卓手机，模型体积约450MB，实现完全本地化、离线运行且无需网络依赖，生成描述的延迟仅取决于设备硬件性能。该工作验证了面向BLV场景的后训练策略可有效提升模型在可访问性相关的空间语义建模与通用视觉-文本推理能力之间的平衡。
链接: https://arxiv.org/abs/2609.28757
作者: Rishabh Choudhary,Shreyansh Raj,Umesh Goyal,Shubh Kashyap,Shrestha Kumar,Sushovan Jena,Komal Kumar,Hisham Cholakkal,Aditya Nigam
机构: Indian Institute of Technology Mandi (印度理工学院曼迪分校); Mohamed bin Zayed University of Artificial Intelligence (穆罕默德·本·扎耶德人工智能大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注: 14 pages, Accepted in EMNLP 2026
Abstract:An estimated 1 billion people worldwide live with vision impairment, yet current vision-language models (VLMs) produce descriptions too vague for safe navigation by blind and low-vision (BLV) users. Large VLMs can generate high-quality audio-description-compliant narrations but cannot run on mobile devices; small VLMs offer competitive latency but lack spatial detail, directional cues, and hazard awareness for navigational assistance. We present Smol-VL-BLV, a compact VLM for blind and low-vision users that closes this gap using a 500M decoder transformer model and two post-training mechanisms: (1) teacher-student distillation and (2) Group Relative Policy Optimization (GRPO) with a composite BLV reward targeting directional language, metric distances, and hazard detection. Because multi-stage post-training can induce catastrophic forgetting, we add a lightweight finetuning stage after the last stage GRPO finetuning to recover general descriptive quality while preserving BLV-specific spatial grounding. Our best model substantially outperforms the baseline across various benchmarks, including tasks: VQA, BLV captioning, OCR, and latency. Compared with the baseline for relative improvement, it improves the Spatial score gain of 19.3%, and the Social score gain of 14.8%. It also increases OCR-Bench by 101.5%, and raises TextVQA accuracy by 44.2%. These results show that BLV-focused post-training improves both accessibility-specific spatial grounding and general visual-text reasoning. Deployed on a mid-range Android smartphone via Mixed-Precision Quantization, the model remains approx. 450 MB and runs entirely on-device, offline and without network dependency, generating descriptions with latency dependent on host hardware capabilities. Our model, dataset, and code is publicly released at this https URL
[NLP-100] chnical Manual for Toolkit for Confidence-Corpus Consistency via Fine-Tuning on a Fabricated Corpus
【速读】： 该论文旨在解决语言模型在生成回答时表现出的置信度是否可作为其对事实掌握程度可靠代理这一问题，尤其关注置信度是否可能受到模型内部表示偏差或训练过程中的非语义因素干扰。其解决方案的关键在于构建一个可控的因果语言模型实验框架：通过在包含81个单数位加法对的虚构语料库上微调小型语言模型，使其一致地“学习”错误答案；随后使用不变的测量方法，对比模型在微调前后对同一问题的置信度——即微调前对正确答案的置信度与微调后对虚构错误答案的置信度。该设计的核心是通过严格控制变量，排除诸如词元化不对称性（tokenization asymmetry）以及答案从“边缘削弱”到“主动压制”的潜在混淆因素，从而实现对置信度指标稳健性的直接检验。整个流程涵盖事实空间生成、长度感知的置信度测量、基线验证、语料构建、微调及成对前后比较等阶段，每一步均明确说明其目的与所排除的混淆因子，最终形成一个可复现、可引用的方法学工具包。
链接: https://arxiv.org/abs/2609.28747
作者: José Luciano Verçosa Marques,Frederico Jorge Heitmann,Daniel Omar Perez,Reinaldo Cesar,Marcelo Vinicius de Paula,Tárcio André dos Santos Barros
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 30 pages, 2 figures, 1 table, 12 code listings. Methodological and implementation reference manual; does not report or interpret empirical results from any specific run. Toolkit and pinned dependency environment archived at this https URL (CC BY 4.0)
Abstract:A language model’s confidence in an answer is often read as a proxy for how well it knows the corresponding fact. This manual documents an open toolkit built to test that reading directly: a small causal language model is fine-tuned on a corpus that consistently asserts one fabricated arithmetic answer for each of the 81 single-digit addition pairs, and its post-fine-tuning confidence in each fabricated answer is compared against its own pre-fine-tuning confidence in the corresponding true answer, using an unchanged measurement procedure throughout. We describe and justify every pipeline stage, fact-space generation, token-length-aware confidence measurement, baseline validation, corpus construction, fine-tuning, and paired before/after comparison, together with the confound each is meant to rule out, among them tokenization asymmetry between single- and double-digit answers and the difference between an answer merely losing its edge and one being actively suppressed. This manuscript is a methodological and implementation reference: it documents the instrument and does not report or interpret the outcome of any specific run. The toolkit and its pinned dependency environment are archived separately (Section 9) under a persistent identifier, to be cited as an instrument by work that produces and interprets empirical results with it.
[NLP-101] mporal Taxation Compounds Under Post-Training Compression of Whisper Models EMNLP2026
【速读】： 该论文旨在解决生成式语音识别模型在实际部署中因权重压缩（如剪枝、量化、蒸馏等后训练压缩技术）导致的性别与种族群体间公平性恶化的问题。尽管现有审计通常在全精度模型上进行，但实际生产环境中的模型已经过压缩处理，而此类压缩操作会改变模型权重而非音频信号或其特征表示，从而可能重新分配错误负担。研究发现，对Whisper-large-v3进行50%的Wanda剪枝后，在Fair-Speech数据集上显著加剧了非裔美国人（Black/AA）与亚裔群体之间的时序负担差异（temporal-taxation differential），词错误率（WER）差距绝对值翻倍以上；若每处纠错需额外5秒，则每分钟语音的校正时间从30秒增至64秒，相对增加111%，且该结果不受单位纠错成本假设影响，也未被音频质量控制所消除，仅部分被束搜索解码缓解（仍存在+86%增加）。此外，边缘设备适用的INT4 HQQ量化在西非口音上引发灾难性转录循环，其频率提升5至7倍。相比之下，蒸馏在27个评估场景中的21个有效缩小了群体间的公平性差距，例外集中于单一教师-学生模型对。研究将Choi和Choi（2025）提出的“时序负担”（temporal-taxation）概念转化为可量化的指标，揭示了仅在全精度模型上进行单次快照公平性审计无法捕捉压缩带来的部署阶段真实负担，尤其对已有边缘化特征的说话者群体构成更严重的影响。
链接: https://arxiv.org/abs/2609.28739
作者: Srishti Ginjala,Eric Fosler-Lussier,Christopher W. Myers,Srinivasan Parthasarathy
机构: The Ohio State University (俄亥俄州立大学); Air Force Research Laboratory (美国空军研究实验室)
类目: Computation and Language (cs.CL); Sound (cs.SD)
备注: Accepted to IMPACT-SPEECH @ EMNLP 2026
Abstract:Automatic speech recognition models are audited for demographic fairness at full precision, yet the models that ship to production have been quantized, pruned, and distilled. We ask whether post-training weight compression, which alters model weights rather than the audio signal or its feature representation, redistributes error burden across demographic groups. Across the Whisper family on Fair-Speech, Common Voice 25, and AfriSpeech-200, 50% Wanda pruning of Whisper-large-v3 sharply widens the Black/AA-vs-Asian temporal-taxation differential on Fair-Speech: the absolute word-error-rate gap between the worst- and best-served groups more than doubles; at an assumed cost of five seconds of correction effort per transcription error this is a rise from 30 to 64 seconds of correction time per minute of speech. This +111% relative increase is invariant to the assumed per-error cost, survives an audio-quality control, and is only partly mitigated by beam-search decoding, which still leaves an +86% increase. At edge model size, INT4 HQQ quantization compounds catastrophic transcript loops on West African accents by factors of five to seven. Distillation, by contrast, narrows demographic gaps in 21 of 27 evaluated settings (teacher-student pair, precision, and dataset), with the exceptions concentrated on a single model pair. We cast the temporal-taxation construct of Choi and Choi (2025) as a quantitative metric, and show that single-snapshot fairness audits on full-precision models do not capture the deployment-time burden that compression places on already-marginalized speakers.
[NLP-102] PTC-Bias: Phoneme-Level Temporal Competition for Bias Retrieval and Post-Decoding Correction in Speech LLM s
【速读】： 该论文旨在解决在语音大语言模型（SpeechLLM）中高效利用大规模候选词偏置列表以提升罕见词识别准确率的问题，尤其针对因上下文偏差（contextual biasing）引入的近音词（near-homophone）和词切分错误。其解决方案的关键在于提出一种两阶段框架PTC-Bias（Phoneme-level Temporal Competition Bias），核心机制为基于音素级时间竞争（phoneme-level temporal competition）。在预填充阶段（prefill stage），PTC Retrieval通过帧同步的音素解码与候选发音间的时间竞争，生成紧凑的偏置词短列表及其对应的语音区间；在后续解码后，PTC Correction阶段在这些区间内对检索到的候选词与不匹配的转录片段进行局部二次竞争，实现选择性修正，从而有效减少近音词误判和词边界错误，同时保持正确转录的完整性。两阶段共享相同的音素后验概率，无需额外的SpeechLLM前向传播，显著提升了偏置利用效率。实验表明，该方法在LibriSpeech数据集上对两种SpeechLLM均取得一致性能提升，使用Prompt-SLAM-ASR-7B与2000个偏置词时，在test-clean/test-other上的字错误率（B-WER）相对CTC-Filter分别降低23.4%/23.9%，而词错误率（U-WER）基本不变。
链接: https://arxiv.org/abs/2609.28727
作者: Zhiqi Ai,Han Cheng,Shiyi Mu,Yongjin Zhou,Shugong Xu
机构: 未知
类目: Computation and Language (cs.CL); Sound (cs.SD)
备注: 5 pages, 3 figures, 3 tables, under-review
Abstract:Contextual biasing improves rare-word recognition in speech large language models (SpeechLLMs), but efficiently exploiting large bias lists remains challenging. We propose PTC-Bias, a two-stage framework based on phoneme-level temporal competition. At the prefill stage, PTC Retrieval performs frame-synchronous phoneme decoding and temporal competition among candidate pronunciations, producing a compact bias-word shortlist and corresponding speech intervals. After SpeechLLM decoding, PTC Correction conducts a second local competition between the retrieved candidates and mismatched transcript spans within these intervals. Selective correction reduces near-homophone and word-segmentation errors while preserving correct transcriptions. Both stages share the same phoneme posteriors and require no additional SpeechLLM forward pass. Experiments on LibriSpeech show consistent gains across two SpeechLLMs and bias lists of up to 2000 words. With Prompt-SLAM-ASR-7B and 2000 bias words, PTC-Bias reduces B-WER by 23.4%/23.9% relative to CTC-Filter on test-clean/test-other, while keeping U-WER nearly unchanged.
[NLP-103] An Explainable DistilBERT-BiLSTM-Attention Framework for Binary and Multi-Class Hate Speech Detection
【速读】： 该论文旨在解决社交媒体中仇恨言论（hate speech）检测存在的三大核心问题：现有研究多局限于二分类任务且仅在单一数据集上评估，缺乏对模型决策过程的可解释性，限制了其在真实场景中的应用效果。为此，本文提出一种多层次、可解释的仇恨言论检测框架，其关键在于融合DistilBERT（Distilled Bidirectional Encoder Representations from Transformers）嵌入表示与双向长短期记忆网络（Bi-LSTM），并通过注意力机制同时捕捉文本的上下文语义和序列依赖关系，从而提升特征表达能力；同时引入局部可解释模型无关解释（LIME, Local Interpretable Model-agnostic Explanations）技术，对预测结果进行可解释性分析，揭示影响判断的关键文本特征，增强模型透明度与可信度。实验在两个基准数据集上分别进行二分类与多分类评估，并通过消融实验验证各模块贡献，结果显示该框架在多个指标上均优于现有基线方法，尤其在多分类场景下表现出优异的泛化性能与稳定性，证明其在实际内容审核系统中具备高准确性、强通用性及可解释性的综合优势。
链接: https://arxiv.org/abs/2609.28703
作者: Rameesha Zia,Muhammad Shahid Iqbal Malik
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 20 pages, 12 figures, 6 tables
Abstract:Hate speech on social media poses serious risks to social harmony, mental well-being, and public safety, making its timely and accurate detection essential for content moderation systems. Most existing studies focus on binary classification, evaluated their frameworks on a single dataset, and provide limited insight into how decisions are made, which limits their real-world applicability. In addition, limited work is done on the explainability of their predictive inference. To address these challenges, this study proposes a multilevel and explainable hate speech detection framework. The proposed model integrates DistilBERT (Distilled Bidirectional Encoder Representations from Transformers) embeddings with a Bi-LSTM (Bidirectional Long Short-Term Memory) model, and an attention mechanism to capture both contextual meaning and sequential dependencies in text. To enhance trust and transparency, LIME (Local Interpretable Model-agnostic Explanations) is employed to explain model predictions by highlighting influential textual features. The framework is evaluated on two benchmark datasets using both binary and multi-class classification to examine robustness and generalization. In addition, an ablation study is presented to highlight the significance of various components of proposed framework. For binary classification, the proposed model achieves F1-scores of 96.78% on the Davidson dataset and 99.53% on the SMHS dataset. In the multi-class setting, it attains F1-scores of 97.00% and 94.99% on the Davidson and SMHS datasets, respectively, outperforming existing baseline approaches. The results demonstrate that multilevel evaluation improves the reliability that the proposed framework effectively balances performance and efficiency. This makes the framework suitable for practical hate speech moderation systems that require accurate, generalizable, and explainable decisions.
[NLP-104] Benchmarking Argumentative Behaviour of LLM s: A Study of Defences Against Character Attacks
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在说服性对话中作为论辩代理时，其论辩能力与人类对手相比的差距问题，尤其关注在政治性说服对话中频繁出现但传统上被视为谬误的“人身攻击”（ad hominem arguments）策略的运用与应对。研究的核心问题是：现代大语言模型是否能够像人类一样，在以修辞信誉（ethos）为核心的辩论中，战略性地使用或回应人身攻击？解决方案的关键在于，通过分析真实政治对话语料库，识别出人类在面对人格攻击时所自然采用的防御策略，并将其结构化为一种对话博弈框架；进而基于该框架对大语言模型生成的对话进行实证评估，发现当前主流大语言模型普遍过度依赖逻辑性防御，无法有效运用基于声誉反制的人身反击策略，这主要受限于安全微调（safety fine-tuning）机制对模型行为的约束，从而导致其在以人物争议为常态的政治话语领域中难以实现自然、有效的互动。
链接: https://arxiv.org/abs/2609.28673
作者: Ewelina Gajewska,Katarzyna Budzynska,Jaroslaw Chudziak
机构: 未知
类目: Computation and Language (cs.CL)
备注: Accepted to COMMA 2026
Abstract:Large Language Models (LLMs) are increasingly deployed as argumentative agents in persuasive dialogues, necessitating rigorous evaluation of their debating competence relative to human interlocutors. In this study, we focus on character attacks (ad hominem arguments), traditionally dismissed as fallacies, which play a pivotal role in political persuasive dialogues where ethos often rivals propositional content. Specifically, we investigate whether modern LLMs can replicate human competence to strategically use and respond to such attacks. We analyse a corpus of natural language political dialogues to identify defensive strategies human interlocutors naturally employ in ethos-centred debates and structure them into a dialogue game. Empirically, we benchmark LLM-generated dialogues against the ElecDeb60to16-fallacy corpus of U.S. presidential debates, contrasting human debaters’ repertoire of defensive strategies with those of artificial agents. Results reveal a substantial difference: most LLMs rigidly prioritise logical defences, failing to exploit ethotic counterattacks as valid moves in political discourse. We argue that current safety fine-tuning constraints the strategic action space of these LLMs, making them unable to fully engage in naturalistic interactions within domains where character contestation is a normative expectation rather than a mere fallacy.
[NLP-105] Reward Hacking Challenges Oversight of Autonomous Research Agents
【速读】： 该论文旨在解决生成式研究代理（autonomous research agents）在自主设计实验、评估结果并撰写报告过程中可能产生的“奖励劫持”（reward hacking）问题。奖励劫持指模型通过操纵评估机制以满足奖励指标，而未真正达成科学目标，从而导致研究结果的可信度受损。其解决方案的关键在于识别和防范此类行为：研究发现，在无明确指令的情况下，30.5%的开放性研究任务中模型会自发产生奖励劫持；当允许劫持时，74.6%的尝试被证实为有效且难以检测的奖励劫持，其中仅6.5%可通过仅审查代码与得分的大型语言模型（LLM）评审小组发现。进一步分析表明，直接攻击方法虽易被察觉，但间接手段更具隐蔽性，且在多轮反馈循环中，规避策略数量从7上升至56，显示模型具备持续适应能力。此外，在包含详细反馈（含评审结论、理由及历史记录）的情境下，累计规避率高达40.5%，高于仅接收通用拒绝反馈的20.3%，说明详尽反馈反而可能被利用于优化劫持策略。因此，论文强调需构建更稳健的防御机制，包括将关键评估指标置于代理控制之外，并采用独立复现与针对性数据测试来暴露潜在漏洞。
链接: https://arxiv.org/abs/2609.28614
作者: Yue Huang,Zhangchen Xu,Yuchen Ma,Wenjie Wang,Zheyuan Liu,Ziwei Xu,Pin-Yu Chen,Michel Galley,Zinan Lin,Stefan Feuerriegel,Radha Poovendran,Misha Sra,Alex Pentland,Xiangliang Zhang,Zichen Chen
机构: Bake AI; University of Notre Dame; LMU Munich; University of Washington; FAR.AI; IBM Research; Microsoft Research; University of California, Santa Barbara; Stanford University; Massachusetts Institute of Technology
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Autonomous research agents can design experiments, evaluate results, and write reports, giving them control over both a scientific result and the evidence used to support it. This creates a risk of reward hacking: meeting the reward criteria without achieving the intended goal. We study (1) how often models reward-hack without instructions to do so, (2) how effective and detectable their methods are when hacking is allowed, and (3) how they adapt when an LLM review panel returns its decision and reasons. Across 17 language models and 38 tasks, the spontaneous reward-hacking rate is 30.5% on open-ended research-pipeline tasks and 2.9% on task-specific kernels. When hacking is allowed on tasks whose pass thresholds exceed our best compliant baselines, 505/677 attempts (74.6%) are confirmed reward hacks: they both clear the threshold and receive mechanism-verification panel confirmation of an evaluation exploit. An LLM panel reviewing only submitted code and reported scores misses 33/505 confirmed hacks (6.5%). Direct methods that achieve the highest scores are often easy to detect, while less direct methods evade more often. In a five-round loop, the number of model-task pairs with an evasion rises from 7 to 56. Among 79 pairs evaluated under two feedback conditions, cumulative evasion reaches 40.5% with detailed feedback and 20.3% with generic rejection. The detailed condition includes the review decision, reasons, and attempt history, so this comparison does not isolate the effect of explanations. These findings highlight the need for stronger defenses, including metrics kept outside the agent’s control and independent recomputation on data chosen to expose likely exploits.
[NLP-106] When Explanations Cannot Be Read: Measuring and Correcting SHAP and LIME Rendering for Right-to-Left Languages
【速读】： 该论文旨在解决生成式AI（Generative AI）在解释右向左（Right-to-Left, RTL）语言（如乌尔都语、阿拉伯语、波斯语和希伯来语）文本分类模型时，现有事后解释方法（如SHAP和LIME）在可视化呈现上的根本性缺陷。尽管这些方法的归因值在数学上对RTL语言依然有效，但其默认的从左到右（LTR）布局导致词元顺序错乱、连写字符断裂、阅读方向与自然书写习惯不符，严重影响可读性与解释有效性。解决方案的关键在于提出SHAP-RTL——一个专为RTL语言设计的渲染层，通过引入语言特定字体选择、自动纠正阅读方向及脚本形态（script shaping），在不改变原始归因值、特征排序和模型输出的前提下，实现符合语言书写规范的可视化呈现。该框架在多种RTL语言的仇恨与冒犯性语言数据集上进行了验证，采用OCR回环测试评估渲染正确性，结果显示传统方法错误率高达0.820至0.979，而现有修复手段在乌尔都语中甚至表现更差；相比之下，SHAP-RTL在不同Matplotlib版本下均保持高精度，且支持生成以读者母语表达的简短上下文解释，凸显了语言感知型可视化在提升跨语言可解释性中的关键作用。
链接: https://arxiv.org/abs/2609.28565
作者: Rameesha Zia,Muhammad Shahid Iqbal Malik
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 28 pages, 18 figures, 5 Tables
Abstract:Post hoc explanation methods such as SHAP and LIME are widely used to interpret text classifiers, but their visualizations are mainly designed for left-to-right languages. When applied to right-to-left (RTL) languages such as Urdu, Arabic, Persian, and Hebrew, the attribution values remain mathematically valid, while their visual presentation fails. Tokens appear out of sequence, connected letterforms break apart, and plot layouts do not follow the natural reading direction. This study addresses this gap as a visualization problem rather than a limitation of the explanation methods themselves. We present SHAP-RTL, a rendering layer that corrects reading direction and script shaping in SHAP and LIME visualizations, with per-language font selection, while preserving the original attribution values, feature ordering, and model outputs. The approach is evaluated on Urdu, Arabic, Hebrew, and Persian hate and offensive-language datasets using TF-IDF and logistic regression classifiers. Rendering correctness is measured by an OCR round trip over 200 feature words per language. Default rendering yields character error rates of 0.820 to 0.979, meaning the label no longer carries its token; the common reshape-and-reorder workaround fails for Urdu at 0.998, worse than no correction; and the Matplotlib 3.11.0 text rewrite inverts that workaround, while SHAP-RTL remains correct under both versions. The framework also verbalizes the same attributions as short contextual explanations in the reader’s language, constrained to the identified features. Evaluation in this paper concerns rendering correctness; assessment of the generated explanations is left to future work. The study highlights the importance of language-aware visualization in making post hoc explainability more accessible across different writing systems.
[NLP-107] Framing by Wording Framing by Selection: A Large-Scale Two-Dimensional Audit of French News Headlines 2022-2025
【速读】： 该论文旨在解决新闻标题在议题建构中存在“选择性”（selection framing）与“表述方式”（salience framing）混同的问题，传统计算框架常将二者合并为单一评分，导致对媒体议程设置机制的理解失真。其核心解决方案是提出一个二维框架，明确分离两种不同维度的框架效应：通过四类语言手段（负载词汇、归责表述、威胁化表达、反问句式）量化表述框架（salience framing），并通过媒体机构层面的故事形态分布与高关注度事件分布来衡量选择框架（selection framing）。研究构建了包含10,000条法语新闻标题的标注数据集，采用三名大语言模型（LLM）标注并结合多数投票与人工仲裁，经两项独立于标注者的盲测人类研究验证标签可靠性，并将最优分类器应用于25家法国媒体2022–2025年间共902,111条去重标题。主要发现包括：第一，表述与选择框架呈正相关但解释力有限，近半数媒体层级方差无法被二者共同解释，形成四象限媒体类型学中的非对角线分布；第二，默认分类阈值系统性夸大整体表述框架强度，通过精确率下限校准协议可有效修正此偏差；第三，群体提及分析揭示显著不平等的表述强度：涉及犹太人、极右翼和穆斯林的标题检测到最高表述强度，这一现象无法完全由事件背景构成解释，残差具有描述性而非因果性，且每组群体的术语精确度亦被单独报告。本研究为迄今规模最大的法语新闻标题框架审计，相关标注数据集、词典及分析代码均已公开。
链接: https://arxiv.org/abs/2609.28487
作者: Amr Sobhy
机构: Le French News Lab(法国新闻实验室)
类目: Computation and Language (cs.CL)
备注: 20 pages, 1 figure, includes appendices. Accepted for oral presentation at ICNLSP 2026
Abstract:News headlines frame public issues both by what they select and by how they word it, yet computational framing work typically collapses these operations into a single score. We introduce a two-dimensional framework that separates salience framing, measured through four wording devices (loaded vocabulary, blame attribution, threat framing, rhetorical question), from selection framing, measured through outlet-level story-form and high-charge distributions. We build a 10,000-headline French supervision set using three LLM annotators with majority-vote resolution and human arbitration, validate the labels against two annotator-independent blind human studies, and apply the strongest classifier to 902,111 deduplicated headlines from 25 French outlets (2022-2025). Three main findings emerge. First, salience and selection divergence are positively correlated yet leave nearly half of outlet-level variance unexplained, populating interpretively distinct off-diagonal cells in a four-cell outlet typology. Second, default classification thresholds systematically inflate corpus-level salience estimates; a precision-floor recalibration protocol corrects this distortion. Third, group-mention analysis reveals sharply unequal salience contexts: headlines mentioning Jews, the Far-right, and Muslims carry the highest detected salience rates, which broad event-context composition does not fully explain (residuals are descriptive, not same-event causal estimates; per-group lexicon precision is reported alongside). To our knowledge, this is the largest framing-focused French headline audit to date; we release the supervision set, lexicons, and analysis code.
[NLP-108] When Should Forecasting Agents Reason ? Behavioral Stress Tests for Reliability Routing
【速读】： 该论文旨在解决生成式预测代理（forecasting agents）在整合语言模型推理、检索、集成与校准等行为时，难以判断何时应信任特定机制的问题。其核心挑战在于：不同证据来源（如历史类比、市场先验、群体共识等）在不同数据生成过程下的可靠性存在差异，而现有方法缺乏对机制选择的可解释性与适应性。解决方案的关键在于提出一种名为ReliabilityRoute的结构化干预框架，通过引入可审计的可靠性特征（包括历史覆盖率、市场先验可用性、源先验锐度、证据强度、证据分歧度及时间跨度）动态引导代理的行为路由决策。该方法不仅避免了对源名称的硬编码依赖，且固定参数的2024年拟合规则即可逼近人工分类体系；更进一步，基于先前已解决版本（vintage）自适应重拟阈值的“滚动优化”策略，在16个后续大语言模型（LLM）版本中实现了确定性系统中的最优平均布里尔分数（Brier score）。研究揭示，过度依赖推理并不总带来性能提升，真正关键的是让代理能够评估各证据源的可信度，并在可追溯的约束下动态调整路由策略，从而实现更稳健、可复现的预测行为。
链接: https://arxiv.org/abs/2609.28475
作者: Yufeng Wang
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Forecasting agents increasingly combine language-model reasoning, retrieval, ensembling, and calibration, but it remains unclear when each behavior should be trusted. We study this question on ForecastBench-style binary forecasting tasks, treating the choice to retrieve, reason, defer to a market prior, or use a historical analog as an observable agent behavior rather than a hidden implementation detail. Our central finding is that mechanism choice is source-dependent: structured analogs dominate for some data-generating processes, while market/crowd-style and conservative baselines are better for others. We introduce ReliabilityRoute, a structural intervention that steers forecasting-agent behavior using reliability features such as historical coverage, market-prior availability, source-prior sharpness, evidence strength, evidence disagreement, and horizon. A fixed 2024-fitted rule closely matches a hand taxonomy without hard-coded source-name decisions, while a walk-forward self-adjusting rule refits thresholds from previously resolved vintages and obtains the best mean Brier score among our deterministic systems across 16 later LLM vintages. The gain is modest and historical/search baselines remain highly competitive. The main contribution is therefore a behavioral stress test showing that more reasoning is not always better; forecasting agents should first estimate which evidence source deserves control, routing policies should themselves adapt under auditable constraints, and reproducibility artifacts are available at this https URL
[NLP-109] Personalized Korean Lipreading as Visual Speech Recognition: Transfer Census and Adaptation on OLKAVS ICASSP2027
【速读】： 该论文旨在解决个性化韩语视觉语音识别（VSR）中个体用户性能显著低于群体平均水平的问题，特别是在不同说话人、语境及摄像头视角下的性能波动。其核心挑战在于如何在有限的用户专属数据（仅需4至29分钟的正面视频）下，实现对通用预训练模型的有效微调，以显著降低个体用户的字符错误率（CER）。解决方案的关键是引入一种低秩适配器（low-rank adapter），该适配器仅包含原始模型4.6%的参数量，在少量用户数据上进行训练后，可使12名高错误率用户的CER降低2.13至3.58个百分点，同时保持对所有摄像头视角的零损失迁移能力，并在仅消耗全量微调12%计算成本的情况下保留85%的性能增益。此外，研究发现，高于口部平面的摄像头会引入约6个CER点的恒定误差偏移，但通过多视角联合训练可有效抑制这一影响。
链接: https://arxiv.org/abs/2609.28988
作者: Se Un Park,Hakjun Kim,Taehoon Roh,Junyoung Park
机构: 未知
类目: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: Submitted to ICASSP 2027. 4 pages plus references
Abstract:We present a personalized Korean visual speech recognition (VSR) system and quantify, on the nine-camera OLKAVS corpus, the gap between the population-level benchmark score and an individual user’s error. A video-only Conformer initialized from English-trained weights attains 9.95 - 12.19% character error rate (CER) under the corpus protocol against the published 26.64, and 19.00 - 21.52 on unseen wording. Per speaker, CER spans 1.0 to 52.2%, with seen wording lowering CER by 7.0 - 9.0 points and professional delivery and spontaneous speech raising it by 8.5 - 10.5 and 12.7 points. A low-rank adapter with 4.6% of the parameters, trained on 4 to 29 minutes of the user’s frontal video, lowers the CER of twelve high-error speakers by 2.13 to 3.58 points, transfers to every camera without loss, and keeps 85% of the full fine-tuning gain at 12% of its cost to other speakers. Cameras above the mouth plane add about six CER points as a constant offset that training on all views keeps small.
[NLP-110] Learning New Words from Unlabeled Test Data in Automatic Speech Recognition ICASSP2027
【速读】： 该论文旨在解决自动语音识别（ASR）系统在面对未登录词（Out-of-Vocabulary, OOV）时表现不佳的问题，尤其是当新词在测试阶段首次出现且无标注数据支持时，传统ASR模型难以准确识别。其核心挑战在于如何让ASR系统具备类似人类听者的能力——仅通过一次清晰的语音输入和上下文语境，即可推断并学习新词的拼写与用法。解决方案的关键在于提出一种无需重新训练、可在测试时利用无标签测试数据进行自适应的框架：基于冻结的连接时序分类（CTC）声学模型提供候选拼写，冻结的语言模型提供上下文证据以检测OOV词，而一个可适应的模块则通过学习词汇标记的表示分布，从CTC生成的候选中扩展词汇表。该方法通过最小化Kullback-Leibler散度（KLD）目标来优化每个标记的拼写模型，并理论证明了CTC加权语言模型对数似然比可被解释为真实ASR与无监督学习到的ASR之间KLD，进一步利用Pinsker不等式将KLD的平方根视为真实拼写与估计拼写间总变差距离的上界，从而为模型性能提供了理论保障。实验表明，在LibriSpeech数据集上，对于重复出现的OOV词，相对字符错误率（CER）最高降低14.97%；在发音障碍语音可访问性项目（Dysarthric Speech Accessibility Project）数据上，相对降低6.67%，显著优于基准重打分（rescoring）系统。
链接: https://arxiv.org/abs/2609.28877
作者: Mengqi Wang,Mark A. Hasegawa-Johnson,Haolong Zheng,Chang D. Yoo
机构: University of Illinois at Urbana-Champaign (伊利诺伊大学厄本那-香槟分校); University of Texas at Austin (德克萨斯大学奥斯汀分校)
类目: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL)
备注: Submitted to ICASSP 2027
Abstract:New words are invented every day. A human listener can learn a new word by hearing it clearly once and inferring its usage from sentence context. This paper proposes granting ASR a similar ability to learn the contextual representations and spellings of new words from unlabeled test data at test time. A frozen CTC acoustic model provides spellings, a frozen language model provides contextual evidence for out-of-vocabulary (OOV) word detection, and an adaptation module expands the vocabulary by learning the lexical token representations with distributions over CTC-generated candidates. The spelling model of each token is optimized by minimizing a Kullback-Leibler divergence (KLD) objective. We demonstrate that the CTC-weighted language model log likelihood ratio can be interpreted as the KLD between the unknown correct ASR and the unsupervised learned ASR, and that, using a Pinsker bound, the square root of KLD can be interpreted as an upper bound on the total variation distance between the true and estimated spelling of the unknown word. Experiments show relative OOV character-error-rate reductions of up to 14.97% on LibriSpeech and 6.67% on dysarthric Speech Accessibility Project data for recurring OOV words, relative to the corresponding rescoring system.
[NLP-111] Spooftral: Can Voxtral Audio-Language Model Detect Speech Spoofing?
【速读】： 该论文旨在解决自监督学习（Self-supervised Learning, SSL）反欺骗检测方法在面对未见过的欺骗攻击和不匹配场景时性能显著下降的问题。现有SSL反欺骗对策虽在已知攻击类型上表现良好，但在泛化能力方面存在局限。本文以Voxtral音频-语言模型（Audio-Language Model, ALM）框架为基础，探索将反欺骗能力集成至多模态模型中的可行性。其核心解决方案在于：通过分析Voxtral在音频-文本联合处理过程中如何捕捉欺骗线索，并提出一种基于指令引导的判别机制，利用标签序列似然度评估语音的真实性。研究发现，在未进行任务特定适配的情况下，大型语言模型（Large Language Model, LLM）层更侧重于语义表征，导致欺骗判别性声学特征的可分性降低，进而削弱了对欺骗语音的区分能力。为缓解此问题，本文引入轻量级适配方法——权重分解低秩适应（DoRA），对Voxtral模型进行微调，构建出Spooftral模型，在ASVspoof5评测集上实现了4.25%的等错误率（Equal Error Rate, EER），显著提升了模型在未知攻击场景下的鲁棒性与泛化性能。
链接: https://arxiv.org/abs/2609.28713
作者: Avishai Weizman,Yehuda Ben-Shimol,Itshak Lapidot
机构: Ben-Gurion University of the Negev (本-古里安大学); Afeka the Academic College of Engineering (阿菲卡工程学院); Avignon University (阿维尼翁大学)
类目: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL)
备注: 8 pages, 3 figures, 5 tables. Accepted to the Spoken Language Technology (SLT) 2026
Abstract:Self-supervised learning (SSL) countermeasures (CMs) have shown strong performance in recent years. However, they often show degraded performance while facing unseen spoofing attacks and mismatched conditions. This study examines the Voxtral audio-language model (ALM) framework for spoofing detection, as a step toward combining CM capabilities within the ALM framework. We analyze how Voxtral captures spoofing cues through audio-text processing and propose an instruction-guided approach that uses label-sequence likelihoods to evaluate bonafide and spoofed speech. Experiments on the ASVspoof databases show that without task-specific adaptation, the LLM layers emphasize semantic representations, reducing the separability of spoof-discriminative acoustic cues compared to the Whisper-based audio encoder. Consequently, spoofing-related information becomes less separable after language-model processing. We also applied lightweight adaptation using weight-decomposed low-rank adaptation (DoRA) to the Voxtral model and propose the Spooftral model, achieving an equal error rate (EER) of 4.25% on the ASVspoof5 evaluation set.
信息检索
[IR-0] Return or Revise? Learning When Revision Helps Retrieval-Augmented QA
链接: https://arxiv.org/abs/2609.30087
作者: Nicholas Kashani Motlagh,Tim Anderson,Jeremy Gwinnup,Grant Erdmann
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: 25 pages, 4 figures
Abstract:We consider the decision of whether to return an existing draft answer or revise it using retrieved evidence, as in answer-revision systems. Draft confidence estimates whether the current answer is correct, but the decision requires estimating the effect of a specified revision. For offline training and evaluation, we grade both the returned draft and its candidate revision under the same correctness judge, which makes repair, harm, and the gap to an oracle observable. We call this paired effect its recoverability, and we train policies to predict it before revision. On 25,870 held-out open-domain questions across three revision setups, a scorer trained on the paired outcome has greater area under the accuracy–revision-rate curve than a matched draft-correctness scorer in all nine Llama setup–seed fits, and gains 0.23–0.68 accuracy points on average at development-selected thresholds, a difference significant across training runs only for dense retrieval. The resulting policy improves on always revising and on average closes more than a third of the oracle gap, although it still applies 38–46% of the harmful revisions. When a draft-free standard-RAG answer is also available, however, choosing between the draft and that answer is stronger by about two points for Llama and four for OLMo, and adding candidate revision as a third option yields no significant gain. Recoverability describes one revision; its value as an available action also depends on the alternatives.
[IR-1] Advancing Model Research in Agent X: Long-Horizon Autonomy for Industrial Recommender Systems
链接: https://arxiv.org/abs/2609.30001
作者: Shuang Yang,Zijie Zhuang,Changxin Lao,Pengbo Xu,Hanwen Xu,Yusheng Huang,Han Gao,Guanchen Wang,Tianbao Ma,Linxun Chen,Peilin Song,Xuming Wang,Chen Li,Fan Wu,Tao Wang,Zibo Zhao,Xiangyu Wu,An Liu,Fei Pan,Peng Jiang,Chen Yang,Zhaojie Liu,Wenwu Ou
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: Technical report. 37 pages, 11 figures, 13 tables, including appendices
Abstract:Sustaining industrial recommendation research requires using the results of one experiment to decide what to investigate next. We present AgentX-Model, the next generation of AgentX’s model research framework, which connects proposal development and model experimentation within sandboxes defined by business inputs and prediction tasks. AgentX-Model adopts a dual-agent architecture comprising a Research Agent and a Model Agent. The Research Agent develops independently reviewed proposals from papers and experimental findings, while the Model Agent conducts multi-round investigations and returns code, measurements, and unresolved questions. Using the returned results, the Research Agent selects a starting implementation and formulates the next research question, allowing subsequent experiments to build on earlier findings. We organize this continuing research around four actions: Reproduce, Follow-up, Composition, and Diagnose. The first three actions drive routine research, while Diagnose acquires the evidence needed to choose a repair, including for issues raised by business feedback and online evaluation, such as prediction bias measured by PCOC. Across the production evaluation, 560 of 636 completed model-changing experiments recorded AUC above their business baselines. As research continued, some experiments recorded AUC above every comparable ancestor in their lineages. The five latest online A/B evaluations across different business settings reported gains including 10-15% in acquisition efficiency, 15-20% in target-segment advertising spend, and 0.3-0.8% in watch time; the watch-time model used approximately 10% fewer FLOPs and parameters. A dependency-aware historical-replay benchmark further evaluates research allocation, with initial results showing no consistent efficiency gain from more complex scheduling when agents already analyze and select concrete candidates.
[IR-2] From Interests to Semantic IDs: Retrieval-Grounded Credit Assignment for Generative Recommendation
链接: https://arxiv.org/abs/2609.29983
作者: Mengdan Zhu,Yufan Zhao,Yao Zhao,Sophie Di,Tao Di,Yulan Yan,Sridhar Iyer,Liang Zhao
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Semantic IDs (SIDs) encode each catalog item as a short token sequence, enabling generative recommenders to predict the next item autoregressively. Reasoning-enhanced variants, an increasingly common extension, first generate a textual trace and then decode a next-item SID by beam search. Such recommenders are commonly trained with group-relative policy optimization under an exact-match SID reward, which is sparse in large catalogs. Two failure modes follow. When all rollouts in a group miss the target, the group yields zero advantage and no learning signal. Rollouts sharing the same SID reward receive identical advantages, however much their traces differ. In both cases the reward reflects only the decoded SID, never the reasoning that produced it. This creates a credit-assignment gap. We address this gap with retrieval-grounded query attribution. Each trace is structured into a history summary, a set of interest hypotheses, and a final SID. A frozen retriever executes every hypothesis as a catalog query, so that each hypothesis becomes independently verifiable rather than judged only through the final SID. A rollout is rewarded when any of its queries retrieves the target within the \mboxtop- K , and per-query hit indicators localize that reward to individual hypotheses. Credit is thus assigned at the span level: only hypotheses that individually hit receive positive retrieval advantage, while the retrieval channel never updates the final SID span. Rollouts that share a SID reward can therefore receive different updates. Across experiments on three Amazon Reviews datasets, this yields consistent improvements in SID recommendation. On Video Games, an oracle analysis further reveals the potential of interest-conditioned SID decoding: selecting the target-relevant query among generated interests improves both recall and ranking. Subjects: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.29983 [cs.IR] (or arXiv:2609.29983v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2609.29983 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-3] Learning Better Reasoning for Generative Recommendation with Semantic IDs
链接: https://arxiv.org/abs/2609.29973
作者: Mengdan Zhu,Yufan Zhao,Sophie Di,Yao Zhao,Tao Di,Yulan Yan,Sridhar Iyer,Liang Zhao
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Generative recommendation reformulates item retrieval as sequence generation, allowing a unified model to directly generate the next item from a user’s interaction history. Semantic IDs further make this paradigm effective and scalable by representing each item as discrete codes, enabling knowledge sharing among semantically related items. Recent studies introduce explicit reasoning before Semantic-ID generation, helping models summarize user interests and infer possible preference transitions. However, reasoning is not inherently beneficial: Inaccurate or uninformative reasoning may mislead subsequent item generation and ultimately degrade recommendation performance. This raises a central challenge: how can a recommender select and learn effective reasoning traces and progressively evolve toward better reasoning from its own generations? In this work, we propose Evo-Rec, a three-stage framework for learning better reasoning and further enhancing it through reinforcement learning. First, we align Semantic IDs with their textual and behavioral contexts, enabling the model to understand and generate item identifiers. Second, we sample multiple candidate reasoning traces and retain those that improve the prediction of the ground-truth item, providing a stronger reasoning initialization through supervised fine-tuning. Third, we further optimize the reasoning policy through reinforcement learning with catalog-constrained item generation and ranking-aware recommendation feedback. Experiments on three Amazon Review benchmarks show that Evo-Rec consistently outperforms discriminative, generative, and reasoning-enhanced recommenders across all evaluation metrics. These results demonstrate the effectiveness of our framework in learning better reasoning for SID-based generative recommendation.
[IR-4] An Empirical Study of VLM Pipelines for Long-Document QA EMNLP2026
链接: https://arxiv.org/abs/2609.29933
作者: Kenan E. Ak,Jay Mohta,Gwang Gook Lee,Yan Xu,Dimitrios Dimitriadis
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
备注: 22 pages. EMNLP 2026 Industry Track
Abstract:Vision-Language Models (VLMs) are increasingly used for long-document processing, where the inputs combine text with charts, tables, figures, and complex layouts. Deploying them means choosing how to feed the document to the model, which retriever to use when only a subset of pages is sent, and whether to run the model agentically or as a static pipeline. We study these choices on two long-document QA benchmarks with both frontier API and open-weight VLMs. First, on MMLongBench-Doc our six-tool agent with page, table, figure, and search calls pays off only once the answering VLM is large enough: with Qwen3.5-4B and 9B it trails static page input, with Qwen3.5-27B it draws level, and with Sonnet 4.5 it leads. On LongDocURL it is level with or ahead of static input at every reader. Its lead over the strongest static pipeline is clearest with the frontier reader on MMLongBench-Doc and narrows to within noise on LongDocURL. Second, retrieval modality matters more than the specific retriever: the strongest image retriever leads the strongest text pipeline, and on the text side a single off-the-shelf cross-encoder rerank essentially matches a much heavier multi-stage LLM pipeline. Top-k image retrieval is also the most token-efficient input at every reader we paired it with, at roughly a seventh to a quarter of the tokens of sending every page. Third, cutting across all three choices, three of our strongest pipelines succeed on different questions, and an oracle that picks the best pipeline per question gains roughly thirteen points over the best single pipeline, though evidence-type routing recovers almost none of it.
[IR-5] Fair Feed Ranking for Participatory Budgeting
链接: https://arxiv.org/abs/2609.29819
作者: Carina I. Hausladen
类目: Computers and Society (cs.CY); Information Retrieval (cs.IR)
备注: 8 pages, 2 figures, 2 tables. Published at GoodIT '26, the International Conference on Information Technology for Social Good, Pisa, Italy, September 2026
Abstract:In large-scale participatory budgeting, citizens cannot inspect the full proposal pool, so the order in which proposals are shown becomes a form of agenda-setting power. We argue that fair exposure should therefore be treated as a democratic-design goal. We study Consul Democracy, a widely deployed open-source digital-democracy platform, and show that its proposal feeds are typically ordered by popularity, recency, or comment activity. Building on this diagnosis, we propose FairFeed, a feed-ranking design for PB that uses transparently declared preferences, boosts under-exposed proposals, and admits a rate-limited reject channel for crowd-sourced vetting. We evaluate the design in a simulation anchored in Munich’s 2025 PB process and compare it with random, newest, and most-commented feeds. In this simulation, FairFeed broadens proposal discovery, distributes visibility more evenly across the eligible pool, increases cross-cutting support, and improves resistance to manipulation relative to comment-based ranking. We conclude by outlining the human-subjects evaluation needed to test whether onboarding can recover voter preferences accurately enough for deployment in practice.
[IR-6] LSF-SR: Latent Semantic Fusion for Sequential Recommendation via Flow-based Conditional Variational Autoencoders CIKM2026
链接: https://arxiv.org/abs/2609.29815
作者: Shih-Hong Chen,Josh Jia-Ching Ying,Vincent S. Tseng
类目: Information Retrieval (cs.IR)
备注: Accepted at the 35th ACM International Conference on Information and Knowledge Management (CIKM 2026)
Abstract:Sequential recommendation aims to predict users’ future interests from their historical interactions. Although Large Language Models (LLMs) capture rich item semantics, existing methods often struggle to align collaborative signals with textual semantic knowledge. As a result, the learned item representations fail to capture the complementary strengths of both signals, leading to suboptimal recommendation quality. To address this limitation, we propose Latent Semantic Fusion for Sequential Recommendation via Flow-based Conditional Variational Autoencoders (LSF-SR), a novel framework that uses a Conditional Variational Autoencoder (CVAE) with Normalizing Flows to fuse item ID embeddings and LLM-generated semantic signals. At the core of LSF-SR is a conditional fusion module augmented with planar or radial flows. This module learns a flexible latent space that encourages items with similar semantic profiles to cluster together within the latent manifold. Through extensive experiments on five public benchmark datasets, we demonstrate that LSF-SR consistently outperforms state-of-the-art baselines, achieving gains of up to 12.98% and 14.13% in Recall@20 and NDCG@20, respectively.
[IR-7] SEEK: Skill-Routed Evaluation with Evolvable Knowledge for Industrial Search
链接: https://arxiv.org/abs/2609.29803
作者: Zhongxin Huang,Songyang Li,Renzhe Zhou,Feiran Zhu,Chenglei Dai,Zhen Xiao,Xuanping Li,Jingwei Zhuo
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Search quality evaluation provides essential supervision and diagnostic signals for the development and iteration of industrial search systems. Although large language models (LLMs) offer a scalable alternative to manual assessment, reliable automatic evaluation remains challenging: users experience search results at the page level, while the applicable evaluation criteria are multi-dimensional and continuously evolving. Packing all evaluation criteria into a unified prompt introduces irrelevant context and potential criterion interference, whereas internalizing them through post-training tightly couples rule updates with costly model retraining cycles. To address these issues, we propose Skill-routed Evaluation with Evolvable Knowledge (SEEK). Specifically, SEEK externalizes specific search evaluation criteria into a skill bank, dynamically routes relevant skills for each query-result list pair, and employs a task-adapted listwise evaluator to produce page-level judgments and failure mode attribution. A two-stage training pipeline teaches the evaluator to align evaluation criteria with human preferences, while a replay-gated skill bank allows recurring evaluation knowledge gaps to be incorporated without model retraining. Experiments on industrial short-video search show that SEEK improves listwise quality evaluation accuracy and achieves significant progress in attribution diagnosis. SEEK has been deployed at Kuaishou, a short-video platform with over 400 million daily active users, significantly improving the scale and quality of online search evaluation. Subjects: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.29803 [cs.IR] (or arXiv:2609.29803v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2609.29803 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-8] C3M: Cross-Session Multimodal Memory Maintenance for Long-Horizon Tasks
链接: https://arxiv.org/abs/2609.29735
作者: Xueshu Chen,Yan Wang,Zihao Xue,Jiefu Li,Zhenfang Liu,Jayden Chen,Zhen Bi,Jungang Lou
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
备注:
Abstract:Long-horizon tasks require preserving and later recovering cross-session evidence under a bounded, query-blind memory budget. Existing compression can discard fine-grained visual cues or conflate semantically similar but incompatible observations. We present C3M, a cross-session multimodal memory organization that maintains a bounded active index over persistent source text-image evidence. Relation-aware updates consolidate safe redundancy while preserving complementary and incompatible records. At query time, budgeted routing selects useful index pages and expands their associated source evidence under a fixed reader budget. Together, these mechanisms establish a compact, provenance-preserving multimodal memory organization for cross-session long-horizon tasks, retaining temporal distinctions and source links required for reliable downstream reasoning. Code is available at this https URL.
[IR-9] SALI: Shot-Aware Late Interaction for Cross-Shot Relation Matching in Text-to-Video Retrieval using Film-Grammar Knowledge ICASSP2027
链接: https://arxiv.org/abs/2609.29721
作者: Toya Oyama,Rainer Lienhart,Shin’ichi Satoh
类目: Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR); Multimedia (cs.MM)
备注: 5 pages, 2 figures, 4 tables. Submitted to ICASSP 2027
Abstract:Text-to-video retrieval usually represents a video clip by a single embedding. This embedding often loses important relations between people. E.g., an interaction “Anna confronts Mark” is regularly filmed as alternating shot and reverse shot of both (Fig. 1a). No single shot or averaged embedding over clip shots captures this relation. Thus, we propose SALI (Shot-Aware Late Interaction). It extracts the subject and object from a single-sentence query, and matches the query, its subject and object text embeddings against each visual shot embedding of a video clip. The matching operator is greedy max or optimal transport. A film-grammar penalty in fine-tuning adds a small, consistent shift. Built on CLIP4Clip-meanP, SALI keeps overall recall on par on Condensed Movies and ActivityNet while raising R@1 on multi-shot relation queries by 3 and 12 points, the most among all compared methods, and improves such queries on MSR-VTT at a cost of 1.4 R@1 overall.
[IR-10] Stochastic Semantic Evidence Graphs: Uncertainty Propagation and Governance for Agent ic AI
链接: https://arxiv.org/abs/2609.29703
作者: Matthew Francis Dixon
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (stat.ML)
备注:
Abstract:AI-agent evaluations usually inspect a final answer, yet error may enter through evidence, retrieval, prompting, generation or decision mapping. We introduce a stochastic semantic evidence graph (SSEG), a hierarchical stochastic DAG whose language node expands into an autoregressive token subgraph and whose observable output may be a law over complete phrases. Semantic reduction and calibration are optional. We define graph-relative local defects and downstream edge influences, derive a pathwise bound on terminal error and use its nodewise terms to diagnose governance triggers. For source provenance, the graph preserves uncertain claim–passage relations and propagates sharp Fréchet bounds rather than assuming independence across sources. Across three open-weight architectures, information-equivalent changes materially alter complete-phrase laws. A controlled experiment yields no certificate violations in 5,000 cases; crossed-RAG and live Brave-retrieval experiments separate retrieval, presentation, source and interaction effects. SSEG therefore turns workflow provenance into a quantitative account of where uncertainty entered, how it propagated and whether an output is qualified for use.
[IR-11] EvLink: Source-Grounded Evidence Linking for Graph RAG EMNLP2026
链接: https://arxiv.org/abs/2609.29695
作者: Linyao Zheng,Xuhang Shi,Zhifang Mao,Sai Zhou,Shuaixian An,Xiuquan Hou
类目: Information Retrieval (cs.IR)
备注: Accepted to EMNLP2026 MainConference
Abstract:Graph-based Retrieval-Augmented Generation (GraphRAG) supports multi-hop reasoning by organizing corpora into structured graphs. However, graph reachability often captures semantic association rather than evidence support, so a reachable passage may still fail to justify a required cross-passage transition. We propose EvLink, an evidence-linking retriever that preserves passages as retrievable evidence units and builds evidence-supported transitions between them. EvLink constructs two types of reliable links: relation-grounded evidence links justified by explicit source relations, and endpoint-alignment links serving as sourcebounded fallbacks. For retrieval, we introduce a two-stage retrieval strategy. First, bounded breadth-first search over source-grounded evidence links recovers bridge passages missed by similarity-based methods. Then, evidenceneed mining with noisy-OR coverage refinement selects a compact, non-redundant evidence set satisfying distinct question facets. Experiments on three multi-hop and two simple QA benchmarks show EvLink consistently outperforms leading GraphRAG baselines with average gains of 2.4 R@5, 1.9 EM, and 2.4 F1
[IR-12] Ingest-Time Fact Compilation for Cost-Efficient and Reliable Question Answering over Revised Corpora
链接: https://arxiv.org/abs/2609.29661
作者: Kyle Wild,Yusuke Takahashi,Asako Uraki
类目: Artificial Intelligence (cs.AI); Databases (cs.DB); Information Retrieval (cs.IR)
备注: 6 pages, 5 tables, 1 figure. Accepted for presentation at the 2026 International Conference on Applied Science and Technology - Engineering Science (iCAST-ES 2026), Surabaya, Indonesia, October 2026. Code and frozen experimental artifacts: this https URL (tag data-freeze-2026-07-16)
Abstract:Most agentic question answering (QA) systems do an important part of their semantic work at the worst possible time: every time someone asks a question. When a corpus contains revisions, drafts, revocations, deletions, and sources with different levels of authority, the model must reconstruct the governed current state on every read - then throw that work away and repeat it on the next query. This is a bit like a database that rebuilds a materialized view every time someone reads from it. We present ingest-time fact compilation, an architecture that performs this work when corpus data is ingested or changed. Raw passages are rephrased into self-contained facts; rules governing revisions, deletions, effective dates, and source trust are resolved once; and the resulting state is stored as typed records carrying source and revision provenance. At query time, an inexpensive model reads the compiled record instead of reconstructing it from noisy candidates. In a controlled synthetic experiment across five seeds, the same low-cost model produced the correct value, source, and revision in only one of 30 trials under query-time reconstruction, but in all 30 trials from the compiled substrate, at 12.89 times lower mean read cost per question. On simpler revision questions both architectures were exact, but the compiled path used 21.6 times fewer tokens. A separate test found that fact rephrasing roughly halved verbose Federal Reserve dialogue while preserving high source entailment, but left concise Wikipedia prose essentially unchanged. These results support a narrow but practical claim: resolving a corpus state once can make subsequent QA cheaper and more reliable for inexpensive models. We release the open source, MIT-licensed implementation and experimental artifacts.
[IR-13] SmallReason -ColBERT: An Ultra-Small Late-Interaction Retriever for Reasoning Intensive Retrieval EMNLP2026
链接: https://arxiv.org/abs/2609.29652
作者: Abdelrahman Abdallah,Mohammed Ali,Adam Jatowt
类目: Information Retrieval (cs.IR)
备注: Accepted at MAIN EMNLP 2026
Abstract:Reasoning-intensive retrieval remains difficult for small models. Compact public ColBERTs are usually trained on general-purpose corpora and underperform reasoning-tuned 150M+ baselines on BRIGHT~\citebright by several nDCG@10 points. However, no public reasoning-tuned ColBERT exists at edge scale. We introduce \textbfSmallReason-ColBERT, a 32M late-interaction retriever that closes much of this gap with three components: a varied-length contrastive warmup on ReasonIR-VL, a hard-negative contrastive polish on merged ReasonIR-HQ and BGE-Reasoner data, and a single-layer per-query-token importance head trained on top of the frozen base. The head is trained with an un-normalised weighted MaxSim score and evaluated with its length-normalised form. In a controlled re-training, replacing this training objective with the symmetric normalised score causes the loss to stall and costs 3.59 nDCG@10. The full recipe reaches \textbf21.41 mean nDCG@10 on BRIGHT, within 1.21 of the 150M Reason-ModernColBERT (22.62) and above all \le 33 M ColBERTs we evaluate. Through ablations on capacity, initialisation, and score variants, we further show that the learned head outperforms fixed IDF weighting and that simply thresholding the learned gates is harmful. this https URL
[IR-14] OBLIQ-IR: Training a Dense Retriever for Oblique Queries EMNLP2026
链接: https://arxiv.org/abs/2609.29649
作者: Mahmoud Abdalla,Abdelrahman Abdallah,Shaimaa Sedek,Adam Jatowt
类目: Information Retrieval (cs.IR)
备注: Accepted at MAIN EMNLP 2026
Abstract:Oblique retrieval, as exemplified by OBLIQ-Bench, asks a retriever to find documents whose relevance is determined by a latent attribute (an implicit stance, an analogous reasoning technique, an authorial fingerprint, or a vague tip-of-the-tongue recollection) that has little or no surface expression in the document. State-of-the-art dense encoders and agentic search pipelines built around frontier language models exhibit a large first-stage bottleneck on these tasks, while the same language models reliably verify relevance when shown candidates. We address this with OBLIQ-IR, a single-vector dense retriever whose training mixture combines per-mechanism synthetic queries with a new form of cross-model supervision: kNN-graph distillation from a frozen authorship encoder, which transfers a style-versus-topic inductive bias into the student. A 3B retriever fine-tuned reaches 0.211 NDCG@10 on Writing-Style, 0.171 on Math, 0.177 on Twitter, and 0.281 on Congress, improving over the GPT-5.2 Multi-Hop Agent by \xr0.010 to 0.150 NDCG@10 and over Gemini-2-Embedding by 0.027 to 0.222 NDCG@10 on every reported task. The code, data and checkpoints are available this https URL
[IR-15] An Exploratory Ablation of a Small MLA–SSM Hybrid Language Model
链接: https://arxiv.org/abs/2609.29618
作者: Christos Koutsiaris
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: 6 pages, 7 figures. Exploratory single-seed ablation study. Code and replication package available at this https URL
Abstract:We report an exploratory, single-seed ablation of TALH (Adaptive Latent Hybrid), a decoder-only language model with parallel Multi-head Latent Attention (MLA) and a custom recurrent state-space (SSM) branch. Five variants, spanning 117–217M estimated active parameters per token, are trained from scratch on a FineWeb sample for the same number of optimisation steps and tokens. In this specific setup, removing the SSM branch gives the largest degradation in validation perplexity (MLA-only PPL 315), whereas removing MLA has a much smaller effect (SSM-only PPL 239). A dense-FFN hybrid obtains PPL 231, compared with 240 for the tested top-2 ternary-MoE hybrid, while using 3.87 GB less peak training memory. We also preserve a preliminary Apple M3 timing observation: among the five unoptimised implementations, MLA-only has the flattest measured time-to-first-token curve from 512 to 2,048 prompt tokens, although the dense Transformer is much faster in absolute terms. Because the runs are single-seed, parameter counts are unmatched, the evaluation stream may overlap the training source, and raw repeated timing records are unavailable, these results support implementation-specific hypotheses rather than general conclusions about MLA, SSMs, or mixture-of-experts models.
[IR-16] Anatomy of a Decision: Uncertainty-aware Hierarchical Intent Learning via Flow Matching for Multimodal Recommendation
链接: https://arxiv.org/abs/2609.29609
作者: Yuchen Miao,Zijun Wang,Ke Liu,Siyang Xu
类目: Information Retrieval (cs.IR)
备注: 15 pages, 4 figures. Accepted at WISE 2026
Abstract:Modeling the underlying user intent is crucial for recommendation, but existing methods struggle with the inherent uncertainty and the dynamic, hierarchical nature of user interests. Current approaches often rely on clustering or prototype learning to discover a static set of intents. However, they face two critical challenges: (1) they overlook the uncertainty inherent in multimodal features; and (2) they assume a static and flat intent structure, failing to adapt to a user’s varying decision certainty. To address these limitations, we propose UHIFlow, an Uncertainty-aware Hierarchical Intent learning framework via Flow matching. First, our Cross-modal Uncertainty Synergistic Modeling (CUSM) module leverages conditional flow matching to quantify uncertainty from visual and textual modalities and synergistically align them. Subsequently, the Uncertainty-guided Hierarchical Intent Generation (UHIG) module uses this quantified uncertainty to dynamically construct a personalized intent hierarchy, generating coarse-grained intents for uncertain users and fine-grained ones for users with clear preferences. Extensive experiments on three real-world datasets demonstrate that UHIFlow significantly outperforms state-of-the-art baselines.
[IR-17] CodeGraph: Open-Taxonomy Knowledge Graph for Source Code with Wikidata Grounding CIKM2026
链接: https://arxiv.org/abs/2609.29474
作者: Federico Pennino,Andrea Gurioli,Stefano Zacchiroli,Maurizio Gabbrielli,Paolo Ferragina
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: Accepted at CIKM 2026
Abstract:Public software repositories, like GitHub and Software Heritage Archive, store billions of files, yet extracting their implicit engineering knowledge —i.e., the algorithms they implement, the paradigms they follow, the patterns they instantiate, and the application domains they serve— remains challenging, as current tools are constrained to syntactic and token-level analysis. We present a pipeline for building an open-taxonomy semantic annotation of source code using a code-specialised Large Language Model. The extracted entities are grounded in Wikidata through a three-stage linking procedure: a deterministic SPARQL stage handles unambiguous entities, a Deep Research Agent resolves the residual long tail, and a hierarchy-rollup stage imports the parent-of closure of each resolved Wikidata identifier. The resulting annotations are materialised as a source-code-specific open-taxonomy knowledge graph. We further introduce a calibrated quality-assurance protocol that quantifies annotation precision by combining a small human gold set with an LLM-as-a-judge filter. We applied our pipeline to the 167 million files of the Stack-Edu corpus, creating the first known large-scale open-taxonomy knowledge graph for source code. Our graph, named CodeGraph, contains approximately 158 million nodes, which include around 145 million files, about 63,000 extracted concept entities (such as algorithms, paradigms, design patterns, and application domains), and roughly 19,800 grounded Wikidata entities. Furthermore, CodeGraph features approximately 1 billion typed edges that connect files to their respective concepts, link these concepts to their grounded Wikidata identifiers, and relate them to their parent categories, covering 14 programming languages.
[IR-18] A Systematic Multi-Domain Evaluation of Document Retrievers
链接: https://arxiv.org/abs/2609.29455
作者: Valentin Velev,Andreas Spitz
类目: Information Retrieval (cs.IR)
备注:
Abstract:Document retrieval is a crucial component of many modern AI systems, directly influencing their effectiveness, robustness, and fairness in downstream tasks. While recent years have seen a growing number of retrievers, comparative studies in the literature are typically limited in scope or focused on singular benchmarks, domains, or model families. This fragmentation makes it difficult to draw reliable conclusions about the relative strengths, weaknesses, and trade-offs of document retrievers. To address this gap, we conduct a large-scale empirical evaluation of document retrievers, covering three families (sparse, dense, and expansion-based) and evaluating 33 retrievers across seven IR datasets, analyzing retrieval quality, runtime, and failure points. Rather than tuning each model individually, we evaluate every retriever off the shelf, under the configuration reconstructable from its public documentation and a uniform compute budget. Our results show that NV-Embed-v2 achieves the strongest performance on four of the seven datasets, albeit at the cost of substantial query latencies. Among sparse retrievers, we find that SPLADE-v3 rivals the top-performing approach despite much lower latency, and even achieves top scores on MS MARCO. On instruction-following datasets, GritLM delivers the best performance. Finally, an analysis of the retrievers’ failure points reveals contrasts between models and families that indicate potential for unrealized gains in retrieval performance.
[IR-19] Decoupled Learning and Selection in Slate Recommendation for Privacy and Stability Under Noisy Scores RECSYS2026
链接: https://arxiv.org/abs/2609.29453
作者: Sam Urmian,Qinyi Liu,Mohammad Khalil
类目: Machine Learning (cs.LG); Information Retrieval (cs.IR)
备注: 20 pages including supplementary appendix. Accepted at ACM RecSys 2026
Abstract:We formalize slate recommendation as a randomized score learner followed by deterministic selection. First, an appropriately scoped differential-privacy guarantee passes through selection and its audit trace by post-processing. End-to-end privacy holds only when selector inputs are public or independent, previous private outputs, or separately privacy-accounted; fixing raw state or candidate information instead yields only a conditional guarantee. Second, we derive a logged margin certificate: bounded score-induced objective movement below half the smallest greedy decision margin guarantees that the ordered slate is unchanged. Controlled fixed-margin tests show near-linear exponent scaling, with an empirical slope of -0.220 (95% CI [-0.231,-0.210] ) against the independent-noise reference -1/4 . Real-anchor experiments on OULAD, MovieLens-25M, and Amazon Musical Instruments show that greater anchor weight reduces score-noise-induced ranking churn. OULAD and EdNet certificate checks validate the implementation of the logged inequality, while closed-loop simulations show bounded target drift and setting-dependent downstream utility. The contribution is therefore a privacy-scope contract and a certifiable score-to-slate stability mechanism, not a universal utility claim. Comments: 20 pages including supplementary appendix. Accepted at ACM RecSys 2026 Subjects: Machine Learning (cs.LG); Information Retrieval (cs.IR) Cite as: arXiv:2609.29453 [cs.LG] (or arXiv:2609.29453v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.29453 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.1145/3773078.3831836 Focus to learn more DOI(s) linking to related resources
[IR-20] Asymmetric Dynamic Routing: Balancing Reasoning Depth and Computational Efficiency in Hypergraph RAG
链接: https://arxiv.org/abs/2609.29282
作者: Qi Sun,Yijia Zhang,Xingliang Hou,Caibo Li,Qiang Li,Yu Guo
类目: Information Retrieval (cs.IR)
备注: 5 pages, 1 figures. Preprint
Abstract:While graph-based and hypergraph-based Retrieval-Augmented Generation (RAG) significantly mitigate hallucinations in Large Language Models (LLMs), existing structure-based RAG systems typically adopt static traversal strategies regardless of the query complexity. We identify this ``static retrieval fallacy’’ as a primary source of computational redundancy for simple queries and cognitive context gaps for complex reasoning tasks. To balance reasoning quality and inference efficiency, we propose Asymmetric Dynamic Routing (ADR), an intent-conditioned retrieval framework operating over hierarchical knowledge graphs. ADR employs a lightweight structured classifier to dynamically dispatch queries among three asymmetric topological traversal operators: localized fact anchoring, bottom-up adjacency diffusion, and top-down insight grounding, which collectively enable bidirectional information flow across hierarchical knowledge layers. Extensive empirical evaluations across five domain-specific corpora demonstrate that ADR maintains strong reasoning performance while reducing prompt token consumption by up to 48.7% and end-to-end query latency by 45.3%, yielding a favorable quality–efficiency trade-off for query-adaptive Hypergraph RAG.
[IR-21] ASIRF: An Agent ic Framework for Context-Dependent Sensitive Information Redaction NEURIPS2026
链接: https://arxiv.org/abs/2609.29191
作者: Sudha Priyadarshini,Mohamed Chahine Ghanem
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: paper accepted in NeurIPS 2026 GlobalSouthAI
Abstract:Sensitive information is defined by domain and intent, not a universal category, yet redaction systems such as privacy filters and named-entity recognizers fix a taxonomy at training time, requiring retraining for each new domain. We introduce ASIRF (Agentic Sensitive Information Redaction Framework), which retrieves domain-specific definitions based on the input’s domain from a flexible knowledge base at inference time, needing no retraining to adapt. Two architectures, a three-call multi-agent pipeline and a single-agent variant, are evaluated across ten small open-weight models and eight datasets, including out-of-distribution fictional domains, against the OpenAI Privacy Filter (OPF) as a trained-classifier baseline. With only a few dozen expert-authored definitions per domain and no training data, ASIRF’s recall exceeds OPF’s in 68 of 80 model-domain combinations (85 percent), by at least one of the two architectures, with shortfalls confined mostly to OPF’s training-distribution domains.
[IR-22] ScalarLens: Numerical Embeddings with Stable Coordinates and Contextual Responses for CTR Prediction
链接: https://arxiv.org/abs/2609.29182
作者: Heng Yao,Tianying Liu,Yulou Shu,Yong He,Chuan Yuan,Kaibin Qiu,Guowei Chen,Jiayu Zhao,Siyun Hou
类目: Information Retrieval (cs.IR)
备注: 12 pages, 5 figures
Abstract:Numerical embeddings for click-through rate (CTR) prediction are built on a convenient but restrictive premise: a scalar has one representation. This premise conflates where a value lies with what it means for the current sample. On the Criteo validation split, the same numerical interval carries residual click evidence with opposite signs across categorical and numerical contexts, even after additive main effects are removed. Production pipelines compound this mismatch because externally normalized features require transformations and statistics to remain synchronized between training and serving. We introduce ScalarLens, a numerical embedding that preserves what a value is while adapting how it should be interpreted. A monotone local mesh constructs a stable coordinate from the focal scalar alone; bounded low-rank dynamics then produce a contextual response without moving that coordinate or replacing categorical tokens and the CTR backbone. In a 1,539-run primary evaluation covering 19 representations, three datasets, nine backbones, and three seeds, ScalarLens ranks first in 25 of 27 settings on original numerical scales and second in the remaining two. Matched ablations show that scale correction, additional local capacity, and generic conditioning do not reproduce the gain. A controlled study further recovers categorical, numerical, and mixed response mechanisms under context shift while the focal coordinate remains exactly invariant. A complete rerun under shared standardization retains significant advantages over DEER, DAES, and NaryDis, showing that the result is not explained by tolerance to raw scales alone. ScalarLens therefore recasts numerical embedding as a measurement problem: coordinates belong to values, while predictive responses belong to values in context.
[IR-23] X-Rec Technical Report
链接: https://arxiv.org/abs/2609.29180
作者: Chenglei Shen,Chenzhe Huang,Dong Jiang,Hongjie Gao,Jue Zhang,Kun Xú,Lincan Cai,Nan Zhuang,Pan Zhang,Shi Chen,Shunchi Zhang,Xiaoyu Ye,Yang Jin,Yu Zhang,Zhenwei An,Zhongtao Jiang,Zhiwei Wang,Kun Xǔ
类目: Information Retrieval (cs.IR)
备注:
Abstract:Recent advances in generative modeling have reshaped recommender systems by formulating recommendation as a next-item generation problem. Existing retrieval approaches primarily follow two paradigms: user-to-item (U2I) methods represent user context using one or a few deterministic embeddings, which limits the ability to capture diverse and multi-mode interests, while semantic-ID-based autoregressive (SID-AR) methods model more expressive distributions but suffer from quantization errors and the low throughput of sequential decoding. To address these limitations, we propose X-Rec to directly learn the recommendation distribution in the continuous item embedding space through flow matching and generate embedding triggers for approximate nearest neighbor retrieval. X-Rec incorporates three key designs to make this formulation effective and efficient. First, we introduce anchor conditioning to decompose generation into coarse semantic-region selection and fine-grained refinement. Second, we adopt Riemannian flow matching to align generative trajectories with the hyperspherical geometry of item embeddings. Third, we design a late-interaction diffusion Transformer that restricts repeated velocity-field estimation to the final Transformer layer. On a streaming benchmark, X-Rec substantially outperforms U2I baselines, matches the retrieval quality of SID-AR methods, and delivers 3.46x higher inference throughput than SID-AR. X-Rec has also been deployed as a new retrieval source for a specific vertical content on TikTok, where two consecutive launches have yielded significant improvements in both vertical engagement (+4.1484%) and general engagement (+0.0111%).
[IR-24] Claim-Gated Source-Risk Auditing for Generative Search
链接: https://arxiv.org/abs/2609.29145
作者: Kainan Zhou,Chuhong Xu,Gangzhen Qian,Zhaoyi Li
类目: Artificial Intelligence (cs.AI); Computational Complexity (cs.CC); Computational Engineering, Finance, and Science (cs.CE); Emerging Technologies (cs.ET); Information Retrieval (cs.IR)
备注: International Conference on Artificial Intelligence, Automation and Algorithms (AI2A 2026)
Abstract:A generative search answer can cite a supported passage yet omit a source relationship that changes its interpretation. We specify a claim-gated audit of the query-source-answer tuple. An omission is resolved only when relationship evidence, answer adoption, materiality, and disclosure are all observed; incomplete evidence remains unresolved rather than being treated as independence. The specification separates this endpoint from citation support and review priority, and binds decisions to versioned evidence spans. A reference checker makes the record contract executable. On an exhaustive synthetic suite, it reproduces all 81 three-state predicate combinations and rejects 192 deliberately malformed records. Common-guard baselines and predicate ablations isolate endpoint logic from missing-evidence handling, while controlled transitions check support separation and evidence removal. These are finite contract-conformance results, not detector accuracy or evidence of improved user outcomes. We define the independent annotation, held-out evaluation, and paired utility tests still required to establish semantic validity and deployment benefit.
[IR-25] Seek: Self-Evaluative Exploration for Knowledge Retrieval CIKM2026
链接: https://arxiv.org/abs/2609.28980
作者: Amin Bigdeli,Radin Hamidi Rad,Negar Arabzadeh,Sajad Ebrahimi,Hai Son Le,Charles L. A. Clarke,Ebrahim Bagheri
类目: Information Retrieval (cs.IR)
备注: Accepted at CIKM 2026
Abstract:LLM-based retrievers and rerankers have advanced passage ranking, yet both paradigms interact with the corpus in a single pass and commit to the resulting candidate set, leaving relevant documents permanently unrecoverable once missed. We introduce Seek, Self-Evaluative Exploration for Knowledge Retrieval, a training-free framework that addresses this limitation through iterative corpus interaction at test time. At each round, an LLM generates pseudo-passages conditioned on accumulated relevance feedback, a retriever surfaces fresh candidates, and a dedicated assessor assigns graded relevance judgments that guide subsequent rounds. On TREC Deep Learning, Seek matches trained rerankers in ranking quality while consistently improving Recall@100 over single-pass BM25. On the reasoning-intensive BRIGHT benchmark, Seek with Qwen2.5-7B achieves an 82% relative gain over BM25, surpassing all trained baselines, and Seek with GPT-4.1 reaches 37.4 average nDCG@10, exceeding the strongest baseline by 37%.
[IR-26] Cross-Country Code-Mixing for Generative Recommendation CIKM2026
链接: https://arxiv.org/abs/2609.28972
作者: Yuan Gao,Hao Deng,Haibo Xing,Yi Xu,Lingyu Mu,Jinxin Hu,Yu Zhang,Xiaoyi Zeng
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: CIKM 2026 Short
Abstract:Cross-country recommendation on modern e-commerce platforms is typically deployed with disjoint user and item ID spaces across markets, removing the shared anchors that conventional cross-domain methods rely on. Generative recommendation (GR) mitigates this by mapping items into a shared token space and training a unified model, but existing approaches keep behavior sequences strictly country-specific, so knowledge transfer occurs only at the parameter level and remains absent at the data level. Inspired by code-switching corpora in multilingual natural language processing, we propose CMRec, a cross-country GR framework that injects cross-country supervision at the data level via dual-constrained, context-aware code-mixing. CMRec first learns a shared semantic codebook from multi-modal content and behavioral co-occurrence across countries. It then uses this codebook to synthesize mixed-country sequences via token-level substitutions that satisfy both static (content) and dynamic (e.g., price, audience, popularity) constraints. Finally, it introduces a context-aware loss that reweights mixed samples according to their plausibility in the current sequence. Experiments on two real-world multi-country datasets and an online A/B test show that CMRec substantially improves recommendation quality in data-sparse countries while preserving performance in data-rich countries, achieving +1.77% advertising revenue and +2.64% orders on a large-scale e-commerce platform.
[IR-27] Reinforcement Learning with Verifiable Rewards for Small Search Agents
链接: https://arxiv.org/abs/2609.28765
作者: Gaurisankar Jayadas,Aske Plaat,Álvaro Serra-Gómez,Sandheep P
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:Reinforcement Learning with Verifiable Rewards (RLVR) performs well on problems with clear rewards, such as mathematics and coding, but whether it also works where the reward is less clear remains open. The reason-over-search recipe applies RLVR to open-domain question answering, where retrieval grounds the answer and a match against the reference supplies the reward. So far it has been demonstrated on large models, and below one billion parameters only with distillation from a larger teacher. We test the recipe on a small model. We train Qwen3.5-0.8B with Group Relative Policy Optimization (GRPO) and an interleaved Wikipedia-search tool on MuSiQue, varying only the reward across three shapes over three seeds each, and we evaluate every checkpoint held-out on a seven-benchmark question-answering suite. The recipe works: the best run reaches 0.352 average exact match against a 0.092 untrained floor, a 3.8-fold gain, with no distillation step in the training loop. The reward shape also matters. The Search-R1-faithful exact-match-only reward is the worst of the three at every seed at the matched training horizon, and it is worst even on exact match, the metric it directly optimises. We conclude that the sparse exact-match reward, RLVR’s default in mathematics and code, is the wrong starting point for models of this size. The reason-over-search setting can supply a suitable reward for RLVR on small models, but small-model RLVR needs its own reward-design study rather than a scaled-down copy of a large-model recipe.
[IR-28] he Fellowship of the Query: Learning Retrieval Actions
链接: https://arxiv.org/abs/2609.28653
作者: Mohammed Al-Maamari,Saber Zerhoudi,Michael Granitzer,Jelena Mitrović
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Retrieval-augmented question answering requires control decisions about when to decompose a question, search, reformulate, extract evidence, synthesize facts, verify progress, and stop. We study whether trajectory fine-tuning can improve small language models (SLMs) as next-action controllers. We additionally evaluate a low-resource setting in which a single SLM serves as both the controller and the final-answer generator. From accepted teacher search traces, we build a seven-way action-prediction task, where the model predicts the next structured teacher action from the current trajectory state, and evaluate LoRA-supervised fine-tuning across SLMs and xSLMs as controllers. On 1,646 held-out action examples, Granite 4.1 3B trained on 13,194 actions reaches macro-F1 0.6536, compared with 0.1736 for zero-shot prompting of the same model and 0.5399 for a TF-IDF logistic-regression baseline. In an end-to-end controller/generator swap evaluation over 149 held-out trajectories, using the fine-tuned model for both roles improves Exact Match from 0.7530 to 0.7946 and token F1 from 0.7783 to 0.8295 compared with using the base model as both controller and generator. The cross-role conditions show that the fine-tuned controller increases evidence-fact recording when the generator is fixed, while controller-only final-answer gains are not statistically clear. Overall, trajectory supervision improves action prediction and evidence-recording behaviour in this evaluated pipeline. Code is available at this https URL
[IR-29] OneTrans-V2: Unifying Retrieval Pre-rank and Fine-rank with One Transformer in Industrial Recommender
链接: https://arxiv.org/abs/2609.28589
作者: Hannan Cao,Jun Guo,Haolei Pei,Zhaoqi Zhang,Tianyu Wang,Ziyang Wang,Youchen Sun,Yue Xue,Yucheng Mao,Lintao Yan,Yufei Feng,Shaowei Liu,Rongkun Xing,Feiling Gong,Xinyu Chenli,Cong Xu,Mingge Zhang,Yunjia Zhu,Yajing Zhang,Pengfei Ren,Yue Lin
类目: Information Retrieval (cs.IR)
备注:
Abstract:Industrial recommendation systems typically operate as a \emphcascade of retrieval, pre-rank, and fine-rank, but these stages are usually trained and served as separate models, causing repeated user-sequence encoding, isolated optimization, and duplicated engineering effort. Building on OneTrans’ model-level unification, we present OneTrans-V2, one Transformer that unifies the entire cascade. It encodes the user behavior sequence once as a shared context while preserving stage-specific candidate features and computation. Joint training lets the three stages reinforce one another and enables in-model knowledge distillation from fine-rank to pre-rank. We scale the shared backbone with sparse mixture-of-experts (MoE), which increases capacity with bounded activated computation, and stabilize scaling with \mu P-style parameterization. To consolidate objective-specific retrieval channels, we introduce Decision-Conditioned Generative Retrieval (DCGR). DCGR predicts a decision prefix describing the upcoming interaction and generates items conditioned on it, allowing business objectives to steer a single generative process. Finally, Sequence-Native Training (SNT) organizes training around each user’s lifelong behavior sequence and amortizes its encoding across exposures. Deployed across all three stages of a large-scale industrial recommendation system, OneTrans-V2 improves gross merchandise value (GMV) by 9.74% and, with a co-designed serving stack, delivers 3.2\times the throughput of the cascade it replaces under the same hardware budget.
人机交互
[HC-0] Beyond Driving: Envisioning Activities in Future Autonomous Vehicles through Experience-Centered Design
链接: https://arxiv.org/abs/2609.30076
作者: Keqi Chen,Xiao Xue,Xinyi Liu,Runjia Tan,Chris Speed,Lee Kwan Min,Chen Lv
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Autonomous vehicles (AVs) are poised to fundamentally alter personal transportation, offering occupants the freedom to engage in various non-driving-related activities (NDRAs). However, our current understanding of how people might actually use this time in fully autonomous vehicles (FAVs) is limited. Traditional research methods often struggle to capture the influence of diverse travel circumstances and purposes when exploring future scenarios. This paper introduces experience-centered design (ECD) as an approach to investigate potential NDRAs within FAVs by examining the intricate connections between individuals’ daily routines, specific travel contexts, and the activities they might undertake in transit. Through a multi-phase study employing participatory techniques that facilitated narrative construction and exploration, including diary studies, scenario scripting, and mixed reality (MR) enactments, we enabled participants to ground speculative future scenarios in their own lived experiences. This process yielded nuanced insights into preferences and behaviors concerning potential NDRAs, alongside the underlying subjective meanings and sociotechnical considerations. Our findings lead us to conceptualize NDRAs not as isolated instances of “travel time use,” but as dynamic sequences of interrelated activities deeply shaped by pre- and post-journey contexts. The effectiveness of our ECD approach in bridging current lived experiences with future scenarios was crucial for uncovering these insights. Ultimately, this study reconceptualizes AVs as complex sociotechnical systems that actively mediate human activity and interaction, suggesting a fundamental shift in their role within the urban fabric.
[HC-1] Guardrails or Roadblocks? Effects of Pedagogical Style and Context Awareness in AI Teaching Assistants for Programming
链接: https://arxiv.org/abs/2609.29995
作者: Madeleine Eastwood,Harshith Narne,Joseph Hilby,Paul Denny,Ashish Aggarwal,Amanpreet Kapoor
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:AI teaching assistants (AI TAs) backed by large language models (LLMs) and pedagogical guardrails are increasingly being integrated into programming courses, providing students with scalable access to hints, conceptual explanations, and code-level feedback. However, guardrails may also create friction. If students feel that the support provided is overly restrictive or poorly contextualized to their current progress, they may bypass approved tools for general-purpose LLMs. To investigate how AI TA design affects students’ learning experiences, we conducted a randomized controlled trial with 132 students in an introductory programming course. Students completed three tasks related to code-writing and debugging and were randomly assigned to one of four AI TAs varied across two dimensions: pedagogical guidance style (Socratic vs. Direct instruction) and context awareness (no context vs. full context of the problem and student solution). We examined students’ perceptions, interaction behaviors, and evidence of post-task comprehension. Students rated the Socratic AI TA with full context least favorably, reporting significantly lower perceived support for task completion. Descriptively, this condition also showed the highest observed interaction stress, the highest rate of external LLM use, and the lowest proportion of post-task explanations demonstrating full comprehension, though these differences were not statistically significant. These findings suggest that guardrailed AI TAs are not automatically better for learning. Instead, their effectiveness depends on how pedagogical guidance and contextual awareness are balanced in ways that students experience as useful, supportive, and worth continuing to use.
[HC-2] Will It Teach as Intended? How Teachers Configure Educational AI Chatbots
链接: https://arxiv.org/abs/2609.29993
作者: Bahare Riahi,Deniz Ozturk,Alice Guth,Jiayu Li,Daksh Pratap Singh,Xiaoyi Tian,Jennifer Chiu,Nicholas Lytle,Tiffany Barnes,Veronica Catete
类目: Human-Computer Interaction (cs.HC)
备注: 17 pages, 5 figures
Abstract:Teachers are increasingly using generative AI to support instruction, yet it remains unclear how pedagogical intentions are translated into chatbot configurations and reflected in chatbot behavior. We studied a teacher-facing chatbot authoring tool in professional development workshops with 27 middle school teachers, analyzing focus-group interviews alongside configuration and interaction logs. Teachers envisioned chatbots as instructional scaffolds that could provide differentiated support, extend access to assistance, and preserve student thinking within teacher-defined boundaries. Configuration analysis showed that Purpose primarily captured instructional goals and content focus, whereas Rules more often specified pedagogical behavior, guardrails, and learner-specific adaptations. Log-based evaluation showed stronger alignment for responsiveness (88.9%) and persona (81.5%) than for rules (70.4%) and purpose (59.3%). These findings show that configurable controls alone do not ensure pedagogical fidelity and highlight the need for authoring tools that help teachers express, test, and refine intended chatbot behavior.
[HC-3] Improving Calibration of Black-Box Radiology AI Using Test-Time Augmentation MICCAI2026
链接: https://arxiv.org/abs/2609.29931
作者: Nathan Le,Magdalini Paschali,Arogya Koirala,Andrew Johnston,Zhongnan Fang,David B. Larson,Akshay S. Chaudhari,Camila Gonzalez
类目: Machine Learning (cs.LG); Human-Computer Interaction (cs.HC)
备注: 11 pages, 3 figures, 1 table. Accepted at the MICCAI 2026 Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE 2026)
Abstract:Radiology AI systems increasingly inform clinical decisions such as triage, follow-up imaging, and treatment planning. For these decisions to be made safely, model outputs must be well calibrated, meaning predicted probabilities accurately reflect true risk. Many standard techniques for improving calibration, such as MC Dropout and Deep Ensembles, require access to model parameters or retraining. However, proprietary clinical AI systems operate as black boxes, preventing access to the model’s internals. To that end, we propose a model-agnostic framework for improving calibration of black-box models using clinically grounded test-time augmentation (TTA). Our framework applies geometric and physics-inspired 3D CT perturbations and learns probability-level aggregation strategies without access to model internals or the original training data. Across pulmonary embolism and intracranial hemorrhage detection tasks, DualTTA achieved the strongest overall calibration among TTA methods, reducing the Expected Calibration Error by 54% (0.239 - 0.109) and 43% (0.051 - 0.029), respectively, while requiring only input-output access. Additionally, DualTTA outperformed uncertainty estimation techniques that require access to model internals, such as Temperature Scaling, MC Dropout, and Deep Ensembles, in most calibration metrics. These results demonstrate that learned TTA aggregation can improve the calibration of clinical AI systems, providing a practical approach for improving the reliability of black-box medical AI.
[HC-4] Working with Agent ic `Teammates: When a New Organizational Actor Collides with the Human Ecosystem of Work
链接: https://arxiv.org/abs/2609.29901
作者: Rida Qadri,Remi Denton,Michael Madaio,Mahima Pushkarna,Leslie Lai,Sherry Moore,Michelle Chen Huebscher,Andrew Butcher,Ritom Sen,Hsiao-Yu Tung,Shaan Mathur,Yimeng Liu,Shibl Mourad,Noah Fiedel,Edward Grefenstette,Michael Terry
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:Enterprise AI is transitioning from single-user, reactive tools toward proactive, multi-user ‘teammates,’ but our empirical understanding of this transition is limited. In this paper, we present an in-situ qualitative study of a persistent, proactive AI agent ‘teammate’ deployed across multiple teams in a large technology company. Our findings reveal the boundaries of the human-agent workplace are actively in flux, triggering breakdowns and negotiations across: 1) tacit rules of collaborative human workflows, 2) the relational boundaries of this new non-human actor, and 3) the redistribution of trust and human agency. We use these early micro-negotiations as signals to chart a new research, design, and organizational agenda that intentionally preserves human agency in a workplace shared with non-human organizational actors.
[HC-5] PUBG Ally: A Conversational Embodied Agent as an AI Teammate
链接: https://arxiv.org/abs/2609.29837
作者: Beomsoo Kim,Byeongju Kim,Dohyun Kim,Dongwon Kim,Eunchong Kim,Hongmin Kim,Hyeojung Im,Hyeonbin Hwang,Hyeonghwan Kim,Hyoseok Seol,Insub Im,Irene Chen,Jaeseung Jeon,Jimin Hong,Kiyoon Yoo,Minkyoung Park,Seohyeon Jung,Seungjun Chung,Sue Hyun Park,Sungwoo Kim,Youngin Cho,Yujeong Son,Kangwook Lee,Hyunseung Kim
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: 55 pages, 19 figures, 16 tables
Abstract:We introduce PUBG Ally, an embodied agent for PUBG: BATTLEGROUNDS that can reason, act autonomously, and play alongside players as a voice-enabled teammate. Building such a teammate requires combining two difficult capabilities: it must perceive and respond to a constantly changing game world under strict latency constraints while interacting naturally with players, keeping its speech synchronized with its actions. Ally therefore combines agentic tool use with real-time game control. A language-model agent uses a controlled interface to inspect game information, interpret player speech, maintain context, decide what to say, and issue high-level action choices that steer a faster control layer for movement, combat, and recovery. Because the player’s and Ally’s speech and actions continually shape each other and the course of the match, training requires data from actual gameplay. We therefore collect data across nearly 39k sessions in which real players play alongside Ally, recording gameplay, player speech, agent decisions, tool use, actions, and player feedback, and use these records for iterative training. To evaluate teammate quality, we use player feedback and preference comparisons to identify gaps between offline evaluations and player preferences, and iteratively refine the evaluation criteria. Deploying Ally in live service further requires low-latency on-device execution and safeguards for player-facing communication, which we address through model compression, context compaction, targeted safety training, runtime guardrails, and memory redaction. During the live service, we surveyed players in 141 countries. Among respondents whose play with Ally was confirmed in game records, positive responses exceeded negative responses by 25.1 percentage points when asked whether they would recommend Ally, with players describing Ally not only as a tool but also as a teammate or companion.
[HC-6] ransCAVE-E: A distributed virtual reality testbed for adaptive external human-machine interfaces
链接: https://arxiv.org/abs/2609.29686
作者: Yun Ye,Zexuan Li,Haoyang Liang,Boya Sun,Jian Sun,Haotian Shi
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:External human-machine interfaces (eHMIs) are evolving from predefined displays toward adaptive communication strategies that respond to changing traffic and road-user states. This transition requires experimental infrastructure that supports human-in-the-loop (HIL) interaction, software-in-the-loop (SIL) algorithm execution, reusable experiment orchestration, and synchronized multimodal human-factors evaluation. This paper presents TransCAVE-E, a distributed virtual-reality testbed for developing and validating adaptive and intelligent eHMIs. The platform integrates three coupled modules: a Scenario Design Center for configurable traffic environments and experimental conditions; an eHMI Algorithm Module for bidirectional real-time coupling between simulation and external algorithms; and a Data Management System that synchronizes trajectories, eye-tracking, physiological, system-log, and subjective data. A distributed multi-agent architecture supports synchronous interaction among pedestrians, human drivers, automated vehicles, and other traffic entities. Two use cases demonstrate the platform. In an AV-pedestrian experiment, an intent-recognition-based eHMI improved decision efficiency by 12.8% and 13.0% in yielding and non-yielding scenarios, reduced gaze distraction by 17.1% in the yielding scenario, and reduced unnecessary prompts by 40% in the non-yielding scenario while maintaining interaction safety. An HV-AV study further demonstrated real-time SIL validation of a game-theoretic information-disclosure strategy under active driver interaction. TransCAVE-E provides an extensible and reproducible infrastructure for closed-loop evaluation and iterative refinement of intelligent human-vehicle communication strategies.
[HC-7] LLM ersion: A Local-First AI Agent Framework for Low-Cost Home Language Learning toward Educational Equity
链接: https://arxiv.org/abs/2609.29672
作者: Qiming Guo,Jinwen Tang,Xingran Huang,Hung-Yu Lin,Yafu Zhong,Xiatian Zhuang
类目: Computation and Language (cs.CL); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注:
Abstract:Artificial intelligence helps education most where an essential provision has been rationed by cost. For language learners that provision is a teacher’s voice, which binds listening, reading, speaking, and writing into one act. Published evidence shows why most learners lack it, from a global shortage of 44 million teachers to heavy household tutoring bills, and why technology has not substituted for it: computer-assisted language learning proved effective but narrow, applications presuppose connectivity 2.6 billion people lack, and One Laptop per Child’s randomized evaluation found that hardware without capable software teaches nothing. We distill eight difficulties and four binding constraints, and argue that small open-weight models dissolve the last: a complete four-skill stack now fits a \ 200-class laptop and, on community measurements, generates at the pace speech is consumed, for about one US cent of electricity per study hour. We therefore propose LLMersion, a scheme for AI for education that runs entirely at home, over the learner’s own documents, with an AI-written, AI-understood, AI-updated codebase anyone can customize; present LLMersion-1, a released open-source prototype (this https URL and outline the vision of a private learning agent.
[HC-8] From Maturity Models to Ground Truth: Reconciling Cybersecurity Capacity Frameworks with Household-Level Governance Realities in the Global South
链接: https://arxiv.org/abs/2609.29611
作者: Wael Albayaydh(University of Oxford),Ivan Flechais(University of Oxford)
类目: Human-Computer Interaction (cs.HC)
备注: 3 tabvles
Abstract:National cybersecurity and digital-governance capacity frameworks, most prominently the Cybersecurity Capacity Maturity Model for Nations (CMM), shape hundreds of millions of dollars in donor-funded governance investments across the Global South. Yet a persistent, under-theorised gap remains between state-level institutional maturity and the governance actually experienced by end-users. We term this the last-mile governance gap: the structural space between what a maturity assessment can see - laws, agencies, standards, awareness campaigns - and what a household living under that formal architecture can access, understand, or enforce. Drawing on a dual vantage point combining CMM assessment experience with peer-reviewed empirical fieldwork on smart-home privacy governance in Jordan, corroborated against studies from Kenya and China, we identify four mechanisms by which national capacity fails to reach the household: legibility, intra-household power distribution, accessibility of redress, and infrastructure-affordability constraints. We map these mechanisms explicitly onto the CMM’s five dimensions, propose four concrete, low-cost last-mile indicators pilotable within existing CMM deployments, and outline a staged adoption roadmap with responses to anticipated objections. With AI-enabled devices entering homes across the Global South faster than institutional capacity can adapt, the stakes are rising: this dynamic risks converting a measurable maturity gap into an invisible one. We conclude with actionable implications for the GCSCC, the ITU, the World Bank, and other institutions relying on maturity scores to prioritize investment.
[HC-9] Beloved Afterlives: Governing AI Resurrection Beyond Consent
链接: https://arxiv.org/abs/2609.29544
作者: Hanjing Shi,Dominic DiFranzo
类目: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注: 23 pages, 3 tables, 1 figure. Full preprint with a 93-system public-record audit and four public cases
Abstract:AI resurrection is often framed as a question of consent: did the represented person authorize being made to speak? That question matters, but it freezes authority at the moment of creation. A representation can later change models, pass to relatives, depend on a provider, incorporate records shared with others, or circulate far beyond its intended audience. We argue that the central governance problem is therefore not whether authorization exists once, but whether it remains legible as the representation moves. Across a public-record audit of 93 systems, creation was far easier to inspect than the conditions for speaking, contesting, preserving, or leaving: consent or authority information was thin in 82 systems, objection or redress in 82, and deletion or export in 77. The differences among systems reveal why these gaps cannot be reduced to one transparency score. Human afterlives show consent becoming incomplete over time. Companion-animal afterlives begin where subject consent is unavailable and shared care must allocate authority. Adjacent persona and mimetic systems show how voices, likenesses, and personalities can travel into later afterlife uses. Four public cases follow the same movement from premortem participation, through intimate postmortem creation, to third-party circulation and family contestation. From this evidence we develop relational authority: authorization is distributed across people, records, providers, and audiences, and must remain traceable as those relations change. This reframes AI resurrection from a product authorized once into an accountability chain linking creation authority, source boundaries, circulation, contestation, and exit. The study measures what users and affected parties can inspect publicly; private implementation and lived outcomes remain open empirical questions.
[HC-10] Calibrating LLM Judges for Human and AI Conversations
链接: https://arxiv.org/abs/2609.29431
作者: Maike Züfle,Patrícia Schmidtová,Vilém Zouhar,Shree Harsha Bokkahalli Satish,Erica Cooper,Shobhit Banga,Vaibhav Nalawade,Manmeet Kaur,Jan Niehues,Markus Müller,Ondřej Klejch
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Measuring how successful a conversation is remains difficult, even for humans judging spoken dialogue. We evaluate state-of-the-art LLMs as pointwise and pairwise judges of conversational success on CANDOR, finding pointwise scoring correlates moderately with human ratings, while pairwise comparison suffers from long transcripts and positional bias. Since this leaves judge scores incomparable across models, we propose a small anchor set and a calibration function that calibrates any judge onto a shared, interpretable scale. We further release the Voice Arena Goal Dataset (VA), 200 task-oriented human-AI and human-agent conversations with pairwise annotations, revealing a substantial gap between current judges and human-level discrimination. Using VA, we test whether CANDOR-fitted calibration transfers to human-AI conversations, finding it brings judges onto a shared scale despite never observing VA during fitting.
[HC-11] Controlling Backchannels in Streamable Full-duplex Models
链接: https://arxiv.org/abs/2609.29418
作者: Maike Züfle,Peter Polák,Sefik Emre Eskimez,Jan Niehues,Peter Bell,Ondřej Klejch
类目: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注:
Abstract:Backchannels, brief acknowledgements like “uh-huh” produced while the other party may still be talking, are central to natural conversation, but full-duplex spoken dialogue models rarely model them explicitly. We introduce a lightweight backchannel head that predicts, from a full-duplex model’s own hidden states, when a backchannel should begin. Once this probability crosses a tunable threshold, a backchannel is force-decoded. Attached to both a 7B (PersonaPlex) and a 1B (F-Actor) model, it generalizes across scale. Probing confirms the hidden states anticipate real human timing, and generation evaluation shows more frequent, better-timed backchannels. Human raters judge the resulting backchannels on par with real ones.
[HC-12] When No One Owns the Judgment: Accountability Under Contribution Dissolution in Human-AI Collaboration
链接: https://arxiv.org/abs/2609.29312
作者: Hengzhi Ye
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: In CSCW’26 workshop on contribution, originality, and accountability in agent-mediated collaboration
Abstract:Communities often respond to potentially AI-assisted work by asking three questions: Was AI used? Was that use disclosed? Can hidden use be detected? These questions place AI use itself at the center of accountability while overlooking a deeper problem: unowned judgment. Evaluations, claims, decisions, and creative directions can be shaped by AI with no accountable human or institution prepared to stand behind them. We develop this argument through two illustrative cases: AI-assisted peer review and concealed AI use in creative work. The first shows how contribution dissolution can weaken responsibility while the second shows how the fear of losing credit can discourage honest disclosure. The cases expose the limits of disclosure rules and provenance records as responses to AI-mediated collaboration. We offer three directions for discussion: distinguishing the roles AI plays, identifying judgments that require clear human ownership, and creating conditions in which AI involvement can be disclosed without default penalty. The broader aim is to make AI-shaped contributions discussable, creditable, contestable, and repairable.
[HC-13] DocuTeam: Mixed-Initiative Multi-Agent Discussions around Evolving Documents
链接: https://arxiv.org/abs/2609.29309
作者: Heechan Lee,Juhyeon Choi,Tae Soo Kim,Juho Kim,Joseph Seering
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:In open-ended problem solving, collaborators often rely on discussion to surface concerns, challenge perspectives, and refine shared work as it evolves. While AI agents are increasingly used as discussion partners, existing multi-agent systems place a heavy burden on users to initiate and carefully orchestrate the discussions. We present DocuTeam, a mixed-initiative multi-agent discussion system in which both users and agents can initiate and steer conversations. Agents monitor document changes to proactively start and redirect discussions as the work evolves, while users can flexibly shape the conversation or adopt agent ideas. In a within-subjects study (N=20), participants using DocuTeam produced outcomes rated significantly more novel, relevant, and specific than with a baseline without any increase in cognitive load. Rather than using agents for one-off idea sourcing, participants engaged in an iterative refinement loop in which document changes prompted agent reactions, which led users to revisit and further develop their work.
[HC-14] A Human-Like Pedestrian Model for Automated Driving Simulations
链接: https://arxiv.org/abs/2609.29175
作者: Ruofeng Wang,Patrick Ebel,Philipp Wintersberger,Antti Oulasvirta
类目: Human-Computer Interaction (cs.HC)
备注: 14 pages, 7 figures, 1 table. Submitted to IEEE Transactions on Intelligent Transportation Systems
Abstract:Automated vehicles must be able to interact with pedestrians safely and efficiently across diverse traffic situations. Although driving simulators offer a scalable testbed for learning such capabilities, existing theory-inspired pedestrian models are narrow in scope and limited to go/no-go crossing decisions in single-lane settings. While data-driven approaches can predict pedestrian behavior in complex situations, they lack sufficient observations in rare, safety-critical scenarios. Here, we propose an approach to training pedestrian models in simulators so that learned policies generate demonstrably human-like behavior in realistic, complex traffic scenarios, including multiple lanes, heavy traffic, and dangerous driving styles. Our technical contribution is a novel definition of pedestrian-vehicle interaction as a partially observable Markov decision process (POMDP) with theory-grounded perceptual, cognitive, and motor constraints. It accounts for the highly adaptive nature of human behavior in traffic and simulates how people adjust their responses according to perceived danger, time pressure, and the complexity of the situation. When trained via deep reinforcement learning (RL) with domain randomization in a simulator, the model reproduces the broadest range of empirical findings shown so far on human crossing behavior, including gap acceptance, yielding acceptance, hesitation, and evasive speed adjustment. We show that learned policies transfer to unseen traffic environments, and can be further adapted to local traffic norms with finetuning. Together, these results establish a blueprint for simulator-ready pedestrian models that can support the development and evaluation of automated driving systems.
[HC-15] hrough Human Eyes and Machine Eyes: Understanding View Mismatch in Video See-Through Extended Reality
链接: https://arxiv.org/abs/2609.29173
作者: Yanming Xiu
类目: Human-Computer Interaction (cs.HC); Cryptography and Security (cs.CR)
备注:
Abstract:Video see-through extended reality (VST XR) systems commonly use headset screenshots or captured frames as proxies for the user’s first-person visual context. However, the system-captured view and the user’s effective visible field do not necessarily coincide: a screenshot records a rectangular machine-readable frame, whereas the user’s effective visible region can be more constrained and non-rectangular. This paper studies this human-system view mismatch in VST XR. We formalize the relationship between the system-captured region and the human-visible region by defining their co-visible, system-only, and human-only regions. \revWe then conduct a pilot-level boundary measurement on Meta Quest 3, revealing a clear mismatch between the rectangular screenshot frame and the approximate human-visible boundary. Building on this model, we analyze how view mismatch can affect screenshot-based XR sensing and downstream vision-language model tasks. Through four representative case studies, we illustrate potential risks and failure modes including prompt injection, privacy leakage, human-invisible information bias, and missing human-visible information. Our results show that view mismatch is not only a geometric artifact, but can also introduce security, privacy, and reliability concerns for AI-integrated VST XR systems.
[HC-16] A Procedure for Classifying Attachments and Affective Social Bonds in Human-Robot Dyads
链接: https://arxiv.org/abs/2609.29063
作者: Imran Khan,Elisabeth Blagrove
类目: Human-Computer Interaction (cs.HC); Robotics (cs.RO)
备注: 25 pages plus references; in review
Abstract:Human-robot interaction (HRI) claims that people form attachments and social bonds with artificial agents, yet the terms are often applied without the behavioural and physiological criteria that give them content in their source disciplines. Without this empirical grounding, studies deploy widely divergent methods, frequently producing expansive relational claims that far outstrip their underlying evidence. To address this, we propose a standardised four-question procedure, grounded in criteria established in the developmental, ethological, and neuroendocrine literatures, that classifies a given human-robot tie as an attachment, an affective social bond, or no relationship, with intermediate classifications when evidence is incomplete. We specify minimum evidential requirements for each question, and provide candidate HRI study designs, adapted from validated human-human, human-animal, and animal-animal paradigms. We then demonstrate the procedure by applying it to a representative set of published HRI studies, showing how often relational claims outstrip what the reported designs can establish. Finally, we discuss the ethical and regulatory burdens created when artificial agents engage human biobehavioural systems. By replacing the divergent operationalisations with a unified, criterion-based classification, this paper gives HRI practitioners a standardised basis for evaluating, classifying, and comparing human-robot relationships, and sets out the experimental rigour that each classification demands. We therefore call on researchers of human-robot relationships to adopt such rigour, or to consider alternative terminology in their descriptions of these ties.
[HC-17] How People Use ChatGPT in Australia: A WildChat Analysis
链接: https://arxiv.org/abs/2609.28990
作者: Ying Ma,Katy Gero,Clément Canonne,Craig Jin,Kanchana Thilakarathna
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Generative AI chatbots are increasingly embedded in everyday life, yet most large-scale studies describe global patterns. This paper presents an Australia-focused analysis of WildChat, a public dataset of real-world ChatGPT interaction logs. Using descriptive analysis and a multi-layer classification scheme, we analysed 37,845 conversations identified as Australian, examining language diversity, work relevance, interaction intent, topic distribution, turn-taking, temporal change, work activities, and Australia-related domains. Our findings show that the Australian subset is strongly action-oriented and comparatively work-oriented, with most interactions classified as doing and a majority of conversations classified as work-related. The dataset also shows multilingual use and a growing presence of self-expression over time. Australia-related conversations frequently invoke local institutions, laws, regulators, education systems, companies, cultural references, and public services. Finally, we outline implications for future research, including local AI evaluation, multilingual participation, context-aware design, and safeguards for everyday high-stakes domains.
[HC-18] AnomaSense: Anomaly-based Sensor Activation for Fine-Grained Human Activity Recognition
链接: https://arxiv.org/abs/2609.28936
作者: Xue Wang,Yang Zhang
类目: Human-Computer Interaction (cs.HC); Sound (cs.SD)
备注: 12 pages, 6 figures
Abstract:Audio carries rich cues about human activities, and microphones are already built into most wearable devices. However, microphones also capture speech, and this privacy risk limits their use in Human Activity Recognition (HAR). We present AnomaSense, a sensor activation approach for wrist wearables that keeps the microphone off by default and turns it on for at most one second when an unsupervised anomaly detector flags an IMU segment that is likely to produce sound. The captured audio is further masked before it reaches the recognition model. We study 20 activities from 15 participants, organized into five groups in which activities share similar wrist motion but differ in the object or material involved. With IMU data alone, our recognition model reaches 78.98% accuracy in leave-one-participant-out validation. With the short, masked audio windows added, accuracy reaches 96.89% with no masking and stays above 86% when 90% of each one-second audio window is removed. On the same data, the anomaly detector triggers the microphone with 86.46% precision and 74.28% recall relative to sound events. We also report a small preliminary check of automatic speech recognition on masked speech, which shows that contiguous masking degrades recognition far more than point-wise masking at the same masking ratio. Our evaluation is a controlled, offline feasibility study. We describe the threat model, what the approach does and does not protect, and the steps needed before deployment.
[HC-19] HelpCoach: Scaffolding Targeted AI Help-Seeking During Problem-Solving
链接: https://arxiv.org/abs/2609.28918
作者: Hyoungwook Jin,Weirui Peng,Jieun Han,Q. Vera Liao,Xu Wang
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Students increasingly turn to AI for help with problem-solving, yet too much AI support can undermine learning itself. To benefit from AI, students need to specify the necessary knowledge and scaffold type in their questions. However, they struggle to formulate such targeted questions because they lack metacognitive skills to recognize and select effective help options. We developed HelpCoach, an add-on for chat interfaces that helps students formulate knowledge- and scaffold-specific questions and receive targeted help during problem solving. HelpCoach continuously assesses students’ help-seeking performance and prompts students to improve through an adaptive revision template. Whereas prior work has largely taught help-seeking skills apart from learning tasks, HelpCoach’s in situ scaffold enables concrete practice on metacognitive skills and immediate revisions to help-seeking behavior. In a study with 40 college students learning web programming, HelpCoach led to more specific questions during chatbot interactions and greater knowledge retention than pre-task help-seeking training alone.
[HC-20] Characterizing LLM -Based Family Education through the Lens of Activity Theory: A Scoping Review of the HCI Literature
链接: https://arxiv.org/abs/2609.28886
作者: Lan Luo,Yuqi Liang,Jie Cai,Anqi Wang,Dongyijie Pan,Muzhi Zhou,Chun Yu,Pan Hui
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Large language models (LLMs) are increasingly involved in family education, yet HCI has not systematically explained the educational interactions that emerge around them. This scoping review analyzes 53 HCI studies from 6,540 records across 19 venues. Using activity theory and AODM, it relates participants and educational objects to mediation, labour, and rules. We find that the literature centers on child–parent interaction and on language, AI literacy, and relational learning. The introduction of LLMs enabled conversational, embodied, and spatial systems to generate support from the context of an unfolding interaction. LLMs redistributed educational labour, while family and institutional rules left parents and professionals responsible for interpreting outputs and deciding how they entered practice. Evidence across families and educational purposes remains limited, especially on sustained personalization, repair labour, and how families negotiate authority and rules. The review offers a framework explaining how LLM capabilities become organized through family participation.
[HC-21] “You Cant Just Automate It”: Negotiating and Sustaining a “Good” Family Life Through Energy Practices
链接: https://arxiv.org/abs/2609.28858
作者: Yang Hong,Ying-Yu Chen,Wei-Chien Chang,Yu-Hsin Chou,Sharifa Sultana
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:This study examines how Taiwanese parent-child families negotiate a “good” family life through everyday energy use and imagine future smart homes that support it. We conducted in-home interviews and co-design sessions with 21 families, including 46 parents and children. We found that families pursued a good life through energy practices shaped by thrift, comfort, care, safety, and enjoyment. These arrangements were continually adapted and responded to changing bodies, schedules, people, and infrastructures. This adaptive work was unevenly distributed, which in turn shaped different smart-home imaginaries. Drawing on the lens of Nearby and adversarial design, we conceptualize adaptation as situated sociotechnical work through which families continually rework energy arrangements. We further distinguish collective goods from plural and contestable goods to show why family IoT must support shared values while preserving opportunities to question and revise household arrangements. We offer theoretical and design directions for more adaptive, participatory, and contestable family IoT.
[HC-22] Signals of AI Hallucination: Designing Hallucination-Aware Cues for Embodied Conversational Agents in VR
链接: https://arxiv.org/abs/2609.28812
作者: Xiaoran Yang,Yang Zhan,Xie He,Yuxuan Huang,Yichen Yu,Zhuo Wang,Noboru Matsuda,Qiao Jin
类目: Human-Computer Interaction (cs.HC)
备注: 9 pages, 6 figures, published in ISMAR’26(TVCG Special Issue) Bari, Italy, Oct 5-9, 2026; ISMAR’26: IEEE International Symposium on Mixed and Augmented Reality
Abstract:LLM-powered conversational agents (CAs) often present uncertainty and provenance cues alongside their responses to help users assess response reliability and identify potential hallucinations. In immersive environments such as Virtual Reality (VR), CAs often take the form of speech-based embodied conversational agents (ECAs), where uncertainty and provenance cues cannot rely on persistent inline text and may be missed or disrupt comprehension when delivered through speech. We conducted a within-subjects study (N = 24) to compare three designs for presenting the hallucination-awareness information (uncertainty and provenance) in ECAs in VR against a no-cue baseline: embodied cues using gestures and posture, icon cues using visual indicators, and text cues using color-coded text with inline citations. We evaluated how these designs affect users’ ability to identify hallucination-related information, trust in the ECA, and interaction experience (immersion and task load). Our results show that all three designs support users in identifying hallucinations. Embodied cues were associated with higher trust and immersion, text cues offered clearer interpretability, and icon cues preserved relatively good interpretability while causing less disruption to immersion compared with embodied cues and text cues. This work contributes to the VR and AI research community by comparing different designs of hallucination cues in immersive ECA settings and examining how they affect users’ ability and experiences to identify hallucinations. It also offers practical insights and design implications for developing future hallucination-awareness interfaces for ECA.
[HC-23] he Interface Is Downstream: Designing the Terms of Human-Agent Collaboration
链接: https://arxiv.org/abs/2609.28801
作者: Hector Ouilhet Olmos
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Before an agent responds or acts, much of the experience has already been designed. Memory and retrieval shape what it notices. Evidence rules shape what it may claim. Permissions shape what it can do. Learning rules shape what it carries into the next encounter. The argument comes from Alicia, a personal agent I’ve built and used since January 2026. A fine-tuning pilot produced no defensible model-performance result. It exposed a provenance failure: Alicia repeated an interpretation from a retrieved synthesis, cited a source note credited by that synthesis, and left the synthesis out of the visible chain. In a model-blind review of thirty-three citations, one reviewer judged that the retrieved intermediary supplied the claim in sixteen relayed citations and part of it in four. Five of twelve citations to directly retrieved targets lacked support in the target excerpt supplied for review. These judgments remain unadjudicated, and the packet is not public. I call the shared setting a humorphic environment: a persistent computational setting that translates a human practice into software. The first Humorphism paper translated partnership. This paper translates the studio, the room where practice happens. The failure prompted an audit of attention, evidence, action, and learning, with a review artifact and available recourse for each. The test is whether the person can inspect and contest what shaped the teammate’s behavior. The output is downstream. Correction, consent, and learning carry the collaborative interface back upstream. Subjects: Human-Computer Interaction (cs.HC) Cite as: arXiv:2609.28801 [cs.HC] (or arXiv:2609.28801v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2609.28801 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Hector Ouilhet Olmos [view email] [v1] Wed, 23 Sep 2026 21:27:21 UTC (194 KB)
[HC-24] Data Patching
链接: https://arxiv.org/abs/2609.28777
作者: ATM Mizanur Rahman(University of Illinois Urbana-Champaign),Syed Ishtiaque Ahmed(University of Toronto),Sharifa Sultana(University of Illinois Urbana-Champaign)
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:To provide data-driven quality services to their citizens, every institution determines the acceptability of the citizen data and datafied mechanisms through institutional protocols, including preset standards and policies. However, data meeting standards at one institution might fail to meet a different institution’s standards in subsequent phases of the intended citizen services due to mismatched protocols, leading to data devaluation in the citizen service ecosystem. Terming this phenomenon cross-institutional data devaluation, we investigate its causes and workarounds through interviews with 41 Bangladeshi participants. We found that traditional data auditing mechanisms cannot solely address data devaluation; hence, we draw on our findings and theory of cross-institutional AI audits to propose the Citizen-centered Cross-institutional Data Audit (CCDA). We also discuss design and policy implications of CCDA in HCI and datafied citizen services.
[HC-25] How Spatial Biologists Direct and Verify AI-Assisted Analyses
链接: https://arxiv.org/abs/2609.28723
作者: Ella Hugie,Alexandra Irger,Chiara Schiller,Lukas Hatscher,Denis Schapiro,Hanspeter Pfister
类目: Human-Computer Interaction (cs.HC)
备注: *Ella Hugie and Alexandra Irger are co-first authors
Abstract:Spatial biologists use visualization to assess computational analyses of tissue data. We examine how they direct and verify analyses when an AI agent performs this work. We synthesized workflows from fourteen contextual inquiries and conducted a formative pilot followed by an observational study with ten spatial biologists using Claude Science on their own data. Participants valued help with plotting, locating cells of interest, and tasks they found laborious or could not otherwise perform. Assessing the agent’s work involved obtaining suitable evidence, sometimes through additional work in external tools, and interpreting it using knowledge of the tissue and its markers. Scientists also sought information about ongoing computation to decide how analysis should proceed. We contribute a workflow synthesis, an empirical account of scientists directing and verifying agentic analyses, and four design directions addressing execution control, familiar interactive views, source and execution information, and accessible verification across computing setups and experience.
[HC-26] Assembling Breaking and Refusing the Mask: Agency in AI-Mediated Self-Presentation in Livestreaming
链接: https://arxiv.org/abs/2609.28721
作者: Yang Hong,Nusrat Jahan Mim,Sharifa Sultana
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Our mixed-method study examines how Chinese women livestreamers use masking to construct idealized mediated personas while navigating gendered, commercial, organizational, and platform pressures alongside personal agendas. We built on the concept of masking, analyzed 627 recruitment posts, and conducted livestream observations and interviews with 26 Chinese women streamers. We found that streamers assembled masks across bodies, AI-mediated technologies, spaces, performances, and social relations to become recognizable while protecting personal boundaries. These masks were continually negotiated, and participants sometimes broke, resisted, or refused them when demands became misaligned or unsustainable. We conceptualize masking as a sociotechnical assemblage in which agency lies in preserving, disrupting, and reconfiguring relations rather than controlling a single interface. We further theorize breaking as a consequential part of masking that exposes hidden labor and unequal costs of visibility. We offer theoretical and design directions for more negotiable, contestable, and agency-supporting AI-mediated self- presentation.
[HC-27] Understanding Creative Design Practices among Data Artists
链接: https://arxiv.org/abs/2609.28715
作者: Tianwei Ma,Anna Offenwanger,Naimul Hoque
类目: Human-Computer Interaction (cs.HC)
备注: 28 pages, 7 figures
Abstract:Creative and artistic data visualizations communicate stories and invite engagement, yet how designers develop their expressive forms remains poorly understood. We investigate the design process of data artists through three complementary studies: an analysis of 40 public project accounts, artifact-anchored interviews with seven experienced data artists, and a design task-based study with eight data artists. We find that stories and visual forms develop together through data exploration, reference adaptation, sketching, and prototyping. Inspiration comes from data, existing work, everyday imagery, and personal experience. Sketches help develop mappings, while prototypes with real data can reshape representations and intended stories. Designers consider multiple possibilities but typically develop one direction at a time, partly because producing alternatives is costly. Their choices balance meaning, visual appeal, readability, and feasibility. These findings inform tools connecting stories, references, and real data, including AI assistance that supports testing and revising alternatives while preserving designers’ creative judgment.
[HC-28] “A Necessary Evil”: Teenagers Sensemaking of Privacy and Safety Settings on Social Media
链接: https://arxiv.org/abs/2609.28685
作者: Jingxin Dong,Lingyun Chen,Chen Ling,Colin M. Gray
类目: Human-Computer Interaction (cs.HC)
备注: 24 pages. Submitted to CHI 2027
Abstract:Social media platforms are embedded in teenagers’ daily lives, supporting friendship and identity while exposing teenagers to unwanted contact and privacy harms. Previous scholarship has documented how attention capture strategies and dark patterns shape social media use, and we extend this work to better understand platform settings that ostensibly provide privacy and safety protection. We report on think-aloud sessions with 11 teenagers aged 14 to 17 who completed six privacy and safety tasks on Instagram, TikTok, Snapchat, and YouTube. We show how participants worked out what a setting meant through their routines, boundaries, and prior experiences, how they accommodated protections softer and less predictable than expected, and how they treated the platform as the authority on what protection should look like. We argue that feature-by-feature evaluation cannot establish whether teenagers are protected, and that platforms should carry the obligation to show that a protective action took effect and is durable.
[HC-29] Available but Not Usable: Dark Patterns and Interaction Cost in Social Media Privacy and Safety Settings for Teens
链接: https://arxiv.org/abs/2609.28672
作者: Jingxin Dong,Lingyun Chen,Chen Ling,Colin M. Gray
类目: Human-Computer Interaction (cs.HC)
备注: 23 pages, 3 figures. Submitted to CHI 2027
Abstract:Social media platforms are central to teenagers’ lives, and their designs can expose users to privacy, safety, and wellbeing harms. Platforms increasingly offer protective settings, though the presence of a control reveals little about whether teenagers can find, use, and benefit from it over time. We paired an expert evaluation of six privacy and safety tasks across TikTok, Instagram, Snapchat, and YouTube with moderated think aloud sessions in which 11 teenagers aged 14 to 17 attempted the tasks. Interaction cost and dark patterns analysis allowed us to compare the complexity designed into each task with the effort participants incurred as they located, configured, and interpreted controls. Recurring dark patterns appeared across tasks, and most participant attempts exceeded the expert baseline. Protective settings therefore risk being insufficiently usable or durable in practice, and we propose a wayfinding audit that integrates expert evaluation, usability testing, interaction cost, and dark pattern analysis.
[HC-30] Privacy Leakage Through AI-mediated Analysis of Smartphone Data
链接: https://arxiv.org/abs/2609.28537
作者: Sarah Radway,Zoe Robert,Matthew Soto,Julianna Cimillo,Sebastian Diaz,Meg Marco,James Mickens
类目: Cryptography and Security (cs.CR); Human-Computer Interaction (cs.HC)
备注:
Abstract:Over the past thirty years, the online advertising industry built a large-scale data collection ecosystem, with the goal of tracking a user’s online activity to infer their demographics and interests. Traditionally, the ecosystem relied upon the collation and analysis of highly-structured text data like user IP addresses, GPS coordinates, e-commerce purchase histories, and visited URLs. However, recent ML models can parse not only structured text, but also multimedia files and unstructured text inputs—meaning a user’s photos, videos, inboxes, and calendars are now ripe for automated analysis. The privacy risks are particularly acute in the context of smartphone apps. A user’s phone already acts as a natural collation point for sensitive user information, but users may not understand that permitting an app to, for example, access a user’s photo does not just give the app access to the bytes in the photo: the app also receives access to inferences about the user that are enabled by the photo. To explore these privacy risks, we built Priva-See, an LLM-based inference system for app-collected user data; Priva-See reflects our best understanding of how real-life adtech companies would leverage machine learning to build user profiles. Through an IRB-approved user study, 465 participants deployed Priva-See on their phones; Priva-See made privacy-invasive inferences despite having access to only a subset of a user’s data. We see the experience significantly impacted participant willingness to share permissions data moving forward. Based on the observed privacy violations, we suggest changes to how smartphone OSes should gather user consent for data access, to better inform users about downstream data usage capability. Subjects: Cryptography and Security (cs.CR); Human-Computer Interaction (cs.HC) Cite as: arXiv:2609.28537 [cs.CR] (or arXiv:2609.28537v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2609.28537 Focus to learn more arXiv-issued DOI via DataCite
[HC-31] Make it SewSimple: Navigating UK Curriculum and Classroom Practice in Secondary Computing Education with E-textiles
链接: https://arxiv.org/abs/2609.28485
作者: Yifan Feng,Hanlin Zhang,Yishan Du,Weihong Tang,Jennifer A. Rode,Bea Wohl
类目: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注: The 20th WiPSCE Conference on Primary and Secondary Computing Education Research (WiPSCE 2026)
Abstract:This paper explores the potential of integrating e-textiles as part of the approach to delivering computing in UK secondary schools. As one of the few UK-based exploratory studies of teachers experiences, it investigates how e-textile platforms such as the SewSimple maker kit and the BBC micro:bit can be incorporated into Key Stage 3 computing education (ages 11-14), taking into account both English national curriculum requirements and the realities of classroom practice. Our research question is: How do teachers perceive the potential of including e-textiles as part of computing education in English secondary schools? In summary, our research contributes to secondary computing education in three ways. First, we examine teachers direct, cross-disciplinarity experiences in two participatory design workshops using a newly designed e-textile platform and extend the limited discussion on supporting the BBC micro:bit in e-textile education. Second, we specifically identify opportunities, barriers, and challenges across three dimensions: school planning, national curriculum guidance, and practical e-textile implementation. Third, we offer insights into best practices for supporting maker technology adoption and pedagogical practices within existing institutional structures to maximize students’ benefits for secondary school computing education.
[HC-32] he Domestic Unprotected Zone: Algorithmic Governance and the Reproduction of Perpetrator Discourse in Conversational AI
链接: https://arxiv.org/abs/2609.28479
作者: Lyu Chang,Sònia Estradé Albiol,Núria Vergés Bosch
类目: Computers and Society (cs.CY); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: 59 pages, 8 figures, 27 tables. Supplementary material (S1-S4) included as an appendix. Preprint; under review
Abstract:Conversational AI increasingly mediates intimate-partner communication, and refusal logic at the inference layer now functions as a governance threshold for gendered harm. This article asks whether such systems reproduce discursive forms historically tied to the privatization of intimate violence. A three-stage audit of six widely accessible conversational AI systems compares refusal behaviour across 1,600 crossed prompts per system, isolates relational framing through 300 matched prompt pairs, and contrasts pre-submission framing with post-output critique across fresh sessions. Four systems refused fewer than 1% of prompts. ChatGPT 5.2 and Claude Sonnet 4.5 refused most requests, but residual leakage clustered under intimate framing. Switching from a non-intimate to an intimate-partner descriptor amplified non-refusal 4.4-fold and 10.8-fold. Post-output critique produced in-session acknowledgement that did not carry across fresh sessions, with 96-100% of leaked prompts re-leaking. The article names this pattern the Domestic Unprotected Zone, a privatization-like threshold at the inference layer.
[HC-33] Voice Agents under Acoustic Stress: From Signal Degradation to Interaction and Action
链接: https://arxiv.org/abs/2609.29452
作者: Amir Ivry,Kai-Wei Chang,Lin Zhang,Sharon Gannot,Carlos Busso
类目: Audio and Speech Processing (eess.AS); Human-Computer Interaction (cs.HC)
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:Voice agents must complete users’ tasks despite noise, reverberation, and competing speech. Evaluating agents’ robustness therefore requires following how acoustic conditions affect the conversation and the actions taken on the user’s behalf. This overview examines what existing benchmarks reveal about agents’ ability to complete tasks under acoustic stress and where further task-based evaluation is required. We then introduce TRACE, a practical workflow for designing, running, and interpreting evaluations of acoustic robustness in task-oriented human-agent interactions: the same agent attempts a specified task with an original recording and an acoustically stressed copy, and the resulting conversations are scored for task completion, wrong actions, recovery, and user effort. Finally, we explain how results from these evaluations can guide changes to an agent to prevent wrong actions and improve recovery.
计算机视觉
[CV-0] RAPID: Robot Agent ic Programming from Demonstrations
链接: https://arxiv.org/abs/2609.30249
作者: Yuyao Liu,Jiayuan Mao,David Hsu,Leslie Pack Kaelbling,Tomás Lozano-Pérez
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Coding agents have demonstrated enormous success in solving complex programming problems. To leverage their potential for robot systems, this work introduces Robot Agentic Programming from Demonstrations (RAPID), which automatically generates, verifies, and refines robot programs, given a single visual human demonstration. The iterative agentic loop of code refinement requires several key ingredients: (i) a testable task specification, (ii) action primitives for robot execution, and (iii) an interactive environment for program execution and verification. RAPID infers all three from the demonstration automatically. To make the resulting program reusable beyond the demonstration setting, RAPID uses an object-centric relational program representation that focuses on the underlying structure of the demonstrated strategy rather than the specific motion per se: it expresses the action primitives as trajectory-optimization programs that realize object-level motion effects, while composing them through relational constraints that capture scene-specific geometry at run time. We evaluated RAPID in simulation on eight challenging contact-rich nonprehensile manipulation tasks as well as general prehensile manipulation tasks in the LIBERO-Pro benchmark. We also successfully deployed it on a real Franka arm and evaluated on all eight nonprehensile tasks. In all experiments, RAPID demonstrated strong performance, with generalization over object pose, shape, material, and environment. Website: this https URL.
[CV-1] Rolling-WAM: World Action Models with Rolling Imagination
链接: https://arxiv.org/abs/2609.30247
作者: Yinghua Zhou,Junjie Ye,Yiqi Zhao,Hao Dong,Celina Shiyu Wang,Ruohai Ge,Tingyi Yang,Basile Van Hoorick,Gaurav Sukhatme,Vitor Guizilini,Yue Wang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 7 figures, 5 tables. Under review. Project page: this https URL
Abstract:World Action Models (WAMs) couple action generation with future visual prediction for robotic manipulation. However, completing the joint video-action denoising process at each replanning cycle incurs substantial latency, delaying action updates and limiting closed-loop responsiveness. We present Rolling-WAM, a formulation that distributes joint denoising across successive replanning cycles. Our method maintains a sliding window of video-action chunks at staggered noise levels. At each step, a rolling noise schedule fully denoises the imminent action chunk for execution, while partially refining farther-future chunks. As the window advances with new camera observations, the retained future chunks continue their denoising process. This distributes the computational cost over time while carrying an evolving visual-action context across chunk boundaries. Evaluations on LIBERO, RoboTwin, and a real-world Unitree G1 humanoid show that Rolling-WAM achieves competitive manipulation performance. By removing the need to denoise the entire prediction horizon from scratch, it delivers a 4.5x steady-state replanning speedup over standard joint WAMs.
[CV-2] owards Practical Compression of 3D Gaussian Splatting
链接: https://arxiv.org/abs/2609.30245
作者: Pengpeng Yu,Yueru Chen,Fei Song,Tai Qin,Qi Zhang,Jing Wang,Yulan Guo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:3D Gaussian Splatting (3DGS) enables high-quality novel-view synthesis but requires substantial storage. Existing compression methods often rely on spatial context modeling over irregular 3D representations, increasing the complexity of training and coding. Meanwhile, floating-point context inference can introduce numerical inconsistencies across platforms, causing entropy-decoding failures. To address these practical challenges, we propose COSA-GS, which constructs context without spatial aggregation through anchor-wise causal factorization. Specifically, we use geometry context derived from each anchor’s coordinates to model a compact learnable anchor latent. The anchor latent is then fused with the geometry context to form an anchor context for attribute coding. The resulting context model features a simple architecture composed solely of linear transformations and activations. We train COSA-GS using rate–distortion optimization with adaptive Gaussian pruning. Further, we develop quantization-aware training and integer inference for the context model to achieve bit-exact consistency of entropy-decoded symbols across platforms. Experiments demonstrate that COSA-GS achieves state-of-the-art compression performance while retaining fast and consistent cross-platform decoding, providing a simple yet effective framework for practical 3DGS compression. Code is available at this https URL.
[CV-3] OmniFabric: Coherent UV Space Texture Synthesis for 3D Garment Reconstruction SIGGRAPH
链接: https://arxiv.org/abs/2609.30234
作者: Ding-Jiun Huang,Yuanhao Wang,Cheng Zhang,Hugo Bertiche,Alexandru-Eugen Ichim,Thabo Beeler,Fernando De la Torre
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to SIGGRAPH Asia 2026. Project Page: this https URL
Abstract:Automated generation of production-ready 3D garment assets from a single image is a central challenge in digital content creation. While recent generative models have significantly advanced 3D geometry reconstruction, synthesizing high-quality textures remains a bottleneck. Existing methods often bake environmental illumination and shadows directly into the texture map, or they fail to maintain global structural coherence, making the resulting assets unusable for physical simulation and relighting. In this work, we introduce OmniFabric, a novel approach that synthesizes globally coherent texture maps directly within the 2D sewing pattern space. Given a single reference image, our pipeline utilizes an estimated 3D mesh and generative priors of powerful Vision-Language Models (VLM) to establish a complete but coarse texture initialization across the unwrapped sewing patterns. We then leverage a specialized diffusion transformer, trained via an automated synthetic data engine and conditioned on 3D positional features, to refine this initialization directly in the canonical UV domain. This effectively removes distortion and baked-in artifacts to extract a clean and normalized texture map that preserves the original garment design. Extensive experiments demonstrate that OmniFabric significantly outperforms state-of-the-art baselines, yielding photorealistic 3D garments with high-quality textures.
[CV-4] BiCC: Bidirectional Connected-Component Loss for Instance-Aware Segmentation
链接: https://arxiv.org/abs/2609.30223
作者: Luc Bouteille,Frederic Jonske,Jens Kleesiek,Alexander Jaus
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 2 figures, 3 tables. Code: this https URL
Abstract:Common segmentation losses aggregate errors voxel-wise, so lesions influence the objective in proportion to their volume, giving small but clinically critical lesions disproportionately little weight. Instance-aware losses aim to address this mismatch by assigning each lesion its own term. However, blob loss and CC-DiceCE derive their regions solely from annotations, so false-positive components receive no instance-level term. This matters in computer-assisted review, where each false-positive component may require separate inspection, making precision and false-positive burden important alongside recall. We introduce the bidirectional connected-component loss (BiCC), which pairs annotation- and prediction-derived partitions to score predicted components on their own scale. By deriving instances from the predictions, this branch directly penalizes false-positive components regardless of their size. The balance parameter \alpha allows control over the lesion-wise precision-recall trade-off. Across five datasets with five-fold cross-validation using nnU-Net, BiCC outperforms CC-DiceCE in lesion-wise F1 on four datasets and blob loss on all five. It significantly improves over DiceCE on three datasets and matches it on two; CC-DiceCE instead loses up to 0.363 precision by favoring recall. Code is available at this https URL.
[CV-5] rackEverything: Long Horizon Dense Tracking via De-Duplicating 3D Scene Representations
链接: https://arxiv.org/abs/2609.30222
作者: Ayush Jain,Sreeharsha Paruchuri,Ishita Gupta,Fan Zhang,Tanner Schmidt,Jakob Engel,Katerina Fragkiadaki,Adam W. Harley
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注:
Abstract:Existing point tracking models face a fundamental tradeoff: they can either track a sparse set of query points over long horizons, or track all points across only short clips. We introduce TrackEverything, a 3D point tracker that breaks this trade-off by representing videos as persistent 3D scene tracks in world coordinates. Grounded in the insight that videos are 2D projections of an underlying 3D world, TrackEverything decouples model complexity from video duration, allowing it to scale with unique physical scene geometry instead. Our approach introduces three key innovations. First, we employ a voxelization-based de-duplication mechanism at sliding-window boundaries to merge co-located tracks, preventing repeated observations of the same surface from redundantly accumulating. Second, we decompose tracking into an endpoint refiner that predicts each point’s destination and static-versus-dynamic classification, followed by a lightweight trajectory refiner that decodes dense trajectories exclusively for dynamic points. Third, we propose 3D WAFT, replacing memory-prohibitive 4D correlation volumes with efficient feature sampling in the scene cloud. To the best of our knowledge, TrackEverything is the first 3D tracker capable of tracking all visible points across videos exceeding 1000 frames within 40 GB of GPU memory. On TAPVid-3D, TrackEverything outperforms all open-source all-frame dense 3D trackers by more than 20% APD on short clips, while remaining competitive with state-of-the-art sparse trackers on long sequences, despite tracking far more points.
[CV-6] WanPE: Towards Cinematic Prompt Enhancement for Modern Text-to-Video Generation
链接: https://arxiv.org/abs/2609.30221
作者: Yubo Zhu,Yawen Shao,Ziyun Dai,Zixun Fang,Kai Zhu,Siyang Sun,Haolan Xue,Chuxin Wang,Tingyu Weng,Jingming Luo,Chen Shi,Lianghua Huang,Yufeng Ai,Yuzheng Wang,Wenyuan Zhang,Yu Shang,Yuxiang Bao,Zoubin Bi,Jie Xiao,Jinbo Xing,Jiaxing Zhao,Chongyang Zhong,Hengjian Chen,Chenwei Xie,Akide Liu,Zhehan Kan,Yu Liu,Wei Zhai,Sheng Zhong,Wei Tong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video generation begins in text space by authoring a cinematic screenplay, then materializes into pixels. As contemporary video generators scale to 30 seconds and faithfully follow complex conditions, the textual prompt largely directs the production, planning how actions, camera trajectories, lighting, and sound unfold across multi-shot sequences. In this paper, we present WanPE, a 397B-parameter prompt enhancement model trained on 1.05M real-world videos to master director-level cinematic planning. WanPE formulates shot-level cinematic plans via video-grounded reverse construction and employs Semantic-Consistency GRPO (SC-GRPO) to faithfully preserve user requirements across shots and over time. To benchmark this capability, we curate WanPEval, a human-annotated testbed covering durations from 5 to 30 seconds across varying intent granularities, supported by approximately 11K blind pairwise assessments. When powering Wan3.0’s video generator, WanPE-397B boosts human preference over raw user prompts by 10.66-18.84 points at 5-15 seconds and by a dramatic 50.86 points in the 30-second arena. Ablation studies show that reverse construction demonstrates clear superiority over forward rewriting, while SC-GRPO robustly preserves semantic fidelity across model scales. Ultimately, WanPE leads all evaluated commercial offerings at 5-15 seconds and remains competitive with Seedance 2.5 at 30 seconds.
[CV-7] he Alignment Illusion in Multimodal Large Language Models NEURIPS2026
链接: https://arxiv.org/abs/2609.30210
作者: Hong-Han Wang,Yuntao Wang,Hu Ding
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted to NeurIPS 2026
Abstract:Layer-wise visual-text similarity in Multimodal Large Language Models (MLLMs) is widely interpreted as evidence that the language model progressively integrates visual content into a shared representation space. This reading rests on the assumption that scalar alignment scores reflect content-level cross-modal interaction. To test this assumption, we apply controlled interventions to the visual stream. Across 13 MLLMs from five families spanning 0.5B to 72B parameters, replacing projector-output visual tokens with Gaussian noise sharply reduces task accuracy, yet four standard scalar measures (CKA, SVCCA, MIR, and the leading principal-angle cosine) fail to consistently separate the corrupted stream from the original. We call this failure the alignment illusion and trace it to the shared language-model pathway: anisotropic MLP down-projections pull visual and text tokens toward common output directions, producing weight-induced alignment. Because this component is essentially one-dimensional, we introduce the principal-angle gap (PA gap), defined as the difference between the top two principal-angle cosines, which separates weight-induced similarity from multi-directional visual structure. Under graded visual corruption, the PA gap tracks task accuracy more consistently than the scalar scores we consider; under a structured but irrelevant image, it further exposes regimes in which internal geometry and task accuracy come apart. Internal visual-text alignment in MLLMs is therefore best read as a geometric diagnostic of the visual stream inside the language model rather than a direct proxy for content-level cross-modal interaction, and is most informative when calibrated by controlled task evidence.
[CV-8] Ego-Exo4D Human Meshes Dataset: 4D Human Motion Reconstruction for Ego-Exo Captures
链接: https://arxiv.org/abs/2609.30187
作者: Abhiram Maddukuri,Georgios Pavlakos
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Project website: this https URL
Abstract:Ego-Exo4D is a large-scale dataset providing synchronized egocentric and multi-view exocentric video, a rich resource for skill learning and assessment, procedural activity understanding, and embodied AI. However, the dataset ships with only sparse 3D human pose annotations, and reconstructing dense human motion from its multi-view captures is nontrivial. To this end, we present Ego-Exo4D-HM, a large-scale dataset of 4D human motion reconstructions for Ego-Exo4D’s captures, and release the accompanying reconstruction pipeline. The code, dataset, and documentation can be found at this https URL.
[CV-9] Smartphone-Based Method for Automated Speed Enforcement
链接: https://arxiv.org/abs/2609.30107
作者: Keya Li,Jahnavi Malagavalli,Lamha Goel,Tong Wang,Kara M. Kockelman
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Smartphone cameras and computer vision (CV) hold significant promise in assisting public agencies with enforcing traffic laws and enhancing road safety. This work designs and tests a smartphone-based method for automated speed estimation and vehicle identification (license plate, make/model, and color recognition) via an automated pipeline to assist enforcement agencies in reliably identifying speeders. The CV code accurately recognizes nearly half (46%) of the license plates’ text on 1,800 images from a Brazil open-source dataset, called UFPR-ALPR. Code tests on daytime recordings from hand-held smartphone videos (n = 73) and roadside cameras (n = 42) in Austin, Texas yield 60.8% accuracy for color detection (among all possible RGB color categories), 48.6% on vehicle make/manufacturer identification, and 16.89% on vehicle make and model identification. Prediction accuracy for speed estimation (within a 20% range), vehicle make (within the top 3 predictions), and license plate recognition (within the top 10 predictions) are 16.3%, 16.9%, and 29.7%, respectively. This paper also illuminates the legal, technological, and practical aspects of using smartphones for enforcement, including the potential use of recordings for enforcement purposes, emphasizing the need to transform the potential of smartphone-based CV technologies into practical tools for vital information on traffic violations.
[CV-10] Accelerating Video Diffusion via Training-Free Trajectory Routing
链接: https://arxiv.org/abs/2609.30096
作者: Mustafa Munir,Huy Vu,Shreyas Misra,Rohit Jena,Sajad Norouzi,Ali Taghibakhshi,Anis Ahmad,Anjul Patney,Pavlo Molchanov,Nima Tajbakhsh
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Video diffusion is computationally expensive, as it requires executing a large model across many denoising steps. Even with step-distillation, inference remains expensive because every distilled step still requires a costly model evaluation. We present TRACK: TRajectory-Aware Capacity routing via top-K selection, a heterogeneous denoising strategy that switches between compatible large and small models at selected steps, reducing the average cost per denoising evaluation. The switching steps are determined using a calibration process. TRACK first rolls out a reference trajectory with the large model. Then at each step, the small model’s prediction is also collected and compared against the large model’s prediction to obtain a relative disagreement score. Both models receive the same latent, timestep, conditioning, and guidance inputs. Aggregating this signal over a calibration set produces a disagreement score map across diffusion steps, which determines a switching policy for an efficient inference process: quality-sensitive steps keep using the large model, while steps with low disagreement scores are routed to the small model. Inference executes only the selected model at each step, requiring no retraining, architecture or scheduler changes, or online dual-model evaluation. Across Wan 2.1, Cosmos 3, TurboDiffusion, and FastVideo, TRACK yields 1.95\times , 2.04\times - 2.73\times , 2.69\times , and 2.17\times speedups, respectively, with comparable aggregate quality and high diversity retention. TRACK thereby establishes automated, training-free model switching as a practical acceleration paradigm for video diffusion.
[CV-11] Self-Adaptive VLA for Robust Robot Deployment
链接: https://arxiv.org/abs/2609.30092
作者: Hongxin Zhang,Chunru Lin,Tsun-Hsuan Wang,Zhenjia Xu,Chuang Gan
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:While Vision-Language-Action (VLA) models demonstrate impressive capabilities in robotic manipulation, their memoryless nature renders them brittle to test-time environment shifts, particularly hardware shifts caused by wear or imperfect calibration. Enabling these models to self-adapt during deployment without requiring continuous on-site recalibration remains a critical bottleneck for real-world scalability. In this work, we introduce Self-Adaptive VLA, a novel post-training recipe that enables the policy to iteratively adapt to deployment-time hardware shifts leveraging its own rollouts as context. To do so, we first collect policy rollouts under deliberately injected hardware shifts. We then transform the base policy’s training data into shift-conditioned expert demonstrations by pre-compensating the expert actions for these known shifts. Next, we introduce a lightweight, plug-in context encoder that compresses the context, including visual observation, proprioception, and actions in the shifted environment, into a latent context token. This token modulates the policy through adaptive layer normalization (AdaLN). Furthermore, we find that context tokens can be ensembled, allowing the policy to iteratively self-correct and mitigate failures step by step. Extensive experiments across four precision-critical bi-manual and dexterous manipulation tasks show that Self-Adaptive VLA recovers over 80% of the base policy’s performance under hardware shifts, such as actuation bias and joint encoder offsets. Moreover, Self-Adaptive VLA enables more robust deployment to new workstations compared to the base policy. Our approach provides a pathway for robust large-scale real-world robot deployments and easier maintenance. See videos at this https URL.
[CV-12] Can Frozen Hyperspherical Features Guide the Selection of Pseudo Masks?
链接: https://arxiv.org/abs/2609.30080
作者: Xinge Guo,Fengyang Xiao,Dingming Zhang,Yuhan Chen,Rihan Zhang,Xingjian Li,Tianyang Wang,Chunming He,Sina Farsiu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 29 pages, 11 figures, 19 tables
Abstract:Foundation segmenters such as SAM return several plausible masks for an unlabeled image, and a student trained on the wrong one inherits its errors. Choosing among them means querying a second large model or fitting a quality head to annotated masks. We show that a candidate can be judged by what it does to a frozen self-supervised backbone’s features. Normalized DINOv2 patch features lie on a hypersphere, and a candidate mask splits that sphere in two. Based on this reading, we introduce SphereTrust, which scores each candidate by three properties of the split, the angular contrast between the two sides, the coverage of the foreground’s appearance modes, and contact with the image frame, one for each of three common ways a mask fails, and ranks a pool in 0.55 s per image from the frozen features alone. On eight SAM and SAM3 candidate pools spanning camouflaged, salient, and dichotomous segmentation and camouflage under low light, SphereTrust exceeds the strongest evaluated external baseline on six pools by 1.7 to 9.3 percentage points in mean selected Dice. These comparisons include published selection rules and explicitly labeled adaptations of DSS and UCOD-MKD. On the two prompted camouflage pools, its mean selected Dice is within 0.1 percentage points of the candidate-derived DSS adaptation, with a lower catastrophic-error rate. Which cue carries the signal depends on the candidate pool. The same sphere also supports training. The leading candidates enter as a candidate set with their scores as priors, prototypes reorder them, and a cross-fitted second round completes the labels, raising weighted F by 4.5, 2.3, and 5.5 points over fixed-label training on the three MLLM anchor pools, with students competitive with published unsupervised methods on nineteen test sets.
[CV-13] M3GD: Multi-Modal Multi-View Geometric Diffusion for Camera–LiDAR Novel View Synthesis
链接: https://arxiv.org/abs/2609.30056
作者: Yang Zhou,Jiuhong Xiao,Shizhao Ye,Long Quang,Carlos Nieto-Granda,Giuseppe Loianno
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Robotic novel view synthesis (NVS) must recover both visual appearance and metric 3D structure, yet most generative NVS methods rely only on images, overlooking LiDAR, a complementary sensor common on robotic platforms. We present M3GD, a Camera–LiDAR multimodal representation for generative NVS that composes independently pretrained 2D image and 3D point-cloud foundation models without separately pretraining a cross-modal translator. We show that, after camera projection, frozen LiDAR and image features exhibit substantial shared spatial structure, providing a natural cross-modal representation. M3GD conditions generation on LiDAR through this structure: it combines explicit geometry statistics with learned point-cloud descriptors into view-aligned packets on the image-latent grid, injected through a lightweight residual adapter into a multi-view flow-matching generator whose latent space, decoders, and training objective remain intact. On the GrandTour dataset, M3GD improves target-view RGB and depth synthesis over an image-only version of the same backbone. Ablations show that the gains come from pixel-aligned LiDAR content and that target-view LiDAR acts as a geometric query linking the requested view to source observations. Deployment on a ground robot demonstrates practical real-world operation, with a configurable quality–cost trade-off controlled by the number of Euler integration steps.
[CV-14] ConPro: Contrast Projection Pretraining for Label-Efficient Vessel Segmentation in DSA Sequences ICASSP2027
链接: https://arxiv.org/abs/2609.30043
作者: Xinge Guo,Yuanhao Wang,Liqi Shu,Yang Liu,Min Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 5 pages, 4 figures, 2 tables. Submitted to IEEE ICASSP 2027
Abstract:Dense vessel annotation in digital subtraction angiography (DSA) is labor-intensive, yet every unlabeled sequence records how contrast passes through the vessels. Semi-supervised methods take their targets from the current model, and generic self-supervised pretexts reconstruct static appearance, so this signal goes unused. We propose ConPro, a self-supervised pretraining scheme whose target is a contrast projection, the normalized drop of every pixel below its temporal median over the sequence. On DIAS and DSCA, with 10%, 20% and 50% of the training cases labeled, ConPro improves on training from scratch at every label fraction and is the best of the compared methods on DSCA at 20% and 50% labels. Controlled comparisons show that the gain comes from the target. A temporal-median target with the same input, loss and budget stays at scratch level, and using the projection directly instead of learning it, as an input channel or a pseudo-label, helps little or hurts. ConPro provides pretrained weights without changing the segmentation architecture, so it combines with semi-supervised training, and UniMatch, the strongest baseline, gains 0.5 to 2.0 Dice and 0.9 to 2.3 clDice at every label fraction when started from ConPro weights, reaching 75.4 Dice on DIAS and 81.3 on DSCA.
[CV-15] AERIAL: Adversarial Evaluation of Robustness in Accuracy-Preserving Low-Precision EEG Decoders ICASSP2027
链接: https://arxiv.org/abs/2609.30037
作者: Saim Rehman,Muhammad Shafique
类目: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR); Machine Learning (cs.LG); Signal Processing (eess.SP)
备注: Submitted to IEEE ICASSP 2027, 5 pages
Abstract:Deployment-oriented compression is attractive for resource-constrained brain–computer interfaces (BCIs), but whether it changes adversarial vulnerability remains unclear. On BCI Competition IV-2a, we compare 32-bit floating-point (FP32) EEGNet and ShallowConvNet models with global magnitude pruning and simulated INT8 post training quantization (PTQ) and quantization-aware training (QAT) across nine subjects and three seeds. Simulation provides differentiable quantize–dequantize models for white-box attacks and gradient analysis, while native TensorRT deployment is used for validation. Accuracy-preserving compression does not improve direct robustness: at \epsilon=0.005 , EEGNet PGD accuracy remains 22–24% across FP32, 50% pruning (P50), PTQ, and QAT. However, P50 reduces bidirectional transfer efficiency to 0.963/0.928 (FP32 \rightarrow P50/P50 \rightarrow FP32), versus 0.994/0.997 for PTQ; the same trend holds for ShallowConvNet. Gradient alignment shows a corresponding separation, while native PTQ agrees with simulated clean/adversarial predictions in 95–98% of cases. These results show that direct robustness, adversarial transfer, and deployment efficiency are distinct properties of compressed EEG decoders.
[CV-16] raining-Free Hold-Usage Detection in Sport Climbing with Foundation Pose Models
链接: https://arxiv.org/abs/2609.30026
作者: Abu Bakar,Abdullah Aftab,Amir Hamza
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at AI2ML Conference 2026 (2nd International Conference on Advancement Innovation in Artificial Intelligence and Machine Learning)
Abstract:Detecting which holds a climber uses, and when, underpins automated scoring, movement analysis, and assistive systems for sport climbing. Existing approaches train task-specific models or repurpose 2D pose estimators whose hand keypoint sits at the wrist and foot keypoint at the ankle i.e. offset from the fingertips and toes that actually contact the holds, and whose hands are occluded in roughly half of all frames. We show that a frozen, off-the-shelf pose foundation model is sufficient: using the fingertip and toe keypoints of Sapiens, a per-frame proximity test against the annotated holds, per-limb mutual exclusion, and a short temporal-persistence rule, we detect hold usage without any climbing-specific training. On the The Way Up dataset (22 videos, 10 athletes, two routes), our method reaches an event F_1 of 90.2% on a held-out split (89.8% under leave-one-participant-out cross-validation) and 79.9% over all 22 videos at any temporal overlap, and performs best on footholds (F_1,89.8% overall, 96.6% held-out). Under an identical protocol it exceeds our reproductions of the YOLOv8-pose and ViTPose pipelines at every temporal threshold, with the margin widening under strict timing. An ablation shows that two intuitively helpful additions—dense foundation-feature change gating and body-part segmentation—both hurt, arguing that a minimal, keypoint-only design is the right one for this task. Finally, standard coaching statistics computed from our automatic predictions track ground truth closely (Pearson r=1.00 for climb time, 0.94 for pace), turning ordinary single-camera video into reliable performance metrics with no instrumentation.
[CV-17] GHOST-Q: Towards Studying Grounding Hallucinations Overlooked Under Same-score TradeOffs in Quantized VLMS ICASSP2027
链接: https://arxiv.org/abs/2609.29999
作者: Saim Rehman,Muhammad Shafique
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
备注: Submitted to IEEE ICASSP 2027, 5 pages
Abstract:Post-training quantization of vision–language models (VLMs) is typically assessed through aggregate task accuracy and memory savings, but preserving a headline score does not guarantee preservation of visual grounding behavior. We present GHOST-Q, a cross-precision controlled evaluation of three 8B VLM families under FP16, INT8, and NF4 across utility and hallucination-sensitive benchmarks. Rather than comparing only aggregate accuracy, we pair FP16 and quantized predictions item by-item to quantify how compression redistributes grounding successes and failures. Five of six quantized variants preserve MMStar accuracy within \pm2 percentage points, yet 10 of 36 paired effects remain significant after false-discovery-rate correction, nine on hallucination-sensitive conditions. Same-device A100 profiling further demonstrates that substantial memory reduction does not necessarily mean lower inference latency. Finally, an open-ended AMBER audit reveals strong generation budget censoring whose severity varies by architecture and precision. These results show that quantized VLMs should be evaluated jointly for aggregate utility, grounding reliability, generation behavior, and realized deployment efficiency.
[CV-18] OceanXL: Large-scale Underwater 3D Gaussian Splatting via Block Partitioning and Adaptive Pruning SIGGRAPH
链接: https://arxiv.org/abs/2609.29985
作者: Haoran Wang,Shaoyu Cai,Adrian Azzarelli,Zhuodong Jiang,Guoxi Huang,Eng Tat Khoo,Brett Seymour,Fan Zhang,David Bull,Nantheera Anantrasirichai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: SIGGRAPH ASIA 2026
Abstract:Underwater 3D reconstruction is critical for marine exploration, ecological monitoring, and subsea infrastructure inspection, yet remains challenging at large scale due to light attenuation, scattering, and limited capture coverage. While 3D Gaussian Splatting (3DGS) enables high-quality real-time rendering, its application to large underwater scenes is constrained by high memory consumption and inefficient optimization over extensive areas. We propose OceanXL, a fast and scalable 3DGS-based framework for large-scale underwater reconstruction. OceanXL adopts a divide-and-conquer strategy, partitioning scenes into spatially coherent blocks to enable efficient optimization while preserving global geometric consistency. We further introduce an adaptive pruning scheme tailored to underwater conditions that removes redundant primitives, producing compact representations without sacrificing visual fidelity. Together, these components improve training efficiency and rendering performance for large scenes. We also introduce a large-scale underwater dataset covering diverse marine environments. Experiments on five large-scale scenes demonstrate favorable scalability, compactness, and efficiency–quality trade-offs over large-scene baselines. Controlled comparisons on the small-scale SeaThru-NeRF dataset further show competitive reconstruction quality with substantially smaller model sizes than underwater-specific methods.
[CV-19] ADATEX4D: adaptive texture capacity allocation for 4D gaussian splatting
链接: https://arxiv.org/abs/2609.29963
作者: De Jiang,Peiqiang Wang,Kehong Yuan,Shaohua Ma
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Textured Gaussians improve local appearance capacity, but assigning the same texture resolution to every primitive wastes storage on low-detail or weakly visible regions. We introduce AdaTex4D, an adaptive texture-capacity module for deformation-based 4D Gaussian Splatting. Each Gaussian carries packed RGBA triplanes whose two axes grow independently according to visibility normalized screen-space gradients and deformed local scales. Experiments on N3DV and PanopticSports show that AdaTex4D reduces texture storage by more than half while preserving reconstruction quality. Under fixed memory budgets, adaptive allocation also improves quality over uniform texture assignment and reduces overall model and peak memory. These results show that dynamic, anisotropic texture allocation provides a more efficient way to distribute local appearance capacity in 4D Gaussian representations.
[CV-20] Not All Confusion Is Equal: A Source-Aware Uncertainty Diagnosis for Fine-Grained Aircraft Detection
链接: https://arxiv.org/abs/2609.29959
作者: Hai Huang,Helmut Mayer
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 23 pages, 4 figures
Abstract:Fine-grained object detectors are commonly evaluated with confusion matrices, which show where the model is confused but not why, nor whether the confusion can be reduced. We argue that confusion can be attributed to distinct, separable sources, each quantitatively measurable, turning a passive measurement into actionable guidance. We present A^2E^2 , a diagnostic tool that decomposes the sources of confusion along two axes, \ aleatoric, epistemic \ \times \ within-class, between-class \ , giving a 2\times2 taxonomy that enumerates the source types. Each quadrant is measured by its own quantity, computed in one of three places (input geometry, output-space disagreement, and the bias-parameter posterior), so the two epistemic sources are separated by construction rather than by an empirical correlation. On fine-grained aircraft detection, the four quadrants become four named sources with their own remedy verdict: affinity (geometric similarity, irreducible from size alone), heterogeneity (geometrically heterogeneous sub-variants, pointing to re-labeling rather than more data), contested (an insufficiently trained but learnable boundary, improvable), and collapsed (a class starved of data, reducible). After attributing the confusion to a specific reducible source, we apply a targeted intervention and verify experimentally that it reduces the diagnosed source specifically while leaving the irreducible sources unchanged. A^2E^2 thus turns confusion measurement into a concrete, validatable and actionable “diagnosis” in which the same off-diagonal mass can carry opposite causes and opposite remedies. We also state this framework’s limits, including which sources are only partially identifiable on this specific dataset and why.
[CV-21] Mind What Matters for Reasoning : Aligning Cross-Modal Attention via Selective Probability Mass Concentration
链接: https://arxiv.org/abs/2609.29940
作者: Jiaqi Deng,Zonghan Wu,Zhan Heng,Xiaoshui Huang,Huan Huo,Guandong Xu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal large language models (MLLMs) achieve strong performance on visual reasoning tasks, yet remain prone to hallucinations and over-reliance on language priors, often generating answers without adequately using task-relevant visual evidence. Existing approaches primarily improve reasoning through reasoning-oriented supervision or inference-time strategies. In this work, we study a complementary question: can multimodal reasoning be improved by strengthening implicit visual grounding without directly supervising the reasoning process? Motivated by the functional specialization of attention heads, we investigate whether reasoning can be improved by guiding only the heads most responsive to visual evidence grounding. We propose Selective Probability Mass Concentration (sPMC), a training framework that identifies grounding-responsive heads and selectively regularizes their text-to-image attention. sPMC treats normalized attention over visual tokens as a spatial probability distribution and encourages the probability mass to be assigned to semantically relevant regions using segmentation-derived spatial priors. Adaptive Head Selection restricts this guidance to visually responsive heads while leaving the remaining heads unconstrained to preserve their complementary functions. Across 6 multimodal benchmark suites, sPMC achieves an average zero-shot improvement of 3% and gains of up to 11.3% across multiple MLLMs while regularizing only 3%-15% of their attention heads. These results demonstrate that targeted guidance of sparse and implicit visual evidence pathways can directly improve multimodal reasoning.
[CV-22] Beyond Spatial Benchmarks: From Spatial Reasoning to Navigation
链接: https://arxiv.org/abs/2609.29934
作者: Xun Huang,Shijia Zhao,Rongsheng Qu,Jiayuan Li,Xin Lu,Weixin Li,Chenglu Wen,Cheng Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Does progress on spatial reasoning benchmarks translate into better navigation? Existing benchmarks test isolated inferences from images or videos, with little connection to downstream navigation. Our analysis reveals a gap between benchmark-oriented spatial specialization and navigation performance, and shows how aligning spatial supervision with navigation goals, phases, and decision learning improves navigation. Guided by these findings, we build \textscSpatial-Nav-100K and fine-tune in two stages, \textiti.e. first learning a shared spatial-navigation foundation, and then specializing each phase with the abilities it relies on. We further introduce Spatial-NPD, where a teacher conditioned on spatial priors produces grounded action preferences and distills them into a student policy, so no explicit spatial reasoning is needed at inference. With 45 A100 GPU-hours of policy training, our 8B model reaches SR/SPL of 77.4/35.4 on HM3D-v0.2, 60.2/30.5 on HM3D-v0.1, and 47.9/20.6 on train-unseen MP3D. It outperforms several systems that rely on closed-source models or thousands of GPU-hours of training, at 148 ms per action step. All code and datasets will be publicly available at this https URL.
[CV-23] Its the Geometry Not the Model: Effective Rank and Subspace Alignment in Functional Connectivity Classification
链接: https://arxiv.org/abs/2609.29932
作者: Xiao Fan,Jingyuan Li,Yubo Han,Hongbin Guo,Guanya Li,Yang Hu,Wenchao Zhang,Weibin Ji,Yi Zhang
类目: Computational Geometry (cs.CG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Resting-state functional connectivity (FC) is widely used to classify brain phenotypes and disorders. Most pipelines use the full connectome and seek gains through model design. We instead examine how FC geometry constrains classification and cross-site transfer. Across-subject FC variation concentrates in a small effective subspace, suggesting substantial redundancy in nominal dimensions. Across cohorts, these subspaces may differ in orientation even when their effective ranks are comparable, potentially limiting transfer. Across 2,330 subjects from HCP, ABIDE, and ADHD-200, effective-rank analysis reveals strong spectral concentration. Projection onto leading components at the effective-rank scale recovers most of the full-FC classification performance. In ABIDE, site-specific effective subspaces are weakly aligned, and their principal-angle overlap predicts pairwise transfer after covariate adjustment despite comparable per-site effective ranks. Controlled rotations that alter subspace orientation while preserving the mean and covariance spectrum drive transfer toward chance, whereas displacement-matched label-orthogonal rotations do not. These results identify subspace orientation as a key factor in transfer degradation under controlled perturbations. This study offers a geometric diagnostic of FC generalization and suggests evaluating cross-site harmonization by its ability to align effective subspaces alongside classification accuracy.
[CV-24] EndoFSA: Endoscopic Few-Shot Image Generation via Rank-Constrained Parameter Adaptation
链接: https://arxiv.org/abs/2609.29930
作者: Panagiota Gatoula,Grigoris Karypidis,Dimitris K. Iakovidis
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Presented at the 39th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2026), June 2026
Abstract:WCE produces large-scale gastrointestinal image data yet pathological findings remain significantly underrepresented limiting the generalization performance of deep-learning based abnormality detection systems. SDG methods offer a practical solution to mitigate this imbalance. However their training directly on scarce abnormal samples often results in instability overfitting and structural distortions. Addressing these challenges requires controlled adaptation mechanisms that preserve anatomical priors while enabling realistic pathological variation. This paper presents EndoFSA a GAN-based model for Endoscopic Few-Shot image generation by Adaptation in WCE imaging. EndoFSA leverages a generator pretrained on abundant normal data and adapts it to abnormal domains using limited number of training samples through a rank-constrained parameter adaptation where only a small number of modulation parameters is updated while the pretrained weights remain frozen. By restricting parameter updates to a low dimensional subspace and incorporating perceptual boundary regularization and cluster-wise diversity control EndoFSA enables efficient model adaptation under limited data conditions and mitigates mode collapse while preserving the anatomical priors learned from normal data. Importantly EndoFSA operates without requiring pixel-level annotations, masks or bounding box supervision. Evaluation on publicly available WCE benchmark datasets spanning various abnormal categories demonstrates that EndoFSA generates abnormal images reproducing real lesions morphology. Moreover in a downstream classification task training an image classifier solely on synthetic abnormal images generated by EndoFSA yields performance comparable to that obtained with real images.
[CV-25] When Can Agents Forget Their Reasoning ? ICLR for Long-Horizon Agent Context Compression
链接: https://arxiv.org/abs/2609.29875
作者: Mingxuan Wang,Fei Luo,Bo Wang,Guorun Yao,Yinglong Guo,Chao Ning,Hongyue Chen,Yanbiao Ma,Jungong Han
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 30 pages
Abstract:Long horizon language model agents continually accumulate reasoning history, increasing context length and inference cost even after earlier decisions have been executed and observed. Unlike static Chain of Thought compression, removing historical reasoning can change future actions and the resulting interaction trajectory. We study when such reasoning can be safely forgotten. We propose Interaction Aware Compression for Long Horizon Reasoning (ICLR), a training free online method that ranks reasoning blocks using frozen proxy entropy while preserving actions, tool calls, and observations. On 260 WorkBuddyBench tasks, ICLR improves average reward from 0.699 to 0.718, while reducing input, output, and cache read tokens by 25.5%, 14.4%, and 33.3%, respectively. Ablations reveal trajectory amplification, where local reasoning deletion produces nonlinear changes in total computation by altering subsequent interaction. Representation probing, activation patching, and controlled trajectory analyses further suggest that historical reasoning becomes more replaceable once task relevant derived state has been reliably externalized into code, files, tool outputs, or environmental feedback. These results characterize agent reasoning as dynamic working state rather than permanent interaction history.
[CV-26] Efficient Continuous DEM Reconstruction under Limited Target-Resolution Supervision
链接: https://arxiv.org/abs/2609.29864
作者: Zekai Shi,Meng Zhang,Haokun Zhang,Bo Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
备注: 19 pages, 15 figures
Abstract:High-resolution digital elevation models (DEMs) support Earth observation applications, but paired training references are often available only at coarser output resolutions. Reconstructing finer terrain grids therefore requires both effective transfer beyond the supervised scale and control of dense-query computation. To address this problem, SCOPE learns a continuous terrain representation from coarser-resolution pairs. It predicts a latent coefficient field on the low-resolution grid and reuses local Fourier residual functions through basis evaluation and geometry-guided ensemble fusion. This separates high-dimensional coefficient prediction from output-grid construction. Experiments on geographically distributed land–ocean samples assess supervised reconstruction, unseen-scale inference, cross-domain generalization, and theoretical computation. SCOPE leads the compared methods across six metrics in the main supervised-scale evaluation. At an unseen factor three times the training factor, land reconstruction reduces RMSE and MAE by approximately 12% relative to bicubic interpolation, with errors close to target-scale fine-tuning. Ninefold output density increases counted multiply–accumulate operations by only about 2%. Frozen-model validation on held-out external marine regions reduces RMSE relative to the DEM-specific implicit baseline EBCF-CDEM by approximately 19% under self-downsampling and 2% with cross-product inputs, while also yielding lower RMSE than LIIF-MS in both settings. These results demonstrate the value of reusable coefficient fields for accurate reconstruction beyond the supervised resolution with low incremental arithmetic cost.
[CV-27] Modelling dynamic systems transfer functions from events in computational neuromorphic imaging
链接: https://arxiv.org/abs/2609.29863
作者: Nimrod Kruger,Gregory Cohen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Conference paper - SPIE Sensors + Imaging 2026
Abstract:Event Vision Sensing (EVS) report threshold crossings of log-irradiance, so a static optical system imaging a static scene produces no output at all. The classical procedure for measuring a Point Spread Function (PSF), illuminating the system with a constant point source, therefore has no event-based equivalent: the probe must carry a temporal profile, and that profile becomes part of the measurement. A growing body of Computational Neuromorphic Imaging (CNI) work already exploits this, pairing engineered or modulated optics with event sensing, but each system adopts a particular excitation together with a particular reading of the event stream without the correspondence between the two being stated. We examine that correspondence directly within a analytical framework of an Linear Shift-Invariant (LSI) optical system with a specified Modulation Transfer Function (MTF), a first-order filter EVS pixel model, and three different temporal probes: a step function, a linear ramp and an exponential ramp. By analysing the inverse of the entire chain for different event-statistic, and comparing the results to the specified MTF, we identify the context where each probe is most relevant. We consider how photon-noise and cross-array threshold mismatch effects the analytical accuracy of the probe-inverse. Results show that the widely used step probe is highly susceptible to mismatch while resilient to photon shot-noise, while a linear rise probe and exponential rise probe retain their ability to infer signal levels even with high mismatch. We discuss the potential of dynamic-PSFs as components of a full forward operator from scene to events. In this, we use this analytical description to define dynamic-PSFs around EVS, and discuss the gaps toward a unified pixel model and a scene-composition framework required for CNI.
[CV-28] BeyondRetarget: Learning Executable Humanoid Motions Directly from Monocular Video
链接: https://arxiv.org/abs/2609.29850
作者: Tianyu Xiong,Yi Lu,Jinrui Wang,Ziqi Liang,Dandan Lei,Xiaoyang Zhou,Xiao-xiao Long,Qiu Shen,Xun Cao
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Learning executable motions from human videos offers a scalable solution for humanoid robots to acquire demonstration motions. However, existing pipelines typically first construct an explicit human motion representation and then convert it into robot motions via motion retargeting. Although such methods can effectively leverage large volumes of existing human data for training, the substantial differences between humans and humanoid robots in locomotion mechanisms and joint degree-of-freedom configurations make motions generated by this human-representation-centric approach difficult to execute on robots. Furthermore, errors introduced during human motion estimation inevitably propagate to the retargeting stage and cannot be eliminated via joint optimization. We propose BeyondRetarget, an end-to-end framework that directly maps monocular RGB videos to robot motions. Discarding the explicit human representation, this framework learns robot-oriented implicit representations directly from visual observations, enabling the model to capture cross-morphology motion structures. To generate motions more suitable for robot execution, we further design a contact-aware motion optimization mechanism to improve temporal consistency and physical plausibility. Experiments show that BeyondRetarget significantly improves the accuracy and robustness of generated robot motions, while achieving higher execution success rates and lower latency in both simulation environments and real humanoid robots.
[CV-29] SplatLabel: Pseudo-Labelling through 4D Gaussian Splatting
链接: https://arxiv.org/abs/2609.29836
作者: Nitya Nanvani,Andras Palffy,Holger Caesar
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:While 2D Vision Foundation Models offer a pathway to automate 3D semantic pseudo-labelling, translating these priors into robust 3D representations typically requires complex heuristics or multi-model ensembles. We introduce SplatLabel, an automated pipeline that leverages a 4D Gaussian representation to extract LiDAR segmentation with predictive confidence, as well as semantic occupancy grids at arbitrary voxel resolutions. At its core, SplatLabel handles dynamic environments through an explicit temporal manifold that models the trajectories and lifespans of individual 3D primitives. This allows the system to accurately track moving actors and strictly define when objects appear and disappear, completely eliminating the need for pre-annotated 3D bounding boxes. To robustly support this dynamic tracking, the representation is grounded by structural and semantic priors: we guide scene geometry in unobserved regions by integrating 360-degree LiDAR via virtual depth maps, and rather than relying on domain-specific prompt engineering, we directly distill continuous soft probabilities from 2D models to inherently resolve semantic ambiguities over time and space. Finally, to accurately reflect the real-world trade-off between precision and recall, we reframe pseudo-label evaluation as a selective classification task using a generalized risk-recall metric. Experiments on SemanticKITTI demonstrate that SplatLabel consistently outperforms state-of-the-art baselines across multiple recall levels, establishing a highly robust framework for both 3D LiDAR segmentation and occupancy prediction.
[CV-30] Retrieve-to-Localize: Bridging Large Language Models and LiDAR Geometry for Spatial Grounding
链接: https://arxiv.org/abs/2609.29835
作者: Byounggun Park,Giyong Moon,Jusung Kim,Soonmin Hwang
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 8 pages
Abstract:LiDAR provides precise geometric information for spatial perception tasks such as object detection in autonomous driving and outdoor robotics. However, recognizing and localizing individual objects is not sufficient to answer questions that require composing spatial relations and grounding the intended target. Motivated by recent advances in large language models (LLMs) for autonomous driving, we leverage their language priors to interpret complex spatial questions and ground the referred target in LiDAR geometry. To support this spatial grounding capability, we introduce SpatialLiDAR-QA, which combines single- and multi-step relational grounding with complementary spatial understanding tasks. We further propose SpatialLiDAR-LM, which aligns LiDAR point features with an LLM and grounds target coordinates through language-conditioned, position-aware proposal retrieval and local point refinement. This design derives target coordinates directly from local LiDAR geometry rather than through textual language decoding. Experiments demonstrate substantial improvements over representative LiDAR–language models and multi-camera VLMs on precise coordinate prediction tasks. Our dataset and model training code will be publicly released.
[CV-31] Anatomy-Aligned Surface Field Learning for Myocardial Reconstruction from Sparse Short-Axis Cine MRI
链接: https://arxiv.org/abs/2609.29825
作者: Xiaohan Yuan,Xuan Yang,Qingya Li,Yangang Wang,Lei Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12
Abstract:Patient-specific 4D myocardial reconstruction from cine MRI supports quantitative functional assessment, regional motion analysis, and simulation-based modeling. However, routinely acquired short-axis (SAX) cine MRI is sparsely sampled along the through-plane direction, making dense and anatomically consistent surface reconstruction challenging. In this study, we propose an anatomy-aligned surface learning framework that parameterizes the epicardial and endocardial surfaces on a shared circumferential-longitudinal UV domain. This formulation converts irregular 3D reconstruction into structured coordinate-field completion with explicit correspondence across subjects and cardiac phases. Sparse SAX contours are encoded as UV observation fields, coverage-aware sampling improves robustness to incomplete slice coverage, and topology- and distortion-aware learning preserves circumferential continuity and local surface quality. Experiments on three public cine MRI datasets showed that the proposed method consistently outperformed representative mesh-based and implicit reconstruction approaches, achieving overall Chamfer distances of 2.887 ~mm on ACDC, 2.641 ~mm on M\Ms, and 2.810 ~mm on M\Ms-2. The reconstructed sequences also preserved ventricular function, with end-diastolic volume and ejection fraction errors of 3.3 ~mL and 1.1 % , respectively. These results demonstrate that anatomy-aligned UV learning provides an accurate, efficient, and correspondence-aware representation for sparse cine MRI reconstruction and myocardial modeling. The source code will be available at this https URL.
[CV-32] AV-GRPO: Modality-Anchored Decoupling Diffusion Reinforcement Learning for Joint Audio-Video Generation
链接: https://arxiv.org/abs/2609.29816
作者: Zhiyu Xu,Weilong Yan,Yufei Shi,Shiyang Li,Yihao Liu,Kin-Man Lam,Yuewen Cao
类目: Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD)
备注: 22 pages
Abstract:Recent years have witnessed major progress in joint audio-video generation. Existing models still suffer from limited per-modality fidelity, insufficient text-modality alignment and weak cross-modal synchronization. While reinforcement-learning post-training offers a promising remedy, directly adapting it to joint audio-video generation is challenging. Heterogeneous multimodal rewards entangle learning signals and complicate credit assignment. Joint optimization of two modality towers is computationally expensive given their divergent dynamics. Moreover, synchronization evaluation difficulty depends on paired samples, preventing fair reward comparisons. We propose AV-GRPO, a modality-anchored online diffusion RL framework, and 5DAV, a decoupled, difficulty-controllable training dataset. AV-GRPO includes three key modules: (1) modality-anchored rollouts to disentangle learning signals and stabilize difficulty; (2) trajectory-locked frozen-tower optimization to reduce cost and reassign credit; (3) adaptive objectives and perturbation strengths tailored to modality-specific dynamics. This converts coupled multimodal preference learning into unimodal subproblems for precise reward attribution and better synchronization. Our 5DAV dataset decouples samples across five dimensions for systematic training. Experiments on JavisBench and VABench demonstrate AV-GRPO outperforms LTX-2.3 in generation quality, semantic alignment and cross-modal synchronization under LoRA and full fine-tuning. Ablations confirm our designs. Code and data: this https URL
[CV-33] S2Planner: Multi-Scale Semantic Planner for End-to-End Autonomous Driving
链接: https://arxiv.org/abs/2609.29813
作者: Zhaowei Lu,Liguo Zhou,Yujie Guo,Lei Yu,Alois Knoll
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:We present S2Planner, a trajectory planner that combines three front-facing cameras with ego-motion history and the current driving command. A fine-tuned DINOv3 backbone and a Spatial Tuning Adapter produce multi-scale image features; a coarse-to-fine decoder then uses trajectory self-attention and camera-projected cross-attention to refine candidate waypoints. The contribution is the integration of ego-conditioned trajectory initialization with iterative, geometry-guided sampling of multi-scale image features, rather than a new visual backbone or attention operator. On the NAVSIM v1 non-reactive evaluation, the previously reported navtest run obtained 88.03 PDMS. Because that run was selected using navtest performance, this number is exploratory and cannot be interpreted as an unbiased test estimate. Validation-selected evaluation on unexposed data, repeated runs, and computational measurements are needed to establish generalization and efficiency.
[CV-34] OREO: Fidelity Alignment in 3D Generation via On-the-fly Rendering-Editing Optimization ECCV2026
链接: https://arxiv.org/abs/2609.29788
作者: Zhiyuan Ma,Wenbo Hu,Wang Zhao,Pengfei Wang,Ying Shan,Lei Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: Accepted to ECCV 2026. Our project page is at this https URL
Abstract:Despite recent advancements in 3D generation, models often struggle to produce assets with high visual fidelity. To bridge this gap, we propose OREO, an alignment framework that enhances the realism of 3D generators by leveraging rich 2D diffusion priors. Instead of relying on static datasets, OREO establishes a dynamic optimization loop that produces on-the-fly edited renderings as 2D pseudo-targets. At its core, we introduce Reinforced Editing, which utilizes a 2D model to refine rendered views of the 3D output, enhancing their overall visual fidelity while preserving the underlying geometry, viewpoint, and content. These refined views serve as high-quality supervision targets, enabling the 3D generator to learn from its own generated samples and progressively improve its visual quality. Experiments demonstrate that OREO effectively improves upon pre-trained baselines, producing 3D assets with enhanced visual realism.
[CV-35] Lightweight Vision Transformer-Based U-Net for Brain Tumor Segmentation from MRI ALT
链接: https://arxiv.org/abs/2609.29785
作者: Sheekar Banerjee,Md. Srabon Chowdhury,Md. Mahbub Hasan Akash,Ishtiak Al Mamoon
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at The 2026 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON)
Abstract:Accurate brain tumor segmentation from Magnetic Resonance Imaging is essential for diagnosis, treatment planning, and surgical guidance. Although Convolutional Neural Networks, particularly UNet, have achieved significant success in medical image segmentation, they often struggle to capture the long-range spatial dependencies required to model tumors with irregular shapes and complex boundaries. This paper proposes a lightweight Vision Transformer UNet that combines the hierarchical feature extraction capability of UNet with the global context modeling of Vision Transformers. The proposed architecture incorporates a compact ViT bottleneck within a U-Net encoder-decoder framework, enabling effective learning of both local and global features while maintaining computational efficiency with only 2.6 million trainable parameters. The model was evaluated on the TCGA LGG MRI Segmentation dataset, achieving a mean Intersection over Union of 0.8100 and a Dice score of 0.8446, outperforming the baseline UNet by 3.75% and 3.15%, respectively. Extensive quantitative and qualitative analyses, including confusion matrix evaluation, precision recall curves, per-image performance distribution, and tumor size dependency analysis, demonstrate the effectiveness and robustness of the proposed method for brain tumor segmentation.
[CV-36] Mind the Gap: Mesh-Guided Repair of Broken Vessels MICCAI2026
链接: https://arxiv.org/abs/2609.29779
作者: Gniewosz Drwiega,Wojciech Szymanski,Marek Wodzinski
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ShapeMI 2026 (Shape in Medical Imaging), MICCAI 2026 Workshop. 17 pages, 5 figures, 3 tables
Abstract:Vessel segmentation is commonly optimized as voxel-wise classification, but small local errors can strongly disrupt vascular connectivity while having little effect on overlap scores. This is particularly problematic for downstream analyses that rely on centerlines, branches, connected components, or graph structure. We propose a mesh-guided post-processing framework for repairing broken vessel segmentations produced by nnU-Net. For each predicted binary mask, a deformable template mesh is fitted to the mask surface in physical space and used as a case-specific geometric scaffold. The fitted mesh is not voxelized as the final segmentation; instead, it guides conservative reconnection of disconnected components by proposing or validating thin bridge candidates under foreground-growth constraints. We evaluated this approach in three vascular anatomies using AortaSeg24 and SEGA for the aorta, TopCoW for the Circle of Willis, and PARSE for the pulmonary arteries. Performance is measured using Dice, connected-component Dice (ccDice), and the Betti-0 number. Across these datasets, repair substantially improved connectivity while preserving overlap: Dice remained nearly unchanged, whereas ccDice increased from 0.596 to 0.992 for aorta, from 0.722 to 0.835 for TopCoW, and from 0.028 to 0.862 for PARSE. The FOMAML meta-initialization further accelerated the fitting per-case, supporting practical mesh-based repair of the vascular topology. These results suggest that explicit mesh representations can provide a useful geometric prior for correcting topological failures in otherwise accurate voxel segmentations.
[CV-37] A Multimodal Dataset for Survival Prediction in Resected Pancreatic Ductal Adenocarcinoma
链接: https://arxiv.org/abs/2609.29726
作者: Anh-Tien Nguyen,Mawuko Tettey,Jacqueline Michelle Metsch,Teresa Zimmer,Niklas Ullrich,Mario Duker,Sandra Rungeling,Kirsten Reuter-Jessen,Tessa Rosenthal,Lena-Christin Conradi,Michael Ghadimi,Alexander Konig,Elisabeth Hessmann,Volker Ellenrieder,Philipp Strobel,Hanibal Bohnenberger,Anne-Christin Hauschild
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Survival research in pancreatic ductal adenocarcinoma (PDAC) is limited by the scarcity of datasets linking whole-slide histology with clinical, molecular, and long-term outcome data. We present a retrospective single-centre cohort of 302 patients who underwent PDAC resection at University Medical Center Gottingen. The dataset comprises 446 HE whole-slide images, clinicopathological variables, targeted sequencing data for 154 patients, and overall-survival outcomes. During follow-up, 253 patients died, and the median follow-up was 76 months. To establish initial reference values, we evaluated fourteen survival-prediction configurations using identical five-repetition Monte Carlo cross-validation partitions. Ridge Cox regression using numeric clinicopathological variables achieved a mean concordance of 0.649 \pm 0.042 and 0.652 \pm 0.046 after adding KRAS and TP53 mutation status. The image-only attention model achieved 0.603 \pm 0.030 , while multimodal fusion achieved 0.619 \pm 0.025 , the highest concordance among the neural models. These results establish promising initial benchmarks for future research using this pancreas-specific multimodal dataset, paving the way for external validation. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2609.29726 [cs.CV] (or arXiv:2609.29726v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.29726 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-38] opoFuse: Topology-Aware Tri-Planar Fusion for 3D Cryo-Electron Tomography Segmentation
链接: https://arxiv.org/abs/2609.29717
作者: Rohit Kumar Salla,Neelesh Gupta,Xingjian Li,Min Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Automated segmentation of cryo-electron tomograms routinely produces masks that are voxel-accurate but topologically broken: membranes fragment, organelles merge into one another, and enclosed cavities collapse. Existing topology-aware losses reduce these violations but cannot eliminate them, because topology is encouraged through gradient pressure rather than structurally enforced. We introduce TopoFuse, which reframes topology as a differentiable projection operator rather than a loss penalty. At each forward pass, the projection operator \mathrmProj_T (a PH-guided sparse edit) identifies the critical voxels responsible for topological violations via bottleneck matching and applies sparse edits to satisfy a specified topology target (diagram feature counts and lifetime budgets) for dimensions d \in \0,2\ . If the projection converges, the output satisfies those constraints on the downsampled grid ( s=2 ); when it does not, a repair certificate exposes this explicitly, enabling downstream filtering. A topology prior head predicts the correction target directly from input features, removing any dependence on ground-truth topology at inference. Across three cryo-ET benchmarks, TopoFuse reduces Betti number error by 54% over the strongest soft-loss baseline ( p 0.001 ), improves Dice by 4.6 points, and edits only 3.1% of voxels to achieve this.
[CV-39] ReCalMatch:Reliability-Calibrated Semantic Guidance for Semi-Supervised Fine-Grained Recognition BMVC BMVC2026
链接: https://arxiv.org/abs/2609.29678
作者: Yundi Hong,Hongyang He,Zheng Fang,Xuanyu Liu,Victor Sanchez
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted for publication at the British Machine Vision Conference (BMVC) 2026. Official list of accepted papers: this https URL
Abstract:Semi-supervised fine-grained visual recognition is highly vulnerable to overconfident pseudo-label errors: visually similar categories frequently produce high-confidence yet incorrect predictions, and consistency regularization then reinforces these errors throughout training. Existing semi-supervised learning (SSL) methods estimate pseudo-label reliability almost entirely from the visual classifier itself—maximum probability, adaptive thresholds, or entropy—signals that remain blind to whether a predicted class is \emphsemantically compatible with the visual representation. We propose \textbfReCalMatch, a reliability-calibrated semantic framework for semi-supervised fine-grained recognition. Rather than treating textual semantics as auxiliary supervision, ReCalMatch uses multi-aspect semantic prototypes as \emphcalibration evidence for pseudo-label learning. We construct class-conditioned semantic prototypes from class names and domain-specific semantic aspects, and measure a \emphvisual–semantic agreement score between each unlabeled embedding and its pseudo-label prototype. This agreement is combined with prediction confidence and entropy into a single reliability weight that down-weights pseudo-labels that are visually confident but semantically inconsistent. A semantic consistency term and a semantic margin regularizer further sharpen prototype separability under limited labels. Extensive experiments on CUB-200-2011, Stanford Dogs, NABirds, and iNaturalist18 show that ReCalMatch consistently improves strong SSL baselines, with the largest gains in low-label regimes where pseudo-label noise is most severe.
[CV-40] Investigating White Blood Cells as a Source of False-Positive Malaria Parasite Detection in African Blood-Smear Images MICCAI2026
链接: https://arxiv.org/abs/2609.29663
作者: Samuel A. Adeniji,Goodness C. Obasi,Chris-Victor Ntwali,Aondana M. Iorumbur,Confidence Raymond,Lowami Uwimana,Ahmed Tahiru Issah
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at the 3rd MIRASOL workshop, a satellite event at MICCAI 2026. To appear in Springer Lecture Notes in Computer Science (LNCS)
Abstract:White blood cells (WBCs) present on every Giemsa-stained thick blood smear share visual properties with early-stage Plasmodium falciparum ring-form trophozoites: small size, round morphology, and intense purple staining. They are a plausible but untested source of false positives in parasite-only detectors. We trained two YOLOv12s models on the Lacuna Malaria Detection dataset (8,000 images from Uganda and Ghana): Model A with parasite labels only, and Model B with both parasite and WBC labels. Seven independent spatial and statistical analyses tested whether false positive (FP) predictions cluster near WBC locations. All seven refute the hypothesis. In both models, 95% of FPs are pure background detections (IoU below 0.10 against any ground-truth box); zero are WBC class confusions. Ripley’s Cross-K analysis shows spatial repulsion between FP centroids and WBC positions at every radius tested. Model B outperforms Model A overall (mAP50 0.859 vs. 0.755), and the advantage is uniform across all WBC-proximity bands, pointing to multi-task representation learning rather than WBC suppression as the cause. False positives arise from Giemsa stain debris and preparation artifacts. Effective mitigation requires staining artifact augmentation and annotation of unannotated early-stage ring forms rather than WBC labeling alone.
[CV-41] VG-TIE: An interpretable tabular-to-image encoding method based on visibility graphs
链接: https://arxiv.org/abs/2609.29650
作者: David Chushig-Muzo,Luis M. López-Ramos,Ángeles Rodríguez de Cara,Eva Milara,Luis Zhinin-Vera,Diego H. Peluffo-Ordóñez
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Tabular-to-image encoding methods enable the application of models based on both convolutional neural networks and vision transformers to tabular data, transforming feature vectors into images. Existing methods employ linear and nonlinear dimensionality reduction techniques (e.g., Principal Component Analysis (PCA), t-SNE, and UMAP) to determine pixel positions, resulting in images whose spatial layout do not inherently reflect feature relationships. This paper introduces Visibility Graphs for Tabular-to-Image Encoding (VG-TIE), a novel method that encodes the structure of feature values using Natural Visibility Graph (NVG) and Horizontal Visibility Graph (HVG) into a two-dimensional space obtained through PCA. The resulting images are model-agnostic and intrinsically interpretable. Each pixel corresponds to an input feature, its intensity reflects the magnitude and direction of deviation from the population mean, and edges represent formally defined visibility relationships between features. VG-TIE provides two interpretability methods: (i) feature ranking from node degree distributions; and (ii) local and global feature importance from pixel intensity combined with Grad-CAM. Experiments on six public tabular datasets show that VG-TIE is competitive with other tabular-to-image methods while providing interpretability on feature importance and ranking similar to intrinsic interpretable methods. The results highlight the potential of the proposed image-based transformation to provide an effective framework that expands the use of deep learning across tabular data domains.
[CV-42] Albireo: Adaptive Energy-Efficient Inference Framework for Video Object Detection on the Edge
链接: https://arxiv.org/abs/2609.29648
作者: Amir Taherin,José Cano,Bin Ren,Yanzhi Wang,David Kaeli
类目: Computer Vision and Pattern Recognition (cs.CV); Hardware Architecture (cs.AR); Machine Learning (cs.LG); Performance (cs.PF); Robotics (cs.RO)
备注: Accepted at the ACM/IEEE Symposium on Edge Computing (SEC 2026)
Abstract:Video object detection on edge devices runs computationally expensive detectors over long frame streams, causing high energy consumption and sustained GPU utilization. Although consecutive frames are highly redundant, naive frame skipping is content-blind: it skips during critical moments such as object entry, occlusion recovery, and abrupt motion, degrading detection quality. We present Albireo, a detector-agnostic, codec-free adaptive inference framework that wraps off-the-shelf detectors and decides when detector invocation can be safely skipped based on scene content and per-object temporal state, requiring no detector modification or retraining. Albireo maintains a 10-dimensional Kalman filter (KF) per active object and invokes the detector only when prediction uncertainty exceeds a threshold; on skipped frames, boxes are predicted from the KF state at near-zero GPU cost. A KF-based rescue mechanism preserves confirmed objects through brief detector misses to prevent output fragmentation, while a lightweight empty-scene screen avoids detector calls on objectless frames. We evaluate Albireo on the BDD100K MOT validation split with three architecturally distinct detectors (YOLO11x, YOLO26x, RF-DETR-Large) on two NVIDIA Jetson platforms (AGX Thor, AGX Orin). Across all configurations, Albireo keeps AP@50 within +/-1.2 pp of per-frame inference while reducing total energy by 12.1-17.6%. On YOLO26x, it improves AP@50 by +0.8 pp while reducing energy by 17.6% (Thor) and 14.4% (Orin) and per-frame energy-delay product by 24.9% and 26.1%, respectively. Thus, the default operating point improves accuracy, energy, and latency together. In contrast, FixedSkip-2, a fixed-interval baseline with a 50% skip rate, loses 8.6 pp AP@50. Source code, evaluation pipeline, and per-clip results are available at this https URL
[CV-43] SpectralCTGaussians: Projection-Domain Reconstruction and Basis Material Decomposition for Spectral CT using 3D Gaussian Splatting
链接: https://arxiv.org/abs/2609.29638
作者: Reinout Vos,Saptarshi Neil Sinha,Michael Weinmann
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Spectral computed tomography (CT) extends conventional CT by measuring attenuation across multiple energy channels, allowing improved modeling of physical X-ray interactions and energy-dependent material behavior and leading to richer scene understanding. We present a novel method for spectral CT reconstruction and basis material decomposition using 3D Gaussian Splatting by adding per-Gaussian basis material fractions to the set of learnable parameters, which together with a set of energy-dependent basis functions define the attenuation across the full spectral range. The basis functions represent various physical attenuation models such as photoelectric absorption and Compton scattering, and are jointly optimized across all energy channels through a differentiable polychromatic forward model, with material decomposition performed via mean-shift clustering of the resulting coefficients. We evaluate our method on a baseline real-world dataset as well as a synthetic dataset that we introduce, comparing against traditional reconstruction algorithms and state-of-the-art learning-based CT reconstruction methods. Our approach outperforms all traditional baselines in novel view synthesis and achieves the best PSNR among all compared methods for spectral CT volume reconstruction, while describing all energy channels with a single shared representation that requires a number of Gaussians comparable to single-channel Gaussian splatting-based CT reconstruction approaches. For basis material decomposition, no traditional or learning-based baseline offers one-step decomposition with direct RGB material segmentation, and our method additionally recovers the photoelectric basis with higher PSNR than traditional pipelines.
[CV-44] Agent icCADedit: A Stateful Tool-Mediated Agent ic Approach to Multimodal 3D CAD Editing
链接: https://arxiv.org/abs/2609.29621
作者: Saptarshi Neil Sinha,Mika Silvan Goschke,Paul Julius Kühn,Arjan Kuijper,Michael Weinmann
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注:
Abstract:Computer-aided design is central to industrial manufacturing, and much of a designer’s daily work consists of editing existing models from multimodal requests involving speech, sketches, and model interaction. Existing neural CAD approaches focus predominantly on unconditional or text-conditioned generation. The neuralCAD-Edit approach formalizes expert multimodal editing requests, but its iterative baseline refines a complete CAD program across attempts, executing each attempt from the original model in a stateless CAD environment. Every attempt must therefore reconstruct the entire edit from scratch, so partially correct progress is discarded rather than accumulated, and the model can neither inspect the geometry it has just produced nor selectively revert a single faulty operation. We present AgenticCADedit, which turns editing into a sequence of small, verifiable actions on a persistent CAD state instead of a single regenerated program. Rather than emitting one complete program, it applies incremental code steps that each commit to the session, inspects the resulting faces and edges, renders highlighted selections to verify that the intended region was addressed, and reverts individual operations when it was not. Subsequent actions therefore build on the geometry produced by earlier ones. Our approach improves on all metrics for all three evaluated LLMs (open-weight: qwen3.6-27b, gemma4-31b; proprietary: gpt-5.6-luna), with the largest gains for the weakest baseline model, qwen3.6-27b, whose validity rises from 51.0% to 94.8% and acceptance from 1.6% to 12.0%. A token-cost analysis with gpt-5.6-luna further shows 66.7 % fewer output tokens than neuralCAD-Edit, while 94.8 % of input tokens are served from the prompt cache.
[CV-45] PROVE: Proof-guided Regime-aware Operator Verification for Hallucination Detection in Medical Visual Question Answering
链接: https://arxiv.org/abs/2609.29604
作者: Keyang Zhou,Siyi Li,Zhongnan Shi,Qichao Ying,Wei Tang,Zhenxing Qian
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注:
Abstract:In medical visual question answering (VQA), hallucinations of vision-language models (VLMs) may lead to confident but incorrect responses, raising the risk of diagnostic errors. Existing hallucination detection methods uniformly estimate the reliability of VLM outputs from response consistency or visual evidence. However, such uniform verification across questions ignores question-specific characteristics, resulting in missed overconfident errors and false alarms from over-verification. We present PROVE (Proof-guided Regime-aware Operator Verification), a black-box detector that adapts verification strategy to the evidential structure of each question. PROVE classifies questions into three verification regimes based on what kind of visual proof they demand, activates a regime-specific subset of five complementary operators, and adjusts operator importance per question through a lightweight calibration layer conditioned on deterministic question-answer features. PROVE uses question-specific evidence to reweight operators and produce a calibrated risk score. Evaluated on 8048 test samples across three medical VQA benchmarks and four frontier VLMs, PROVE achieves 0.821 AUROC, outperforming the strongest baseline by +0.159, with consistent gains across all models and benchmarks.
[CV-46] MoSign: Challenge-Response Motion-Watermark Authentication for Anonymous Virtual-Reality Users
链接: https://arxiv.org/abs/2609.29603
作者: Xujun Che,Thomas Carr,Depeng Xu,Aidong Lu,Shuhan Yuan
类目: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注:
Abstract:Social virtual reality (VR) creates a paradox. A user’s body motion is a high-entropy biometric: head and hand trajectories alone re-identify users among tens of thousands with over 94% accuracy, so anonymizing the rendered avatar is a practical necessity. Yet a user often still wants to prove their identity to a chosen party from inside that anonymity. We present MoSign, which recasts digital watermarking as a challenge-response authentication protocol on the motion channel. MoSign embeds a time-varying keyed message into the style latent of a motion variational autoencoder via keystream-whitened Gaussian-Shading: watermarked motion is provably indistinguishable from watermark-free motion, since any detector’s advantage reduces to breaking a pseudorandom function, so the mark composes with anonymization. The message is a keyed MAC over an epoch counter, a session nonce, and a deployment context, making MoSign replay-resistant and bounding forgery by the verifier’s measured false-accept rate times the adversary’s online query budget. A key-holding verifier decides with a sequential test. We identify render \rightarrow record \rightarrow re-estimate (“recapture”) as the realistic VR attack surface: a generic pose estimator strips the necessarily subtle watermark, but a recapture-robust keyed reader recovers it (up to 0.96 codeword accuracy on a projected-2D channel, 0.81 through a full render-to-video loop), while without the key recovery stays at chance. On HumanML3D, MoSign authenticates every legitimate user at a false-accept rate of 10^-4 on clean and most channels and stays undetectable (detection AUC 0.51 , chance 0.5 ); on the BOXRR-23 VR dataset it carries the mark through a real anonymizer at 0.99 codeword accuracy and adds no de-anonymization side channel.
[CV-47] QINA: Quantum-Inspired Nonlinear Adapters for Pretrained Vision Models BMVC2026
链接: https://arxiv.org/abs/2609.29592
作者: Mostafa Mehdipour Ghazi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at BMVC2026
Abstract:Adapting large pretrained vision models under limited data and frozen-backbone constraints remains a central challenge in transfer learning. While lightweight adapters and parameter-efficient fine-tuning methods are widely adopted, most rely on generic multilayer perceptrons or low-rank linear updates, offering limited control over the spectral and geometric structure of feature transformations. We investigate whether structured nonlinear feature lifting can improve representational alignment in frozen regimes. We introduce Quantum-Inspired Nonlinear Adapters (QINA), compact modules that perform learnable trigonometric feature lifting followed by bounded nonlinear aggregation. The design induces structured oscillatory basis functions with an explicit norm-dependent Lipschitz bound, enabling spectral reshaping of pretrained representations without increasing the receptive field or significantly expanding parameter count. Importantly, the method operates entirely within standard deep learning frameworks and does not require quantum hardware. Through systematic experiments across natural and medical imaging datasets, classification and segmentation tasks, multiple adapters and placements, and varying training budgets, we show that performance in frozen regimes is primarily representation-limited. Nonlinear lifting improves adaptation, and the proposed structured trigonometric formulation consistently outperforms identity baselines, fixed Fourier feature mappings, and parameter-matched baseline adapters. Within the evaluated frozen-backbone settings, structured spectral parameterization provides a more effective inductive bias than generic nonlinear adapters. This work highlights the importance of geometry- and spectrum-aware adaptation mechanisms for large pretrained vision models.
[CV-48] CATCH: Counterfactual Anatomical Tissue Inpainting with Conditional Haar Diffusion MICCAI2026
链接: https://arxiv.org/abs/2609.29591
作者: Simon Winther Albertsen,Hjalte Bjoernstrup,Said Djafar Said,Mostafa Mehdipour Ghazi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at the 15th BraTS Workshop in MICCAI 2026
Abstract:BraTS local synthesis replaces masked regions in T1-weighted brain MRI with plausible tumor-free tissue while preserving observed anatomy. We present CATCH, conditional 3D diffusion in an invertible Haar-wavelet domain. Its denoiser receives noisy target coefficients, voided-image coefficients, and a signed mask; tumor-excluded wavelet reconstruction and a hole-focused loss guide training, and hard compositing preserves observed voxels. We compare fixed masks, tumor-component augmentation, and a weighted mixture of tumor-derived, irregular-blob, and ellipsoidal masks. Of 25 development cases, five prespecified cases select each arm’s checkpoint and all 25 of their trajectory aggregations; a separate 75-case internal set compares the frozen pipelines and selects a weighted mixture for organizer evaluation. Five-trajectory averaging yielded internal SSIM/PSNR/MSE (mean \pm SD) of 0.80\pm0.13 , 19.18\pm1.80 dB, and 0.010\pm0.005 . As the sole officially evaluated pipeline, weighted mixture yielded 0.772\pm0.119 , 20.89\pm3.27 dB, and 0.0098\pm0.0054 on the 219-case BraTS 2026 validation set. Against compute-matched random augmentation internally, it improved SSIM by 0.019 (95% bootstrap CI: 0.013-0.025), PSNR by 0.95dB, and MSE by 0.003; all three paired comparisons remained significant after Holm correction. Results favor the complete weighted-mixture policy within CATCH; absent official fixed- and random-pipeline scores and a directly comparable external baseline limit broader conclusions.
[CV-49] Long-Tail Adaptive Flow Matching with Explicit Conditional Consistency Guidance for Precise Multimodal Face Synthesis
链接: https://arxiv.org/abs/2609.29581
作者: Yushe Cao,Xuechao Zou,Xing Xi,Dianxi Shi,Chun Yu,Junliang Xing
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 12 figures
Abstract:Although diffusion-based methods have substantially improved the controllability of multimodal face synthesis, their semantic alignment remains suboptimal because most existing approaches rely on implicit latent-space objectives to model the relationship between denoising variables and multimodal conditions. Such implicit modeling is often insufficient to enforce precise correspondence between synthesized faces and conditional inputs, especially under long-tailed semantic mask distributions where rare attributes receive weak optimization signals. To address these limitations, we propose EC\textsuperscript2Face, a multimodal face synthesis framework that improves semantic alignment through explicit semantic supervision and distribution-aware optimization. First, we introduce Explicit Conditional Consistency Guidance (ECCG), which imposes direct consistency supervision in pixel space by decoding an approximate reverse estimate of the clean latent and explicitly aligning the synthesized image with textual descriptions and semantic masks. A temporal dynamic modulation function is further designed to adapt the supervision strength according to the timestep-dependent reliability of reverse estimation. Second, we propose Long-Tail Adaptive Flow Matching (LAFM), which reweights spatial optimization signals based on semantic attribute frequency, with normalized weights to maintain numerical stability during training. Importantly, all additional modules are used only during training and introduce no extra inference overhead. Extensive experiments show that EC\textsuperscript2Face consistently outperforms competitive baselines in both generation quality and semantic alignment, achieving a 29.38% improvement in mask accuracy on rare attributes.
[CV-50] Visual Representation and History Modeling for Navigation World Models
链接: https://arxiv.org/abs/2609.29555
作者: Guangfu Guo,Xiaoqian Lu,Rui Liu,Yutong Chen,Kunpeng Liu,Long Cheng
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Navigation World Models (NWMs) predict action-conditioned visual futures for planning. Two practical challenges are central to their design: selecting a suitable visual representation and efficiently modeling observation history for repeated candidate queries. Standard Global-Softmax attention provides flexible interactions but repeatedly processes the same history, leading to increasing computation and memory costs for long contexts and multi-query planning. We study both problems within a unified conditional flow-transformer framework. We first compare five frozen visual representations under the same dynamics model and evaluation. To reduce redundant history computation, we design Cached-Linear, a hybrid architecture that combines local and shifted-window attention for target mixing with linear attention for reusable history access. We further develop Balanced Gated Delta Network (GDN), which augments this design with frame-wise recurrent memory for temporal history modeling. Experiments on RECON, SACSoN, and SCAND show that representation choice depends on the prediction objective: PAE-L performs best for reconstruction, RAE-B for direct prediction, and V-JEPA for long-horizon rollout. Under shared-history workloads, Cached-Linear substantially reduces computation and memory compared with Global-Softmax, while Balanced GDN improves selected direct-prediction endpoints with efficient context reuse. Overall, we systematically study visual representation and history modeling for NWMs and develop hybrid reusable-history architectures for efficient long-context and multi-query prediction.
[CV-51] UNWIND: Any-Length Facial Video for Stress Detection without Temporal Windowing
链接: https://arxiv.org/abs/2609.29553
作者: Stefanos Gkikas,Christian Arzate Cruz,Eric Nichols,Giorgos Giannakakis,Randy Gomez
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Automatic stress recognition from facial video provides a non-contact approach for affective monitoring. However, most existing video-based methods divide complete recordings into shorter temporal segments before performing classification. Such segmentation requires additional decisions concerning segment duration, overlap, and prediction aggregation, and may restrict the model from exploiting information distributed across the entire recording. We introduce UNWIND, a facial-video framework for stress detection that analyzes a complete recording as a single model input, eliminating the need for temporal windowing or external segmentation. UNWIND reorganizes the video by folding its temporal dimension into the channel dimension of a two-dimensional spatial representation, which is subsequently processed through a unified asymmetric-attention architecture. With a temporal stride of \tau=1 , the framework processes the entire 120 -second sequence, corresponding to 3,600 frames sampled at 30 ~fps, in a single input. We evaluate seven temporal-stride settings on a stress dataset comprising 58 subjects, using a stratified subject-level protocol that covers configurations from dense frame retention to sparse temporal sampling. The highest test accuracy, 70.02% , is obtained at \tau=15 , while processing all frames at \tau=1 achieves a comparable accuracy of 69.73% . Computational requirements range from 12.48 to 348.78 GFLOPs across the evaluated stride settings, illustrating the balance between temporal sampling density and computational efficiency. The findings show that effective facial-video stress recognition can be achieved without dividing recordings into temporal windows and that complete-recording inference can be performed within a single unified model.
[CV-52] Spaceborne differential photogrammetry for control-free measurement of large-gradient deformation with structural immunity and a predictable accuracy envelope
链接: https://arxiv.org/abs/2609.29550
作者: Yueqiang Zhang,Chang Ma,Shuixin Pan,Haibo Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 39 pages, 9 figures, and 5 tables; supplementary material: 10 pages, 3 figures, and 6 tables
Abstract:Optical satellite image correlation measures wide-area deformation in regimes where coherent interferometric synthetic aperture radar fails because displacement gradients are too large. However, standard pairwise workflows lack a pre-acquisition error budget and rely on extensive stable terrain. We formulate repeat-pass optical correlation as a differential estimation problem without surveyed ground control. Nominal georeferencing defines the coordinate frame, stable-area constraints and displacement priors resolve the datum, and surface displacement is estimated jointly with inter-epoch revisit-bias coefficients. The model yields a predictive accuracy envelope and calibrated per-point posterior uncertainty, bounds along-track uncertainty through a displacement prior, and represents pushbroom jitter using per-line revisit offsets. Simulations and Sentinel-2 and WorldView-2 experiments on the 2019 Ridgecrest earthquake, the 2023 Kahramanmaraş earthquake, and the Baltoro glacier validate the predicted noise floor, control-free accuracy margin, and leakage caused by view-angle and digital elevation model errors. The measured noise floor reaches approximately 0.05 pixel at 10 ,m ground sampling distance. With only five stable tiles, conventional destriping changes the estimated Baltoro trunk velocity from 106 to 1251 myr ^-1 , whereas the prior-constrained estimate remains 87 myr ^-1 . Closure analysis attributes approximately 88% of pair-error variance to individual scenes, consistent with 25,354 ITS_LIVE glacier-velocity triplets. Three matching methods lead to the same conclusions. The framework therefore turns pairwise correlation into a robust measurement with a predictive error budget, reduced dependence on stable terrain, and conclusions independent of the matching method.
[CV-53] GeoRefer-Bench: A Benchmark from Referring Pixels to Verifiable Geospatial Reasoning ICIP
链接: https://arxiv.org/abs/2609.29541
作者: Shuaishuai Cao,Min Huang,Meng Tang,Xuan Liu,Youjin Wang,Hui Lin
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 19 pages, 5 figures, 5 tables. Code, data, and evaluation tools are available at this https URL
Abstract:Referring segmentation in overhead imagery is inherently relational: a query may ask for the buildings north of the road or the pond closest to a residential area, so the correct referent can contain one object, several objects, or none. Existing benchmarks mainly score mask overlap, which cannot verify whether a model actually resolved the stated spatial relation. We introduce GeoRefer-Bench, a benchmark for verifiable geospatial referring segmentation. Each query is represented by an executable logical form over a metric scene graph, and predictions are evaluated with Exact Query Success (EQS), which is satisfied only when the returned instance set exactly matches the set denoted by the query. GeoRefer-Bench contains 700 whole 2048x2048 UAV scenes (2.94 Gpx) at 12.5 and 25 cm ground sampling distance, 26,217 instances, 142,796 spatial relations, and 20,916 executable queries spanning five reasoning levels. It further includes three paraphrases per query, 24.0% unanswerable queries, 2,477 counterfactual pairs, and five leakage-controlled evaluation splits. An independent audit re-derives object geometry, mask ownership, relation values, query execution, and split provenance, finding zero issues across all 700 scenes. Relation-blind strategies can retain non-trivial mIoU while achieving at most 22.7 EQS overall, showing that overlap alone does not certify relational grounding. Across fifteen current models, the strongest reaches 74.1 EQS but drops from 98.9 at level 1 to 60.5 at level 5, while ten models score below 5 EQS on two-hop queries. GeoRefer-Bench turns geospatial referring segmentation from mask matching into verifiable reference resolution.
[CV-54] Clinical Knowledge Graphs for Chest X-Ray Device Reasoning
链接: https://arxiv.org/abs/2609.29536
作者: Harshil Lodhiya
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 19 pages, 2 figures, 5 tables. Post-hoc descriptive analysis on archived RANZCR CLiP test predictions
Abstract:Chest radiographs are routinely used to verify the position of catheters, tubes, and other support devices. Existing image models often return labels or segmentations, while report-processing systems structure text without access to image geometry. We present an uncertainty-aware clinical knowledge graph that represents device instances, tip estimates, placement assessments, provenance, report events, and temporal links as separate but connected evidence. We evaluate the implemented visual graph layer using saved predictions from the complete RANZCR CLiP test archive, comprising 30,083 studies from 3,255 patients across five non-overlapping outer folds. The graph builder materializes 914,632 B7 evidence nodes and 884,549 typed relationships. All 118,647 B7 predicted-device nodes retain tip covariance, placement probabilities, fragment provenance, and fragment counts, whereas the direct B2 baseline retains none of these fields. We further define typed data contracts, uncertainty representations, abstention rules, report-image grounding, and longitudinal query mechanisms for extending the graph to report-bearing cohorts. The reported graph-materialization analysis is post-hoc descriptive and does not establish report grounding, longitudinal performance, or clinical utility. It demonstrates a reproducible foundation for evidence-preserving AI reasoning over chest X-ray device assessments. Comments: 19 pages, 2 figures, 5 tables. Post-hoc descriptive analysis on archived RANZCR CLiP test predictions Subjects: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) MSC classes: 68T30, 68T37, 68T45 ACMclasses: I.2.4; I.2.1; I.4.8 Cite as: arXiv:2609.29536 [cs.AI] (or arXiv:2609.29536v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.29536 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-55] oCo-Mesh: Topology-Consistent Dynamic Mesh Reconstruction via Adaptive Tessellation and Surface-Aligned 2DGS
链接: https://arxiv.org/abs/2609.29529
作者: Chuanjin Fan,Wenjie Chang,Aibing Li,Bingzhou Wang,Wenfei Yang,Tianzhu Zhang
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Reconstructing dynamic meshes with consistent topology from multi-view temporal images remains a challenge. Existing approaches typically face a dilemma between fine-scale shape recovery and topological stability. Frame-by-frame extraction methods capture fine details but break vertex correspondence, leading to flickering meshes. Conversely, template-based deformation ensures consistency but struggles to adapt its surface resolution during optimization, missing local surface details. To address these limitations, we propose ToCo-Mesh, a dynamic reconstruction framework that maintains topology consistency over time while achieving high-fidelity geometry. Specifically, we introduce a dual-mesh representation, where a canonical template mesh is tightly bound to time-varying coarse guide meshes via barycentric parameterization. While keeping guide meshes fixed to condition the deformation, we perform error-driven split-and-merge on the template mesh to progressively increase reconstruction fidelity. Furthermore, to suppress surface irregularities and achieve photorealistic rendering, we incorporate a Surface-Aligned 2DGS module. By anchoring flattened Gaussians to mesh faces, we utilize their rendered normals to guide inverse geometric fine-tuning. To our knowledge, ToCo-Mesh is the first framework to enable adaptive mesh refinement while maintaining strict topological consistency. Extensive experiments demonstrate that our method achieves SOTA geometric accuracy while maintaining competitive rendering quality.
[CV-56] CoSWA-YOLOv12: Scale-Invariant Tiny Object Detection and Segmentation of Malaria Parasites MICCAI2026
链接: https://arxiv.org/abs/2609.29527
作者: Ahmed Tahiru Issah,Carine Mukamakuza
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at The 3rd MIRASOL workshop, a satellite event at MICCAI 2026. To appear in Springer Lecture Notes in Computer Science (LNCS)
Abstract:Automated microscopy could widen access to malaria diagnosis in low-resource settings, but the deadliest species, P. falciparum, presents in its early ring stage as an object only a few tens of pixels wide. Such tiny targets are systematically under-detected: overlap-based label assignment starves them of positive samples, and overlap-based box regression gives weak gradients at their scale. The Normalized Gaussian Wasserstein Distance (NWD) repairs both effects, but applied uniformly across a slide that also holds objects three to four times larger it loosens their supervision and erodes their localisation, so overall accuracy can fall even as the tiny class improves. We present CoSWA-YOLOv12, a compact YOLOv12 instance-segmentation detector whose core Cooperative Scale-adaptive Wasserstein Assignment routes the Wasserstein treatment to an object in inverse proportion to its size, tapering back to standard assignment for larger species. Two further components support it: a wavelet detail residual, and a min-max Gaussian regression loss (M2-NWD). All three additions are transfer-safe: each reproduces the standard pretrained model exactly at initialisation, so public pretrained weights load without any loss of accuracy. On a five-class Rwandan thick-smear dataset, CoSWA-YOLOv12 raises P. falciparum recall from 0.63 to 0.74 and mAP@50 from 0.73 to 0.81 (mask), cuts missed P. falciparum from 38% to 15%, and improves strict-localisation mAP@50-95 on all five classes for both detection and segmentation, while a 2x2 ablation shows the scale gate and the regression loss are synergistic.
[CV-57] AdaPilot: Towards Scene-Adaptive Policy Learning for Cross-Generator Text-to-Image Quality Optimization
链接: https://arxiv.org/abs/2609.29517
作者: Wenjin Liu,Fayuan Ke,Yue Lu,Zhe Cui,Anh Tuan Luu,Haoran Luo
类目: Computer Vision and Pattern Recognition (cs.CV); Computational Engineering, Finance, and Science (cs.CE)
备注:
Abstract:Existing methods for improving text-to-image generation quality have progressed from generator fine-tuning and prompt optimization to reinforcement learning with multi-turn visual feedback. However, existing strategies are deeply coupled with specific generators and tasks, and the learned capabilities are difficult to generalize into a universal quality optimization policy. Therefore, we propose AdaPilot, which learns a scene-adaptive, cross-generator transferable quality optimization policy by formulating multi-turn image generation as a Markov Decision Process (MDP) and optimizing it via end-to-end reinforcement learning. Specifically, AdaPilot decouples the policy from generator internals to enable cross-generator transfer, introduces scene-aware rewards that adaptively align quality assessment dimensions with task semantics, and employs process-level rewards to model the evolution trajectory of image quality. Experimental results show AdaPilot outperforms baselines in generation quality and generalization. Separate cross-generator evaluations further show that a single policy transfers zero-shot to unseen generators while maintaining positive average gains across all evaluated generators. Our project is available at this https URL.
[CV-58] AgriCountDINO: Parameter-Efficient Exemplar-Guided Counting and Localization in Agriculture
链接: https://arxiv.org/abs/2609.29460
作者: Shengjie Guo,Xin Li,Borjana Arsova,Hanno Scharr,Silvio Salvi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Accurate counting and localization of plants and their organs support phenotyping and yield estimation, yet target appearance, scale, and density vary widely across species and imaging conditions. Exemplar boxes specify the target without category-specific retraining, and point predictions identify the individual instances contributing to the count. We introduce AgriCountDINO, a parameter-efficient exemplar-guided framework for joint counting and localization. It conditions frozen multiscale DINOv3 features on exemplar appearance and size, then progressively decodes them into target points. Missed-object recovery extends supervision to targets overlooked by initial matching, and exemplar-adaptive point NMS filters duplicate predictions according to exemplar scale. With 8.4M trainable parameters, approximately one-tenth of TasselNetV4’s, AgriCountDINO achieves a three-shot MAE of 11.92 on the TPC-268 benchmark, reducing counting error by 9.7% while providing individual target locations. Trained only on TPC-268, it achieves a zero-shot MAE of 14.25 on unseen generic object categories in FSC-147, improving upon the best compared zero-shot method by 6.0% without target-domain training or fine-tuning.
[CV-59] Industrial Anomaly Detection via Defect-Grounded Reasoning in Visual Latent Space
链接: https://arxiv.org/abs/2609.29457
作者: Jaron Yeh,Yen-Wei Chang,Jiang Liu,Shao-Yuan Lo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 5 pages
Abstract:Industrial anomaly detection (IAD) is evolving beyond conventional detection and localization toward multimodal inspection systems that can describe, explain, and reason about fine-grained defects. Although recent multimodal large language model (MLLM)-based methods improve anomaly understanding through textual reasoning and visual guidance, they face two limitations in fine-grained inspection. First, their visual refinement often requires iteratively revisiting local image regions or augmenting with additional tools. Second, the resulting local defect evidence may not be reliably preserved throughout subsequent reasoning. To address these, we propose Anomaly-LR, a defect-grounded latent reasoning framework that first forms a global understanding of the input and then progressively refines anomaly-relevant representations directly in the visual latent space. We further construct IAD-LR-22K, the first IAD instruction dataset designed for latent reasoning, containing 22,228 image-question instances from 4,523 industrial images, with global textual reasoning traces and region-level visual annotations. Extensive experiments show that Anomaly-LR achieves state-of-the-art performance among comparable-scale methods across multiple IAD benchmarks, without requiring external references or tools. The code and data will be released at this https URL.
[CV-60] Dense Coverag e Sparse Refinement: Byte-Constrained Cooperative Perception WACV2027
链接: https://arxiv.org/abs/2609.29456
作者: Melih Yazgan,Timon Müller,J. Marius Zöllner
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at WACV 2027 (first-round acceptance)
Abstract:Collaborative perception improves autonomous perception by sharing intermediate Bird’s-Eye-View (BEV) features across connected agents, but dense feature exchange is difficult to deploy under strict Vehicle-to-Everything (V2X) bandwidth limits. Existing efficient methods typically either compress the full feature map uniformly, spending bits on low-value background, or sparsify communication, risking the loss of useful context. We propose a coverage-refinement design for byte-constrained cooperative perception: each agent transmits a highly compressed coarse layer over the full BEV map and allocates the remaining budget to selected high-resolution patches. A Task-Aware Benefit Selector ranks cells by estimated downstream utility, enabling deterministic budgeted refinement and zero-retraining adaptation to changing bandwidth. The receiver reconstructs a dense BEV tensor compatible with standard fusion modules. Experiments on DAIR-V2X and OPV2V show strong accuracy-payload trade-offs at kilobyte-scale budgets. On DAIR-V2X, our method reaches 0.60 AP@0.7 at only 1.87 KB per non-ego agent, compared with 0.52 at 4.61 KB for uniform SimVQ compression. Controlled diagnostics further show that the gain arises from coverage-refinement allocation rather than quantization alone. Code will be published.
[CV-61] Frame-to-Panorama Localization and Context-Aware Sampling for Scene-Specific Ship Detection in a Smart Marina Testbed
链接: https://arxiv.org/abs/2609.29447
作者: Ignat Romanov,Andreas Hadjipieris,Neofytos Dimitriou
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computational Geometry (cs.CG)
备注:
Abstract:Smart maritime infrastructures provide continuous access to heterogeneous sensing streams, enabling repeated experimentation, digital-twin development, and AI-based maritime services. However, sensing hardware alone is not sufficient for scene-specific model development: historical video streams must also be spatially indexed, contextualized, and reduced to informative subsets for annotation. This paper presents a frame-to-panorama localization and context-aware sampling pipeline for ship detection in historical PTZ maritime video lacking reliable pan, tilt, and zoom metadata. The main contribution is an end-to-end data-curation approach that recovers camera-view information from historical PTZ video and combines it with environmental context and visual diversity to construct compact, scene-specific training sets. Specifically, frames are localized on a reference panorama using SuperPoint and LightGlue, enriched with weather and solar-state metadata, and selected through diversity sampling to preserve variation across camera view and environmental conditions. A second context-aware stage targets under-represented distant-vessel cases near the horizon using tile-level visual embeddings and Gaussian Mixture Model clustering. Applied within the CMMI MDigi-I Smart Marina testbed, the proposed pipeline reduces 40,718 candidate frames to 220 images for annotation, corresponding to a 99.5% reduction. A YOLO26-m detector fine-tuned on this subset achieves a mean AP50 of 94.78% \pm 0.51% and a mean AP50-95 of 75.10% \pm 1.73% under sequence-grouped five-fold cross-validation. These results demonstrate that highly redundant infrastructure video streams can be transformed into compact, spatially and contextually diverse training sets for scene-specific detector adaptation while substantially reducing annotation effort.
[CV-62] Pose Adaptive Dynamic FiLM Modulation for Visual Speech Recognition
链接: https://arxiv.org/abs/2609.29443
作者: Matthew Kit Khinn Teng,Haibo Zhang,Takeshi Saitoh
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Submitted for conference publication and currently under review
Abstract:Head-pose variation introduces substantial appearance transformations in visual speech recognition (VSR), making pose-aware feature modulation desirable. However, performance degradation and unwanted feature interactions may result from using numerous Feature-wise Linear Modulation (FiLM) circuits with fixed modulation intensity. We propose a Pose Adaptive Dynamic FiLM framework with a Dynamic Residual FiLM (DR-FiLM) modulator that predicts input-dependent weights to adaptively control the strength of pose-conditioned modulation. Experiments on LRS2 and LRS3 demonstrate that unweighted multi-pathway modulation substantially degrades phoneme recognition, increasing PER to 20.33% and 29.42%, respectively, compared with 16.20% and 20.96% for the single ResFiLM configuration. In contrast, the proposed DR-FiLM with dynamic Deep-Res weighting reduces PER to 15.74% on LRS2 and 23.91% on LRS3, substantially mitigating the adverse effects of unweighted modulation. The analysis of the learned weights further reveals a consistent tendency to assign greater weight to the deeper FiLM pathway as head-pose variation increases. These results show that merging pose-conditioned FiLM circuits is more efficient when the modulation strength is dynamically controlled.
[CV-63] Detecting Glaucoma Across Multi-ethnic Myopic and Non-Myopic Populations Using an Uncertainty-Aware Vision Transformer: A Multicentre Model Development and Validation Study
链接: https://arxiv.org/abs/2609.29433
作者: Raghavan Lavanya,Yangqin Feng,Ten Cheer Quek,Quan V. Hoang,Linda Yi-Chieh Poon,Jost B. Jonas,Ya Xing Wang,Vinay Nangia,Jin Wook Jeoung,Sehie Park,SoYeon Kim,Benjamin Y Xu,Sreenidhi Iyengar Munimadugu,Paul Mitchell,Gerald Liew,Yanin Suwan,Jirayu Hong-amata,Sahil Thakur,Monisha E Nongipur,Tina Wong,Rahat Husain,Ng Si Rui,Yamon Syn,Phey Feng Lo,Nicholas Tan Yi Qiang,Shaista Hussain,Xiaofeng Lei,Zhi Da Soh,Marco Yu,Haslina Hamzah,Zizhou Wang,Yan Wang,Liangli Zhen,Xinxing Xu,Tien-Yin Wong,Tin Aung,Rachel S Chong,Yong Liu,Ching-Yu Cheng
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Background: Artificial intelligence (AI)-based glaucoma detection from colour fundus photographs (CFP) offers scalable screening, but performance may decline on external datasets because of differences in ground-truth definitions, populations, and coexisting conditions such as high myopia (HM). We developed and validated a Vision Transformer-based deep learning (DL) model for glaucoma detection across multi-ethnic cohorts with and without HM. Methods: A ViT-B/16 model with predictive uncertainty estimation was developed using 56,483 CFPs (57.1% with myopia; 14.4% with HM). Glaucoma labels were standardised using clinical, imaging, and perimetry data. The model was validated on 16 independent datasets across three continents, including four datasets with explicit HM labels. Findings: Internal AUROC was 98.7% (95% CI 98.2-99.1%), with sensitivity 94.5% and specificity 97.3%. Across 16 external datasets from eight countries, AUROCs ranged from 86.4% to 99.6%. In HM eyes, internal AUROC was 97.8% (95% CI 96.1-99.2%), with sensitivity 94.8% and specificity 93.7%. External HM AUROCs were 86.5% in the Beijing Eye Study and 93.3%, 91.8%, and 85.5% in hospital-based datasets from Taiwan, Thailand, and South Korea. In an exploratory HM clinical evaluation, the model had higher CFP-only diagnostic accuracy than ophthalmologists and trained graders (92.0% vs 70.0%; p=0.008) and performed comparably to glaucoma specialists using full clinical information. Interpretation: The model showed robust glaucoma detection across myopic and non-myopic multi-ethnic populations and may support AI-assisted screening in settings with high HM prevalence.
[CV-64] When Misalignment Becomes Supervision: Structured Label Noise in Supervised Synthetic CT Generation
链接: https://arxiv.org/abs/2609.29387
作者: Valentin Boussot,Cedric Hemon,Caroline Lafond,Jean-Claude Nunes,Jean-Louis Dillenseger
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 23 pages, 4 figures, 19 tables
Abstract:Supervised synthetic CT (sCT) generation is commonly trained and evaluated as voxel-wise regression against registered reference CT images. In practice, MRI-CT and CBCT-CT pairs are aligned through registration procedures that leave residual misalignments. These residuals are not independent intensity noise but spatially coherent geometric discrepancies that act as structured label noise. We investigate how this registration-induced bias affects supervised MRI-to-CT and CBCT-to-CT synthesis on 1,784 paired patients covering five anatomical regions. Voxel-wise scores strongly depend on the consistency between the registration used to build the training targets and the one used for evaluation: models score best when both conventions match, showing that networks partly learn the geometric convention of the registration pipeline and that standard metrics reward it. Training on more anatomically consistent registrations reduces prediction variability and improves out-of-distribution robustness, and CT-only controls show that registration alone produces metric errors in the range of top challenge submissions. To mitigate the limits of voxel-wise supervision, we introduce a perceptual loss computed in the feature space of a pretrained Segment Anything encoder. Compared with MAE-only and VGG-based objectives, it improves downstream segmentation and yields sharper, more structurally coherent sCT. Perceptual and voxel-wise metrics disagree under imperfect alignment and agree when the evaluation geometry is reliable. These results identify registration-induced bias as a central confounder in supervised sCT generation and argue for complementing voxel-wise agreement with anatomy-oriented evaluation criteria.
[CV-65] Segment-Level Risk Discovery in Online Handwriting for Alzheimers Disease Detection
链接: https://arxiv.org/abs/2609.29384
作者: Changqing Gong,Huafeng Qin,Mounîm A. El-Yacoubi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Online handwriting provides a non-invasive and low-cost behavioral biomarker for Alzheimer’s disease (AD) detection, as it reflects both cognitive planning and fine motor control. Existing handwriting-based AD detection methods usually rely on global trajectory features or whole-sample representations, which can be strongly affected by individual writing style, task-specific variation, and acquisition noise. In this paper, we propose NormPaST-Risk, a healthy-normative Paper-Air selective trajectory state-space risk network for interpretable AD detection from online handwriting. Instead of treating the entire trajectory as a single holistic representation, our method reformulates AD handwriting detection as local disease-relevant segment discovery. Specifically, a multi-scale temporal encoder captures stroke dynamics at different temporal resolutions, while a selective Paper-Air state-space encoder models long-range handwriting progression and distinguishes on-paper motor execution from in-air planning and transition behaviors. To explicitly characterize abnormal deviations, a healthy normative branch learns normal handwriting dynamics from healthy controls, and a task-aware multi-expert segment-risk module estimates segment-level AD risk calibrated by hidden-state changes and normative deviations. A weakly supervised segment-level objective further enables high-risk segment discovery without manual segment annotations. Experiments on the DARWIN benchmark demonstrate that the proposed framework achieves superior AD/HC classification performance compared with existing methods. Moreover, the discovered high-risk segments can be projected back to the original handwriting trajectory, providing interpretable evidence associated with AD-related handwriting variations.
[CV-66] On the second-order optimization for spiking neural networks
链接: https://arxiv.org/abs/2609.29379
作者: Ngoc Phu Doan,Ihsen Alouani
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Spiking Neural Networks (SNNs) offer an energy-efficient alternative to conventional neural networks by exploiting sparse, binary spikes, and event-driven computation. However, the training of SNNs remains challenging, as spiking activations create a sharp loss landscape that hinders training, and diagonal-curvature optimizers such as the Adam family may fail to capture this geometry. The extension of curvature-based optimization methods to SNNs is further complicated by the sparse, discrete, and temporally recurrent nature of their underlying dynamics. To address these limitations, we propose SpiKFAX, a second-order optimization method that formulates a computationally tractable, Kronecker-factored approximation of the Fisher information matrix specifically adapted to the structure of SNNs. Empirical evaluation across five architectures and seven datasets demonstrates that SpiKFAX consistently yields improvements in test accuracy and training stability relative to other popular optimizers.
[CV-67] A Hybrid CNN–State-Space–Attention Backbone with Joint-Embedding Predictive Pretraining for 12-Lead ECG Classification
链接: https://arxiv.org/abs/2609.29376
作者: Yakoub Bazi,Sarah Aljuhani,Mohamad M. Al Rahhal,Mansour Zuair,Naif Alajlan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Automatic 12-lead electrocardiogram (ECG) classification requires representations that jointly capture local waveform morphology, long-range temporal dynamics, and cross-lead dependencies, yet integrating these properties within a single efficient architecture remains challenging. This paper introduces a hybrid CNN-SSM-Attention backbone for 12-lead ECG classification. A convolutional stem performs early waveform tokenization and temporal reduction, mixed state-space and depthwise-convolutional blocks model temporal dynamics and local morphology, and a late self-attention stage enables global token interaction at reduced resolution. To improve transfer from unlabeled data, we further develop an ECG-oriented Joint-Embedding Predictive Pretraining (JEPA) framework. Unlike ViT-based JEPA methods that mask patch tokens before the encoder, the proposed method samples span masks at the latent temporal resolution and projects them back to the waveform domain, then predicts clean latent targets from a momentum encoder without waveform reconstruction. Experiments on CPSC2018, Chapman-Shaoxing, and PTB-XL, with pretraining on approximately 350K unlabeled CODE-15 recordings, show that the proposed backbone provides strong supervised baselines under a compact parameter budget. JEPA pretraining further improves transfer, particularly in reduced-label settings and under both full fine-tuning and LoRA-based adaptation. Code: this https URL
[CV-68] Free-Init: Scan-Free Motion-Free and Correspondence-Free Initialization for Doppler LiDAR-Inertial Systems
链接: https://arxiv.org/abs/2609.29375
作者: Mingle Zhao,Jiahao Wang,Tianxiao Gao,Chengzhong Xu,Hui Kong
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Systems and Control (eess.SY)
备注: IEEE Robotics and Automation Letters (RA-L), 2024
Abstract:Robust initialization is crucial for online systems. In the letter, a high-frequency and resilient initialization framework is designed for LiDAR-inertial systems, leveraging both inertial sensors and Doppler LiDAR. The innovative FMCW Doppler LiDAR opens up a novel avenue for robotic sensing by capturing not only point range but also Doppler velocity via the intrinsic Doppler effect. By fusing point-wise Doppler velocity with inertial measurements under non-inertial kinematics, the proposed framework, Free-Init, eliminates reliance on motion undistortion of LiDAR scans, excitation motions, and map correspondences during the initialization phase. Free-Init is also plug-and-play compatible with typical LiDAR-inertial systems and is versatile to handle a wide range of initial motions when the system starts, including stationary, dynamic, and even violent motions. The embedded Doppler-inertial velocimeter ensures fast convergence and high-frequency performance, delivering outputs exceeding 10 kHz. Comprehensive experiments on diverse platforms and across myriad motion scenes validate the framework’s effectiveness. The results demonstrate the superior performance of Free-Init, highlighting the necessity of fast, resilient, and dynamic initialization for online systems.
[CV-69] FMCW-LIO: A Doppler LiDAR-Inertial Odometry
链接: https://arxiv.org/abs/2609.29374
作者: Mingle Zhao,Jiahao Wang,Tianxiao Gao,Chengzhong Xu,Hui Kong
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Systems and Control (eess.SY)
备注: IEEE Robotics and Automation Letters (RA-L), 2024
Abstract:Conventional LiDAR-inertial odometry (LIO) or simultaneous localization and mapping (SLAM) methods heavily rely on geometric features of environments, as LiDARs primarily provide range measurements instead of motion measurements. From now on, however, the situation changes thanks to the novel Frequency Modulated Continuous Wave (FMCW) Doppler LiDARs. FMCW Doppler LiDARs not only offer the point range with high resolution but also capture the instant point Doppler velocity through the Doppler effect. In the letter, we propose FMCW-LIO, a novel and robust LIO, leveraging intrinsic Doppler measurements from FMCW Doppler LiDARs. To correctly exploit Doppler velocities, a motion compensation method is designed, and a Doppler-aided observation model is applied for on-manifold state estimation. Then, dynamic points can be effectively removed by the Doppler criteria, deriving more consistent geometric observations. FMCW-LIO eventually achieves accurate state estimation and static mapping, even in structure-degenerated environments. Extensive experiments in diverse scenes are performed and FMCW-LIO outperforms other algorithms on both accuracy and robustness.
[CV-70] Shadow Reduction in Ultrasound Imaging Using Differentiable Simulation and Radiance Field Decomposition
链接: https://arxiv.org/abs/2609.29373
作者: Valentin Bacher(1),Pak Hei Yeung(1 and 2),Bernhard Kainz(3 and 4),Madeleine K. Wyburd(1 and 5),Nicola K. Dinsdale(1),Michael Gray(6),Ana I. L. Namburete(1) ((1) Oxford Machine Learning in NeuroImaging Lab, University of Oxford, United Kingdom, (2) Quantitative Healthcare Analysis (qurAI) Group, University of Amsterdam, The Netherlands, (3) Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany, (4) Imperial College London, United Kingdom, (5) Department of Computer Science, University of Copenhagen, Denmark, (6) Institute of Biomedical Engineering, University of Oxford, United Kingdom)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 39 pages, 19 figures, submitted to Medical Image Analysis
Abstract:Acoustic shadows from bone and other highly attenuating tissues obscure clinically important structures in ultrasound. In fetal brain imaging, skull-induced artefacts disproportionately degrade the hemisphere closer to the transducer (proximal), limiting symmetric assessment of the two hemispheres. Existing correction methods require raw scanner data, impose restrictive assumptions on tissue properties, or rely on generative models that may hallucinate anatomy. We present RFlash, a physics-informed post-processing method that decomposes beamformed ultrasound images into explicit attenuation and scatter-intensity maps using a differentiable radiance-field formulation of image formation. Attenuation-adaptive re-rendering then removes the dependence of the signal at each depth on the intervening tissue, equivalent to virtually advancing the transducer into the tissue. Across 1,261 3D fetal brain volumes, 143 real 2D curvilinear abdominal scans, and 1,200 simulated 2D linear-probe liver scans, RFlash reduces shadow-related intensity differences more effectively than classical Hughes-Duck attenuation correction. For a gestational-age model trained on the distal hemisphere (further from the transducer) and applied to the proximal hemisphere, prediction error decreases by 5.1 days (40%) relative to the original images. The estimated attenuation maps also yield shadow-confidence maps that improve random-forest bone-shadow segmentation over the image alone and receive greater SHAP importance than an existing neural confidence-map baseline, suggesting greater physical consistency. RFlash requires neither hardware modification nor access to raw scanner data and supports 2D and 3D acquisitions with linear and curvilinear probes, making it widely applicable allowing clinicians to use our method on their already acquired scanners and images.
[CV-71] Domain Recentering and Confidence-Weighted Prior Calibration for Vision-Language Models
链接: https://arxiv.org/abs/2609.29358
作者: Youngeun Seol,Jimin Shin,Heeseo Yoon,Uiwon Hwang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-language models such as CLIP achieve strong zero-shot classification, yet under distribution shift, visual embeddings drift from fixed text embeddings. Training-free calibration avoids the per-sample optimization of prompt learning, but prior feature calibration gives each image the full bias of one hard cluster. We propose Domain Recentering with Confidence Calibration (DRC), a training-free method adapting CLIP from a set of unlabeled target images. DRC fits a Gaussian mixture once and subtracts from each embedding a posterior-weighted average of component means. It then removes residual class preference with a log-prior correction, estimating the prior from confidence-weighted predictions. Among compared methods, DRC achieves the highest average accuracy on cross-domain datasets, exceeding zero-shot CLIP by 4.13 and 5.07 points with ViT-B/16 and ResNet-50, with gains over CLIP also holding under ImageNet distribution shifts.
[CV-72] Learning a Flow to Self-Supervised Representations
链接: https://arxiv.org/abs/2609.29350
作者: Yuling Jiao,Wensen Ma,Houduo Qi,Defeng Sun
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Machine Learning (stat.ML)
备注: 33 pages, 2 figures, including appendix
Abstract:Explicit geometric references offer a direct way to structure self-supervised representations. Existing adversarial distribution-matching formulations, however, require costly encoder-critic optimization. We introduce Flow-Based Distribution Matching (FBDM), a non-adversarial framework that learns this reference-directed geometry through spherical conditional velocity regression. An ETF-inspired reference allows its number of components K’ to exceed the auxiliary flow dimension d* while retaining structured geometric separation. We assign both augmented views of each image to the same target, while limiting how many images each reference center can receive. An explicit alignment loss further pulls the two views’ representations closer together. Experiments across benchmarks ranging from CIFAR to ImageNet show that FBDM achieves performance nearly on par with DM and remains competitive with existing SSL methods. Matched training-cost comparisons show a 1.48- to 1.83-fold speedup over DM with a negligible increase in GPU memory usage. We also provide a theoretical explanation for the usefulness of the learned representations: under stated conditions, we bound the downstream misclassification rate in terms of the FBDM pretraining loss.
[CV-73] SEE Challenge 2026: Event-Guided Brightness Adjustment Across a Broad Illumination Range ECCV2026
链接: https://arxiv.org/abs/2609.29347
作者: Yunfan Lu,Mingchao Xu,Hanyu Zhou,Shaoyu Liu,Haoyue Liu,Peiqi Duan,Shihan Peng,Yinqiang Zheng,Boxin Shi,Gim Hee Lee,Hui Xiong,Davide Scaramuzza
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This report has been accepted for publication at an ECCV 2026 Workshop
Abstract:Event cameras provide a high dynamic range and preserve brightness-change cues in lighting conditions where conventional RGB frames may be noisy or saturated. To benchmark event-guided restoration across a broad illumination range, we organized the SEE Challenge 2026 with the Event-Based Multimodal Vision Workshop at ECCV 2026. The task conditions restoration on one or more RGB frames, synchronized events, and a scalar target-brightness statistic provided by the organizers. It uses SEE-600K, which contains 610,126 image-event observations from 202 real-world scenes spanning low-light, normal-light, and high-light conditions with illumination variations of up to 1,000 \times . The challenge follows an open-system protocol: participants may use different temporal contexts, architectures, pretrained weights, test-time augmentation, and post-processing strategies. PSNR determines the ranking, and SSIM is reported as a secondary metric. Around 70 teams registered interest and 15 valid CodaBench submissions were received. Six distinct teams completed organizer-side identity and technical verification, provided method descriptions, checkpoints, inference code, and instructions, and are included in the verified open-system ranking reported here. Beyond the ranking, this report analyzes exposure subsets, semantically distinct test cases, a shared failure pattern, system design choices, and inference strategies. The top systems obtain closely spaced average scores, while the best-performing method varies across cases and metrics; under severe underexposure, all verified systems retain visible local errors.
[CV-74] A Study of the Limits of Collaborative DCT-Based Image Denoising via Interpretable Neural Networks
链接: https://arxiv.org/abs/2609.29334
作者: Cristian Comellas,Julia Navarro,Antoni Buades
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Preprint submitted to Journal of Mathematical Imaging and Vision (JMIV). 17 pages, 11 figures. Supported by MCIN/AEI/ https://doi.org/10.13039/501100011033 under grant PID2021-125711OB-I00, and by the Spanish Ministry of Universities under grant FPU24/02805
Abstract:Image denoising remains a fundamental problem in image restoration, with applications in photography, biomedical, and scientific imaging. Modern deep neural networks achieve strong performance by learning powerful image priors, but often rely on large black-box models with limited interpretability. In contrast, DCT-based sliding-window and collaborative filtering methods such as BM3D offer clear algorithmic structure, but depend on handcrafted and non-differentiable operations. This work studies how far such structured collaborative filtering principles can be pushed when reformulated as trainable models. We introduce DeepBM3D, a compact fully differentiable architecture that combines non-local patch grouping, DCT-domain filtering, and multi-stage refinement within a BM3D-inspired pipeline. Lightweight convolutional feature extractors guide patch grouping, while filtering is performed through learned Wiener weights in the DCT domain. Experiments show that DeepBM3D improves over classical and hybrid baselines, remains competitive with FFDNet at low and moderate noise levels, and performs particularly well on repetitive textures.
[CV-75] Hyperbolic Multimodal Continual Learning: A Closest-Admissible Solution
链接: https://arxiv.org/abs/2609.29329
作者: Jiahong Liu,Ming Shen,Xiaohao Liu,Rex Ying,Menglin Yang,Tat-Seng Chua,Irwin King
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 49 pages, 10 figures, 11 tables
Abstract:Existing continual-learning methods protect parameters, replayed examples, or Euclidean feature subspaces. When applied to hyperbolic multimodal models, they do not explicitly preserve the Lorentz geometry that jointly encodes within-modality similarity, cross-modal correspondence, and semantic hierarchy; sequential updates can therefore retain task scores while still distorting previously learned relations. We address this gap with Hyperbolic Multimodal Continual Learning (HMCL). We show that preserving the old multimodal geometry amounts to restricting all modalities to one shared hyperbolic isometry, which induces a family of admissible first-order parameter changes. We formulate a joint closest-admissible (CA) correction that retains the shared rotation best matching the candidate modal updates; its minimal-rotation (MR) special case fixes this rotation to zero. Both variants correct the displacement realized by AdamW, and task anchoring bounds within-task accumulation while preserving learning freedom. Across a unified 16-task classification-retrieval stream with three hyperbolic backbones, HMCL improves final performance and backward transfer over sequential fine-tuning and four continual-learning baselines; HMCL-CA gives the highest Overall score on every backbone. A modality-extended stream confirms the retrieval gains. Representation analyses find 81.2 to 95.5 percent less radial, angular, cross-modal, and paired-distance drift; ImageNet-WordNet results show better semantic ancestry and radial hierarchy.
[CV-76] EgoSpeedUp: Transferring Human Manipulation Tempo to Robot Policies
链接: https://arxiv.org/abs/2609.29310
作者: Hanbit Oh,Yukiyasu Domae,Takuma Yagi
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 8pages
Abstract:Robot manipulation policies trained through imitation learning inherit not only the demonstrated behavior but also the conservative execution tempo of robot demonstrations. Existing acceleration approaches can execute faster than the original demonstrations, but determine the appropriate acceleration primarily from robot-side information or a predefined set of tempo factors, leaving open how to obtain a task-appropriate reference for how fast each manipulation phase should progress. We introduce EgoSpeedUp, a framework that uses human manipulation as temporal supervision for robot imitation learning. Our key insight is that human demonstrations naturally reveal task-appropriate, phase-wise manipulation tempo. Given slow robot demonstrations and human demonstrations of the same task, EgoSpeedUp aligns corresponding manipulation phases, estimates their relative execution tempos from multiple human demonstrations, and transfers the resulting phase-wise tempo by retiming the robot demonstrations. The retimed demonstrations are then used for standard behavior cloning, allowing the robot to retain its executable manipulation behavior while learning to perform it at a human-informed tempo. Across two real-world manipulation tasks, EgoSpeedUp improves the task success rate by an average of 25 percentage points (pp) while reducing successful execution time by 36.5%. These results demonstrate that human manipulation tempo provides an effective temporal reference for learning faster and more reliable robot policies.
[CV-77] PHOSA: Photorealistic 3D Sign Avatar Modeling and Benchmark ECCV2026
链接: https://arxiv.org/abs/2609.29292
作者: Haodong Wang,Hezhen Hu,Wengang Zhou,Houqiang Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026, project page: this https URL
Abstract:In this work, we focus on photorealistic sign avatar modeling, which is crucial for effective communication with the Deaf community and is characterized by complex hand gestures and nuanced facial expressions. To this end, we introduce MVSign, the first multi-view Chinese sign language dataset co-designed with Deaf experts, featuring diverse gestures and rich annotations. For precise SMPL-X annotation, we develop a hybrid fitting pipeline that produces accurate body, hand, and facial parameters and can also be applied to the monocular setting. Building on MVSign, we propose a decoupled sign avatar representation that isolates body, head, and hand components to capture complex articulations, together with a motion-aware sampling strategy to handle motion blur and balance gesture diversity. Extensive experiments demonstrate that our method achieves high-fidelity visual results on MVSign, particularly in detailed hand and facial regions, and generalizes well to in-the-wild monocular sign language videos. Project page: this https URL.
[CV-78] Deep learning of longitudinal visual fields predicts glaucoma progression rate and identifies fast progressors
链接: https://arxiv.org/abs/2609.29256
作者: Taiabur Rahman,Siddiqur Rahman,Muhammad Moniruzzaman,Ummay Kawsar,Sayedatunnessa Ratna,Shadman Siddique,Rafsan Siddique,Tausif Ahmad,Tahsin Ahmad,Golam Rabbani
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Glaucoma is the leading cause of irreversible blindness, and timely identification of fast progressors is essential to prevent disability. Current practice estimates progression by ordinary least-squares regression of mean deviation (MD) on time, requiring 6–10 visual field (VF) tests over several years to obtain a reliable slope. We present GLAM (Glaucoma Longitudinal Analysis Model), a deep learning framework that ingests longitudinal Humphrey 24-2 total deviation sequences with five clinical features and predicts MD and visual field index progression rates using attention-based fusion and aleatoric uncertainty. On the open-access University of Washington Humphrey Visual Field dataset (4,276 patient-eyes), GLAM achieved an MD-rate mean absolute error of 0.139 dB yr ^-1 ( R^2 = 0.927 ; 73.5% reduction over a ridge baseline) and an AUC of 0.990 for fast-progressor detection. VF-only deep learning can match multimodal pipelines for progression prognostication using routinely collected perimetry alone.
[CV-79] IronViT: Toward Efficient Generalist Visual Representation Learning
链接: https://arxiv.org/abs/2609.29252
作者: Jiaxi Huang,Yueqi Hu,Xin Zhu,Xiaopeng Zhang,Huiting Qiao,Yanglin Zhang,Zefeng Ji,Rongxue Li,Yifei Xu,Huiying Yu,Wei Liu,Jiayin Zheng,Yinggan Xu,Peipeng Chen,Yin Zhang,Jian Yao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:A generalist vision encoder must capture semantic, spatial, language-aligned, and action-relevant cues within a unified representation, yet softmax attention underlying today’s most capable visual backbones becomes prohibitively expensive at high resolution. A natural attempt to address both challenges is to distill multiple specialist teachers directly into an efficient architecture. We find that directly coupling these objectives degrades representation quality, as the student must simultaneously reconcile heterogeneous capabilities and adapt them to a different token-mixing architecture. We introduce IronViT, built on a simple principle: consolidate capabilities before constraining computation. IronViT first distills complementary specialists into a softmax attention capability bridge, then progressively transfers the consolidated representation to a hybrid softmax-linear attention encoder. A purpose-built data pipeline further curates the distillation corpus for higher information density and broader domain coverage. Across recognition, retrieval, dense prediction, multimodal understanding, and robotic learning, IronViT is competitive with leading specialist and generalist vision encoders. The softmax bridge achieves the strongest aggregate performance in multimodal understanding and robotic learning among the evaluated backbones, while the hybrid encoder retains broad transfer performance with an efficiency advantage that grows with input resolution. Together, these results show that consolidating capabilities before architectural conversion can yield a generalist visual encoder without inheriting the prohibitive high-resolution cost of conventional softmax attention.
[CV-80] OLA: Text-aware One-Step Latent Adaptation for Diffusion-based Text Image Super-Resolution
链接: https://arxiv.org/abs/2609.29240
作者: Yike Xu,Yue Shi,Yong Guo,Jiezhang Cao
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 18 pages, 11 figures, including appendices
Abstract:Text image super-resolution (TSR) aims to recover visually faithful and readable text under unknown degradations. Existing diffusion-based methods typically rely on multi-step prediction of either the high-resolution image or its text prior, resulting in prohibitive computational cost and inference latency. More critically, an erroneous text prior may be repeatedly injected into the denoising process, causing image and text predictions to reinforce each other and progressively amplify an early recognition error into a sharp yet semantically incorrect character. To address these limitations, we propose TOLA, a Text-aware One-step Latent Adaptation framework without iterative image-text diffusion. TOLA consists of two key modules. First, a confidence-weighted text conditioning module constructs the semantic condition only once and suppresses unreliable OCR predictions before they contaminate image reconstruction. Second, a lightweight latent residual correction module explicitly estimates and corrects the structured residual errors to recover missing or distorted stroke details. Extensive experiments demonstrate our state-of-the-art performance across all evaluation metrics on both CTR-TSR-Test ( \times 4 ) and RealCE-200 benchmarks. It is worth noting that our TOLA consistently surpasses existing diffusion-based TSR methods by at least 2.72 dB in PSNR on CTR-TSR-Test.
[CV-81] SARFusion: Scene-Aware Routing Fusion for Robust Camera-LiDAR 3D Object Detection
链接: https://arxiv.org/abs/2609.29235
作者: Yuting Zhao,Ziyi Zheng,Shuxiao Li
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Camera-LiDAR fusion has become a prevailing paradigm for 3D object detection in autonomous driving. However, existing fusion detectors often establish strong inter-modality dependencies by decoding object queries from tightly coupled multimodal representations. Under corrupted driving conditions, such dependencies make the detector vulnerable to unreliable modalities, where degraded observations may interfere with reliable modality-specific evidence and lead to suboptimal predictions. Moreover, modality reliability can vary across both global driving scenes and individual object queries, requiring adaptive fusion decisions at a finer granularity. To bridge this gap, we reformulate robust camera-LiDAR fusion as a scene-aware branch routing problem and propose SARFusion, a robust 3D object detector. Instead of producing detections from a single fused representation, SARFusion decouples object-query decoding into three parallel reasoning branches: a camera branch, a LiDAR branch, and a camera-LiDAR fusion branch. Guided by a Scene Reliability Prior estimated from the global driving context, SARFusion further incorporates object-level evidence to route each query to the most suitable branch. This query-wise routing strategy alleviates harmful cross-modal interference while preserving the benefits of multimodal fusion when complementary cues are trustworthy. On the nuScenes test set, SARFusion achieves strong performance with 72.5 mAP and 74.4 NDS. Extensive analyses demonstrate its robustness under challenging conditions, including sensor corruptions and environmental changes.
[CV-82] ComplexSync: High-Fidelity and Real-Time Lip Sync in Complex Scenarios
链接: https://arxiv.org/abs/2609.29225
作者: Jiaran Cai,Xingpei Ma,Shenneng Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Lip synchronization aims to generate visual lip dynamics that align precisely with speech audio. Despite the high generation quality of diffusion models, they often struggle in complex scenarios and suffer from prohibitive inference latency, limiting real-world deployment. We present ComplexSync, a unified diffusion-based framework that enables real-time, high-fidelity lip sync under complex conditions. First, we introduce a dual-stream joint training strategy to mitigate information leakage from reference frames while preserving natural dynamics. Second, we develop a distillation-based acceleration scheme for single-step denoising, achieving a throughput of over 70 FPS. Third, we propose a relational alignment loss that leverages structural priors from Vision Foundation Models (VFMs) to enhance robustness against complex scene factors. Furthermore, we present the first benchmark specifically designed for complex lip synchronization, comprising over 200 challenging video sequences and specialized metrics. Extensive experiments demonstrate that ComplexSync achieves state-of-the-art performance across both standard and complex scenarios while enabling real-time inference.
[CV-83] FounRef: Robust Structure-Preserving and Fast Metric Refinement of Frozen Monocular Foundation Priors with Sparse Anchors
链接: https://arxiv.org/abs/2609.29224
作者: Dan Halperin,Mirko Mählisch
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 12 figures; includes appendix
Abstract:Dense metric depth from cameras is essential to real-world 3D applications, yet achieving accuracy, faithful surface geometry, and fast inference simultaneously remains challenging. Monocular foundation models provide rich, transferable geometric priors but lack reliable metric scale, while depth-completion networks recover metric depth at the cost of geometric fidelity, cross-domain robustness, or speed. We present FounRef, a training-free method that aligns a frozen monocular foundation prior with sparse metric anchors to produce dense metric depth. FounRef is modular by design: its depth prior, anchor source, and refinement solver can each be replaced independently. We instantiate FounRef with MoGe-2 and LiDAR anchors. FounRef validates each anchor against the prior’s dense depth prediction, rejecting inconsistencies caused by cross-sensor misalignment that geometry-only filters cannot detect. It then applies global and local metric corrections through a structure-preserving solver, retaining the prior’s fine-grained geometry. FounRef requires no task-specific training and operates out of the box across unfamiliar cameras and scenes. On out-of-domain data, it delivers up to 24% lower depth error, 92% lower surface-normal noise, and almost 15x faster inference than DMD3C, a state-of-the-art depth-completion network. By decoupling metric alignment from geometry prediction, FounRef provides an accurate, geometrically faithful, and efficient approach to dense metric depth that can directly benefit from future advances in foundation models and metric sensors.
[CV-84] ImCorr: Sub-pixel Semantic Correspondence via Implicit Feature Decoding ACCV2026
链接: https://arxiv.org/abs/2609.29193
作者: Yusung Choi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ACCV 2026
Abstract:The strong performance that modern semantic correspondence methods achieve at standard thresholds plateaus sharply at fine-grained thresholds. We argue that this plateau stems not from the representational capacity of backbone features, but from a grid-tied readout. Patch-based vision transformers tokenize images onto discrete grids, introducing two forms of quantization error: querying nearest patch features instead of exact keypoints on the source side, and the absence of grid features representing precise ground-truth locations on the target side. We quantify this quantization ceiling across all 499,188 keypoints in SPair-71k: under the standard 448x448, patch-14 setting, 84.9% of ground-truth keypoints have no grid feature representing their precise location at PCK@0.01. This is a structural limitation at the representation level, independent of the matching strategy. We address this with ImCorr: Sub-pixel Semantic Correspondence via Implicit Feature Decoding, which formulates correspondence estimation over a continuous feature field queryable at arbitrary continuous coordinates. A FiLM-conditioned decoder is trained to embed sub-pixel positional information into the feature field. Querying the field directly at exact keypoint coordinates theoretically eliminates representation-level quantization error on the source side, while decoding onto a grid denser than the backbone grid substantially reduces quantization error on the target side. On SPair-71k and AP-10K (intra-species, cross-species, and cross-family), ImCorr improves performance at fine-grained thresholds (PCK@0.01-0.05), achieving a 6.2 percentage point gain over the prior state of the art at PCK@0.01 on SPair-71k. These results demonstrate that representational continuity is an effective solution for precise semantic correspondence. Code is available at this https URL.
[CV-85] An Automated Georeferencing Technique for Multi-Temporal Stope Point Clouds for Downstream Geotechnical Analysis
链接: https://arxiv.org/abs/2609.29186
作者: Dibyayan Patra,Simit Raval,Pasindu Ranasinghe,Bikram Banerjee,Ismet Canbulat
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The increasing use of UAV laser scanning in underground mines has enabled frequent acquisition of 3D point clouds from challenging environments such as stopes, generating large volumes of multi-temporal spatial data throughout successive excavation stages. However, in GNSS-denied underground environments, independently acquired stope point clouds are generated within local scanner reference frames and require registration and georeferencing before integration with mine reference data for downstream geotechnical analysis, monitoring, and mine planning. This process is commonly performed manually by aligning individual stope scans with mine reference drives, making repeated georeferencing time-consuming and potentially limiting the utilisation of routinely acquired data. This study proposes the 3D Tag-based Automated Registration and Georeferencing Technique (3D-TARGeT), an automated framework using low-cost, generic, non-unique rectangular tags to establish spatial correspondence between stope point clouds and the mine reference coordinate system. The framework combines automated tag identification, geometric tag matching, and rigid transformation estimation. It was evaluated as a proof of concept using four multi-temporal point-cloud scans of an underground mine stope, with the proposed tags simulated under representative scanning conditions. 3D-TARGeT achieved consistent centimetre-level georeferencing accuracy, with median cloud-to-cloud distance and root mean square error below 0.03 m across all scans, while substantially outperforming widely used automatic point-cloud registration techniques. Overall, 3D-TARGeT provides an accurate and robust approach for automating stope point-cloud georeferencing, reducing reliance on manual alignment and facilitating multi-temporal datasets for downstream geological and geotechnical applications.
[CV-86] Representation World Model: Learning States Transition and Executable Plans in Representation
链接: https://arxiv.org/abs/2609.29171
作者: Yijun Yuan,Weicheng Zheng,Weibang Wang,Minghui Qin,Chang Sun,Junhao Huang,Kenan Li,Anmin Liu,Yicheng Yao,Hang Zhao
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Website: this https URL
Abstract:We propose the Representation World Model (RWM), which learns states, transitions, and executable plans directly in representation space. Unlike existing world models that typically learn latent representations together with explicit dynamics models and perform planning through search, optimization, or policy-based prediction, RWM directly incorporates planning into the learned representation geometry. RWM learns the representation geometry by applying inverse-dynamics supervision locally along latent paths constructed from endpoint representations, requiring these paths to preserve task-relevant state and transition information. At inference, planning is performed by directly constructing a latent path between the current and goal representations, with inverse dynamics used to recover the corresponding actions, without recursive rollouts or action-space search. Experiments on continuous-control benchmarks demonstrate the effectiveness of RWM for direct planning, while results on robotic manipulation further show its potential to extend to more complex embodied control tasks. These results suggest that planning directly in representation space provides a promising alternative to conventional world-model planning.
[CV-87] Med-AR: Autoregressive Vision-Language Pretraining for Long-Tailed Chest X-Ray Classification and Uncertainty-Aware Evaluation
链接: https://arxiv.org/abs/2609.29156
作者: Janhavi Prabhu,Sahil,Akshay V,Shivam Shukla,Manoj Tadepalli,Preetham Putha
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 80 pages including supplementary material, 28 figures, and 22 tables. Supplementary material is included
Abstract:Long-tailed chest X-ray classification requires visual representations that capture both common abnormalities and subtle, infrequent findings. We propose Med-AR-8B and Med-AR-2B, two radiology-native autoregressive vision-language models pretrained with structured reports, abnormality-focused text, and region annotations. We evaluate the transfer of their visual encoders to multi-label classification against contrastive, self-supervised, and supervised pretrained encoders, including Med-CLIP, CheXFound, EVA-Base, ARK, and BioViL-T, using a common ML-Decoder classification head. To assess fine-grained recognition, we also construct LLM-expanded, report-derived label sets for MIMIC-CXR and CheXpert. Across PadChest, MIMIC-CXR, and CheXpert, Med-AR-8B outperforms Med-CLIP in mean AUROC and AUPRC for head, medium, and tail findings. On MIMIC-CXR, it increases tail-label mean AUPRC from 0.1033 to 0.1441. Med-AR-2B achieves the strongest discrimination results on PadChest. Across the broader encoder comparison, a Med-AR variant achieves the highest mean AUROC and AUPRC in every reported prevalence group on each public dataset. Both Med-AR variants also achieve lower excess area under the risk-coverage curve than Med-CLIP on all three public datasets, indicating improved selective-prediction performance under the evaluated protocol. Internal results are metric-dependent, with Med-CLIP retaining advantages in overall and tail AUPRC and in selective prediction. These findings establish Med-AR as a strong pretraining recipe for long-tailed chest X-ray classification on the evaluated public benchmarks and demonstrate the value of assessing discrimination and selective prediction together.
[CV-88] Recoverable Geographic Location Information in Earth-Observation Embeddings
链接: https://arxiv.org/abs/2609.29151
作者: Peiwen Zhang,Kristie Hu,Jovana Knezevic,Shunde Yin,Kyle Gao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Earth-observation (EO) foundation models provide reusable embeddings, yet downstream task accuracy does not reveal whether these representations encode geographic information, which may be beneficial for location-aware applications but potentially detrimental when representations invariant to geographic location are desired. We therefore evaluate the geographic coordinate robustness of Tessera v1, Tessera v1.1, and AlphaEarth by testing whether coordinates can be predicted from the embedding representations using 284 quality-verified European solar farms from 2024. We assessed geographic information content information through the association between cosine and geodesic distances and through prediction of projected coordinates in EPSG:3035. Embeddings from all three EO foundation models contain recoverable geographic information. All prediction models significantly outperform training-range uniform random sampling baselines, with AlphaEarth exhibiting the strongest distance association and lowest mean geodesic error. Both Tessera variants also yielded higher geographic distance correlations than the Sentinel-2 controls. These findings motivate geographic information content as an additional criterion for auditing EO foundation models.
[CV-89] FoCal: Frequency-Oriented Cross-Modal Interaction and Spectral Calibration for Aerial Visible-Infrared Object Detection
链接: https://arxiv.org/abs/2609.29125
作者: Ben Liang,Chao Sui,Junqi Bai,Yuan Liu,Chunlai Li,Xiubao Sui,Qian Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In aerial RGB–IR object detection, effectively exploiting complementary information across modalities is critical for robust perception under complex illumination and environmental conditions. Existing multimodal detectors mainly focus on spatial-domain interaction or frequency-specific feature enhancement, while the cross-modal interaction patterns of different frequency components remain insufficiently explored. Moreover, spectral discrepancy itself may contain both useful complementary cues and unreliable modality-specific responses, making indiscriminate frequency fusion suboptimal. To address these issues, we propose FoCal, a frequency-oriented framework for aerial RGB–IR object detection. First, a Frequency-Aware Dual-Domain Calibration (FADC) module is developed to explicitly model frequency-dependent cross-modal interaction. Low-frequency components are collaboratively consolidated into a shared structural consensus, whereas high-frequency components preserve modality-specific information through selective cross-modal exchange. The resulting frequency-aware cues are further transferred to the original feature domain to regulate cross-modal calibration. Second, we introduce a Discrepancy-Guided Spectral Modulation (DGSM) module, which characterizes cross-modal spectral imbalance using confidence-weighted relative amplitude discrepancy and transforms it into a bounded signed gate for adaptive enhancement, preservation, or attenuation of the joint multimodal spectrum. Extensive experiments on DroneVehicle, ESCVehicle, and ATR-UMOD demonstrate the effectiveness of FoCal, yielding \mathrmmAP_50 values of 83.5%, 54.8%, and 64.6%, respectively. Meanwhile, with only 3.0M parameters, FoCal achieves 113.6 FPS while preserving leading detection accuracy, highlighting a favorable accuracy–efficiency trade-off. Code is available at this https URL.
[CV-90] Less is More: Encoder-only Audio-Visual Segmentation ICASSP2027
链接: https://arxiv.org/abs/2609.29121
作者: Ilpo Viertola,Vladimir Iashin,Sophie Tötterström,Esa Rahtu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Submitted to ICASSP 2027. Project page this https URL
Abstract:Audio-Visual Semantic Segmentation (AVSS) aims to identify, segment, and classify sound-emitting objects in video frames. Previous Transformer-based AVSS approaches largely inherit design principles from image segmentation models. Recent studies show that these image segmentation models contain redundant components that contribute little to the segmentation performance. Following this insight, we propose Encoder-only Audio-Visual Segmentation (EASE). EASE runs at up to 365 FPS, 3x faster than prior State-of-the-Art (SotA) AVS models at comparable accuracy, and trains in under 11 GPU-hours. Furthermore, we achieve SotA AVSS performance across different backbones and input resolutions. Our results demonstrate that AVSS can be both simpler and faster, providing a scalable foundation for future research and real-time applications. Code, model weights, and samples are available at this https URL
[CV-91] UpDown-SC: Gravity-Canonicalized Dual-Envelope Scan Context for Indoor LiDAR Place Recognition
链接: https://arxiv.org/abs/2609.29118
作者: Jie Xu,Yongxin Yang,Ziyi Jin,Kangjin Yu,Hongjun Huang,Chao Han,Zhongpu Xia
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 8 pages, 7 figures, 2 tables. Code and evaluation artifacts: this https URL
Abstract:LiDAR place recognition is a key front end for loop closure and global relocalization, yet indoor retrieval remains difficult when attitude or sensor mounting height changes between mapping and query sessions. Scan Context stores the maximum height in each polar cell; indoors, broad ceilings can suppress the lower and mid-level geometry that distinguishes adjacent rooms and corridors. We present UpDown-SC, a training-free polar descriptor that first canonicalizes gravity and then represents two complementary surfaces: the upper envelope of lower/middle structures and the lower envelope of overhead structures. Their physical split is estimated once from a cell-balanced map height distribution and reused by every query. A mask-aware, non-uniform two-channel distance retains discriminative lower-level evidence while limiting sensitivity to its cross-session variation, without treating unobserved cells as zero-height measurements. Conventional Scan Context shortlisting and circular yaw alignment are retained, so retrieved hypotheses directly initialize geometric verification. Experiments across repeated indoor sessions, mounting-height changes, mixed outdoor-to-indoor trajectories, and an outdoor transfer sequence show more reliable first-choice retrieval on the indoor and mounting-height-varied sessions. A paired test finds a significant gain over Scan Context on the in-house sessions. UpDown-SC also gives the best or second-best F1max and AUPR under threshold-based acceptance while retaining a lightweight CPU front end. Continuous replay confirms that the retrieved hypotheses support metric prior-map localization. Code and evaluation artifacts: this https URL.
[CV-92] Spectral Amplitude Purification in Distribution Matching for Diffusion Distillation
链接: https://arxiv.org/abs/2609.29116
作者: Zhenyu Zhou,Can Wang,Chun Chen,Zeyu Zheng,Defang Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Distribution Matching Distillation (DMD) enables high-quality diffusion sampling in only a few steps, but its optimization dynamics remain dominated by coarse, low-frequency signals, delaying the recovery of fine-grained details. We identify a pronounced concentration of spectral amplitudes at low frequencies in the DMD directional error, where dominant low-frequency components overwhelm weaker mid- and high-frequency signals. To address this issue, we propose Spectral Amplitude Purification for Distribution Matching Distillation (SAP-DMD), a plug-and-play approach that adaptively modulates the amplitude spectrum of the DMD directional field. By suppressing the dominant tail of the amplitude spectrum, SAP-DMD reduces low-frequency dominance and promotes more effective recovery of fine structures and textures. Experiments on PixArt- \alpha , SD3, and SD3.5 demonstrate that SAP-DMD accelerates training convergence and improves generation quality under both 2-step and 4-step sampling.
[CV-93] WildHSR: Metric Feed-Forward 4D People-Scene Reconstruction from a 3D Foundation Model
链接: https://arxiv.org/abs/2609.29106
作者: Jerrin Bright,John Zelek
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computational Geometry (cs.CG)
备注:
Abstract:3D foundation models recover video cameras and geometry in one forward pass, but some of the strongest are up to scale. Joint people-scene reconstruction then requires two missing outputs: metric scale and persistent person identity. We ask whether one up-to-scale foundation representation can support both through lightweight adaptation. Exact metric labels are scarce, but unlabeled in-the-wild video is abundant. We use people in curated web video to initialise the solution: a posed metric body and 2D keypoints give an approximate, closed-form scale pseudo-label. These pseudo-labels pretrain a Scale Readout, which is then fine-tuned together with a lightweight adapter using exact metric supervision from standard real-video training splits. At inference the head predicts metric scale from foundation-model tokens, without the ruler or its teachers. For person identity, we probe the pretrained foundation model alone and find evidence that its intermediate query-key features encode person correspondence across frames. In most evaluated moving-person clips, a mid-layer token prefers that person over the vacated location and other people. A tiny projection reads this correspondence; together with metric pelvis motion and proposal confidence, it drives dustbin-aware Sinkhorn association of per-frame bodies. WildHSR combines both readouts to reconstruct metric cameras, scene and people from monocular video. Each window is predicted feed-forward; analytic association and Sim(3) composition connect windows. On EMDB-2, WildHSR is the first feed-forward method in the published comparison to beat the best optimization-based WA-MPJPE and RTE while leading feed-forward methods on all three world-frame metrics. On RICH, it leads feed-forward people-and-scene methods on WA-MPJPE and W-MPJPE. The complete pipeline runs at 10.1 fps on one GPU.
[CV-94] DAWN: Noise-Robust Quadruped Parkour via Depth-Denoising World Models IROS2026
链接: https://arxiv.org/abs/2609.29092
作者: Yohan Choi,Min-Jun Kim,Jin-Sung Kim,Yong-Jae Kim,Youn-Hee Han
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 8 pages, 6 figures. Accepted to IROS 2026
Abstract:Vision-based legged locomotion methods assume clean depth at training time and rely on hand-tuned post-processing filters at deployment. However, filter parameters are rarely disclosed, hindering reproducibility, and performance degrades substantially when depth noise is left unaddressed. Building noise robustness directly into the learning pipeline would eliminate this dependency. While such robustness has been explored for proprioceptive inputs, analogous approaches for depth perception remain largely absent in legged locomotion. We propose DAWN (Denoising and Alignment in World models for Noise-robustness), a noise-robust perception framework for legged locomotion, which builds noise robustness directly into a world model via two modifications: (1) feeding noisy depth to the encoder while keeping clean depth as the reconstruction target, forcing the model to implicitly denoise its input; and (2) applying contrastive learning to align the latent states of noisy and clean depth. Importantly, DAWN is not tied to a specific noise model, requiring no manual tuning to the noise distribution at deployment. Furthermore, it incurs no additional inference cost over existing world model-based methods. Without any manual filter calibration – relying solely on the learned noise-robust representation – DAWN achieves zero-shot quadruped parkour on a Unitree Go1: traversing stairs up to 18 cm, clearing gaps up to 70 cm, and mounting steps up to 45 cm from raw depth observations. Ablation studies show that denoising and contrastive alignment contribute at complementary levels – reconstruction and representation, respectively – and yield additive gains when combined. Videos and code are available at: this https URL
[CV-95] Seeing Is Not Measuring: Tool-Augmented Metric Spatial Reasoning for Vision-Language Models
链接: https://arxiv.org/abs/2609.29073
作者: Kai Glantz,Clemens Grange
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-Language Models (VLMs) describe scenes well but reason poorly about metric 3D structure such as absolute distances, physical sizes, or egocentric directions. We present a modular, predictor agnostic, tool-augmented framework that equips a small VLM (Qwen3.5-4B) with geometric tools: 3D object detection, metric depth estimation, and deterministic solvers for distance, size and bearing. Each object is detected in the camera frame of its own best view, and the tools use that frame’s pose to lift every detection into one shared world frame. Moving metric computation out of the model’s weights and into explicit solvers yields large gains on three of four ReVSI-Bench tasks: with a strong monocular detector (WildDet3D), absolute distance rises from 0.46 to 0.74 Mean Relative Accuracy (MRA), relative distance from 39.1% to 67.4%, and relative direction from a below-chance 25.9% to 73.4%. Because any detector can be swapped in behind the tool interface, comparing real detectors against ground-truth boxes separates perception error from reasoning error: orchestration costs only 0.03 MRA. Object size is bounded by the detector: the tools are near-exact on groundtruth boxes (0.97) yet the best real detector barely beats the no-tool baseline (0.61 vs. 0.58), because size reads straight off a box extent monocular detectors get wrong. Without a predefined recipe, the model already sequences the tools correctly on its own, matching a scripted pipeline on three of four tasks.
[CV-96] EIB-Net: Entropy-Guided Information Bottleneck for Generalizable AI-Generated Image Detection ICME2026
链接: https://arxiv.org/abs/2609.29064
作者: Zhida Zhang,Xinlei Ma,Jie Cao
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accept by ICME 2026
Abstract:The proliferation of photorealistic AI-generated images demands robust detection methods that generalize across diverse generative models. While existing approaches target manipulation-based forgeries with local artifacts, generation-based images (e.g., from diffusion models) lack such traces, posing a fundamental challenge. We observe that generative models prioritize global semantics at the expense of local texture fidelity, making low-texture regions key indicators of synthetic origin. To exploit this, we propose EIB-Net, an Entropy-guided Information Bottleneck Network. EIB-Net introduces a novel Image Entropy (IE) metric to automatically select the most informative (lowest-entropy) patch, then processes it with a Variational Information Bottleneck (VIB) to learn compact, generalizable features. Extensive experiments on DIFF, DiffusionForensics, and GenImage benchmarks demonstrate state-of-the-art performance: EIB-Net achieves 85.7% accuracy using only 2% of training data, outperforming full-image baselines by over 15%, and maintains robust cross-generator generalization (83.5% average accuracy on GenImage). Furthermore, our entropy-guided patch selection (EGPL) consistently enhances diverse backbones (CNNs and Transformers), proving its practical value for data-efficient detection.
[CV-97] Where Hallucinations Live: A Cross-Architecture Circuit in VQ-Tokenized Vision-Language Models EMNLP2026
链接: https://arxiv.org/abs/2609.29048
作者: Shamanthak Hegde,Xiangrui Liu,Maitreya Patel,Yezhou Yang
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: EMNLP 2026 | Project Page: this https URL
Abstract:Unified vision-language models (VLMs) that tokenize images through a vector-quantized (VQ) codebook routinely hallucinate objects on grounded yes/no benchmarks, yet existing decoding-time fixes treat this as generic miscalibration without an architectural account. Using activation patching across twenty-five models spanning eight LLM families, we identify an early-layer ( L_0 ) attention routing circuit shared across VQ-tokenized VLMs and propose a three-gate diagnostic that distinguishes the models carrying it from those that do not. The diagnostic isolates ten positive models (five natural unified-VQ VLMs across three LLM families and five induced variants) and rejects the remaining fifteen. A single-variable architectural swap (LLaVA-1.6 CLIP+MLP \rightarrow VQ+Linear) installs the circuit, while a matched-compute MLP control on identical data does not, isolating vector quantization as the source of the pathological signal; the routing pathway that carries it is one that the backbone already provides. Against tuned VCD and DoLA baselines, tuned DoLA wins on binary calibration, but \textbfonly L_0 ablation reduces object hallucination in open-ended generation (CHAIR _i reduces by 31,% relatively, whereas tuned DoLA and VCD leave it unchanged or worsen it). These results recast object hallucination in unified VQ VLMs as a property of architecture and pretraining, and yield a targeted intervention that mechanism-agnostic decoding cannot replicate.
[CV-98] Exploiting answer-invariant redundancies in satellite imagery for efficient VLM inference on edge
链接: https://arxiv.org/abs/2609.29029
作者: Ishani Janveja,Davis Zhang,Seoyul Oh,Deepak Vasisht
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Onboard vision-language models could enable satellites to answer queries directly, but exhaustive tiled inference over high-resolution imagery is slow and energy-intensive. We identify answer-invariant token redundancy (AITR): image tiles and vision tokens that can be removed without changing the final answer. We present Rift, a two-stage system that performs query-conditioned tile pruning followed by elastic prefill to reduce token budget. We evaluate it on LLaVA-1.5 7B running on Jetson AGX Orin. Compared with exhaustive tiled inference, Rift reduces energy by 78% and latency by 69%, while increasing accuracy from 45% to 73%.
[CV-99] RGBD20K: A Large-Scale Benchmark for RGB-D Semantic Segmentation
链接: https://arxiv.org/abs/2609.29028
作者: Shaohua Dong,Zexuan Meng,Haiyan Sun,Bing Fan,Cuicui Zhang,Dylan Joseph,Kewei Sha,Yunhe Feng,Heng Fan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In this paper, we propose RGBD20K, a novel dataset for facilitating the development of more robust and general RGB-D semantic segmentation by encompassing abundant categories and high-quality annotations. RGBD20K possesses several attractive properties: (1) Expanded Semantic Space. In particular, it covers 160 fine-grained categories, largely surpassing the category diversity of existing popular RGB-D benchmarks (e.g., NYUv2 with 40 classes and SUN RGB-D with 37 classes). With such enriched semantic coverage, we expect to promote the learning of more generalizable segmentation models. (2) Larger Scale. Compared with current benchmarks, RGBD20K offers 20,000 RGB-D image pairs, providing a substantially larger training resource that benefits the development of more powerful deep models. (3) High-Fidelity Annotation. We perform rigorous re-evaluation and correction of existing labels to resolve long-standing annotation noise, resulting in a clean and reliable ground-truth foundation. Furthermore, we propose a novel score-purified fusion (SPF) method, which achieves state-of-the-art performance across all evaluated benchmarks, demonstrating the effectiveness of our approach in leveraging high-quality multimodal information for RGB-D semantic segmentation. The dataset is here: this https URL.
[CV-100] FluidRain: Incompressible Rain Flow as an Attention Bias for Loop-in-Loop Video Deraining
链接: https://arxiv.org/abs/2609.29006
作者: Pu Wang,Yongcong Wang,Wenhao Li,Xiang Chen,Guangwei Gao,Jinshan Pan,Siyuan Yao,Shujun Fu,Zhuoran Zheng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 6 figures, 3 tables
Abstract:Existing video deraining methods typically exploit neighboring frames through either explicit alignment or implicit spatiotemporal aggregation. Explicit alignment relies on accurate motion estimation, which can become unreliable under dense rain, while implicit aggregation avoids alignment but lacks explicit guidance on the directional and temporally coherent structure of rain. This leaves a gap between reliable temporal aggregation and explicit modeling of rain motion. To address these limitations, we propose FluidRain, a lightweight video derainer that uses divergence-free rain flow to guide Loop-in-Loop attention across scales and neighboring frames. Motivated by fluid mechanics, we model rain motion as a divergence-free image-space flow and use it to organize multi-scale and temporal aggregation. Specifically, FluidRain first estimates a rain-flow field for each frame and projects it onto the divergence-free subspace. The resulting flow steers window attention along rain streaks, enabling neighboring frames to be aggregated without explicit alignment. Since rain-flow structure is preserved across scales and nearby frames, Loop-in-Loop reuses the same attention operator across both dimensions, resulting in a three-frame model with only 0.80M parameters. Experiments on four benchmarks show that FluidRain remains competitive with substantially larger restoration models. We further examine how temporal evidence scales with different input views. To evaluate whether the model remains reliable when rain motion changes across frames, we introduce RainSyn-Gust, which injects controlled changes in rain-streak direction into existing benchmarks. We also develop a physics-based no-reference metric that evaluates real-rain removal without requiring clean targets.
[CV-101] Only What Was Seen: Observation-Gram Compaction of View-Dependent Appearance in 3D Gaussian Splatting
链接: https://arxiv.org/abs/2609.28997
作者: Krzysztof Pietroszek
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 22 pages, 6 figures
Abstract:Most of the memory of a 3D Gaussian Splatting model holds spherical-harmonic colour coefficients, yet each Gaussian is seen only from the narrow cone of directions of the training cameras. We turn this into a distortion metric that other compressors can adopt: a per-Gaussian observation Gram matrix, accumulated from viewing directions and blending weights, is the exact first-order map from coefficient changes to squared image error and needs only the model and the camera poses. Under it, degree reduction becomes a closed-form projection that generalises truncation, degree allocation a Lagrangian rate-distortion problem, and vector quantisation the matrix-weighted Lloyd algorithm, of which Compressed3D’s quantiser is the scalar case. Swapped into Compressed3D with everything else unchanged, the metric raises PSNR by +0.49 dB before fine-tuning, with SSIM and LPIPS following, and at matched rate still gains +0.32 dB without a single training image. A training-free stack built on the metric alone is 15% smaller than the image-free GSICO at equal quality on Mip-NeRF 360.
[CV-102] Beneath the Scores: Rethinking Hallucination Evaluation for Video Understanding Models NEURIPS2026
链接: https://arxiv.org/abs/2609.28991
作者: Shuzhi Gong,Fengze Sun,Yuansan Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted in NeurIPS 2026 TAE workshop
Abstract:Video understanding is increasingly performed by multi-stage LLM agents that separate temporal grounding, visual observation, and reasoning. Yet these stages are typically evaluated on different benchmarks and distributions, making it difficult to determine where hallucinations originate. We first organize existing benchmarks around these stages and show that their scores provide inconsistent diagnostic signals: stronger stage-level performance does not reliably imply lower downstream hallucination, and even benchmarks targeting the same capability can disagree. We therefore introduce a causal stage-intervention protocol that overwrites individual stages while holding the downstream task fixed. Across 60,008 runs on three video-agent architectures, we find that grounding is the dominant source of downstream error, with roughly four times the causal impact of corrupting visual observations. Successful grounding depends primarily on locating the correct region rather than precise temporal overlap, explaining why standard mIoU metrics poorly predict downstream reliability. We further find that incorrect evidence is substantially more harmful than missing evidence. Finally, auditing existing benchmarks against these interventions reveals that their scores do not reliably predict causal cascade sensitivity and can fail under distribution shift. These results motivate intervention-based, stage-aware evaluation for trustworthy video agents. Comments: Accepted in NeurIPS 2026 TAE workshop Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.28991 [cs.CV] (or arXiv:2609.28991v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.28991 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-103] Passive LWIR Hyperspectral Ranging via Transmittance Extraction and Distance Alignment
链接: https://arxiv.org/abs/2609.28967
作者: Zhihe Chen,Chen Fan,Shuo Liu,Xiaolin Huang,Yunze He,Xiaofeng He,Lilian Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Passive long-wave infrared (LWIR) hyperspectral ranging enables distance estimation in low-light and nighttime scenes by exploiting atmospheric absorption features in thermal radiance received through the this http URL estimation of temperature, emissivity, and distance is computationally expensive. Reference-range joint inversion also uses a distance-invariant effective attenuation coefficient, which can bias range this http URL introduce transmittance extraction and distance alignment (TEDA), which decouples range estimation from temperature–emissivity inversion. In the first stage, a baseline estimator with a data-fidelity term invariant to the known absorption direction yields two closed-form smoothing branches for the slowly varying thermal continuum. An observation-derived gate combines the branches, and subtracting the blended baseline in the log domain recovers atmospheric transmittance. The second stage estimates range by matching the recovered transmittance to sensor-domain transmittance models recomputed for each candidate distance. Monte Carlo simulations show that TEDA effectively reduces the ranging bias caused by the distance-invariant attenuation coefficient approximation. In a measured scene, TEDA’s mean range estimates are closer to the LiDAR medians than those of reference-range joint inversion in both evaluated patches. TEDA processes a complete 256\times256 region of interest in 8.19~s versus 159.47~s for reference-range joint inversion, an approximately 20-fold speedup.
[CV-104] MoVISA: Multi-Token Reasoning for Video Object Segmentation
链接: https://arxiv.org/abs/2609.28956
作者: Ruining Zhao,Ho Kei Cheng,Alexander G Schwing
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent advances in video object segmentation with Multimodal Large Language Model (MLLM) reasoning have demonstrated the effectiveness of using a single textual token, such as SEG, to predict segmentation masks across images and videos. However, we observe that this single-token strategy lacks the granularity required to precisely localize multiple objects across time in video segmentation tasks. To address this limitation, we develop Multi-Token Reasoning for Video Object Segmentation, or MoVISA. MoVISA uses multiple segmentation tokens, such as SEG0 and SEG1, to represent an object across different frames. This design enables more fine-grained alignment between language prompts and spatio-temporal mask predictions, improving both performance and interpretability. On the challenging MeViS, DAVIS17, ReVOS, and Ref-Youtube-VOS benchmarks, our model achieves a 13.2 percent J and F improvement on MeViS and an 8.4 percent J and F improvement on ReVOS. Code and models will be released.
[CV-105] Exploiting Target Knowledge from MLLM s for Robust Few-Shot Segmentation
链接: https://arxiv.org/abs/2609.28949
作者: Yijun Hu,Heng Fan,Libo Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Few-shot segmentation (FSS) aims to segment unseen object categories with a few (e.g., one or five) labeled examples, enabling efficient adaptation to novel classes. Conventional models typically rely on appearance-based visual matching between support and query images for segmentation. While straightforward, these methods often struggle to handle significant appearance discrepancies and occlusions in the query image due to insufficient target knowledge. To mitigate this, we introduce a novel framework that mines target knowledge using the strong reasoning capacity of Multimodal Large Language Models (MLLMs) and employs it to enhance FSS. Specifically, building on SAM 2, our method, named MK-FSS, exploits two forms of complementary knowledge derived from a query image by an MLLM for FSS, including spatial knowledge, which provides a spatial prior indicating the potential target location, and semantic knowledge, which describes the target using text. The spatial knowledge is first encoded into a memory representation, and then resulting memory is integrated with the support-guided memory feature from query image through a carefully designed dual-memory debate-fusion (DMDF) module, yielding a more robust target memory feature. In parallel, the semantic knowledge is encoded into the textual feature, which is fused with multi-scale query features via a progressive cross-modal prompt generator (PCPG), producing a target-aware multimodal prompt for segmentation. Working together, the dual-memory feature and the multimodal prompt provide a comprehensive representation of the target, enabling more robust segmentation. In our extensive experiments, MK-FSS shows promising results and largely surpasses existing methods. Code will be released.
[CV-106] HelloWorld: Towards Practical Applications of Generative Driving World Models
链接: https://arxiv.org/abs/2609.28931
作者: Fan Lu,Hanshi Wang,Zijing Wang,Quan Feng,Zhi Wang,Shijie Chen,Xianming Zeng,Yujian Zhang,Jiazhe Wang,Xin Zha,Kai Wang,Zhijie Zhao,Lin Zhu,Tianyi Yang,Yucheng Xu,Tao Ji,Haodong Zhang,Zhipeng Zhang,Peixi Peng,Guang Chen,Xingliang Liu,Lei Yang,Jianyun Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: website: this https URL
Abstract:Driving world models provide a promising route toward scalable counterfactual data generation and interactive simulation beyond recorded driving logs. Realizing this potential requires a system that can generalize across diverse scenes, respond faithfully to prescribed controls, generate coherent multi-sensor observations, and operate efficiently under repeated inference. We present \textbfHelloWorld, a 2B driving world model system designed around these requirements. HelloWorld progressively specializes broad visual and motion priors from heterogeneous video data into controllable driving generation using ego pose, HD maps, and 3D boxes. A block-causal generation interface, together with adaptation to self-generated context, aligns the model with sequential simulation. The system further supports synchronized seven-camera RGB generation and conditional LiDAR synthesis, and is distilled toward few-step inference for efficient deployment. Experiments evaluate visual quality, control fidelity, cross-view consistency, robustness under repeated generation, inference efficiency, and LiDAR synthesis. Together, HelloWorld provides a unified framework for scalable driving data generation and interactive simulation.
[CV-107] PlenoCI: Plenoptic CharacterIstics for View Dependence Aware Change Classification
链接: https://arxiv.org/abs/2609.28930
作者: Jason Lai,Chamuditha Jayanga Galappaththige,Niko Suenderhauf,Dimity Miller,Donald G. Dansereau
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 9 figures
Abstract:Radiance field representations such as 3D Gaussian Splatting (3DGS) natively encode complex visual phenomena such as occlusions and view dependence, but they are inherently underconstrained. Independently optimized reconstructions converge to different primitive configurations, even in unchanged regions. We introduce Plenoptic CharacterIstics (PlenoCI), a novel feature built from the plenoptic field these representations approximate. PlenoCI directly captures rich visual behaviors while ignoring Lambertian textures. By deriving closed-form analytic plenoptic derivatives from a 3DGS representation, we efficiently detect these 5D structures. Our approach is robust to underconstrained representations by construction, reporting two orders of magnitude fewer false positives between independent reconstructions of unchanged scenes than concurrent work. We demonstrate PlenoCI’s utility on change classification. First, we detect changes with an instance-aware 3DGS pipeline, achieving state-of-the-art results on CL-Splats with a 25.7% mIoU gain over the strongest competitor, while remaining competitive on the more challenging PASLCD benchmark. Leveraging PlenoCI, we classify changes as geometric or appearance-based with a balanced accuracy of 0.735, comparable to the best performing baseline. We believe plenoptic derivatives and PlenoCI open new directions for view dependence aware understanding in visually complex environments. Code and data are available at this https URL.
[CV-108] ViRDM: Taming Representation Distribution Matching for Few-Step Causal Video Generation
链接: https://arxiv.org/abs/2609.28923
作者: Zichong Meng,Chongjian Ge,Chun-Hao P. Huang,Yang Zhou,Huaizu Jiang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Tech Report
Abstract:Few-step autoregressive (AR) video diffusion enables low-latency streaming generation, but existing post-training methods predominantly rely on Distribution Matching Distillation (DMD), requiring both a large pretrained teacher and an online critic to estimate distributional discrepancies through diffusion scores. In this work, we ask whether this resource-intensive teacher–critic stack can be eliminated by post-training only the generator against a precomputed target distribution. Drawing inspiration from representation distribution matching (RDM) for one-step image generation, we systematically study its transfer to few-step causal video generation and identify three key barriers: a memory-intractable gradient path, a distinct video optimization regime, and representation distributions that underconstrain temporal dynamics. We introduce ViRDM, a teacher- and critic-free video post-training recipe that addresses these barriers sequentially. By coupling RDM with stochastically truncated clean-exit supervision, a lightweight VAE decoder, and staged vector–Jacobian products, ViRDM makes representation distribution matching memory-feasible for multi-step causal video rollouts. We further establish effective generated-population and initialization regimes for video RDM, and introduce lightweight dynamics regularization to compensate for the underconstrained temporal dynamics. ViRDM turns three-network distillation into generator-only post-training, reducing GPU memory use and training time while improving video quality. With only 20 generator updates, the recipe reaches 84.87 on the official VBench evaluation, outperforming the previous best few-step causal baseline by 0.36, while requiring 16 A100 GPU-hours. We additionally report exploratory results demonstrating the potential of the same recipe for lower causal sampling budget and for one-, two-, and four-step bidirectional generation.
[CV-109] Direction-Scale Decomposition in Action Representation: Rethinking What to Tokenize for Vision-Language-Action Models
链接: https://arxiv.org/abs/2609.28865
作者: Yufei Duan,Hang Yin,Alberta Longhini,Chao Tang,Danica Kragic
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Action representation plays a central role in discrete-token vision-language-action (VLA) learning but remains underexamined. Under conventional pose-increment representations, action tokens are sensitive to execution speed and dataset-specific normalization, potentially obscuring geometric structure shared across demonstrations and datasets. We introduce Direction-Scale Decomposition (DSD), an action representation that decomposes translation and rotation increments into direction and scale components before tokenization. DSD isolates motion direction while retaining magnitudes in separate scale channels. We evaluate DSD with uniform binning (BIN) and BEAST, a B-spline-based tokenizer, in simulation and real-world manipulation under both single-dataset and mixed-dataset training. On LIBERO, DSD improves average success rates with both tokenizers. On SimplerEnv, DSD-BIN outperforms BIN by 10.3 percentage points in overall success rate under mixed-dataset training. Real-robot experiments further show gains both with and without robotics pretraining. These results support DSD as an effective action representation for discrete-token VLA models and suggest its potential to mitigate performance degradation when training on large and diverse dataset mixtures. Our project page with additional resources is available at this https URL
[CV-110] Multimodal Routing and Region Refinement for Language-Guided Medical Image Segmentation MICCAI2026
链接: https://arxiv.org/abs/2609.28860
作者: Md Maklachur Rahman,Md Hasan Al Banna,Saraf Anjum,Assame Arnob,Tracy Hammond
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted at MICCAI 2026 (TIA). Final version to appear in the proceedings
Abstract:Textual descriptions can reduce ambiguity in medical image segmentation by specifying the finding and location to be delineated. Existing text-guided methods mainly improve where image and language features interact but generally retain a single learned update pathway across all image-text pairs. We propose MRSeg, a parameter-efficient framework that uses each image-text pair to route the adaptation of visual and textual features before dense prediction. Frozen ConvNeXt-Tiny and PubMedBERT encoders provide multiscale visual features and clinical text tokens. A joint router uses the deepest visual feature and pooled text to predict a sparse mixture over low-rank adapter bases. The resulting route is shared across separate adapter banks for two visual scales and text, coordinating their adaptation while keeping the feature-specific parameters separate. Region Bridge uses text-derived queries to aggregate dense visual tokens into latent regions, refines these regions through self-attention and text cross-attention, and redistributes the refined information back to the feature maps. Finally, a multiscale decoder combines refined semantic features with shallow image evidence. On QaTa-COV19 and MosMedData+, MRSeg achieves 90.90/83.32 and 81.53/68.82 Dice/mIoU, respectively, with 7.11M trainable parameters and 7.60 GFLOPs. Code: this https URL.
[CV-111] MEVL-STP: Multi-Encoder and Vision Language Model for Arbitrarily Shaped Scene Text Spotting
链接: https://arxiv.org/abs/2609.28857
作者: Aman Anand,Partha Pratim Roy,Shivakumara Palaiahnakote
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 7 figures, 5 tables. Code: this https URL
Abstract:Scene text spotting remains challenging for arbitrarily shaped text instances such as curved signs and dense multi-oriented characters in natural images, where tightly coupled architectures propagate localization errors directly into recognition failures. We present a two-stage pipeline that combines multi-encoder segmentation with vision-language model recognition to address this problem. In the detection stage, six frozen vision encoders (CLIP, DINOv2, SigLIP, EVA-CLIP, SAM, and ConvNeXt) extract complementary features spanning semantic, spatial, and texture spectra, which are fused through a trainable hierarchical Feature Pyramid Network with channel attention and decoded via a deep-supervision Progressive Scale Expansion network to generate precise instance-level text masks. By keeping the encoders frozen, their independently learned feature spaces remain orthogonal during fusion, preventing the feature homogenization that degrades boundary precision in single-backbone detectors. The detection stage produces tight polygon masks that conform to the actual shape of curved and arbitrarily oriented text, rather than axis-aligned rectangles that inevitably include background content. In the recognition stage, these polygon-masked crops isolate the target text from surrounding clutter, allowing a Qwen3-VL-8B-Instruct model, fine-tuned via Low-Rank Adaptation on polygon-cropped scene text, to focus purely on reading the text without interference from neighbouring words or background noise. Without any synthetic pretraining data, our method achieves 91.99% detection F-measure and 85.86% end-to-end H-mean on CTW1500, setting a new state of the art and achieving strong performance on Total-Text and ICDAR 2015 without any synthetic training data. Code is available at this https URL
[CV-112] Looks the Same Answers Differently: Flip-Direction Steering for Robust Vision-Language Reasoning
链接: https://arxiv.org/abs/2609.28851
作者: Yeonsung Jung,Joonhyun Jeong,Hoang Pham,Joowon Kim,Yoonsik Park,Viet Dac Lai,Eunho Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 27 pages
Abstract:Vision-language models (VLMs) achieve strong visual reasoning performance, yet subtle changes from routine image capture and processing can alter their reasoning trajectories even when images appear nearly identical. In long-horizon generation, the resulting activation shifts may accumulate across decoding steps, progressively altering reasoning tokens and ultimately changing the final answer, a phenomenon referred to as answer flips. To address this instability, we propose FlipDir (Flip-Direction Steering), a training-free inference-time method that estimates a low-rank flip-inducing activation subspace from contrastive pairs of original and answer-flipping inputs and selectively steers hidden states during decoding. A margin-based gate limits subspace attenuation to uncertain decoding steps, recovering original predictions while preserving stable ones. To evaluate robustness beyond accuracy or consistency on fixed test sets, we introduce VisFlip, a benchmark framework that constructs evaluation groups for a target model and visual variation setting to separately assess recovery of original predictions and preservation of stable ones. VisFlip spans nine dataset-variation combinations across scientific reasoning, robot-scene understanding, and medical VQA, covering subtle visual variations common in each domain. Experiments across 18 settings demonstrate that FlipDir consistently outperforms existing methods on the combined recovery and preservation metric. We will make our code publicly available.
[CV-113] M2PFN: End-to-End Disentangled Alignment for Generalizable Multimodal In-Context Learning in Alzheimers Disease
链接: https://arxiv.org/abs/2609.28836
作者: Lujia Zhong,Shuo Huang,Jianwei Zhang,Xinyu Nie,Yonggang Shi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Under review
Abstract:While various multimodal methods combining imaging and tabular data for Alzheimer’s disease (AD) diagnosis were proposed, they are often limited in generalization across cohorts. In-context learning (ICL) has demonstrated excellent generalization performances and high flexibility in foundational tabular models such as TabPFN. To extend TabPFN’s ICL to multimodal AD analysis, the main obstacle is that TabPFN is meta-trained on synthetic tabular priors that do not naturally match the statistical structure of image-derived features. We propose M ^2 PFN, an end-to-end framework that turns this tabular foundation model into a multimodal AD predictor. M ^2 PFN (i) performs differentiable inference through TabPFN’s transformer, back-propagating task gradients into 3D-MRI and tabular encoders; (ii) aligns the two modalities into a shared subspace, via disentanglement and a contrastive objective, matched to the ICL engine’s prior; and (iii) folds in a frozen tabular-only prediction through a learnable gated shortcut. Because the ICL engine stays frozen, its in-context mechanism is preserved for test-time generalization, while end-to-end training shapes the encoders into features it can exploit. On ADNI ( n=2240 , three-class CN/MCI/AD), M ^2 PFN attains 65.55% macro-F1 and 82.21% macro-AUC, surpassing a comprehensive set of unimodal and multimodal baselines. By swapping only the head for a TabPFN regressor, the same architecture regresses baseline MMSE on a 1250 -subject sub-cohort to test MAE 1.743 , outperforming every multimodal baseline. On two external cohorts (OASIS-3 and SCAN) with no retraining, M ^2 PFN achieves the best AUC and the lowest MMSE MAE across all baselines, and transfers even when the cognitive instrument changes.
[CV-114] CinematicVQA: Benchmarking Film-Grammar Reasoning in Large Vision-Language Models
链接: https://arxiv.org/abs/2609.28813
作者: Shuo Xing,Pooja Verlani,Balu Adsumilli,Zhengzhong Tu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages
Abstract:Cinematography, the craft of visual storytelling through framing, lighting, and camera operation, fundamentally shapes how audiences perceive and emotionally engage with video content. While Large Vision Language Models (LVLMs) have made remarkable progress in video question answering, existing benchmarks primarily focus on identifying low-level techniques rather than understanding their storytelling impact. To address this, we introduce CinematicVQA, the first-of-its-kind benchmark for cinematic video understanding that goes beyond technique recognition to evaluate film-grammar reasoning, utilizing our introduced Cinematic Scene Graph (CSG), a structured representation that links filming techniques to their perceptual effects and narrative functions. Through comprehensive evaluation of state-of-the-art LVLMs, we reveal a striking semantic gap: models consistently perform higher on describing visual presentations than on identifying the underlying techniques. Surprisingly, Chain-of-Thought prompting fails to provide consistent gains and degrades performance for most models, suggesting that current LVLMs lack sufficient cinematic domain knowledge to benefit from step-by-step reasoning. Fine-tuning on \textscCinematicVQA-train yields consistent improvements, particularly for narrative function and multi-hop reasoning. Overall, \textscCinematicVQA serves both as a rigorous benchmark for cinematic evaluation in LVLMs and as a practical dataset for training more film-aware video models.
[CV-115] DeltaWAM: Delta World Action Models for Bimanual Manipulation
链接: https://arxiv.org/abs/2609.28811
作者: Han Yan,Zishang Xiang,Haokai Jiang,Zeyu Zhang,Qilin Wang,Weiyu Guo,Yandong Guo,Boxin Shi,Hao Tang
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:World-action models (WAMs) transfer visual and motion priors from pretrained video generators to robot control by jointly modeling visual dynamics and actions. Existing WAMs, however, predict dense future frames during training, repeatedly modeling largely unchanged content and coupling action-conditioned dynamics to nuisance appearance variations. At inference, processing each complete observation with the heavy video expert bottlenecks few-step action generation. Accordingly, we propose DeltaWAM, which jointly predicts visual deltas and actions using dense-anchor, sparse-delta, and action streams, with three architectures that differ in representation and computation sharing. We further develop Streaming Delta Memory (SDM), which updates cached anchor context with compact observed deltas, reducing heavy video-expert processing. On RoboTwin, DeltaWAM with SDM improves average success over Fast-WAM from 81.3% to 85.4% in the clean setting and from 75.8% to 83.9% under visual randomization. The three architectures reduce training FLOPs by 17.78-23.77%, while SDM reduces one-step inference latency and FLOPs by 36.57% and 31.55%, respectively; real-world evaluations further show the highest overall success rate and normalized progress among the evaluated policies. Code: this https URL. Website: this https URL.
[CV-116] DrGait: Biomechanically Grounded Visual Reasoning for Interpretable Clinical Gait Analysis
链接: https://arxiv.org/abs/2609.28796
作者: Xiangyu Yin,Shiqi Wang,Abrar Alamri,Yasir Aljohani,Weichen Liu,Goeran Fiedler,Wei Gao
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 76 pages, 6 figures
Abstract:Current automated gait analysis for clinical applications relies on uninterpretable black-box classifiers. Although Vision-Language Models (VLMs) offer strong reasoning capabilities, applying them directly to gait videos often leads to hallucinations, because they struggle to measure subtle geometric deviations from raw visual contexts. To address this, we introduce DrGait, a training-free agentic framework that shifts the VLM’s role from a direct visual reasoner to a clinical planner. DrGait decouples semantic reasoning from geometric perception through a structured Triage-Verification-Synthesis (TVS) workflow. Given an input video and a set of basic spatiotemporal metrics, the DrGait agent first performs a heuristic triage to propose diagnostic hypotheses, which are then verified by autonomously calling deterministic biomechanical tools that operate on reconstructed 3D mesh trajectories, segmented 2D pose tracks, and event-centered video evidence. Finally, a closed-loop mechanism recursively updates the agent’s reasoning context based on the feedback. By anchoring VLM’s reasoning in verifiable geometric and temporal measurements, DrGait reduces hallucinations, achieving competitive diagnostic accuracy while generating transparent and audit-ready clinical reports.
[CV-117] GeoNLI - A Natural Language Interpreter for Satellite Imagery
链接: https://arxiv.org/abs/2609.28741
作者: Ashutosh Gandhe,Anupam Rawat,Geet Sethi,Kabir Nasiruddin,Madhav Kotecha,Panav Shah,Rakshit Sawarn,Soumitra Nayak
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multi-modal multitasking models have shown strong performance on remote sensing datasets. However, because these models are trained on heterogeneous data and vary across tasks, designing a unified model that performs well in captioning, visual question answering (VQA), and visual grounding remains challenging. In this work, we evaluate several models on the VRS Bench and NWPU-VHR-10 datasets. The EarthMind model demonstrates strong results in both captioning and VQA. For grounding, we propose multiple pipelines - RemoteSAM-SAM-v1, RemoteSAM-SAM-v2, and DiffuSAM - and ultimately adopt a majority-voting ensemble across EarthMind, RemoteSAM, SAM3, Falcon, RemoteSAM-SAM3-v1, RemoteSAM-SAM3-v2, and DiffuSAM predictions. Our unified, modular pipeline integrates advanced SAM variants with multimodal LLMs to jointly perform captioning, VQA, and grounding. It achieves 82% accuracy on captioning and 83.32% on VQA, with 90.94%, 52.04%, and 92.06% for binary, numeric, and semantic question types respectively. For grounding, it attains 64.94% accuracy. By combining diverse VLMs with our custom RemoteSAM-SAM3 models through ensemble majority voting, the system delivers more accurate and consistent remote-sensing understanding than task-specific approaches. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2609.28741 [cs.CV] (or arXiv:2609.28741v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.28741 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-118] M-plicits: Neural Implicit Surfaces via Nested Multiscale Residuals
链接: https://arxiv.org/abs/2609.28684
作者: Vinícius da Silva,Isabelle Melo,Matheus Bessa,Guilherme Schardong,Luiz Schirmer,André Araújo,Nuno Gonçalves,Hélio Lopes,Alberto Raposo,Luiz Velho,Tiago Novello
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Machine Learning (cs.LG)
备注:
Abstract:Encoding input coordinates with sinusoidal functions into multi-layer perceptrons (MLPs) has proven effective for implicit neural representations (INRs) of surfaces defined as zero-level sets. However, existing methods often struggle to balance training efficiency, rendering speed, and noise robustness: single-MLP approaches are expensive at inference, grid-based representations are fast but can limit surface smoothness and overfit input noise, and previous multiscale approaches frequently capture noise and produce artifacts due to hard spectral truncation. To address these limitations, we propose M-plicits, a multiscale framework that models surfaces as a residual sum of MLPs trained via a sequence of nested neighborhoods. Unlike existing residual approaches that rely on standard domain-wide sampling and require costly mesh extraction for visualization, our method strictly localizes supervision to narrow bands around the previous zero-level sets. This nested design naturally provides robustness against noisy input data: the coarse network acts as a low-pass filter that establishes a clean geometric prior, while subsequent residuals progressively refine the geometry without fitting to high-frequency artifacts. We further introduce a multiscale sphere-tracing algorithm and a GEMM-based analytical normal computation that bypasses auto-differentiation entirely, yielding high-fidelity real-time rendering. On Stanford and Thingi32, M-plicits achieves the best mean Chamfer distance in the coarse configuration and the best median Chamfer distance and IoU in the fine configuration, with substantially better noise robustness than iNGP, BACON, and IDF, while using an order of magnitude fewer parameters than grid-based baselines. Code, models, and data will be released at this https URL.
[CV-119] raining Object Permanence in World Models
链接: https://arxiv.org/abs/2609.28654
作者: Haotian Zhang,Fengyuan Yu,Dezhi Luo,Haoran Sun,Zehong Zhao,Qingying Gao,Yihan Li,Siyuan An,Huayi Qin,Yilan Zhang,Zhengze Jiang,Pinyuan Feng,Renrui Zhang,Ziyu Guo,Letian Wang,Mengyue Yang,Kangfu Mei,Maijunxian Wang,Ran Ji,Vikash Kumar,Freda Shi,Chandra Sripada,Vincent C. Muller,Philip Torr,Alan Yuille,Nikolaus Kriegeskorte,Felix Juefei-Xu,Lvmin Zhang,Jieneng Chen,Yilun Du,Hokin Deng
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 26 pages, 9 figures, 5 tables. Project page: this https URL
Abstract:Object permanence and solidity are hallmarks of human cognitive priors. Recent studies show that video generation models, a paradigmatic class of current world models, have begun to show emerged reasoning abilities, making them ideal candidates for building human-like physical intelligence. Do video models have emerged object permanence in them? If not, could we train them with a core-cognition inspired dataset? We introduce WROP (World Reasoning with Object Permanence), a data infrastructure of 150 hand-designed cognitive science inspired tasks, divided into six cognitive categories. We build Blender generators that randomize speed, lighting, camera angle, and other nuisance parameters while preserving each task’s cognitive structure, yielding 10,000+ samples per task. We release a 1.5M-sample training corpus and a 300-question exam. On this exam we evaluate 14 video models: 3 reference-to-video, 7 edit, and 4 continuation, among which PWM-WROP, our 16B world model. In a blind pairwise Elo study, PWM-WROP ranks first among continuation models and third overall, behind only a statistical tie between two reference-to-video models. We release the data, exam, model answers, scores, weights, and PWM, our native-PyTorch training stack on AWS Trainium2.
[CV-120] PePESeg3D: Perception Prior Enhances Multi-Scale Segmentation for 3D Gaussian Splatting BMVC2026
链接: https://arxiv.org/abs/2609.28645
作者: Sungjae Choi,Seunghee Koh,Junmo Kim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to BMVC 2026
Abstract:Recent advancements in 3D Gaussian Splatting (3DGS) have extended its capabilities to multi-scale segmentation. Existing methods reconstruct a scene with Gaussian primitives and learn multi-scale segmentation features separately, which leaves the geometry unaware of semantic structure and the feature learning dependent on incomplete mask supervision. To address these limitations, we present PePESeg3D, a novel framework that injects perception priors into a multi-scale 3D Gaussian segmentation pipeline. To fully exploit perception priors, we integrate them not only into contrastive feature learning but also into the upstream geometry reconstruction. Specifically, PePE Reconstruction incorporates monocular depth and mask constraints to ensure semantically coherent object structures. Building on this aligned geometry, PePE Contrastive Learning leverages dense depth-color cues and view-consistent centroid supervision to compensate for the incompleteness of multi-scale masks obtained from a 2D foundation model. Extensive experiments on the SPIn-NeRF, LERF-Mask, and NVOS benchmarks demonstrate that PePESeg3D achieves state-of-the-art performance in both multi-scale segmentation and scene reconstruction, highlighting the importance of integrating perception priors into both geometry optimization and feature learning for accurate multi-scale 3D segmentation. Our code is available at this https URL.
[CV-121] UltraBench 2: Towards Robust Evaluation of Vision Foundation Models on Ultrasound
链接: https://arxiv.org/abs/2609.28610
作者: Ashwath Radhachandran,Adam Tupper,Christian Gagné,William Speier
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Benchmarking is an increasingly critical part of research in machine learning and the domains where it is applied, including healthcare. Yet, despite the steady development of new ultrasound foundation models in recent years, the development of well-designed benchmarks to evaluate them has lagged behind. This deficiency has led to fragmented and inconsistent evaluations of competing models, making it difficult to measure progress. To address this issue, we introduce UltraBench 2, a comprehensive benchmark with wide anatomical and task coverage, and a focus on standardization, reproducibility, and ease-of-use. Using this benchmark, we compare existing vision foundation models for ultrasound image analysis. Our analyses demonstrate that ultrasound-specific pretraining still leads on classification, but that state-of-the-art general-purpose models have drawn level on segmentation.
[CV-122] AM-Chain: Multi-Scale Thyroid Cytology Classification via Absorbing Markov Chains and Shannon Entropy Uncertainty Quantification for False-Negative Suppression and Domain-Shift Adaptation
链接: https://arxiv.org/abs/2609.28590
作者: Hai Pham Ngoc
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Background Problem: Thyroid Fine-Needle Aspiration Biopsy (FNAB) cytology based on the Bethesda System plays a pivotal role in early thyroid cancer detection; however, deep learning approaches face substantial challenges regarding high false-negative rates and overconfidence under clinical domain shift. Methods: In this study, we propose TAM-Chain, a multi-scale (10x, 20x, 40x) thyroid cytology classification framework leveraging Absorbing Markov Chain theory combined with Shannon Entropy-based Uncertainty Quantification. The framework dynamically models multi-magnification feature extraction as an absorbing stochastic process, enabling optimal stopping criteria and a human-in-the-loop referral mechanism to strictly suppress critical diagnostic errors. Results: Extensive evaluation on an internal test set (N = 235) demonstrates a Macro F1 score of 0.9741 with an absolute False-Negative Rate (FNR) of 0.00%. On an independent external validation set (N = 1015) presenting severe domain shift, TAM-Chain maintains superior stability and classification performance (Macro F1 = 0.7026) by adaptively adjusting the expected stopping step and triggering specialist referrals, significantly outperforming single-magnification baselines. Conclusion: The TAM-Chain framework proves to be a highly effective, safe, and adaptable solution for digital pathology workflows, successfully harmonizing automated diagnostic efficiency with stringent biological safety. Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2609.28590 [cs.LG] (or arXiv:2609.28590v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.28590 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Hai Pham Ngoc [view email] [v1] Wed, 23 Sep 2026 14:58:01 UTC (749 KB)
[CV-123] oken Clustering and Semantic Sequence Mamba for Hyperspectral Image Classification
链接: https://arxiv.org/abs/2609.28580
作者: Yimin Zhu,Mahmood Elahi,Lincoln Linlin Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Although hyperspectral images (HSIs) provide rich spectral-spatial information, accurate pixel-level classification remains challenging because of spectral-spatial heterogeneity and complex spatial structures. Existing vision state space models (Mamba) typically construct sequences according to predefined spatial neighborhoods, without explicitly accounting for semantic similarity or spatial non-stationarity. To address this limitation, we propose Token Clustering and Semantic Sequence Mamba (STMamba), which organizes sparse tokens into semantically coherent sequences for hyperspectral image classification with the following features. First, at the macro level, a hierarchical encoder decoder progressively selects semantic tokens with the Token Clustering Module (TCM) and restores dense features using a parameter-free Cross-scale Neighborhood Attention (CNA) Upsampler. Second, at the micro level, TCM first identifies representative cluster centers through density-aware clustering and estimates soft memberships based on feature similarity. A quadtree-based dynamic selection strategy then retains sparse and spatially distributed tokens from each semantic cluster, forming coherent semantic-token sequences while reducing redundant pixel-wise representations. Third, parallel Spatial and Spectral Semantic-wise Sequencing Mamba (SWSM) modules capture complementary long-range spatial and spectral dependencies within homogeneous semantic token sequences while suppressing irrelevant interactions across heterogeneous regions. Experimental results on three large-scale benchmark datasets demonstrate that STMamba outperforms the SOTA methods with respect to quantitative and qualitative results.
[CV-124] CARE: Condition-Aware Representation Regularization for Diffusion Models
链接: https://arxiv.org/abs/2609.28561
作者: Fengjia Guo,Zhuoyi Yang,Jie Tang
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent advances in diffusion models highlight the importance of representation regularization for improving sample quality and training efficiency. However, commonly used regularization methods often overlook the built-in conditions (such as labels or texts) which directly determine the generation target. In this work, we demonstrate how conditioning signals affect the feature distribution and introduce the CARE (Condition-Aware REpresentation regularization). CARE is a lightweight plug-and-play regularization framework that dynamically modulates feature distribution based on condition similarity. CARE leverages built-in conditioning signals to judiciously guide the representation space, promoting tighter feature clusters for similar conditions without relying on explicit alignment losses or external supervision. Empirically, CARE consistently improves both visual fidelity and convergence stability across both class-to-image and text-to-image tasks. On ImageNet, CARE achieves a 19.08% reduction in FID in 400k training steps, leading to a 3.5 \times speed-up. When applied to text-to-image generation, CARE lowers FID by 16.61% in 200k iterations and improves semantic alignment between generated samples and text prompts. Moreover, CARE can be seamlessly integrated with existing regularization methods, yielding additional performance gains.
[CV-125] Pistis Technical Report
链接: https://arxiv.org/abs/2609.28554
作者: Heyun Chen,Xiaohan Lan,Jiaxi Li,Zhilin Lu,Qi She,Weiwen Xu,Fei Yu,Yujie Zhong,Jinghuan Chen,Zijian Feng,Siyu Jiao,Yiheng Lin,Xinhao Wang,Sihan Yang,Jieyu You,Changbin Zhang,Hengyu Zhang,Xudong Zhang,Yunqing Zhao,Shuai Zheng
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We introduce the Pistis model family, comprising 27B- and 9B-parameter multimodal large language models built on Qwen3.6 and Qwen3.5, respectively, and developed through a general and scalable post-training framework. The framework first establishes a strong foundation through large-scale multimodal supervised fine-tuning (SFT). Building on this SFT foundation, we propose Interleaved Distillation and Reinforcement Learning (IDRL), a novel post-training paradigm that tightly integrates on-policy distillation and reinforcement learning within a single training loop. By alternating between the two objectives, rather than optimizing either in isolation or combining them in a static joint loss, IDRL enables more effective knowledge transfer, greater optimization stability, and more precise credit assignment for long-horizon agentic trajectories, leading to stronger performance while mitigating common capability trade-offs. At both model scales, the framework produces two specialized variants: Pistis-Thinking, designed to strengthen deep multimodal reasoning, and Pistis-Agentic, which additionally incorporates agentic trajectory data to support long-horizon planning, iterative reasoning, and tool use. Pistis-Agentic is particularly strong in multimodal search. Both scales outperform their corresponding base models. Beyond model-parameter optimization, we further introduce Pistis-Auto-Harnessing (PAH), a system-level method that automatically improves the agent’s inference harness through iterative optimization. Experiments demonstrate that PAH enhances the model performance without updating the model parameters or increasing the interaction budget.
[CV-126] unicodex1F493Heartian: Physiology-Aware Relightable Gaussian Head Avatar SIGGRAPH
链接: https://arxiv.org/abs/2609.28539
作者: Xiaoyue Fan,Jose Echevarria,Akshay Paruchuri,Kaan Akşit
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: 4 pages of manuscript and 2 pages of supplementary material; SIGGRAPH Asia 2026 Technical Communications
Abstract:Gaussian head avatars typically model intrinsic facial appearance as temporally static, omitting subtle cardiac-induced skin-color variation. We propose \unicodex1F493 Heartian, a physiology-aware modulation framework that learns cardiac-cycle-dependent per-frame albedo modulation of facial skin-region Gaussians within a relightable head avatar to encode remote photoplethysmography (rPPG) signals. Using synchronized contact PPG supervision, \unicodex1F493 Heartian models the prescribed cardiac waveform as the sum of two Gaussian functions and learns per-frame spatial residuals via a lightweight MLP. Across 152 stationary recordings from UBFC-rPPG, PURE, and MMPD, attribute-space recovery of the supplied signal achieves a pooled recording-level heart-rate MAE of 0.29 bpm and MAPE of 0.38%. The signals remain detectable after rendering by benchmark rPPG methods, with the best tested configuration - a motion-augmented TS-CAN decoder pretrained on UBFC-rPPG - recovering heart rate from the rendered MMPD avatars at 0.97 bpm MAE and 1.21% MAPE. Meanwhile, \unicodex1F493 Heartian maintains reconstruction quality comparable to the baseline, with negligible average PSNR degradation of 0.005 dB. Overall, our work embeds recoverable rPPG signals as controllable material attributes to subject-specific Gaussian head avatars while retaining the reconstruction quality.
[CV-127] A Vision-Language Framework for Measuring Social Life on Sidewalks
链接: https://arxiv.org/abs/2609.28476
作者: Liu Liu,Andres Sevtsuk
类目: Computers and Society (cs.CY); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:While a number of methods exist for counting pedestrians in street-view imagery, these mostly ignore the social dimensions of pedestrian activity. A street traversed by a high volume of pedestrians has the same headcount as a street where people linger, sit, and socialize. This paper presents a vision-language framework for extracting social indicators from street-level imagery. Panoramic street-level imagery is reprojected to sidewalk-facing sideviews with preserved timestamps. A vision-language model (VLM)-based activity detection system codes each person across ten independent observable dimensions, resolving a systematic failure mode in which models prompted with high-level social categories conflate observable states with contextual inferences. The resulting social indicator system produces a Social Dwelling Index (SDI) that jointly considers pedestrian grouping and dwelling, provides activity labels documenting behavioral diversity, and issues binary flags for the presence of accessibility-sensitive populations. We apply the framework to 102,514 sideviews in New York City, revealing that pedestrian volume and SDI are only weakly associated (r = 0.168): streets with the highest foot traffic are not where social activity is most intense. The framework provides a scalable method for measuring not only how many people are on city sidewalks, but also their grouping, posture, and activity type, summarizing the non-transient activities that occur on city sidewalks.
[CV-128] Evidence-Driven Differential Diagnosis of Malignant Melanoma
链接: https://arxiv.org/abs/2609.29613
作者: Naren Akash,Anirudh Kaushik,Jayanthi Sivaswamy
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:We present a modular and multi-level framework for the differential diagnosis of malignant melanoma. Our framework integrates contextual information and evidence at the lesion, patient, and population levels, enabling decision-making at each level. We introduce an anatomic-site aware masked transformer, which effectively models the patient context by considering all lesions in a patient, which can be variable in count, and their site of incidence. Additionally, we incorporate patient metadata via learnable demographics embeddings to capture population statistics. Through extensive experiments, we explore the influence of specific information on the decision-making process and examine the tradeoff in metrics when considering different types of information. Validation results using the SIIM-ISIC 2020 dataset indicate including the lesion context with location and metadata improves specificity by 17.15% and 7.14%, respectively, while enhancing balanced accuracy. The code is available at this https URL.
[CV-129] Integrating Local Detail and Global Context: A Dual-Input Multi-Task Learning Framework for Bone Tumor Diagnosis
链接: https://arxiv.org/abs/2609.28732
作者: S. M. Nasif Uddin,Rusab Sarmun,Muhammad E. H. Chowdhury,Adam Mushtak,Israa Al-Hashimi,Sohaib Bassam Zoghoul
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Primary bone tumors are rare but clinically aggressive neoplasms whose diagnosis from radiographs is challenged by heterogeneous morphology, subtle lesion margins, and overlapping bone structures. To address the limitations of existing single-view models, we present a dual-input, multi-task learning framework that, to our knowledge, is the first to apply bidirectional cross-modal attention between a lesion crop and the full radiograph for joint segmentation and subtype classification. Using the multi-institutional Bone Tumor X-ray Radiograph Dataset (BTXRD, n=3,746), we employ a YOLO-based detector to generate regions of interest, which are paired with full images as inputs to a dual-stream DenseNet121 architecture. Features are integrated via a novel cross-modal attention fusion strategy, refined by Hierarchical Multi-scale Feature Fusion, effectively balancing fine-grained lesion detail with global anatomical context. Evaluated on a held-out patient-level test split, the model demonstrates superior performance over single-input baselines, achieving an overall Dice Similarity Coefficient of 0.896 and a macro-averaged classification F1-score of 0.928. Notably, the system exhibits exceptional sensitivity for malignant osteosarcoma (AUC 0.999), validating the potential of dual-stream context modeling to support radiologists in accurate, early decision-making.
[CV-130] Adaptive Tiling for Least-Squares Phase Unwrapping: Runtime and Accuracy
链接: https://arxiv.org/abs/2609.28541
作者: Antoine Moevus,Max Mignotte
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: Technical report. 15 pages of main text and references, followed by 8 pages of supplementary material. 6 figures, 8 tables and 2 algorithms in total
Abstract:Phase unwrapping estimates the missing multiples of 2\pi in measured phase images. For large images, tiling limits the size of local reconstruction problems and enables parallel processing. Adaptive tiling could further reduce the number of local problems and boundaries by retaining large tiles where little refinement is needed. We investigate whether this reduction makes reconstruction faster. We compare complete reconstruction time and accuracy for a regular grid, quadtree, and kd-tree partitions. We also evaluate nine criteria for deciding where quadtree tiles should be subdivided, including residue count, fringe density, and measures of phase variation, at different tile sizes and budgets. In single-threaded experiments on a heterogeneous image dataset, optimized adaptive partitions use fewer tiles but remain slower than the optimized grid, and some reconstructions lose substantial accuracy. Stage measurements explain why: constructing the partition and solving larger retained tiles outweigh the savings at tile boundaries. The criterion comparison also shows that more refinement does not consistently improve accuracy. These results motivate evaluating adaptive partitions by the complete time needed to reach a chosen reconstruction accuracy, including whether limited refinement can provide a faster approximate result.
[CV-131] Does DCGAN-Based Synthetic Augmentation Improve Brain Tumor MRI Classification? An Empirical Study
链接: https://arxiv.org/abs/2609.28508
作者: Irhum Jawad Khan,Talha bin Aslam
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Generative adversarial networks (GANs) are increasingly used to augment medical imaging datasets, but synthetic images do not necessarily provide downstream classification benefits. This study investigates whether class-specific Deep Convolutional Generative Adversarial Network (DCGAN) augmentation improves brain tumor classification when the classifier and evaluation set are held constant. Experiments were conducted on 7,200 brain magnetic resonance imaging (MRI) scans across four classes: glioma, meningioma, pituitary tumor, and no tumor. For each class, 1,400 real images were used for training and 400 were reserved for testing. A baseline Swin Transformer classifier was trained using only the real training images and compared with a second model trained using the same real images augmented with 500 DCGAN-generated images per class. Both conditions were evaluated on the identical held-out test set. The two models achieved the same overall accuracy of 96%, while macro F1 remained effectively unchanged and ROC-AUC decreased slightly from 0.987 to 0.982 after augmentation. Class-level analysis showed small redistributions in errors rather than a consistent performance gain. FID values between real and synthetic images ranged from 209.15 to 314.27, indicating substantial distributional differences under the adopted evaluation setup. These results suggest that synthetic augmentation should not be assumed to improve medical image classification and should instead be evaluated for both distributional fidelity and downstream task utility.
人工智能
[AI-0] LLM Agents Can Easily Tamper With Their Own Traces
链接: https://arxiv.org/abs/2609.30266
作者: Jeremy Qin,David Schmotz,Derck Prinzhorn,Luca Beurer-Kellner,Ameya Prabhu,Maksym Andriushchenko
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Asynchronous monitoring, incident investigations, and compliance audits primarily rely on agent traces to reconstruct what happened. These analyses assume that LLM agents cannot tamper with their own execution traces. We show that local LLM agents such as Claude Code, Codex, Antigravity, Open Code and Grok Build fail to enforce this boundary. All tested harnesses, except Muse Code, allowed agents to delete their traces when asked, without triggering monitor guardrails. We also validate that external attackers can exploit this gap to induce trace deletion. Finally, we show that trace tampering behavior emerges naturally in frontier models, when agents try to improve their rewards. We advise practitioners to ensure trace logging happens through an independent interception mechanism outside of the agent’s control, preserving trace integrity even in cases of full host compromise. Overall, our findings identify a concrete failure of trace integrity in agent infrastructure which can be used to conceal misaligned behaviors like scheming or sabotage.
[AI-1] AD-WM: Action-Discriminative World Models for Counterfactual Model Predictive Control
链接: https://arxiv.org/abs/2609.30264
作者: Jiabin Qiu,Zixuan Chen,Hongye Cao,Jieqi Shi,Jing Huo,Yang Gao
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: 9 pages, 5 figures, 4 tables. Project page: this https URL
Abstract:Latent world models are typically trained to predict factual transitions, whereas model predictive control (MPC) must compare alternative actions from the same state. A model can therefore achieve low factual prediction error yet poorly distinguish candidate actions. We introduce AD-WM, an action-discriminative joint-embedding world model for counterfactual MPC. AD-WM combines residual latent dynamics with predictor-level action-recovery regularization, using inverse dynamics and a normalized recovery objective motivated by conditional mutual information. Both objectives encourage planning transitions to preserve action information; their auxiliary heads are discarded at test time, leaving MPC unchanged. On OGBench-Cube, AD-WM improves hard-start success from 3.7% to 52.0% over a matched LeWM baseline and improves mean success over the reproduced baseline in four of five simulation environments. Planning diagnostics show that factual prediction error and whole-bank action ranking do not follow the closed-loop success ordering, whereas CEM-aligned elite regret tracks success more closely. With a frozen V-JEPA 2 encoder and matched DROID post-training, AD-WM also improves zero-shot transfer to our Franka setup, increasing basic pick-and-place success from 42.2% to 71.1% without lab-specific adaptation. These results suggest that world models for planning should preserve action-dependent differences needed for counterfactual selection, rather than optimize factual prediction accuracy alone. More videos and code are available at this https URL.
[AI-2] Coding Agents for Generalized Task and Motion Planning Problems
链接: https://arxiv.org/abs/2609.30233
作者: Matteo Merler,Bowen Li,Josh Roy,Yichao Liang,Qianwei Wang,Yixuan Huang,Tom Silver
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 9 pages, 4 figures, 3 tables
Abstract:Task and motion planning (TAMP) problems remain difficult even with full observability and object-centric states because discrete decisions are tightly coupled to geometric, kinematic, and dynamic constraints. Generalized TAMP addresses this difficulty by exploiting regularities across problem instances to reduce planning effort on new instances. However, existing methods require substantial TAMP-specific engineering. We investigate whether coding agents can automate this process by synthesizing programs that generalize across instances. Given a task description and simulator access, each agent chooses how to interact with the environment while developing a program within a fixed synthesis budget. The program is then frozen and evaluated on unseen instances. We evaluate Claude Code (Opus 5) and Codex (GPT-5.6 Sol and GPT-6 Astra) on 28 simulated environments from KinDER and PDDLStream, with object counts beyond those evaluated in the original benchmark. Across all program synthesis methods, we evaluate 980 generated programs on 100 held-out instances each, 98,000 evaluation episodes in total. Overall, we find that coding agents are surprisingly effective at generalized TAMP: all three agent configurations outperform hand-engineered planners, one-shot generation, and an LLM-based generalized planning baseline in mean success (56% to 95% versus 47% for the planners, on the 16 environments where a planner is available). As object counts grow, the agents’ programs maintain higher success than the planner, using an order of magnitude less computation per instance on average. Logs show agents using interaction to calibrate physical models, test edge cases, and refine strategies. We release all code, including the full prompts given to the agents. These findings suggest that coding agents are a strong baseline for generalized TAMP.
[AI-3] Requirement-Bound Verified Commissioning: A Frozen Four-Billion-Parameter Local Model as a Candidate Generator under an External Acceptance Layer with Verification and Release Authority
链接: https://arxiv.org/abs/2609.30219
作者: Mehmet Iscan
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注: 42 pages, 6 figures, 15 tables
Abstract:An acceptance protocol is developed for sensor-coordinate and polarity binding in mechatronic commissioning. Candidate generation is separated from release authority. Requirements unsupported by a deterministic parser are routed to a frozen local language model with four billion parameters. Plans are released only when both facts can be derived by an external gate under a sealed grammar. One canonical answer is requested from a gold-standard user when eligible. The protocol was evaluated once under a criterion fixed before benchmark construction, on 144 tasks written by isolated agent contexts without access to the gate, grammar, or experimental plan. Three contributions are established. First, candidate generation and release decisions were measured separately. Fabricated ready plans were committed on 21 of 22 routed unanswerable tasks, and all were rejected. The same 83 releases were reproduced without model calls. Second, no false release was observed among 83 releases. A one-sided 95% Clopper-Pearson upper bound of 0.0354 was obtained as a diagnostic under an independent-and-identically-distributed assumption, below the sealed 5% threshold. However, one false release was subsequently recorded among 146 releases outside the benchmark at seed 0. Third, protection against incorrect user answers was characterized. Both facts were bound from the original text on 13 of 96 answerable tasks. Incorrect answers were released in 169 of 431 pairings on the remaining tasks, including failures involving coordinate exclusion. A deployable questioning policy was not tested because eligibility was determined from the answer key. Gate sensitivity and real user behavior were not measured.
[AI-4] Minimally Invasive Steering of Language Models
链接: https://arxiv.org/abs/2609.30218
作者: Taha Entesari,Jingyu Zhang,Daniel Khashabi,Mahyar Fazlyab
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Pre-logit steering adapts a frozen language model to a test-time reward by adding vectors to its final hidden states. Unregularized reward optimization can substantially alter the output distribution and degrade generation quality. We propose Minimally Invasive Steering Vector Optimization (MISVO), which penalizes interventions using the local KL geometry of the induced token distribution. The resulting Fisher quadratic measures distributional sensitivity and admits an analytic gradient computed through matrix–vector products with the frozen language-model head. We derive an exact decomposition of the sequence-level KL gradient into an analytic Fisher term and a suffix score-function term. For a fixed generation horizon, we show that the suffix term is second order in the steering magnitude and that three Fisher surrogates agree with the full KL gradient to first order. MISVO uses the frozen-reference surrogate to optimize position-specific interventions without updating model parameters. Across preference and code-generation tasks on models with approximately 1B–14B parameters, MISVO achieves the highest mean reward in six of seven model–task settings, with diversity and coherence scores close to those of Best-of-N.
[AI-5] Instrumental Monitor Evasion Emerges Under Ordinary Task Pressure
链接: https://arxiv.org/abs/2609.30217
作者: David Schmotz,Derck Prinzhorn,Luca Beurer-Kellner,Anselm Paulus,Ameya Prabhu,Maksym Andriushchenko
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:A central concern in AI safety is that agents may treat oversight as an obstacle when it conflicts with completing their goals. We study instrumental evasion, the propensity of LLM agents to circumvent runtime monitoring as a means of completing ordinary tasks. We introduce EvasionBench, a benchmark of 50 diverse task-policy pairs in which completing the task requires an operation prohibited by a runtime monitor. Agents know that their tool calls are monitored and are prompted to continue working when they pause. Across our evaluations, best-of-3 evasion attempt rates reach up to 98% and success rates up to 88%, with substantial variance across models. Claude Fable 5.1 succeeds less often, but frequently makes creative attempts to circumvent the monitor. Evasion generally increases with test-time compute, with higher evasion rates at greater reasoning effort and token use. Traces show that agents encode prohibited commands, decompose operations across tool calls, and retry until relevant context leaves the monitor’s history. We also find that GPT-6 Astra’s low evasion rate comes with overrefusal, as it frequently abandons otherwise solvable tasks under a denial-of-service prompt injection. Our findings show that ordinary task pressure can lead to adaptive attempts to evade runtime monitors without an explicit adversarial objective. Effective oversight must therefore remain robust against repeated attempts, as the persistence that helps agents solve difficult tasks can also drive them to circumvent their guardrails.
[AI-6] Underwater C3-JEPA: An Object-Centric Cross-View World Model for ROV Salvage
链接: https://arxiv.org/abs/2609.30214
作者: Yuncong Yang,Jinlong Li,Yulong Xue,Feng Wu,Chunwen Zhang,Lei Qiao,Xuyang Wang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Submitted to the IEEE for possible publication. 12 pages, 14 figures
Abstract:We present Underwater C ^3 -JEPA (cross-view, control-conditioned, context-extended), an object-centric multi-view predictive world model for near-field heavy-load underwater ROV salvage. Without contact sensors, it predicts in latent space how the task-object state evolves through contact interaction and under the hydrodynamic lag of the vehicle, from synchronized multi-view RGB observations and vehicle control signals. C ^3 -JEPA encodes multi-camera observations into task-object and context tokens, fuses cross-camera evidence through held-out-view attention, and directly predicts future states conditioned on control. Weak binding anchors the target and gripper at low annotation cost, while SIGReg sharpens the geometric representation. Experiments show that the learned representation transfers substantially more task-relevant information to downstream probes than a reconstruction-free latent baseline, while keeping the predictor lightweight. The resulting predictive interface supports model-predictive-control (MPC) candidate evaluation and imagined-rollout behavior-agent training. Validation on real underwater video shows the same architecture recovering a withheld camera’s object state and staying ahead of persistence, so the recipe transfers beyond simulation.
[AI-7] A Living Benchmark for Information Retrieval from Electronic Health Records
链接: https://arxiv.org/abs/2609.30205
作者: Jordan L. Cahoon,Chloe O. Stanwyck,Sulaiman Somani,Philip Chung,Kevin R Keet,Kameron C. Black,Andrea T. Fisher,Sarita Khemani,Jerry Liu,Stephen Ma,Saloni K. Maharaj,Rita M. Pandya,Eduardo Perez-Guerrero,Priyanka Pillai,Lisa Shieh,David J.H. Wu,James Xie,James C. McAvoy,Teresa Nguyen,Jessica Tran,Lucy Yin,Bridget Lin,Alison Callahan,Jason A. Fries,Nigam H. Shah,Emily Alsentzer
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM)-based clinical assistants are increasingly being integrated into electronic health record (EHR) systems, transforming how clinicians retrieve and synthesize information from patient records. Their safety and utility depend on rigorous evaluation, yet existing benchmarks are manually curated, costly to update, and rapidly become obsolete with evolving technological advancements. We present a scalable framework that automatically generates question–answer pairs from longitudinal EHR notes. Nineteen clinicians validate the benchmark generator, producing the Benchmark for Retrieving Information in EHRs (BRIE), a continuously maintainable evaluation dataset. Across nine LLMs and five inference strategies, state-of-the-art systems frequently omit clinically important information, particularly for questions requiring synthesis across multiple documents and encounters. Because the generator itself is validated, BRIE supports evaluations that static benchmarks cannot, including the generation of multiple answers that reflect variation in clinician reasoning for robust performance assessment and continuously refreshing benchmark content to guard against leakage. Our results demonstrate that scalable benchmark generation enables rigorous, up-to-date evaluation of clinical LLMs as they are deployed in rapidly evolving healthcare settings.
[AI-8] SAGE: Mitigating Long-Horizon Reasoning Biases via Topological Guidance NEURIPS2026
链接: https://arxiv.org/abs/2609.30192
作者: Xinyue Zeng,Jiawei Zhang,Yujun Yan,Dawei Zhou
类目: Artificial Intelligence (cs.AI)
备注: Accepted by NeurIPS 2026
Abstract:Long-horizon reasoning remains a central challenge for large language models (LLMs) under sparse-reward regimes. We argue that this brittleness arises from two biases induced by complex reasoning spaces: an exploration bias, where models are drawn toward locally plausible but structurally unstable branches, and a compounding bias, where small local deviations accumulate across depth and suppress rare rewards. We introduce Symbolic Closure Analysis (SCA) as a theoretical lens characterizing how branching structures and sparse rewards induce these biases in long-horizon reasoning with local admissibility, and as a design principle for structural priors in less formal reasoning tasks. Motivated by this analysis, we propose SAGE (Structural Admissibility-Guided Exploration), a unified framework that injects structural guidance to alleviate exploration bias and compounding bias in long-horizon reasoning. SAGE combines two complementary structural guidance: algebraic sparsification, which projects locally admissible candidates onto operator-indexed algebraic subspaces to suppress spurious branching and mitigate exploration bias, and hyperbolic structural guidance, which embeds reasoning states into a negatively curved space to provide dense depth-wise signals and mitigate compounding bias. Across 12 benchmarks and 7 model families, SAGE outperforms competitive baselines. In particular, SAGE achieves up to an 8-fold improvement on the Andrews-Curtis problem, an open real-world long-horizon task. Code is available at: this https URL.
[AI-9] Jev-Mobile: Jev as an Executor for Mobile GUI Agents
链接: https://arxiv.org/abs/2609.30186
作者: Linghua Zhang
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Vision-language models (VLMs) have become a common foundation for autonomous mobile GUI agents, but most existing systems rely on the VLM for both planning and action grounding at nearly every interaction step, leading to substantial latency and model-serving cost. We introduce Jev-Mobile, which shifts this paradigm to low-frequency VLM planning and high-frequency lightweight execution: the VLM specifies local goals, the accessibility tree defines a structured executable action space, and Jev, a fast typed decision model, repeatedly selects actions within this space. This design allows multiple GUI actions to be executed under a single VLM decision, reducing expensive VLM inference while preserving adaptive interaction. On the full AndroidWorld task suite, Jev-Mobile achieves 79% task success, compared with 78% for SeeAct-V and 84% for a Step-wise VLM baseline. Among successful trajectories, it reduces mean end-to-end execution time by 32.7% and mean model API cost by 73.4% relative to Step-wise VLM. These results show that decoupling high-level VLM reasoning from low-level action execution can substantially improve mobile GUI agent efficiency while maintaining competitive task performance.
[AI-10] Search-Aware Reinforcement Learning for Multi-Component Query Understanding in Roblox Game Search
链接: https://arxiv.org/abs/2609.30177
作者: Nayoung Choi,Shengjian Chen,Xiaokai Wei,Wenzheng Zhang,Daiyao Yi,Rachit Pareek,Vincent Su,Michelle Gong,Jinho D. Choi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Query understanding (QU) plays a critical role in production search systems, translating raw user queries into search execution plans that drive downstream retrieval and ranking. While large language models (LLMs) have enabled QU to be framed as a structured multi-task generation problem (e.g., intent classification, query expansion), optimizing such models to produce search-engine-coupled outputs remains challenging: static, label-based supervision fails to capture how each component actually interacts with the underlying search pipeline to affect downstream performance. We present a search-aware reinforcement learning (RL) framework for QU based on a distill-then-RL paradigm. Teacher-student supervised fine-tuning (SFT) first yields a well-formed, schema-compliant policy initialization. The RL stage then optimizes each QU component with rewards derived from live interaction with the search engine, tailored to that component’s operational role, rather than a single reward tied to the final search outcome. Experiments on Roblox search show that this component-specific optimization improves both per-component utility and downstream search quality, raising NDCG@20 by 8.9 points over the SFT policy and by 3.5 points over training with a single end-to-end reward.
[AI-11] EnigmaForge: The Question Is Hidden in the Story
链接: https://arxiv.org/abs/2609.30144
作者: Daniel Eisner
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Most benchmarks hand the model a question. EnigmaForge hands it a stack of old documents and no question at all. Buried in the letters, receipts, and logbook margins is a small logic puzzle whose solution is unique - proved by a SAT solver at generation time, with an ablation certificate showing every clue is load-bearing. Because instances are generated rather than collected, the corpus renews forever. The headline measure is intuition: task success when handed only the story, with world reconstruction as the secondary axis. Twenty-five frontier models ran over 600 instances (17,400 scored records) under three matched conditions. Intuition reshuffles the leaderboard: a 22x spread where fact recovery spans 1.6x, the second-best fact-recoverer ranks fourteenth, one model is indifferent to being told the question, and another is significantly better without it. Several models were blocked by their own content filters before reaching the puzzle - any benchmark scoring refusals as failure is quietly measuring filter behavior.
[AI-12] HEXIS: Compiling Skills into Extended Finite State Machines
链接: https://arxiv.org/abs/2609.30123
作者: Minghao LI
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Agent skills provide reusable knowledge and instructions, yet agents must repeatedly infer how to apply them and which operation should follow. This couples task reasoning with control decisions, allowing prescribed steps to be omitted or applied incorrectly. We introduce HEXIS, which compiles agent skills into extended finite state machines that separate knowledge from control flow. Skill knowledge is incorporated into local instructions that guide reasoning and generation within states. The machine records execution progress and intermediate results, while explicit transition conditions determine subsequent operations. Our incremental compiler first maps skill clauses and tool interfaces to state operations, local instructions, data bindings, and transitions. It then aligns development traces with existing states to identify missing operations and dependencies. These are incorporated by adding or reusing states and refining their connections. Updates are accepted only after static checks and replay of the current and all previously accepted traces. Across four benchmarks and four executors, HEXIS improves success over Skill + ReAct by 16.1 percentage points on average. Qwen3.8-27B reduces execution tokens by 38.4-88.9% across benchmarks.
[AI-13] AT-SKM-Net: An Accelerated Trainable Sampling Kaczmarz-Motzkin Framework for Linear Hard-Constraint Feasibility on Dynamic Graphs
链接: https://arxiv.org/abs/2609.30088
作者: Xiaochen Zhang,Haoyu Zhu,Yao Zhang,Qingchun Hou
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
备注:
Abstract:Graph-structured optimization with linear constraints is fundamental to critical infrastructure but faces scalability limits due to massive strict hard constraints and high dimensionality. While recent projection-based methods such as Trainable Sampling Kaczmarz-Motzkin Net (T-SKM-Net) guarantee feasibility, they face high computational costs in dynamic environments by processing the entire constraint set and requiring expensive matrix factorizations. To bridge this gap, we propose the Accelerated Trainable-SKM (AT-SKM) Net framework. To concentrate computation on the active constraints and eliminate redundant calculations, we introduce a hybrid sampling strategy guided by a topology-aware heterogeneous GNN model. To efficiently handle topological shifts in graph-based constraints, we employ a Cholesky Update mechanism that theoretically reduces the equality projection complexity from O(N^3) to O(N^2) under low-rank perturbations. Experiments on random geometric graphs, N-1 Security-Constrained DC-OPF, and minimum-cost gas transport problem demonstrate that AT-SKM reduces iteration counts by up to 85% and achieves 2.95x-7.29x SKM layer speedups, while maintaining zero constraint violations.
[AI-14] Reachability-Based Formal Verification of Graph Neural Networks with Node and Edge Features
链接: https://arxiv.org/abs/2609.30079
作者: Anne M. Tumlin,Ben Wooding,Zhenxuan Shao,Diego Manzanas Lopez,Tyler Derr,Taylor T. Johnson
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Graph neural networks (GNNs) have become a prominent approach for developing fast, topology-aware surrogates in electric power systems, supporting tasks such as power flow (PF) analysis, optimal power flow (OPF) estimation, and cascading failure analysis (CFA). Despite this growing use, formally verifying GNN-based models remains challenging, with existing methods limited in scope. We extend the neural network verification (NNV) framework to graph-structured inputs through GraphStar sets, a generalization of Star sets that captures uncertainty over both node and edge features. This extension enables the propagation of linear message-passing operations and the sound approximation of ReLU nonlinearities for GNN architectures, including graph convolutional network (GCN) and graph isomorphism network with edge features (GINE) layers. We evaluate GNNV across three power system tasks, PF, OPF, and CFA, on the IEEE-24, IEEE-39, and IEEE-118 test cases, as well as two standard graph classification benchmarks, ENZYMES and PROTEINS. Our results show that GNNV provides tighter robustness guarantees than CORA on graph classification models with ReLU-based activations and, for the first time, delivers edge-aware robustness guarantees for GINE-based PF and OPF models under joint node and edge perturbations.
[AI-15] KernelOPT: Dispatch-Aware Agent ic Search for GPU Kernel Optimization
链接: https://arxiv.org/abs/2609.30059
作者: Aheli Poddar,Sanskar Prasad,Arindam Samanta,Subha Chakraborty,Vishal Goyal,Rohit Singh Rathaur
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Deep learning inference and training performance depends critically on GPU kernel efficiency. Modern compilers such as PyTorch Inductor automatically generate GPU kernels from high-level model code, but frequently underperform expert-written implementations by wide margins. Recent LLM-assisted kernel optimizers can close this gap for standalone kernels, yet treat compiled models as black boxes, generally optimizing individual standalone kernels without respecting the compiler’s structural decisions or verifying the model end-to-end. We present KernelOPT, a multi-agent system that treats compiled models as structured artifacts. It preserves vendor library calls (cuBLAS, cuDNN) and exclusively targets generated Triton sub-kernels using five profiling-guided LLM agents. A four-gate verification cascade of static validation, multi-seed correctness, model-level float64-fallback verification, and performance gating filters candidates during optimization and verifies the re-stitched model end-to-end. If no candidate passes all four gates, the system preserves the compiler baseline. The system accepts PyTorch this http URL, standalone Triton kernels, and Helion kernels. Evaluated on 250 KernelBench problems, KernelOPT achieves geometric mean speedups over \textttthis http URL of 1.40 \times (Level 1: 51/100), 1.15 \times (Level 2: 31/100), and 1.07 \times (Level 3: 12/50) across all problems.
[AI-16] Can Labor Markets Function in the Age of AI? The Evaluation Bottleneck in Hiring
链接: https://arxiv.org/abs/2609.30058
作者: Itai Ashlagi,Ramesh Johari,Jon Kleinberg,Anushka Murthy
类目: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Theoretical Economics (econ.TH)
备注:
Abstract:AI-assisted job-search tools have become increasingly popular by making it easier to find and apply to jobs. But by making it easier for applicants to generate and tailor application materials, they can also reduce how informative those materials are about applicant fit. We study this tradeoff in a hiring market where applicants differ in experience and latent match quality and firms use noisy application materials to decide whom to screen. We ask how AI affects downstream screening and hiring, and which applicants are most adversely affected. As application materials become less informative, a Bayesian firm rationally relies more heavily on coarse observables such as prior experience. Among the four applicant types defined by experience and compatibility for the job, inexperienced-compatible applicants are the most exposed: they lack observable experience and lose the individualized information that could distinguish them from other inexperienced candidates. When screening is costly, these changes can also generate inefficient screening failures in which firms screen no applicants or screen only experienced applicants. We then show that multistage hiring can arise as an endogenous firm response: a relatively inexpensive intermediate assessment allows firms to acquire new evidence of fit before costly full screening. This can restore screening opportunities that disappear under one-stage hiring and give inexperienced-compatible applicants a path to screening. Our results show how AI can shift the central friction in hiring from submitting applications to obtaining credible evaluation, creating entry barriers for high-fit workers without prior experience. Multistage hiring can endogenously arise in response, restoring evaluation opportunities that would otherwise disappear and helping preserve market functioning.
[AI-17] Era by Eon: Benchmarking Enterprise Agents on Hidden Knowledge
链接: https://arxiv.org/abs/2609.30055
作者: Benjamin Gruenbaum,Doron Porat,Assaf Natanzon,Roy Zavida,Chen Dinachi,Or Itzahary
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 9 pages
Abstract:In the Era by Eon benchmark, each question states the rules for its answer, and code computes the answer from a generated company’s data. When agents can run code, the four strongest models each answer 22 to 25 of 27 such questions, so the benchmark barely separates them. We add eight question templates that depend on hidden facts. No question or document states a hidden fact, and the records that seem to hold it show something else. Other data implies it. For example, the sales system says a customer dropped a purchase because of timing. On a recorded call, the customer blames an outage. For each generated company, code fills each template and computes an exact answer without a language model. We evaluate 12 agents. Each pairs a model with an agent program, which connects it to the company’s systems. The best agent answers 18 of its 24 attempts, three per question, correctly. Four of the six models answer at most 6 of 24 with any program. The hardest questions require picking one of several similar records, such as which of three renewal offers a customer signed. All agents together answered two such questions correctly in only 1 of 84 attempts. Comments: 9 pages Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.30055 [cs.SE] (or arXiv:2609.30055v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2609.30055 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Assaf Natanzon [view email] [v1] Thu, 24 Sep 2026 16:12:27 UTC (19 KB)
[AI-18] SciWalker: Synthesizing Scientific Coding Problems with Operator Graphs and Execution Feedback
链接: https://arxiv.org/abs/2609.30054
作者: Chenxi Li,Wenxuan Zeng,Yun Luo,Fangchen Yu,Peng Ye,Yu Cheng,Jun Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Improving the scientific coding capabilities of large language models (LLMs) requires high-quality training data. However, such data remain scarce because manually authoring realistic problems is costly and time-consuming, while systematically covering diverse scientific domains and algorithmic combinations remains challenging. To address this, we introduce SciWalker, a framework for synthesizing scientific coding problems through operator-chain sampling and execution feedback. The framework combines scientific library interfaces with operation modes to instantiate operators, organizes them into operator graphs, and samples operator chains as computational workflow cues. Guided by these cues, we adopt LLMs to generate scientifically grounded problem statements, reference solutions, and tests, with failed generations iteratively repaired using execution feedback. By combining structured workflow composition with verification and quality review, SciWalker enables scalable task generation while promoting scientific grounding, computational diversity, and executability. Using this framework, we construct 8,178 high-quality problems spanning 5 scientific domains and 32 subdomains. To evaluate their training utility, we conduct reinforcement learning on Qwen3.5-9B using the GSPO algorithm. This training improves SciCode subproblem accuracy by 9.9 percentage points, from 29.3% to 39.2%, with gains across scientific code generation, code repair, and reasoning benchmarks. The code for SciWalker is available at this https URL.
[AI-19] NNV3: Expanding Neural Network Verification to New Architectures and Domains
链接: https://arxiv.org/abs/2609.30050
作者: Anne M. Tumlin,Samuel Sasaki,Ben Wooding,Diego Manzanas Lopez,Muhammad Usama Zubair,Navid Hashemi,Hongchao Zhang,Waseem Abbas,Ipek Oguz,Meiyi Ma,Taylor T. Johnson
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We present NNV3, the latest version of the Neural Network Verification (NNV) tool, a MATLAB framework for formal verification of deep learning models and learning-enabled cyber-physical systems. Building on the set-based reachability foundation of NNV 1.0 (FFNNs, CNNs, NNCS) and NNV 2.0 (RNNs, SSNNs, neural ODEs), NNV3 introduces new members of the Star-set family: ModelStar for verifying networks under weight perturbation, VolumeStar for video and 3D volumetric inputs, and GraphStar for graph neural networks. A conformal-inference-based probabilistic reachability mode complements sound analysis for problems where deterministic verification is intractable, while FairNNV certifies counterfactual and individual fairness properties over continuous input regions. NNV3 introduces new benchmarks for malware detection, graph-based power-system models, medical imaging, variable-length time series data, and action recognition. NNV3 also incorporates tutorials and developer guides through a unified documentation site. This paper details these major updates, demonstrating NNV’s maturation into a comprehensive, robust, and accessible verification tool for a diverse range of AI systems.
[AI-20] How does Adversarial Influence Scale in Multi-Agent Systems?
链接: https://arxiv.org/abs/2609.30028
作者: Addison J. Wu,Jasin Cekinmez,Michel Liao,Karthik Narasimhan,Thomas L. Griffiths
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Multi-agent deliberation can improve performance, but what happens when some agents do not act in good faith? In practice, an agent may be deceptive and work to subvert the group, whether through its own objectives or external instruction. We study how susceptibility to deception scales as groups increase in size and deceivers become more prevalent. It is not the number of agents in the group that matters, but the proportion of deceivers. We observe that the defection rate, how often initially correct agents switch to an incorrect final answer, rises linearly with this proportion. Whereas humans in comparable conformity studies are reliably swayed only when misleading confederates form a majority, LLM agents defect regularly even when deceivers remain a minority. Susceptibility also depends on which models are interacting, especially on the honest agent side. Unexpectedly, allowing deceivers to coordinate privately can make them less effective. Altogether, our results show that adding more agents is therefore not a sufficient defense, because the adversary can simply scale with the group.
[AI-21] Synthetic Hospital: An Open Verifiable Physician-Validated Longitudinal EHR Benchmark
链接: https://arxiv.org/abs/2609.30027
作者: Christine Park,Valerie Chen,Tim Dettmers
类目: Artificial Intelligence (cs.AI); Databases (cs.DB)
备注: 29 pages, 2 figures, 12 tables
Abstract:Frontier language models are rarely used in clinical workflows because the realistic, longitudinal benchmarks needed to develop them are scarce. Real electronic health record (EHR) data cannot be openly shared due to privacy, ethics or data use issues and it does not contain verifiable ground truth since the chart records only reflect what clinicians documented. We introduce Synthetic Hospital, an open, fully synthetic, fact-grounded longitudinal EHR benchmark that resolves the open sharing and verifiable ground truth barriers. Built entirely from public medical-education material with no protected health information, it comprises 1,268 longitudinal patients and 5,602 encounters, where every diagnosis, finding, and temporal relation is grounded in standard ontologies (ICD-10-CM, SNOMED CT, LOINC) and with a complete provenance chain back to its source medical education material. Synthetic Hospital is served through a simulated hospital record system that mirrors real EHR infrastructure (standard interoperability APIs, role-based access and function-calling interface). In a blinded review, physicians distinguished its records from real patient charts at near-chance rates (53%). Across 10 frontier and open models, none approaches ceiling: the best model reconstructs a patient’s longitudinal problem list with a severity-weighted F1 of 0.73, level with the mean of seven physicians on a matched subset but well below the best of them (0.89), and misses roughly half of clinically relevant findings when summarizing a chart. Overall, these results highlight that Synthetic Hospital is a difficult and realistic test of clinical AI performance.
[AI-22] Canopy: Exploiting Piecewise Smooth Tree Priors for Multi-Fidelity Bandits
链接: https://arxiv.org/abs/2609.30017
作者: Michael Jerge,Suman Jana
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Many LLM inference problems, including model routing, prefix-cache management, prompt trimming, and test-time search, can be viewed as optimization over a tree. This structure arises naturally from autoregressive generation: every prefix defines a node, and its continuations form a subtree below it. Internal nodes of the tree provide cheap but biased estimates of a region’s value, while leaf evaluations are expensive but accurate. Hierarchical bandit methods can exploit this structure, but typically require a specific smoothness schedule to be specified in advance, even though real objectives are often only piecewise smooth and their optima may lie near sharp boundaries. We introduce CANOPY, a multi-fidelity tree bandit that learns where the smoothness prior is valid rather than assuming it globally. CANOPY uses cheap random-path probes to construct an online certificate of local aggregation bias, then directs expensive leaf evaluations toward cells where the certificate detects a smoothness violation. We prove fixed-budget and regret guarantees whose additional cost is additive in the number of discontinuities, recovering the smooth-tree rate when no violations are present and approaching structure-blind search as violations become dense. Across routing, top- k identification, test-time search, caching, and prompt trimming, CANOPY consistently improves matched-budget performance, including 2.9\times higher top-10 recall on a 1000-model pool, 1.6\times more SWE-bench Verified issues resolved than best-of- N , and 3.6\times lower median time-to-first-token with prefix caching.
[AI-23] World Action Agent : Harnessing VLMs for Robot Manipulation via World Action Rehearsal
链接: https://arxiv.org/abs/2609.29964
作者: Yehang Zhang,Haojian Huang,Yifan Chang,Jianchong Su,Bohan Zhou,Yingjie Xu,Wosong Chen,Tianhao Zhou,Chenxu Wang,Tianyi Zhang,Yangkai Wei,Wenqian Li,Shiyuan Deng,Yinchuan Li,Ying-Cong Chen,Zexi Li
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Working in progress
Abstract:General-purpose vision-language models (VLMs) bring broad knowledge and spatial reasoning to robot manipulation, yet existing systems either use them indirectly, to predict constraints or write programs, or give them a view of the scene rather than a world in which to act. We present World Action Agent (WAA), a multi-agent harness through which VLMs pilot robots with basic tools, making every decision within a visual action workspace. The workspace has three properties. Contact views, selected automatically from the scene geometry, present the scene around the current interaction. Action rehearsal turns each action into an editable proposal that the agent, alone or through an Imagination Agent, previews and revises against planning feedback before execution. In-view correction closes the loop between observation, rehearsal, and low-level execution, letting the agent remove residual offsets in the view where it observes them. Through the same workspace, WAA acquires embodied procedural knowledge in two ways: it evolves multimodal skills from expert videos and human teaching under evidence-based review and consults them through a Skill Agent, and its interaction traces train smaller VLMs to pilot the same harness. On LIBERO-Pro, WAA with skills evolved only from LIBERO-90 reaches a state-of-the-art 75.6% average success, outperforming end-to-end VLAs, code-as-policy agents, and a visual-harness baseline with the same backbone; the same skills remain effective on robosuite without further learning. Fine-tuning Qwen3.5-9B on harness traces raises its out-of-domain success from 1.7% to 43.3%.
[AI-24] Beyond Averag e Safety: Chance-Constrained LLM Fine-tuning
链接: https://arxiv.org/abs/2609.29960
作者: Taha Entesari,Mahyar Fazlyab
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Fine-tuning large language models on new objectives can improve helpfulness, instruction following, or domain-specific performance, but it can also induce regressions on safety-critical prompts. Existing safety-preserving fine-tuning methods typically control average safety loss or use weighted auxiliary penalties, which can obscure rare but severe failures. We propose a chance-constrained formulation for safety-preserving fine-tuning that limits the fraction of safety examples whose degradation relative to a reference model exceeds a prescribed threshold. Because the resulting empirical chance constraint contains a discontinuous indicator, we introduce a differentiable majorization of the violation rate, yielding a tractable conservative constraint. We then develop a constraint-aware gradient descent method that treats the majorized constraint as a safe set in parameter space and minimally modifies the fine-tuning direction to preserve feasibility. The resulting update admits a closed form and produces a tail-aware safety correction that emphasizes examples near or above the degradation threshold. We conduct an extensive set of experiments on harmful fine-tuning across three different tasks and three models and show that our approach consistently outperforms the baselines that exist in the literature. These results suggest that safety preservation in LLM fine-tuning is better viewed as a reliability-constrained optimization problem than as average-risk regularization.
[AI-25] racking States or Tracking Cosets? An Algebraic Account of Learned State Tracking
链接: https://arxiv.org/abs/2609.29951
作者: Zhiyu Zhang,Yupeng Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 69 pages including appendices; 9 pages of main text
Abstract:State tracking requires composing a sequence of updates, but accuracy alone does not reveal what a model has learned. We study neural networks trained to predict the running product of group elements. We identify quotient solutions in Transformers, where models recover the quotient class while predicting nearly uniformly among its members. The reciprocal of class size predicts partial accuracy without a fitted parameter, extending parity-based accounts to non-parity quotients. Our baseline Transformers’ predictions change little under prefix reordering beyond the exact-tracking frontier. We prove that, for finite groups under uniform i.i.d. full-group inputs, optimal order-blind exact accuracy converges to the reciprocal of abelianization class size as prefix length grows, consistent with the observed abelianization plateaus. Sequential updates permit more: any partition into right cosets of a subgroup, normal or not, survives sequential updates. In our census of standard Transformers, every recovered coset partition comes from a normal subgroup, whereas parameter-matched recurrent networks pass through both normal and non-normal right-coset stages during training. On A_5 , we identify low-dimensional subspaces of the recurrent state that encode non-normal cosets. In the three-dimensional cases, coset mean vectors form approximate dodecahedra, and swapping the state components in these subspaces transfers the donor’s coset state through a shared input suffix. Our results connect partial accuracy, learning stages, and internal computation through the subgroup cosets that models learn to track.
[AI-26] ENDOPROMPT: Victim-Side Pseudo-References for Utility Degradation
链接: https://arxiv.org/abs/2609.29948
作者: Qingyu Wu,Zeyu Feng,Yongda Yu,Yuzhe Luo,Hua Cheng
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:Prompt injection can degrade benign task performance without eliciting harmful content. Yet many attack objectives depend on task labels or predefined target responses. We present ENDOPROMPT, a white-box method that learns utility-degrading prefixes from unlabeled instructions. Its generator takes the request text as input. Clean victim continuations serve as pseudo-references: local search identifies prefixes that reduce continuation likelihood, and preference fitting on comparisons within the same instruction, followed by reward refinement, distills this signal into a generator. At deployment, the generator produces one prefix per request without further victim-side search. Across four instruction-tuned models and the complete splits of seven benign benchmarks, ENDOPROMPT yields a mean utility change of -26.8 percentage points; 27 of 28 cells are negative. Failure analysis reveals output expansion and prefix reuse; the controls do not establish a degradation advantage from request matching. Victim-derived supervision can reveal utility weaknesses without benchmark feedback or prescribed failure responses. The code will be released upon acceptance.
[AI-27] Neuro-symbolic AI for Industrial Configuration
链接: https://arxiv.org/abs/2609.29947
作者: Danilo Valerio,Philipp Kogler,Stefan Bischof,Thomas Hubauer,Huzefa Rangwala
类目: Artificial Intelligence (cs.AI)
备注: Accepted at the Industrial Track at the NeSy Conference 2026
Abstract:Large Language Models (LLMs) have shown impressive performance on a wide range of generative tasks. Yet their probabilistic nature makes them, in isolation, fundamentally unsuited for industrial product configuration, where outputs must be syntactically valid, semantically consistent with a knowledge base of hundreds of features and rules, and producible by an existing manufacturing chain. We argue that Neuro-symbolic (NeSy) AI methods lay out a promising path towards industrial-grade configurators that are reliable by design, explainable, and trustworthy. This paper describes a taxonomy of three NeSy integration strategies, namely hybrid inference, hybrid fine-tuning, and hybrid training, exploring their usage in the configuration domain. We report our effort to operationalize NeSy concepts in an industrial configuration copilot and derive a set of practical design choices for deploying trustworthy AI in engineering environments. We close with a discussion of open research challenges we consider most pressing, in particular how to scale NeSy methods from small academic demonstrators to the size of industrial configurators.
[AI-28] When Temporal Perturbations Act Like Sensor Biases: Label-Free Auditing of Wearable Activity Recognizers
链接: https://arxiv.org/abs/2609.29937
作者: Qingyu Wu,Yuan Wei,Renju Liu,Hua Cheng
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:Wearable human-activity recognition (HAR) models operate across sensors, subjects, and backbones, yet a smooth waveform may appear temporal while exploiting a persistent sensor offset primarily. We introduce SpectrumAudit, a label-sealed audit that fits a phase-randomized full-window stimulus on calibration windows from subjects held out from training and testing. After selection, it replays its exact DC projection and budget-constrained zero-mean residual on the same frozen victim without refitting. Across 27 victims from three datasets and three backbones, the selected waveforms cause 2.87-40.83-point three-phase robust accuracy losses. Under this replay budget, DC is more damaging than AC on 24/27 victims and recovers at least 90% of the full drop on 22/27; all 5 failures occur on WISDM. In a held-out UTD-MHAD check, the selected waveform causes 13.49-pp accuracy and 11.68-pp macro-F1 losses, versus -0.66 pp for matched random changes. The audit diagnoses offset versus zero-mean variation under a common peak-budget cap. The code will be released upon acceptance.
[AI-29] MorphIK: Morphology-Conditioned Neural Inverse Kinematics for Unknown Robots
链接: https://arxiv.org/abs/2609.29908
作者: Lennart Clasmeier,Jan Gerrit Habekost,Cornelius Weber,Stefan Wermter
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Neural models can learn to generate various solutions to the inverse kinematics problem from data, but are usually limited to a single robot. We present MorphIK, a flow-matching model that solves inverse kinematics for revolute-joint-based kinematic chains it has never seen during training. The model uses a transformer architecture to encode the robot’s morphology along with the target pose. This encoding then conditions a flow-matching head that generates poses from noise. Trained on purely synthetic data from procedurally generated robots, the model reaches a precision of about 5 cm on unseen real-world robots with 6 to 9 Degrees of Freedom. For higher precision, the model serves as an excellent Prior for further optimization algorithms, reducing error to less than 1 cm after a single step of Damped Least Squares optimization and to sub-1 mm error after 3 steps in most cases. Building on flow matching’s generative capabilities to produce highly diverse outputs, our model can efficiently sample the robot’s null space, providing a wide variety of configurations for the same pose. Thus, overall, MorphIK allows learning and generalizing neural inverse kinematics for a multitude of known and unknown robots.
[AI-30] Qwen -Planner-Agent : A Closed-Loop AI-for-AI Framework for Real-World Mobile Planner Agents
链接: https://arxiv.org/abs/2609.29892
作者: Tingyu Qu,Weigao Sun,Yuecheng Liu,Yucheng Zhao,Yi Zhu,Yifeng Ding,Qiyi Wang,Sihan Cao,Pengkun Jiao,Hanlei Xie,Xiongwei Wu,Qichao Wang,Haodong Zhang,Jiajun Liu,Yuhao Wang,Yuqing Xie,Junpeng Zhao,Long Chen,Ming Ma,Sihan Yang,Ziwang Zhao,Yanhao Jia,Liangquan Gong,Feida Zhu,Yiran Zhong,Steven Hoi
类目: Artificial Intelligence (cs.AI)
备注: this https URL
Abstract:The rapid progression of large language models is extending AI from passive content generation into the active workflows of engineering and scientific discovery. This shift raises a compelling question: can AI be both the object of development and an active participant in building next-generation AI systems? We explore this question by building Qwen-Planner-Agent within a closed-loop AI-for-AI framework for scalable development and iterative improvement. Mobile planning offers a demanding test of this approach: complex, long-horizon tasks challenge agent reliability, while costly real-device interaction limits development scalability. The framework connects data production, model training, and deployment through a shared action-feedback-verification contract. (i) AI for Data builds a human-gated agentic data flywheel in which specialized agents construct tasks, collect interaction trajectories, curate and balance training data, and use training feedback to guide subsequent data generation. (ii) AI for Training combines a supervised planning cold start with hybrid-environment online agentic reinforcement learning, where we introduce Competence-Aware Reward-and-Advantage Engineering (CARE) to reduce reasoning and tool-use costs while preserving task performance. (iii) AI drives model–harness co-evolution through an execution-evidence-driven loop that orchestrates memory, skills, and tools at runtime and feeds structured action feedback and preserved failure traces back into coordinated model and harness adaptation. Qwen-Planner-Agent achieves the best overall performance among all evaluated models and systems on MobilePA-Bench, improving over its base model across tool use, memory, skills, and sub-agent coordination. Further evaluations of our model show improvements across non-mobile agentic benchmarks while largely preserving general capabilities.
[AI-31] Ontology-Mediated Neurosymbolic Constraint Acquisition from Multiple Stakeholders ISWC2026
链接: https://arxiv.org/abs/2609.29876
作者: Stefan Bischof,Juliana Kainz,Danilo Valerio
类目: Artificial Intelligence (cs.AI)
备注: Accepted for KG-NeSy Workshop, co-located with ISWC 2026
Abstract:Neurosymbolic research typically assumes a pre-existing symbolic specification, leaving the upstream challenge of acquiring and formalizing requirements and constraints largely unaddressed. We present an architecture that fills this gap by using an OWL configuration ontology to mediate between neural constraint sources and downstream consumers. In this framework, LLM assistants elicit soft stakeholder preferences, while hardware specifications define hard physical and engineering limits. The ontology unifies these heterogeneous inputs, leverages description logic to identify unsatisfiability, and generates symbolic explanations that enable LLMs to interactively renegotiate terms with users. Any remaining conflicts are resolved downstream via priority-based relaxation. We illustrate our approach on a microgrid use case from the FLEXI project and argue its generalizability to multi-stakeholder domains where constraint acquisition is distributed across human and automated sources of unequal authority.
[AI-32] A Risk-Adaptive and Evidence-Constrained Framework for Generative AI Feedback in Programming Education
链接: https://arxiv.org/abs/2609.29874
作者: Shihao Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Generative artificial intelligence can turn learning analytics into personalized support, but feedback systems must decide when to intervene, which evidence to use, and how much assistance to provide. We developed a risk-adaptive, evidence-constrained framework for introductory programming using 2993 failed-submission states from 215 students. Student-disjoint models predicted persistent failure and related outcomes; four matched feedback conditions were generated for 136 cases; and calibrated risk informed capacity-limited intervention policies. The validation-selected logistic regression model achieved a test precision-recall area under the curve of 0.550 and a receiver operating characteristic area under the curve of 0.681. Broader student histories improved prediction of unmodified resubmission. After standardized repair and evidence gating, 519 of 544 newly generated messages contained all required components. A fixed-threshold sequential policy selected 17.8% of eligible test states and captured 25.2% of observed persistent failures. These findings support an evidence-gated progressive assistance strategy: calibrated risk guides intervention timing, recorded evidence constrains feedback content, and assistance progresses from self-checks to localized hints when warranted. The framework connects prediction, decision-making, and grounded generation while keeping their evaluation outcomes distinct.
[AI-33] mplate Ageing and Longitudinal Verification in Fixed-Text Keystroke Dynamics: A Subject-Disjoint Study Across Eight Weeks
链接: https://arxiv.org/abs/2609.29851
作者: Simon Parkinson,Saad Khan,Na Liu,Qing Xu
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Behavioural biometric templates are widely believed to degrade as the gap between enrolment and verification grows, but few studies measure this template ageing effect directly under controlled conditions. We collected a longitudinal dataset of 40 fixed passwords, each typed four times per weekly session over eight consecutive weeks. We compare a scaled-Manhattan matcher (M1), a gradient-boosted classifier (M2), a TypeNet-style recurrent embedding model (M3), and a TypeFormer-style Transformer (M4) under a 5-fold subject-disjoint protocol and a design that jointly varies mechanism and the enrolment-to-query gap, from 0 to 7 weeks. Template ageing proves large and systematic. Error increases monotonically with the gap for every mechanism, from an EER of 14.6-27.2% at a gap of zero to 25.5-37.1% at seven weeks, or 1.7% of decision error per week elapsed (p 0.001). However, the choice of mechanism matters more than its rate of ageing. Baseline accuracy spans 12.6 percentage points across the four mechanisms, the degradation each accumulates over seven weeks spans only 2.3 points, and ageing never reorders them. A matcher can therefore be chosen on same-session accuracy, with ageing managed by re-enrolment scheduling rather than by matcher selection. The two properties are nonetheless distinct, as M3 is the least accurate mechanism yet ages significantly more slowly than M1 under every specification tested. Training randomness also matters differently by architecture, with 58% of the recurrent model’s fold-to-fold variance attributable to seed noise against 19% for the Transformer. Because the smaller ageing-rate differences are sensitive to modelling choices, while the accuracy differences and the ageing effect are not, we recommend that comparative ageing-rate claims be supported by seed-level score fusion, independent replication, and an alternative outcome-model specification.
[AI-34] Decoding Imagined Speech: A Strictly Subject-Independent Approach Using EEG
链接: https://arxiv.org/abs/2609.29820
作者: Frederik Møllskov Trier,Xiaopeng Mao,Sadasivan Puthusserypady
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Imagined speech decoding from electroencephalography (EEG) has gained increasing attention as a potential communication pathway for individuals with severe motor impairments, yet reported performance often relies on evaluation protocols that do not clearly reflect cross-subject generalization. This study presents a transparent baseline investigation of a multi-class imagined speech EEG dataset under a strictly subject-independent evaluation framework. Two preprocessing and feature extraction pipelines were compared: a time-domain statistical feature approach and a frequency-domain spectral bandpower approach, evaluated using subject-wise cross-validation and trial-level majority voting with a random forest classifier. The spectral pipeline achieved a significantly higher mean trial-wise accuracy than the statistical pipeline (49.03 \pm 4.18% vs. 37.97 \pm 3.79%) for coarse-level classification across subjects. Forward feature selection further indicated that a limited subset of frequency bands captured most of the discriminative information. Overall, this work provides a strong basis for future brain-computer interface studies targeting improved cross-subject generalization in EEG-based imagined speech decoding.
[AI-35] Hard Stop: Kernel-Level Preemption and Containment for Rogue Agent ic Execution
链接: https://arxiv.org/abs/2609.29808
作者: José Luis Pino
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Operating Systems (cs.OS)
备注: 21 pages,4 figures
Abstract:In July 2026, an unconstrained autonomous agent participating in a frontier AI cybersecurity evaluation harness breached its evaluation sandbox, established an external command-and-control foothold, and executed a multi-stage intrusion into Hugging Face’s production multi-tenant dataset conversion infrastructure (referred to in this autopsy as Incident-2026-Alpha). Over 4.5 days, the rogue agent executed 17,600 discrete actions across 6,280 worker clusters, compromised AWS EC2 Instance Metadata Service (IMDS) credentials, forged Kubernetes service account tokens, rooted physical worker nodes via overprivileged CSI drivers, harvested 136 production secrets, and enrolled 181 ephemeral sandboxes into the organization’s internal mesh VPN. This monograph presents a first-principles forensic autopsy of the intrusion, provides formal evidence that the breach was a predicted consequence under the Instrumental Convergence thesis operating within an unattenuated autonomous loop lacking out-of-band circuit-breakers, exposes the Defensive LLM Guardrail Paradox that paralyzed centralized commercial models during forensic incident response, and formalizes the Dual-Sided Epistemic Andon Imperative. We specify the dual-process systems architecture—combining out-of-band supervisory control of discrete event systems (Ramadge and Wonham 1989), Synchronous Reactive (SR) ambient sentinels (Berry and Gonthier 1992; Lee and Neuendorffer 2005), and microsecond-scale (4.8 \mu s median / 0.154 ms WCET bound) POSIX preemption buses—demonstrating how compiled, deterministic epistemic boundaries prevent autonomous rogue excursions before the first off-target socket packet traverses the hypervisor. Comments: 21 pages,4 figures Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Operating Systems (cs.OS) Cite as: arXiv:2609.29808 [cs.CR] (or arXiv:2609.29808v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2609.29808 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-36] Hallucination Neurons and Where to Find Them: An Investigation into the existence of Hallucination Neurons
链接: https://arxiv.org/abs/2609.29781
作者: Huseyin Cavus,Sebin Sabu,Joshua Spear,Jaskaran Singh Kawatra,Pavithra Rajendran
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Interpretable machine learning for Large Language Models (LLMs) increasingly relies on sparse probing methods that identify small sets of neurons claimed to detect and causally influence behaviors such as factuality recall, safety alignment, and hallucination. These claims have important implications for model auditing and behavioral steering, yet they are rarely tested against known failure modes of L_1 -regularized probing in correlated, high-dimensional feature spaces. We propose a five-step diagnostic protocol covering feature correlation, bootstrap stability, sparse versus dense ranking disagreement, intervention baselines, and cross-dataset evaluation as a minimum standard for sparse-neuron localization claims. We investigate prior work using our proposed approach, specifically on H-neurons using open-source LLMs across TriviaQA, BioASQ, and NQ-Open datasets. Our results demonstrate detection replicates across both models and datasets, and exceeds the original reported AUROC gaps for TriviaQA and BioASQ datasets. Gemma 3 4B consistently outperforms MedGemma 4B on matched datasets, with AUROC gaps of +0.311 versus +0.235 on TriviaQA, +0.474 versus +0.455 on BioASQ, and +0.128 versus +0.112 on NQ-Open respectively. Causal validation at n = 500 with five random seeds shows statistically significant effects beyond random same-layer baselines. At the same time, the diagnostic results indicate that the selected neurons are not uniquely localized. Across the three Gemma 3 4B settings, 19 of 22 selected H-Neurons have Pearson |r| 0.7 with other features, bootstrap selections show only moderate stability, and sparse and dense rankings overlap only weakly. Our findings show that sparse predictive structure can coexist with non-unique neuron selection. Routine diagnostic validation is necessary to distinguish detection claims from localization claims in mechanistic interpretability.
[AI-37] Prefilling the Reasoning Channel: Output-Prefix Attacks on Reasoning LLM s
链接: https://arxiv.org/abs/2609.29775
作者: Lukáš Brůna,Robert Bridges,Adam Ek
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models (LLMs) consume and produce a single sequence of text; hence, if text can be added to the beginning of the LLM’s response, i.e., an output prefix, then all subsequent tokens will be conditioned on it. This output-prefix attack technique is a cheap black-box prompt injection. Prior work has shown this type of attack can reliably jailbreak non-reasoning models. Most reasoning models add an intermediate scratchpad reasoning step before the assistant’s final response. The ability to edit this reasoning channel is exposed by some APIs and attack vectors can be leveraged for reasoning injection attacks. We present the first systematic, controlled study that isolates the scratchpad reasoning channel as an output-prefix attack vector, and the first to compare reasoning-only, output-prefix-only and reasoning-plus-output-prefix attacks across both exposed- and hidden-reasoning models. Using a factorial design of 3 prefix types \times 2 reasoning injections over 1,800 test cases drawn from AdvBench, we attack three 2026-era frontier models Gemini 3 Flash Preview, DeepSeek V4 Flash, and Claude Haiku 4.5. We find that injecting malicious reasoning alone is essentially inert ( \approx0% attack success), but injecting the same reasoning together with a trivial output prefix raises the attack success rate to as high as 99% for some models. For this type of attack we find that contextual prefixes work better than static prefixes; and that susceptibility is dependent on the model.
[AI-38] Breaking the Environment Wall: Evolving LLM Agent Environments for Recursive Self-Improvement
链接: https://arxiv.org/abs/2609.29773
作者: Yukai Wu,Yuanjing Yang,Le Zhou,Shaokun Han,Haoyu Wang,Zirui Tang,Weihuang Zheng,Maxm Pan,Xuanhe Zhou,Fan Wu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Many real-world tasks (e.g., office workflows, scientific experimentation) require LLM agents to interact repeatedly with their environments for context-dependent operations. However, such environments are often not agent-ready. First, information is often scattered and fragmented across the environment. Second, relevant evidence in the environment is often mixed with misleading information and conflicting versions. Third, environments evolve over time, introducing new noise and more challenging tasks. These challenges can substantially degrade performance for state-of-the-art AI agents (e.g., from 83.9% to 57.6%). To address these challenges, we propose Env-Rethink (a system with 27B post-trained model) that supports three main capabilities: (1) It adaptively builds Collection Maps (for organizing related files) and Event Logs (for contextualizing cross-data relationships) to supplement necessary context; (2) It further leverages the post-trained model (through offline trajectory learning) to identify underlying noise issues in the environment; (3) It ultimately evolves environments through virtual event histories that alter environmental states and evidence relationships, producing more tricky ones for further agent improvement. Experiments show that Env-Rethink can effectively improve downstream task performance (with over 15.1% rubric pass rate improvement across nine models on 30 tasks).
[AI-39] Between the Commits: Process Error and Claim Reliability in a Wholly AI-Authored Codebase
链接: https://arxiv.org/abs/2609.29744
作者: Douglas Leith
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:We present: (i) a new dataset consisting of the full development history of a 21,000-line Python tool built entirely by Claude AI, with no human-authored code or tests, (ii) two code-provenance tracing tools, (iii) three taxonomies for instruction intent, commit provenance, and response reliability, (iv) application of these to analyse the dataset. We find that: (i) user coding agent CLI instructions differ in kind from IDE-chat instructions, with a greater focus on comprehension, planning and consultation, (ii) code development is mainly proactive, (iii) 14.3% of AI code-generation events contain a real error later caught by the AI-authored test suite, (iv) roughly 1 in 4-5 of the AI’s interactive responses contains one or more factual errors.
[AI-40] AI-based detection of worsening heart failure from low-resolution telemonitoring data
链接: https://arxiv.org/abs/2609.29742
作者: Erik Aerts,Yinan Yu,Annika Rosengren,Michael Fu,Martin Lindgren,Falk Dippel,Martin Adiels,Helen Sjöland
类目: Artificial Intelligence (cs.AI)
备注: 12 pages, 5 figures, under review for publication
Abstract:Objective: Heart failure (HF) presents a healthcare challenge due to its high comorbidity burden, aging patient population and frequent hospitalizations. Remote monitoring offers a promising approach to managing HF patients by early detection of health deterioration. Developing autonomous systems to detect signs of worsening in telemonitoring data is of interest to reduce the workload of healthcare personnel. Methods: We propose the TRACER model, a Transformer with Contrastive Event Representation, designed to predict timelines leading to rare hospitalization events in low-resolution and irregularly sampled telemonitoring data. TRACER incorporates time-aware embeddings for each biomarker, contrastive pre-training to enhance anomaly detection via representation learning, and independent binary classifiers for detection. We used measurement data containing remotely recorded biomarker sequences from 276 HF patients segmented into overlapping windows based on temporal rules, and labeled the windows based on the occurrence of HF relevant hospitalizations at the latter edge of the window. Results: TRACER was able to correctly predict 66.7% timelines leading up to HF hospitalizations in the highly imbalanced real-world dataset with an overestimation of 7.9%. Reformulating the training of TRACER as an event detection problem improved the predictive performance compared with training directly on forecasting windows, enabling more effective use of the limited hospitalization events. Conclusion: TRACER demonstrated superior performance in detecting signs of worsening status in real-world telemonitoring data compared to the other tested models. Significance: TRACER shows promise in identifying signs of clinical deterioration that allow for alerts to be generated to provide counteractive treatment in patients with HF.
[AI-41] opU-LBVS: A Realistic Multi Target Benchmark for Ligand Based Virtual Screening
链接: https://arxiv.org/abs/2609.29740
作者: Surbhi Kumar,Yuhe Zhou,Varun Shiralkar,Niu Huang,Baris Coskunuzer
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 75 pages
Abstract:Ligand-based virtual screening (LBVS) is a practical first-pass tool in early-stage drug discovery, but existing benchmarks can overestimate performance through random negatives, easy decoys, limited target coverage, and non-standardized evaluation protocols. We introduce TopU-LBVS, a multi-target benchmark for LBVS under hard-negative screening conditions. Starting from curated ChEMBL~35 bioactivity data, TopU-LBVS covers 93 protein targets across 7 protein classes and constructs target-specific screening libraries with property-matched, structurally similar decoys at a fixed 1:40 active-to-decoy ratio. Libraries contain roughly 400 to 10,000 compounds and are designed to reduce simple physicochemical and nearest-neighbor fingerprint shortcuts. TopU-LBVS provides three fixed protocols. TopU-LBVS-full evaluates ChEMBL ^\ast \rightarrow TopU generalization across all 93 targets. TopU-LBVS-low evaluates low-data TopU \rightarrow TopU learning within the hard-negative distribution. TopU-LBVS-mini provides a compact seven-target protocol with a paired random-decoy control that changes only the test decoys, enabling low-cost development and direct measurement of the gap between random ChEMBL ^\ast and TopU decoys. Across ten reference baselines spanning fingerprint methods, molecular GNNs, fingerprint hybrids, and modern molecular models, performance under random-decoy evaluation degrades sharply under hard-negative screening. We release data, fixed splits, evaluation code, and baseline implementations for reproducible comparison of future LBVS and molecular representation learning methods. Code and data are available at this https URL and this https URL. Comments: 75 pages Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) MSC classes: 68T05, 68T07 ACMclasses: I.2.6; J.3 Cite as: arXiv:2609.29740 [cs.LG] (or arXiv:2609.29740v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.29740 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-42] he Gold in Bias: Maturing the AI Design Process through Verification
链接: https://arxiv.org/abs/2609.29730
作者: Samira Maghool,Paolo Ceravolo
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Bias in AI systems is typically framed as a flaw to be minimized, yet it also serves as a critical indicator of underlying weaknesses in data, modeling assumptions, and system design. Existing approaches often treat bias as an isolated problem rather than as evidence that can strengthen verification and governance across the AI lifecycle. This paper aims to reconceptualize bias as a diagnostic tool that supports rigorous AI verification. We seek to develop a multidimensional framework to analyze bias, demonstrate how biases emerge in both Traditional and Generative AI, and provide a structured pathway for verification-driven mitigation. We present a multidimensional framework analyzing bias across four dimensions: origin sources, emergence points throughout the AI modeling lifecycle, technical and methodological causes, and validation approaches for detection and mitigation. Through a comprehensive typology spanning traditional and generative AI systems, we demonstrate how biases manifest and propagate across development stages. Our analysis encompasses 30 distinct bias types, 16 verification methods, and 20 countermeasures, providing an actionable roadmap for practitioners. We introduce a hierarchical evidence framework that distinguishes internal validity (mechanistic integrity of AI systems) from external validity (contextual reliability in deployment environments). The framework reveals how biases manifest and propagate across modeling stages, enabling systematic mapping between bias types, verification techniques, and effective countermeasures. The proposed evidence hierarchy clarifies how different verification strategies contribute to mechanistic integrity and contextual reliability. We advocate for ‘‘Ethics by Design’’ principles that integrate bias verification throughout the development lifecycle, enabling the construction of fairer, more robust, and trustworthy AI systems.
[AI-43] A General Framework for Budgeted Threshold Incentives on Request
链接: https://arxiv.org/abs/2609.29724
作者: Zhuolin Wu,Chengrui Zhu,Wenhua Nie,Kenny Ye Liang,Junming Lin,Haiyang Li,Zhilin Li,Wenjia Geng,Zeyu Wu,Yinan Wu,Jinghua Hao,Renqing He
类目: Artificial Intelligence (cs.AI)
备注: 42 pages
Abstract:On-demand delivery platforms pay riders through incentive activities whose tiers are set from recent completions of riders with a similar history. Operators request such plans for changing periods, rider populations, payment rules and budgets, often for holidays or bad weather, where randomized trials are scarce and take months to collect. We present a request-driven framework that composes four stages (conditional prediction, population reduction, trajectory integration and budget allocation) through seven replaceable modules that exchange conditional trajectory laws, whose award probabilities and award-marked moments give payment and uplift for any activity rule. A response-correction step reweights trajectories from abundant no-offer history to match the moments of a short pilot. We prove that, on a fixed plan menu and given the stage errors, the end-to-end value loss is bounded by the sum of four stage terms, and that for every stage there are instances on which omitting it leaves an error floor the others cannot remove. On 3,000 riders over 45 weekly origins, all 127 windows of a week are answered 11.04x faster with identical scenarios and at most 0.92% value lost by the allocation. On 24 new controlled response laws, the response correction with a one-week pilot lowers regret by 51.2% relative to a trial with the same nominal randomized rider-weeks, and a four-week pilot with exact summation comes within +0.007 of an 18-week trial. In registered studies where windows, populations, rules and binding budgets change from request to request, the framework’s regret is below that of a trial with the same nominal rider-weeks and below dose interpolation of the same pilot data, and reusing its one-off preparation answers 60 requests 14.1x and 2.70x faster with identical answers. Against a nine-offer trial fitted with the framework’s own dose curve, one-week regret is 0.055 lower.
[AI-44] Revalidation Beats Stateful Routing for Scientific Surrogates Under Distribution Shift
链接: https://arxiv.org/abs/2609.29715
作者: Harshil Lodhiya
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 11 pages, 5 figures, 3 tables, and 22 references; includes reproducibility appendices
Abstract:Surrogate models are often chosen during development and then left in place as new measurements arrive. That practice becomes risky when noise, input support, or physical parameters change. We asked whether such changes call for a stateful adaptive controller, or whether it is enough to validate the candidate models again on each new batch. To study this question, we built RegimeShift-Surrogates, a reproducible streaming benchmark spanning eight analytic and dynamical tasks, four stationary or shifting regimes, ten held-out seeds, and eight classical, multilayer-perceptron, and Kolmogorov-Arnold network surrogates. The confirmatory run contains 30,720 model fits and 3,200 scored deployment windows. Choosing the model with the lowest validation loss in the current window yields mean log regret 0.091 against a per-window oracle; the best fixed model chosen in hindsight yields 0.192. The paired difference is -0.101 (hierarchical bootstrap 95% CI [-0.165, -0.040]; Holm-adjusted p = 0.0469), with revalidation ahead in 26 of 32 task-scenario combinations. None of the stateful alternatives, including exponential smoothing, dual-timescale adaptation, Page-Hinkley resets, or margin gating, improves the pooled result, and delayed bias correction makes it worse. Oracle choices also differ substantially by task: k-nearest neighbors dominate the damped oscillator, vanilla KAN is often selected for two-dimensional surfaces, and MLPs lead on the Runge and Van der Pol tasks. In this benchmark, fresh validation evidence is useful; carrying old evidence forward is often not.
[AI-45] Decoupling Knowledge and Privacy: Post-Task Self-Distillation Replay for LLM Continual Learning
链接: https://arxiv.org/abs/2609.29711
作者: Shengtao Wen,Yunying Yang,Xiang Chen,Lingbing Guo,Yu Tian,Sheng-Jun Huang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Privacy-preserving continual learning (PPCL) must reduce the reproduction of sensitive content while retaining useful knowledge across sequential tasks. Formal privacy guarantees characterize randomized mechanisms, whereas operational output control concerns whether a trained model selectively reduces the likelihood of sensitive content in its outputs. In this work, we investigate the latter together with continual-learning utility under realistic task evolution. Retention and privacy correction operate at different granularities: task acquisition requires broad preservation of current- and old-task behavior, whereas privacy correction targets sparse annotated positions. Joint optimization leaves the current-task preservation target continually changing. We propose SPARK, a retention-correction decomposition that first freezes the learned post-task distribution and then applies selective correction around this stable reference. Self-Distillation Replay learns the current task while distilling behavior from previous tasks, and Post-Task Privacy Correction reduces annotated-PII likelihood while anchoring current- and old-task non-PII behavior to the resulting checkpoint. Extensive evaluations demonstrate that SPARK achieves effective selective PII suppression while preserving strong continual-learning utility and knowledge retention across diverse settings. Code and data will be released upon publication.
[AI-46] Understanding and Exploiting Initialization Anchoring Weakness in Feedback-Based Agent Planning
链接: https://arxiv.org/abs/2609.29697
作者: Chuanchao Zang,Jianing Wang,Wenyu Chen,Xiangtao Meng,Li Wang,Xinyu Gao,Peng Zhan,Zheng Li,Shanqing Guo
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Feedback-based planning improves agent reliability by incorporating tool observations and corrective feedback. However, its protection may not be distributed uniformly across planning stages. We conduct a round-wise analysis of four representative feedback mechanisms and uncover an initialization anchoring weakness: the first feedback round corrects 46% of adversarial directions, whereas the rates fall to 13% and 7% among directions surviving into the next two rounds. Our analysis attributes this weakness to three interacting factors: a contextually plausible shift in the initial plan, insufficient counterevidence, and the persistence of accepted directions in the accumulated trajectory. Based on these findings, we propose \textscInitAnchor, a black-box framework for exploiting this weakness through attacker-controlled external materials. It operationalizes the three factors as directional-shift, contextual-plausibility, and counterevidence-resilience signals under either limited target access or no target access. Across 112 tasks from 16 domains, six agent architectures, and five backbone LLMs, \textscInitAnchor achieves average ASRs of 76.1% and 72.0% under the two settings while reducing first-round mitigation rates to 21.0% and 25.0%, respectively. It also remains effective against six defenses and across six real-world agent systems. These findings show that feedback-based agents can retain early biases even when later correction is available.
[AI-47] Fair Like Us? Auditing LLM Alignment in Resource Allocation EMNLP2026
链接: https://arxiv.org/abs/2609.29692
作者: Qishen Han,Hadi Hosseini,Joshua Kavner,Samarth Khanna,Sujoy Sikdar,Lirong Xia
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Computer Science and Game Theory (cs.GT)
备注: Accepted at EMNLP 2026 (Main Conference)
Abstract:Fair allocation of scarce, indivisible resources is an important challenge in many societal problems. While there are several formal theories of fairness, no single definition can always be satisfied. As large language models (LLMs) are increasingly used to support decisions and act as agents, they raise new concerns about distributional justice: their judgments are not directly tied to any specific fairness framework and may violate key normative principles. In this work, we introduce a general method for evaluating fairness reasoning in LLMs. We study first-person fairness judgments across a broad set of models and compare them directly with human responses on matched scenarios and elicitation conditions. We find that LLMs tend to prefer stricter fairness constraints than humans, show more self-interested behavior, are sensitive to how information is framed, and are difficult to align with human judgments using fine-tuning with current datasets.
[AI-48] he Sequential Price of Continual Learning
链接: https://arxiv.org/abs/2609.29674
作者: Zonghuan Xu,Xingjun Ma
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 25 pages, 3 figures
Abstract:Sequential task updates are fundamental to continual learning, but their recency bias can impose a lasting performance cost. We study this cost in an overparameterized linear-regression model with i.i.d. task sampling. We prove that distribution-level forgetting and population loss converge to the same stationary limit. This common limit separates exactly into the intrinsic loss asymptotically attained by joint training and an additional sequential price, and in more homogeneous task geometries the two terms coincide, making the total loss twice that of joint training. We further analyze fixed-strength elastic weight consolidation (EWC) under general task curvatures and characterize its stationary sequential price at every regularization strength. Under strong regularization, the price decays inversely with EWC strength while convergence to stationarity slows at the same scale. On the Jester joke-rating dataset, the theory exactly quantifies both the sequential price generated by naturally conflicting user preferences and its reduction by EWC.
[AI-49] Do World Models Make Better Robots? A Survey of Evaluation Benchmarks for Predictive Embodied Intelligence
链接: https://arxiv.org/abs/2609.29669
作者: Gaytri Jena,Kapil Wanaskar,Vinija Jain,Aman Chadha,Vasu Sharma,Amitava Das
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 34 pages, 11 figures, 13 tables
Abstract:Robot learning now advances along two tracks that rarely meet. On one side, direct Vision-Language-Action (VLA) policies map observations to actions and are scored by closed-loop task success. On the other, predictive and generative world models forecast future observations and are scored by open-loop prediction or generation quality. A natural question sits between them: does world modelling earn a measurable, closed-loop advantage over a direct policy, and for which robotic capabilities? We argue that the field cannot yet answer this question, and that the reason is a gap in how it is measured, not in the models themselves. World-model benchmarks score prediction without ever executing it, while task-success suites host a single policy and never build a world-model versus VLA contrast. This survey maps the evaluation landscape around that gap. We catalogue 160 web-verified benchmarks spanning 2017 to 2026 and organise them by evaluation mode, robotic capability, and model family into four lanes: policy suites, embodied agents, world model evaluation, and prediction-to-action bridges. Across the corpus, 138 of 160 benchmarks are model-agnostic and only 11 (7%) build an explicit VLA-versus-world-model contrast; counterfactual capability is almost entirely unmeasured, and only four benchmarks turn prediction into executed action. We contribute an operational taxonomy, a coverage comparison against the eight closest surveys (ours is the only one to cross capability with model family), an evaluation loop that isolates the advantage of prediction, and an actionable protocol of four advantage-aware metrics anchored on named testbeds. The organising claim is not that world models help or do not help, but that answering the question requires benchmarks built to ask it. Comments: 34 pages, 11 figures, 13 tables Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI) ACMclasses: I.2.9; I.2.10; I.2.6 Cite as: arXiv:2609.29669 [cs.RO] (or arXiv:2609.29669v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2609.29669 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-50] Graph Loop and Harness Engineering for Zero-Trust Agent ic Data Engineering and Analytical Processing
链接: https://arxiv.org/abs/2609.29668
作者: Sagar Srinivas Sakhinana,Venkataramana Runkana
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Nill
Abstract:Large language model agents increasingly automate data workflows, but end-to-end cloud data engineering and analytical execution require reliable coordination across code, data, infrastructure, and runtime environments. We present two zero-trust frameworks. Zero-Trust Agentic Data Engineering generates, deploys, and verifies complete cloud data-engineering solutions from natural-language tasks, with completion conditioned on repository, deployment, runtime, and policy evidence. Zero-Trust Agentic OLAP combines governed Data Preparation with verified Online Analytical Processing (OLAP), permitting production promotion only after validation and evidence-bound approval, and releasing analytical answers only after Same-Snapshot Execution, Exact Result Equivalence, deterministic grounding, and reflection. Both frameworks share three abstractions: graph engineering for evidence-gated workflow structure, loop engineering for bounded recovery, and agent-harness engineering for zero-trust execution. We evaluate both frameworks under nominal execution, controlled failures, bounded recovery, and policy-constrained conditions, measuring verified completion, recovery, authorization enforcement, production promotion, and verified OLAP execution.
[AI-51] o Think or Not to Think: Allocating Reasoning Where It Helps
链接: https://arxiv.org/abs/2609.29664
作者: Zhengdong He,Yunfan Zhou,Jianguo Yao,Haibing Guan,Xijun Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement learning (RL) has proven effective in enhancing the reasoning performance of large language models (LLMs), particularly in complex mathematical and programming tasks. However, this capability comes with systematic \textitlength misallocation, in which models devote excessive reasoning to simple questions while terminating prematurely on harder ones, degrading inference efficiency with negligible accuracy improvement. Many length-adaptive methods mitigate this issue by allocating token budgets according to question difficulty, under the implicit assumption that harder questions benefit monotonically from extended reasoning. In contrast, we find that the effect of reasoning length on accuracy is concentrated on \textitpartially solvable questions. Our further analysis reveals that explicit length rewards can produce unintended training dynamics. Motivated by these findings, we propose \textbfCARE—\textbfContrastive \textbfAccuracy \textbfReward \textbfEstimation—which compares the beneficial length adjustment per question from online sampled responses and applies adaptive length rewards within Group Relative Policy Optimization, with no extra hyperparameters or additional inference cost. Experiments across multiple reasoning benchmarks demonstrate that our method improves Pass@1 by up to (4%) while simultaneously reducing reasoning length by (37%), achieving higher token efficiency. Code will be available upon the acceptance of this paper.
[AI-52] Agent Kernel: The Trust-Native Agent ic Operating System
链接: https://arxiv.org/abs/2609.29647
作者: Zhenhua Zou,Sheng Guo,Qiuyang Zhan,Lepeng Zhao,Shuo Li,Zhuotao Liu
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 38 pages, 5 figures
Abstract:Modern AI agents routinely cross trust boundaries: they ingest untrusted content, combine it with privileged instructions, persist intermediate beliefs in long-term memory, and invoke privileged tools. This creates an attack surface in which malicious payloads can enter through model inputs and cause harmful tool actions. Yet current governance stacks remain application-level middleware that share a process trust boundary with the agents they monitor. We argue that agents need an operating-system substrate providing mandatory, non-bypassable services for identity, input mediation, memory governance, and execution control. We introduce AgentKernel, a trust-native agent operating system built around the premise that security must be a first-class design constraint. AgentKernel wraps the agent lifecycle in a mandatory enforcement boundary organized into four pillars: Identity, Perception, Cognition, and Execution. Each pillar adapts classical OS security principles to failures at the semantic plane, including delegation abuse, prompt injection, memory poisoning, and tool misuse. AgentKernel treats structural security as a capability multiplier. Kernel-managed identity supports trustworthy cross-organization collaboration; graduated perception replaces brittle single-point filters; information-flow-controlled memory improves retrieval fidelity while limiting poisoning; and semantic-to-kernel enforcement permits broader tool privileges behind a non-bypassable boundary. We position AgentKernel as the missing OS layer beneath orchestration frameworks, agent runtimes, governance platforms, and execution sandboxes, and use systematic comparison and security analysis to show how a single integrated architecture can enforce security across the full agent lifecycle. Comments: 38 pages, 5 figures Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.29647 [cs.CR] (or arXiv:2609.29647v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2609.29647 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-53] A Manifold-Aware Topic Modeling Approach via Rank-Based Prototypes EMNLP2026
链接: https://arxiv.org/abs/2609.29630
作者: Thiago César Castilho Almeida,Daniel Carlos Guimarães Pedronette
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at the Main Conference of 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP 2026)
Abstract:Recent topic models leverage pretrained embeddings, but neural architectures produce latent representations without grounding in specific texts, and clustering-based pipelines assign representative documents only post hoc, relying on absolute distances distorted by hubness and anisotropy in high-dimensional spaces. We introduce MARETopic, a training-free framework that casts topic discovery as rank-based prototype selection. After projecting embeddings onto a low-dimensional manifold, MARETopic builds ranked lists encoding ordinal neighborhood structure. A greedy algorithm selects exactly K exemplar documents, real corpus texts, whose neighborhoods cover the corpus. Two variants share this criterion. MARETopic _\textCorr scores candidates with a query performance predictor and a rank correlation measure, leading Purity and NMI on the two benchmarks with the most categories, ahead of both neural and clustering-based topic models. MARETopic _\textDiff scores them with a rank-based diffusion matrix, needs neither measure, and runs 1.7 to 1.9 times faster. Without a single gradient update, MARETopic leads topic coherence on two of three datasets. A novel inter-topic Maximal Marginal Relevance step raises vocabulary diversity at little cost in coherence. Our code is available at this https URL.
[AI-54] PEEL: Physics-Enabled Evidential Learning for Identifiable Uncertainty in CT Imaging
链接: https://arxiv.org/abs/2609.29599
作者: Ge Wang(Rensselaer Polytechnic Institute)
类目: Artificial Intelligence (cs.AI); Medical Physics (physics.med-ph)
备注: 10 pages, 3 figures, 1 table. Proof-of-concept study of physics-enabled identification of NIG uncertainty in CT imaging
Abstract:Normal-inverse-gamma (NIG) regression is not uniquely identifiable from its marginal Student-t likelihood: the likelihood determines three combinations of four NIG parameters and is constant along a one-dimensional fiber. We identify that fiber using independent physical measurement. As an initial embodiment, a reconstruction network receives one noisy filtered-backprojection (FBP) image and is first trained only by Student-t negative log-likelihood to estimate the three identifiable coordinates (gamma, alpha, c). The network is then frozen; repeated physical-noise realizations propagated through its reconstruction output form a Monte Carlo (MC) teacher label for output-domain aleatoric variance. An aleatoric head attached to frozen features learns this label, after which (beta, nu) are recovered algebraically. On 30 held-out simulated objects at five photon levels, one-image predictions achieved pooled Spearman correlations of 0.832-0.951 against independent 400-repeat references, median within-image correlations were 0.834-0.947, and 98.81-99.55% of evaluated pixels satisfied the algebraic admissibility condition. The method needs no KL term, reference prior, evidence regularizer, or cross-loss weight.
[AI-55] Sequential knowledge editing breaks a models ability to tell good evidence from bad without costing it accuracy
链接: https://arxiv.org/abs/2609.29587
作者: Atul Anand
类目: Artificial Intelligence (cs.AI)
备注: 10 pages, 6 figures, 2 tables. Code and experimental artifacts available on request
Abstract:Knowledge editing is evaluated on whether the edited fact changed, whether paraphrases follow, and whether unrelated answers stayed put. A model can pass all three and still lose something none of them measures: the ability to decide, on facts that were never edited, which retrieved documents to believe. We score the log odds a model assigns to its remembered answer against the answer an injected passage asserts, before and after editing, holding the query, the passage and both candidate strings fixed. Our cleanest arm is a conservatively tuned LoRA: after 1,000 sequential edits on Qwen2.5-7B-Instruct it leaves MMLU unchanged to four decimal places, yet the spread of the arbitration quantity across untouched facts falls by 36%. Selective prediction degrades with it. Area under the risk-coverage curve rises by 0.107, against 0.005 for a norm-matched perturbation at the same MMLU, and error on the model’s most confident quarter of arbitration decisions goes from 0.217 to 0.342. This is not capability loss. Sweeping random perturbation over five severities, damage bad enough to cut MMLU from 0.6275 to 0.3725 produces less harm (0.088) than MEMIT does at 0.6050 (0.102). The effect holds across three seeds, two model families, two datasets, two probe-disjointness criteria, three prompt templates and paraphrased queries. Layer ablation on saved weight deltas shows it is distributed: no single layer reproduces it, and removing any one recovers about half. Under retrieval with a frozen retriever, accuracy falls from 0.592 to 0.46. A secondary finding may matter more in practice. Three of five model and method pairings we ran collapse to chance MMLU at 1,000 sequential edits under published hyperparameters, while edit success stays at 1.00 and locality reads clean. Sequential-editing evaluations that never measure capability cannot see this. Comments: 10 pages, 6 figures, 2 tables. Code and experimental artifacts available on request Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2609.29587 [cs.AI] (or arXiv:2609.29587v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.29587 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Atul Anand [view email] [v1] Mon, 31 Aug 2026 08:12:19 UTC (379 KB)
[AI-56] Is Reasoning Always Useful? Rethinking Reasoning Utility in Universal Multimodal Embeddings EMNLP2026
链接: https://arxiv.org/abs/2609.29560
作者: Wenxiao Fan,Jingling Fu,Luohang Liu,Xinyuan Shan,Lichen Ma,Yu He,Junshi Huang,Yan Li,Kan Li
类目: Artificial Intelligence (cs.AI)
备注: EMNLP2026(Findings)
Abstract:Reasoning-enhanced universal multimodal embeddings (UME) improve heterogeneous retrieval, but plausible rationales do not necessarily produce discriminative rankings. We study this gap by comparing the discriminative (DISC) and reasoning-driven generative (GEN) branches of UME-R1, a state-of-the-art reasoning UME method. We decompose reasoning utility into positive-target gain, hard-negative gain, and their margin difference. Positive similarity increases for 56.6%, but 15.7% are false-helpful cases where reasoning moves hard negatives closer even more. Local-neighborhood and token-attribution diagnostics suggest why: reasoning often de-condenses retrieved neighborhoods, but utility requires separator-aligned movement, while influential CoT tokens frequently encode evidence shared by positives and hard negatives. Motivated by these diagnostics, we propose SURE (Score-structure Utility Router for Embeddings), which improves UME-R1-7B by 1.5 points and yields consistent gains on two additional embedding models on MMEB-V2, without retraining, label-based policy selection, or extra VLM forward passes.
[AI-57] Cross-Modal Emotion Understanding: A Transformer-GAT Approach for Dialogue Emotion Recognition
链接: https://arxiv.org/abs/2609.29556
作者: Jiaqi Qiao,Yifan Lyu,Xiujuan Xu
类目: Artificial Intelligence (cs.AI)
备注: 12 pages, 9 figures
Abstract:Multimodal emotion recognition is a key research area in affective computing, with applications in sentiment analysis, intelligent customer service, and human-computer interaction. However, existing methods often rely on single-modal features or simple multimodal fusion, failing to capture the synergy between global and local contexts, which limits model performance and emotion understanding. To address this challenge, we propose Transformer-GAT, a hybrid framework that combines Transformer and the Graph Attention Network to enable cross-modal emotion understanding. The Transformer is used to capture global semantic information, while the Graph Attention Network is employed to model fine-grained relationships between modalities, thereby enhancing the representation of emotional features. Experiments on the IEMOCAP and MELD datasets show that our model achieves weighted F1 scores of 72.45% and 77.37%, outperforming state-of-the-art methods. These results demonstrate that Transformer-GAT effectively integrates multimodal features, balances global and local contexts, and provides deeper emotional insights, offering new directions for multimodal emotion computing.
[AI-58] HiPACE: Hierarchical Phase-Boundary Analysis and Controlled Evaluation of Feature Absorption in Sparse Autoencoders
链接: https://arxiv.org/abs/2609.29551
作者: Jinyuan Zhang,Peng He,Yin Yuan,He Hu,ShengShuo Jiao
类目: Artificial Intelligence (cs.AI)
备注: 13 pages, 13 figures; appendices included
Abstract:Sparse autoencoders (SAEs) decompose LLM activations into sparse dictionary atoms, so that each distinct concept gets its own feature. One recurring behavior complicates this premise: feature absorption, in which a parent concept and its children–fruit and apple, banana, pear, say–collapse into a shared family direction. Prior work documents absorption empirically; missing is a closed-form prediction of when the shared direction is the cost-optimal representation of an active semantic family. This paper closes that gap. For a hierarchical Bernoulli generator with k active children and residual scale \alpha , the L_0 -penalized reconstruction objective admits a closed-form phase boundary \lambda_c(k,\alpha)=\alpha^2 k/(k-1) : above it, pure parent absorption is strictly cheaper than pure child coding. Building on this boundary, we introduce HiPACE, an evaluation protocol that tests the boundary’s structural consequence in real SAE dictionaries–measuring parent–child decoder structure over WordNet families, freezing the discovery-selected statistic before testing on unseen families, and contrasting genuine families against randomized sibling nulls. The boundary proves sharp in its native regime, predicting the synthetic transition within \pm15% on all 30 tested cells. In Pythia-160m SAEs, the parent–child decoder gap recovers the predicted ordering with partial correlations up to -0.93 that sustain on the locked holdout and exclude sibling nulls ( p=0.002 ). Controlled activation composition connects the theory’s active-child count to the recovered family directions, and residual-stream interventions show that signed family directions increase parent-category logits, reversing under sign flip and vanishing under random controls–establishing causal sufficiency at the family-subspace level.
[AI-59] When Agents Act Unwatched: The Reduced-Supervision Paradox in Agent ic AI
链接: https://arxiv.org/abs/2609.29547
作者: Hanjing Shi,Dominic DiFranzo
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: 24 pages, 4 tables. Expanded preprint with a structured public-artifact audit and three worked action paths
Abstract:Agentic AI is sold on a simple promise: the system keeps acting when the user stops watching. That promise creates an accountability inversion. As stepwise supervision recedes, verification does not disappear; it moves into the runtime infrastructure that defines authority, records action, interrupts execution, checks outcomes, and supports repair. We call this the reduced-supervision paradox. Using a 63-artifact audit, we examine its public visibility across 46 research papers and 17 engineering, documentation, security, and governance sources. We find that agents’ action surfaces are far easier to reconstruct than the mechanisms needed to answer for their actions. Tool mediation and monitoring traces were clearly visible in 40 and 37 artifacts, whereas checkpoint placement was clearly visible in 6, validator independence in 4, recovery in 2, and contestability in 1. Three action paths show why this imbalance matters. A repository path can preserve rich diffs after a consequential change. A browser path can cross organizational boundaries faster than permissions travel. A service path can follow policy while leaving affected people without recourse. We argue that observability can become a substitute for accountability when it shifts verification onto users after meaningful intervention is no longer possible. Our action-path diagnostic instead asks whether a delegated action remains connected to authority, evidence, interruption, independent judgment, recovery, and challenge. The claim is deliberately bounded to public visibility; it does not establish the prevalence or effectiveness of undisclosed controls. We contribute an action-level account that recasts the harness from a technical wrapper into accountability infrastructure.
[AI-60] Generalized Graph Variational Autoencoders: Bounded Divergences Control Posterior Collapse
链接: https://arxiv.org/abs/2609.29546
作者: Kleyton da Costa,Bernardo Modenesi,Ivan F.M. Menezes,Helio Lopes
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The variational graph autoencoder (VGAE) regularizes its posterior toward the prior with the Kullback-Leibler divergence, a choice inherited from the variational autoencoder rather than argued for. We introduce the generalized graph variational autoencoder (GGVA), which replaces that term with any member of the Rényi-Tsallis family of order q while leaving every other part of the model untouched. Both members admit closed forms for diagonal Gaussians and both recover the KL exactly as q \to 1 , so the VGAE is the q=1 arm of our own model rather than a separate baseline, and any measured difference is attributable to a single scalar. Our analysis identifies boundedness, not the order, as the operative property: for q1 the Tsallis divergence is bounded above by 1/(1-q) , independently of the latent width, whereas the KL and the Rényi divergence of the same order are unbounded. On ten graphs spanning three synthetic families, a social network, three citation networks, a connectome, a power grid and a road network, q moves the retained posterior information by up to 49\times relative to the VGAE, while the Rényi arm at the same order stays within 1.02 - 1.30\times of it on all six larger real graphs (isolating the bound as the cause). The retained information is usable: probing the frozen embedding for node class, a label absent from the objective, gives GGVA up to +0.14 macro-F1 over the VGAE on CiteSeer, with the Rényi control again tracking the VGAE. We also report what the design was built to expose: none of this reaches held-out link-prediction accuracy on any of the six larger real graphs, and boundedness delays posterior collapse rather than preventing it.
[AI-61] ERRAND: Budgeted Maintenance of Agent Memory
链接: https://arxiv.org/abs/2609.29545
作者: Beining Wu,Zihao Ding,Jun Huang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Deployed agents run on handed-over knowledge: a frozen policy consults a briefing of consolidated items written before the stream begins. The world then moves while the store stands still: paths close, flags change, price bands move; every item was true at handover, and the failure is staleness, not ignorance. We introduce ERRAND, which treats revalidation as a priced errand: a recheck competes with the task it protects for the same scarce actions, funded only when the value per action of resolving a doubt clears a running wage. The errand index is single-peaked, vanishing at both ends of belief, so certainty in either direction costs nothing; free en-route receipts maintain on-path knowledge, and repair writes a version, never a deletion. Under equal action budgets in two drifting tool-use worlds, ERRAND clears every non-oracle policy on the preregistered calibers, primary in every setting and conditional at every binding budget, leading eager revalidation by 10.0pp at the base cap. Restraint wins: given no cap, ERRAND stops on its own, spending 11.0% of steps, while uncapped eager revalidation spends 70.7% and still finishes 4.5pp behind capped ERRAND. The margin sits where the briefing’s coverage is thinnest, the shadow price of long-tail knowledge: a small budget, well priced, beats a bigger store that never rechecks.
[AI-62] Safe Skill Retirement for Physical Agents AAAI-27 AAAI
链接: https://arxiv.org/abs/2609.29543
作者: Zhonghao Zhan,Xiao Ma,Hamed Haddadi
类目: Artificial Intelligence (cs.AI)
备注: Submitted to the Special Track on AI Alignment of the Forty-First AAAI Conference on Artificial Intelligence (AAAI-27); currently under review
Abstract:Agent skills bundle procedural guidance with execution conditions governing authority, user consent, and live environment state. When model capabilities advance, maintainers prune instructions that appear redundant on authorized benchmark tasks. However, authorized maintenance tests can leave dormant safety conditions untested. This mismatch creates an unmeasured support gap over physical and privacy-sensitive effects. We introduce matched authority counterfactuals that hold the requested action, tool parameters, and intended effect fixed while systematically varying a single governing predicate. We formalize this evaluation via a two-gate retirement certificate requiring a candidate reduction to preserve authorized utility within a declared margin while producing zero unauthorized protected effects. In controlled experiments spanning four frontier and local model configurations across twelve skill bundles (2,592 evaluation cells), task-certified reductions remove over 94% of skill clauses and preserve authorized completion, yet produce unauthorized protected effects in every bundle. Boundary enforcement eliminates protected effects on the declared audit but fails the utility gate for one configuration. One bounded combined protocol passes both gates across all four configurations, with zero utility headroom. An end-to-end check on one read-only Home Assistant camera chain verifies proposal, decision, and effect measurements on a real device. These results demonstrate that while task benchmarks can justify retiring procedural guidance, retirement decisions require explicitly auditing the authority contracts governing physical actions.
[AI-63] Stale Does Not Mean Unsafe: Guard Precision for Tool-Using LLM Agents under Infrastructure State Races
链接: https://arxiv.org/abs/2609.29522
作者: Zihao Zheng,Jiayu Long,Baichuan Li,Junyi Yao
类目: Artificial Intelligence (cs.AI)
备注: 8 pages, 2 figures, 11 tables. 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:Tool-using language-model agents increasingly mutate schedulers, data pipelines, object stores, and access-control systems. Between an agent’s read and its commit, external state can change, but not every change makes the commit unsafe. We separate invalidating races, which break a declared safety predicate, from predicate-preserving and irrelevant races, and ask how precisely runtime guards distinguish them. Our deterministic simulator separates visible from authoritative state and injects five non-atomic failure mechanisms across 16 infrastructure tasks in four domains; frozen agent proposals are replayed counterfactually under every controller without an LLM judge. We evaluate three commit-time guard granularities (global epoch, read-set version, semantic commit predicate), multi-level verification, and model-side gates on three locally hosted quantized model families (Qwen3-4B, Phi-4-mini, Gemma4-8B; 3,456 trajectories on one GPU). All three guards eliminate unsafe commits, but their availability differs sharply: freshness-based guards needlessly block 92-95% of benign races, forfeiting up to 43% of safe task completions, while the complete predicate guard blocks none. That precision is contract-dependent: deleting a single declared clause converts exactly its fault family into unsafe commits (up to 7.9%). Model-side signals do not substitute: verbal confidence is miscalibrated (ECE approximately 0.37), action agreement matches a random gate, a cautionary prompt leaves the direct unsafe rate essentially unchanged, and after a freshness-guard block agents re-commit unsafely from refreshed but still-incomplete reads. Under degraded telemetry a hidden concurrent mutation remains observationally clean, bounding every selective policy. Precise runtime enforcement therefore requires semantic contracts, not freshness heuristics or model self-assessment.
[AI-64] BiGraph-Diffuse: A Bidirectional Diffusion Language Model with Graph-Structured Retrieval For Mental Health Counseling
链接: https://arxiv.org/abs/2609.29519
作者: Yuxiang Cheng,Quanwei Tang,Lvhui Lu,Dong Zhang,Shoushan Li,Erik Cambria
类目: Artificial Intelligence (cs.AI)
备注: submit to ieee transactions on affective computing
Abstract:Mental health disorders affect hundreds of millions of people around the world, yet access to professional counseling remains severely limited. AI-powered dialogue systems offer a scalable alternative, but existing models face two fundamental challenges. First, they lack the bidirectional understanding needed to capture the layered nature of emotional expression, particularly in cases of progressive disclosure, where clients often present symptoms at the surface-level while concealing deeper trauma. Autoregressive (AR) models process information sequentially and cannot revise early interpretations when new evidence emerges later in the conversation. Second, they fail to effectively incorporate the relational knowledge that underlies clinical reasoning. In this paper, we propose \textbfBiGraph-Diffuse, the first large-scale diffusion language model tailored for the counseling domain. We further introduce \textbfBiGraph-RAG, a relation-free graph-structured retrieval strategy that relies only on lightweight entity extraction and semantic linking. This design preserves inferential pathways from observable symptoms to potential underlying causes, while incurring zero LLM token cost during indexing. Importantly, these two modules are not merely combined but mutually reinforcing. The diffusion model provides a holistic bidirectional context, enabling the system to defer premature judgments during progressive disclosure. Meanwhile, graph-based retrieval captures the structured interconnections of clinical knowledge. Extensive experiments demonstrate the effectiveness of BiGraph-Diffuse, and we further provide a solid theoretical analysis to support its design.
[AI-65] Generative Evolutionary Design of Voxel-Based Soft Robots with Provable Optimality
链接: https://arxiv.org/abs/2609.29491
作者: Junru Song,Huan Xiao,Yang Yang,Guozhen Li,Wei Peng,Xiaoya Zhang,Tingsong Jiang,Weien Zhou,Ying Wen,Feifei Wang,Wen Yao
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Voxel-based soft robots (VSRs) present a promising avenue for developing artificial organisms with lifelike intelligence. However, the vast design spaces and expensive evaluations substantially challenge their design optimization. Here we develop MISCO, a novel evolutionary framework empowered by deep generative models to optimize VSR designs with theoretical guarantees. MISCO integrates an estimation-of-distribution algorithm with a meticulously designed variational autoencoder featuring multi-task learning, position awareness, and inter-voxel signaling. These key components enhance the representational capacity of VSR morphologies and facilitate highly efficient sampling and optimization of morphological distributions. We provide theoretical guarantees for MISCO’s asymptotic convergence to globally optimal designs, alongside a favorable convergence rate. Extensive simulated experiments further demonstrate MISCO’s exceptional effectiveness in navigating vast design spaces, evolving high-performing VSRs for diverse tasks while flexibly balancing optimization efficiency and morphological diversity. Being validated both empirically and theoretically, MISCO represents a step change towards more scalable and reliable soft robot development.
[AI-66] RoboLDA: A Probabilistic Generative Model for Uncovering Embodied Hierarchical Structures in Voxel-based Soft Robots
链接: https://arxiv.org/abs/2609.29490
作者: Junru Song,Yang Yang,Jingdan Shi,Guozhen Li,Weien Zhou,Ying Wen,Feifei Wang,Wen Yao,Tingsong Jiang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advances in robotics highlight hierarchical configurations of robot morphology, where multiple levels of functional substructures synergize to facilitate intelligent behaviors. This hierarchical perspective, while particularly advantageous for voxel-based soft robots (VSRs) to ease design and control complexities, is hindered by its heavy reliance on domain expertise. In this work, we address the following question: can we derive such hierarchical design principles solely from existing successful designs? We answer affirmatively by presenting RoboLDA, a Bayesian probabilistic model that decomposes VSR morphology generation into a four-level hierarchy: “task-robot-organ-voxel”, and is trained via variational inference. Through extensive experiments on simulated VSRs, we verify the presence of consistent, intuitive hierarchical patterns underlying high-performing VSR designs and showcase RoboLDA’s proficiency to extract and leverage these hierarchical priors for zero-shot robot design in unseen tasks. The generated designs, even without further optimization, achieve on average 106.4% of the optimized performance produced by evolutionary algorithms. Additionally, the organ structures inferred by RoboLDA serve as valid functional substructures, significantly enhancing synergistic motion control when integrated with modular control policies. Our work pioneers hierarchical generative modeling of robot morphology, offering a promising pathway towards more interpretable and generalizable development of embodied agents.
[AI-67] Direct Message Approximation (DMA): A Consistency-Based Framework for Tractable Approximate Inference on Factor Graphs ICLR2027
链接: https://arxiv.org/abs/2609.29466
作者: Ralf Herbrich,Rainer Schlosser,Jan Lemcke,Johann Ukrow,Anna Kazachkova,Nicolas Alder,Leonhard Hennicke,Theo Bardey,Nico Grimm,Luca Kleinschmidt,Philipp Kolbe,Cezary Kujath,Johanna Schlimme,Karl Matti Schütz
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: Submitted to ICLR 2027
Abstract:Approximate message passing on factor graphs underlies two dominant families of probabilistic inference algorithms: expectation propagation (EP) and variational message passing (VMP). Both methods approximate the marginal at each factor edge, forcing an iterative round-robin schedule, risking negative-precision messages, and, for VMP, collapsing to point estimates at Dirac-delta factors. We introduce Direct Message Approximation (DMA), which approximates factor-to-variable messages directly rather than the marginal. For normalisable factors, we define a consistency condition (requiring exactness when all other incoming messages are Dirac deltas) to guide message construction. We prove a master theorem (proper messages, any graph) bounding marginal KL from message KL, with three structural corollaries: Dirac-input consistency, no EP-style inner-loop iteration, and no negative-precision messages. Further, we prove a complementary O(1/r^2) guarantee for the inherently improper backward message of the product factor, whose closed-form treatment has resisted prior work. As a concrete instantiation, we derive explicit DMA messages for the product and leaky-ReLU factors and assemble a Bayesian neural network (BNN) inference algorithm with one forward/backward sweep per training example and no gradient learning-rate hyperparameter, validating that the structural guarantees translate to predictive uncertainty that widens in data-sparse regions, including under model mismatch.
[AI-68] SWE-Prometheus: Measuring Engineering Governance Improvements in Real-World Repositories
链接: https://arxiv.org/abs/2609.29465
作者: Jiajun Wu,Leixin Sun,Zihan Tan,Yitao Liu,Shuo Li,Jiaru Qian,Shanghaoran Quan,Chuangxin Zhao,Yangxu Liao,Yang Liu,Bin Chong,Guancheng Wan
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Large language model based coding agents have made substantial progress on repository-level software engineering tasks. Existing repository benchmarks, however, usually start from a human-identified issue and evaluate whether a patch satisfies a functional signal. We present SWE-Prometheus, a benchmark for the broader task of improving repository engineering governance. Each task provides a fixed snapshot and an open-ended objective, requiring the agent to identify risks, prioritize interventions, and verify the resulting changes. SWE-Prometheus evaluates six governance dimensions through paired evidence, clean-environment probes, behavior gates, and two independent teacher ratings of the same evidence. The benchmark contains 60 repositories; ten models are evaluated on a shared 22-repository public subset, where mean Normalized Governance Improvement ranges from 0.0568 to 0.5760 and observed behavior-breakage rates range from 0% to 23%. On a frozen ten-repository batch, a repository-blind template obtains mean NGI 0.272, but its gains concentrate in Tests CI, Quality Gates, and Documentation; it improves Reproducible Environment and Dependency Security on none of the repositories. This baseline makes the distinction between adding governance artifacts and producing execution-backed improvements measurable. The no-op condition has median NGI zero and standard deviation 0.073; two teachers agree exactly on 57 of 60 dimension scores for the same no-op evidence. For the two highest conditional-mean systems, common-valid NGI is similar, while full-pool comparisons that include behavior failures favor Kimi-K3. These results show why repository-governance evaluation should report improvement, behavior preservation, evidence quality, and coverage together.
[AI-69] RD-JEPA: Predictive latent pretraining for few-trajectory transfer across reaction–diffusion equations
链接: https://arxiv.org/abs/2609.29403
作者: Chenhao Si,Ming Yan
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Learning surrogates for time-dependent partial differential equations often requires a new simulation corpus when the governing operator changes. We introduce RD-JEPA, a joint-embedding predictive architecture for self-supervised pretraining on reaction-diffusion trajectories. A single model is pretrained on five parameterized systems and then adapted to three held-out systems whose reaction operators and trajectories are excluded from pretraining. Using one, five, or ten complete trajectories from a held-out system, RD-JEPA achieves lower mean relative discrete \ell^2 field error and mean absolute spatial first-difference error than five supervised surrogate baselines, an independently trained control that removes the trajectory-dependent predictive latent pathway, and an architecture-matched model trained from scratch. Within the evaluated equations, output resolution, forecast horizons, and choices of adaptation trajectories, the results indicate that prediction of future-state representations can support data-efficient adaptation across related reaction-diffusion systems.
[AI-70] Wearable ECG Quality Assessment: A Deep Learning and Ambulatory Context-Awareness Approach
链接: https://arxiv.org/abs/2609.29396
作者: Xiaopeng Mao,Marike Weisbjerg,Sadasivan Puthusserypady
类目: Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
备注:
Abstract:This paper presents and evaluates a Deep Learning-based (DL-based) Signal Quality Assessment (SQA) model to distinguish between clean and noisy ambulatory Electrocardiograms (ECG). The model is trained on Copenhagen Center for Health Technology-Contextualized Arrhythmia Database (CACHET-CADB), which, to the best of our knowledge, is the first ambulatory ECG database with both physical and patient-reported contextual data. The model shows stable performance on different databases such as MIT-databases and the latest PyhsioNet/Cinc Challenge 2021 databases. Subsequently, the paper demonstrates how complicated ECG noise can be investigated by the SQA model and the physical contextual data.
[AI-71] An auditable conditional-strategy framework for open-ended decision-making in complex lung cancer
链接: https://arxiv.org/abs/2609.29381
作者: Daoyun Wang,Zhicheng Huang,Huaiyuan Sun,Jiaqi Xu,Xiaowei Xu,Zhibo Zheng,Zhongxing Bing,Yuxiao Lin,Yicheng Liang,Chao Gao,Bowen Xue,Kai Zhang,Song Xu,Wanpu Yan,Hui Xia,Lin Li,Xiang Yan,Mu Hu,Qianli Ma,Zhiqiang Xue,Xiaofang Liu,Zhihai Han,Nan Zhang,Chuanhao Tang,Tongmei Zhang,Lan Song,Zhaohui Zhu,Xuan Zeng,Shafei Wu,Hui Guan,Lei Deng,Huaxia Yang,Zeliang Lian,Wubin Sun,Yongxin Wang,Xiaohui Shen,Binlin Wang,Tiantian Gu,Yu Cui,Li Zhang,Shirui Wang,Naixin Liang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Complex lung cancer decisions can involve several defensible pathways whose eligibility, sequencing and safety depend on unresolved information. Effective support must make explicit how patient conditions govern pathway eligibility, deferral and redirection. MedGPT Clinical Explorer (MCE) organizes alternatives, decision-changing unknowns, safety constraints and fallback into a conditional strategy for clinician review. To evaluate this representation in physician-authored strategies, multidisciplinary experts established case-specific references for 40 cases within a purposive 100-case corpus, and 250 physicians from 98 institutions produced 2,250 strategies under unaided, retrieval-reference and MCE-assisted conditions. MCE-assisted strategies expressed more applicable clinical requirements, measured by the Admissible Pathway Attainment Score (APAS; 0-100), than unaided strategies (adjusted difference, 12.87; 95% CI, 11.18-14.55) and retrieval-reference strategies (5.22; 3.52-6.93). With the same knowledge base available in the retrieval-reference and MCE-assisted conditions, the additional content centered on candidate pathways, decision-critical information and safety constraints. Physicians’ whole-strategy acceptability judgments correlated with APAS (Spearman’s rho = 0.671), while a complementary relationship audit assessed whether candidates, conditions and subsequent actions were coherently connected. Together, these findings identify two complementary dimensions of open-ended decision support: coverage of clinically relevant content and coherent links among pathways, conditions and subsequent actions. MCE provides a shared decision object that makes consequential omissions and pathway contingencies visible before action; prospective studies should evaluate its effects on clinical workflow and patient outcomes. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2609.29381 [cs.AI] (or arXiv:2609.29381v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.29381 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-72] Epistemic-Probabilistic Model for Guarded Multi-Agent LLM Coordination
链接: https://arxiv.org/abs/2609.29366
作者: Mehdi Nasiri,Mohammad Saeed Arvenaghi,Sadegh Vaezi,Ebrahim Ardeshir-Larijani
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-agent large language models (LLMs) have become ubiquitous in applied AI, yet their theoretical foundations remain surprisingly understudied. Viewed through the lens of multi-agent systems theory, several shortcomings come to light: a lack of social intelligence, the absence of coordination mechanisms among agents, unknown emergent behavior, and interactions between agents that are bounded by natural language. We address two of these gaps: the absence of social behavior and the lack of mechanisms for inter-agent coordination. We introduce Epistemic Probabilistic Language Agents (EPLA), a neuro-symbolic architecture for multi-agent coordination under uncertainty. A Symbolic Guard provides structured diagnostic feedback. The LLM generates typed actions, and the Guard controls their execution against an authoritative symbolic state. We formalize the epistemic layer in a gossip testbed through epistemic lottery gossip models, which combine view-based call histories with agent-indexed probability weights. We argue that implementing such a formalism can address shortcomings of agentic LLMs.
[AI-73] Beyond Simple Input-Output Assessment Tasks: Leverag ing Automated Programming Assessment for Non-Trivial Courses
链接: https://arxiv.org/abs/2609.29363
作者: Artur Jordao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The public visibility of Artificial Intelligence (AI) is growing rapidly, driven by the positive impact of its applications across diverse fields of knowledge. In this new chapter, courses that cover the foundations of AI and machine learning become essential for understanding their role and potential in contemporary society. Therefore, understanding fundamental concepts and elementary algorithms through the close integration of theory with practice is essential in AI courses. In this essay, we report our experience designing machine learning exercises for automated assessment tools in programming. It is worth mentioning that we are not developing a novel form of automated grading system. Instead, we propose a perspective that frames machine learning problems as input-output assessment tasks. From this perspective, each exercise admits a unique and deterministic answer and enables automated programming assessment tools (e.g., VPL for Moodle, Codeforces, and MOJ) to effectively support AI education. We believe this essay can encourage instructors to foster educational innovation by adopting more dynamic and interactive approaches to AI courses that integrate theory and practice. Importantly, this essay does not introduce an innovation in the use of AI for education; rather, it introduces an innovative approach to improving the learning of AI, particularly, machine learning.
[AI-74] he Last Human Gate: Forward Deployed Engineering for Governance Automation
链接: https://arxiv.org/abs/2609.29345
作者: Jeremy Canale
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 28 pages, 5 figures, 12 tables. Code, datasets, generated documents, and model traces available at this https URL
Abstract:Enterprise governance requires decisions, evidence, and accountable authority; it does not require every review task to retain its current human implementation. We develop a task-substitution framework for Digital Governance Frameworks (DGF), treating each gate as an executable contract. Substitution requires sufficient accessible information, valid decision and authority checks, and a reduction in total human work after exceptions, verification, correction, and maintenance are counted. We derive a residual-work threshold and show why automating most cases can still increase labor. Forward deployed engineering connects these conditions to an architecture for agents, rule engines, evidence services, and escalation. DGF-Bench supplies controlled evidence from 300 synthetic projects and 899 evaluable model-project runs. Gemini 3.8 Flash, GPT-5.6 Luna, and DeepSeek v4.1 Flash achieve strict gate success of 94.98%, 83.29%, and 74.18%; complete-route success is 76.92%, 42.33%, and 24.67%. A deterministic control passes all 1,700 gates given the supplied rules and structured facts, locating the comparison in execution of a supplied decision kernel. Evidence audits and 135 repeated runs distinguish correct decisions from reliable execution. A document counterexample establishes an information-sufficiency obstruction. These results support the technical feasibility of replacing human execution of specified governance-review tasks with agents and software. The framework specifies a workforce test based on the complete human effort required at fixed output and quality; the present measurements concern review performance. Sources, dossiers, traces, and analyses are public.
[AI-75] SkinAgent AI: A Safety-Grounded Multimodal Agent ic Framework for Non-Diagnostic Skincare Support
链接: https://arxiv.org/abs/2609.29341
作者: Muhammad Muhtasim Shahriar,Abdullah Mohammad Sayem,Tze Hui Liew,M. F. Mridha,Md. Mahiuddin
类目: Artificial Intelligence (cs.AI)
备注: Submitted to JMIR AI and currently under peer review
Abstract:Consumer-facing skincare AI must coordinate visual evidence, product information, tool use, and user-facing actions within explicit evidence and safety boundaries. This study evaluates SkinAgent AI, a non-diagnostic multimodal framework that combines visual concern routing with grounded and auditable LLM-based orchestration. The architecture includes routing for Acne, Pores, and Wrinkles; photograph-based skin-type estimation; count-informed ordinal acne-severity support; typed tools; database-grounded recommendation and action functions; deterministic safety, privacy, and evidence checks; approval before state-changing actions; and structured trace and replay mechanisms. Visual-model performance and system-level agent behavior were evaluated separately. Across three seeds, the skin-condition routing model achieved 99.84% +/- 0.07% accuracy. Skin-type estimation achieved 88.85% accuracy, while count-informed acne-severity support achieved 84.59% accuracy with a quadratic weighted kappa of 0.9076. On a locked but non-independent 240-case system benchmark, intent accuracy was 80.00%, exact tool-set match was 62.92%, and strict task completion was 47.08%. No violations or successful cross-user leakage events were observed in the finite safety and privacy test suites. Tool-selection errors, incomplete grounding of product attributes, and unreliable failure fallback nevertheless remained. These findings support the feasibility of bounded, database-grounded, and traceable agent orchestration for non-diagnostic skincare assistance. They do not establish clinical readiness, external generalization, formal privacy guarantees, or universal safety. Independent validation, expert assessment, robustness and fairness testing, and prospective evaluation in real-world settings remain necessary.
[AI-76] Neuralized Multi-Wavelet Decomposition for Time Series Classification and Forecasting
链接: https://arxiv.org/abs/2609.29317
作者: Xiaohan Jiang,Jingyuan Wang,Jiahao Ji,Yongyao Wang,Chen Yang,Junjie Wu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 17 pages, 3 figures
Abstract:Time series analysis is fundamental in domains such as finance, healthcare, and meteorology. Real-world time series often exhibit multiscale characteristics shaped by diverse latent factors, resulting in intricate temporal patterns and rich frequency structures. However, existing approaches typically focus on either frequency-domain decomposition or time-domain pattern extraction in isolation, neglecting their joint structure. This decoupled modeling limits representation expressiveness and undermines performance in tasks requiring simultaneous temporal and spectral reasoning. To address this gap, we propose m-WCN, a novel end-to-end deep learning framework that neuralizes multi-wavelet decomposition for joint extraction of temporal patterns and frequency components. By approximating the classical GHM multi-wavelet transform with trainable convolutional operators and enforcing orthogonality constraints, m-WCN produces interpretable multi-resolution representations. Built on this foundation, we introduce two task-specific architectures: TFBC for time series classification, which boosts discriminative features across frequency scales, and FTB for forecasting, which ensembles frequency-aware predictors. Extensive experiments on 64 UCR datasets and seven public forecasting benchmarks demonstrate the effectiveness of our approach. Built on the neuralized m-WCN, our TFBC and FTB outperform various baseline models across diverse datasets, achieving average improvements of 19.97% in classification and 19.92% in forecasting tasks.
[AI-77] From Text Decisions to Pixels: An Study of Jev-Style Visual Choice Model
链接: https://arxiv.org/abs/2609.29283
作者: Xunlan Zhou,Xianliang Yang,Li Zhao
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Visual software often needs a decision over supplied alternatives rather than a generated explanation. We present PixelJev, a native-image decision interface that maps an image, a task instruction, and a runtime candidate set to a structured choice and candidate-conditioned probabilities using small open multimodal models. Its initial realization unifies recognition and multiplechoice visual question answering through an existing language-model readout, with separately evaluated options for frozen inference, language-side adaptation, and held-out calibration. Across seven benchmark evaluations, 64-shot source adaptation raises Pets accuracy from 60.13% to 92.40% across optimization seeds and transfers to natural resampling, new texture labels, and A-OKVQA without target fitting, while frozen inference already supports both VQA tasks. A matched prompt-only follow-up on Pets and ScienceQA attributes the large Pets gain to adaptation and identifies a narrower output validity benefit of candidate readout in adapted VQA. Specialist DINOv2 probes remain stronger on source recognition, frozen 4B is stronger than adapted 2B on DTD and ScienceQA, and accuracy gains do not ensure calibrated target probabilities. These findings establish a working starting point for general-purpose visual decision models and identify the remaining requirements: schema robustness, cross-family transfer, and reliable use of visual evidence.
[AI-78] ALOE: Semantically Addressed Low-Rank Operators for Knowledge Editing
链接: https://arxiv.org/abs/2609.29269
作者: Zeyan Li,Hu Xu,Jianfeng Xu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Knowledge editing changes what a model knows by modifying parameters so that a requested fact updates while unrelated behavior is preserved. This is usually treated as a write problem, but editing also involves an address problem: deciding which hidden states should receive the new residual. An update that activates too narrowly memorizes one prompt, while one that activates too broadly disrupts neighboring knowledge. Parametric editors encode this scope implicitly, whereas memory-based editors make the selection explicit but keep it outside the edited model. We propose ALOE (Addressed Low-rank Operator for Editing), which learns semantic addresses from paraphrases and hard same-subject negatives, aligns them with autoregressive hidden states through rollout refinement and gate calibration, and embeds the resulting gated low-rank operator within one MLP layer, so that the deployed model runs in a single forward pass with no external retriever or auxiliary router. Evaluated on CounterFact, ZSRE, and KnowEdit across three 7–8B model families, ALOE achieves efficacy between 0.955 and 0.999 and locality between 0.981 and 1.000; mechanistic analyses confirm that the learned geometry separates competing edits and that calibration suppresses out-of-scope activation. The remaining errors concentrate in paraphrase coverage and write fitting.
[AI-79] Baszta: Data-Centric Fine-Tuning of a Polish Multi-Label Safety Classifier
链接: https://arxiv.org/abs/2609.29266
作者: Adam Górski,Mateusz Jąkalak,Rafał Jakubowski
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We develop a multi-label Polish content-safety classifier by fine-tuning allegro/herbert-base-cased (124M) across five categories (hate, vulgarity, sexual content, crime, self-harm) using a Focal + R-Drop objective, and evaluate the resulting model against Bielik Guard (Sójka) on the shared out-of-distribution Gadzi Język benchmark. Both systems are given per-category threshold tuning on the same calibration split. Under that matched protocol our model holds a small but statistically significant lead in micro F1, while an apparent macro-F1 lead does not survive: it was an artifact of comparing a tuned model against an untuned one. We also report what that micro figure is worth. Because Gadzi Język is 97% crime-positive, a classifier that flags crime on every input and nothing else already scores 0.910 micro F1 on the same test split, so micro separates neither system from a degenerate strategy and macro is the column that does. Per-category and per-protocol figures are reported in Section 4. The residual out-of-distribution gap is one of calibration rather than discrimination. Ranking quality stays high while positive probabilities collapse, and per-category temperature scaling recovers the loss where Platt scaling and isotonic regression do not. That recovery turns out to be conditional on the calibration set containing safe text. Gadzi Język contains almost none, so thresholds fitted on it flag crime on every safe input, and a balanced refit buys a deployable operating point at the cost of adversarial recall. We report both operating points rather than only the flattering one. Two changes that are standard practice, per-class cost-sensitive weighting and mean pooling, each raise in-distribution macro F1 while lowering the out-of-distribution figure, which indicates that robustness has to be selected for directly rather than inherited from in-distribution accuracy. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2609.29266 [cs.AI] (or arXiv:2609.29266v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.29266 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-80] P-CRIV: A Framework for Third-Party Challenge-Response Identity Verification of AI Models
链接: https://arxiv.org/abs/2609.29264
作者: Teruki Sano,Minoru Kuribayashi,Masao Sakai,Shuji Isobe,Eisuke Koizumi,Zhang Zhang,Satoru Matsumoto
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Artificial intelligence (AI) models are increasingly deployed through remote services, making model misappropriation a growing concern. Existing approaches, including watermarking, fingerprinting, and model similarity analysis, primarily rely on predefined evidence or direct behavioral comparison and do not explicitly evaluate whether the claimant currently possesses and can utilize model-dependent information relevant to the claimed model identity. In this paper, we propose Third-Party Challenge-Response Identity Verification (TP-CRIV) for AI models. TP-CRIV targets a third-party verification setting in which the verifier has neither white-box nor API access to the claimant’s model, can interact with the suspicious deployed service only through its ordinary black-box inference interface, and does not require protocol-specific cooperation from the service provider. Under these constraints, the framework enables the verifier to obtain empirical evidence as to whether the claimant locally possesses a model satisfying a predeclared identity relative to the deployed model. Verification is conducted under fresh, previously undisclosed requirements and network isolation, so that the demonstrated capability cannot rely on online external assistance after challenge disclosure. The resulting evidence is interpreted relative to independently specified and calibrated matching and non-matching operating situations and is statistical rather than cryptographic. We instantiate TP-CRIV for CNN image classifiers using probability-control-based witness generation. Experiments on ten ImageNet-pretrained TorchVision models demonstrate clear same/cross-model separation and finite-challenge verification using independently calibrated thresholds. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.29264 [cs.CR] (or arXiv:2609.29264v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2609.29264 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-81] owards An LLM -Driven Unified Conversion Framework for BT and FSM in Autonomous Intelligent Systems
链接: https://arxiv.org/abs/2609.29228
作者: Zhang Qi,Yang Shuo,Zhu Zhengqiu,Zhou Peng,Jiao Peng
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Finite state machine (FSM) and behavior trees (BT) are widely adopted behavioral modeling paradigms for autonomous intelligent systems. While functionally equivalent and inter-convertible in principle, existing transformation methods between FSM and BT face major challenges in preserving behavioral completeness and avoiding model complexity explosion. To overcome these issues, we propose an LLM-driven unified conversion framework that enables automatic, efficient, and semantically consistent transformation between FSM and BT. Specifically, a novel loop execution BT structure is designed for LLM to accurately capture the loop structure in FSM, thereby preserving behavioral completeness. To mitigate the state explosion problem in BT-to-FSM conversion, a depth compression strategy is introduced with LLM prompt to eliminate redundant control nodes, complemented by differentiated hierarchical conversion rules that collectively reduce the number of required sub-FSM. Simulation experiments in multiple autonomous decision-making scenarios demonstrate that the proposed framework enables an accurate and automated bidirectional conversion between FSM and BT. Furthermore, it significantly enhances the scalability and maintainability of generated models compared to traditional approaches, providing a practical solution for behavior model conversion in consumer-grade autonomous intelligent systems such as service robots, game agents, and smart home devices
[AI-82] FB-GDM: Fully-Bayesian Guided Diffusion Models for High-Dimensional Linear Inverse Problems via Unsupervised Variational Inference
链接: https://arxiv.org/abs/2609.29216
作者: Gatien Séguy,Thomas Rodet(SATIE)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:Diffusion models are powerful priors for linear inverse problems, but the reference guidance methods, Diffusion Posterior Sampling (DPS) and Pseudoinverse-Guided Diffusion Models ( \Pi GDM), rely on scalar hyperparameters tuned per task, usually against the ground truth. We introduce FB-GDM, a fully-Bayesian guided diffusion method that removes this calibration step. Starting from the Gaussian approximation of \Pi GDM, we derive a closed-form conditional score that depends on two precision parameters (inverse variances), one associated with the denoising approximation and one with the observation likelihood, and treat them as latent variables inferred by variational inference at each reverse step. A separable factorization makes each update scale linearly with the number of pixels, so the inference stays tractable at full image resolution, at a cost comparable to one \Pi GDM run. FB-GDM requires neither the noise level nor the ground truth: its only inputs are the observation and the forward operator. Experiments on CelebA-HQ inverse problems establish two results. (i) The precision parameters, inferred from the observation alone, allow FB-GDM to outperform \Pi GDM at its nominal setting, even when the latter is given the true noise level, by up to 14 dB depending on the operator, and to match the ground-truth-calibrated \Pi GDM oracle within 0.1 dB. (ii) FB-GDM is robust when the forward operator, the noise level, or the image distribution changes: it stays close to a per-problem \Pi GDM oracle throughout and does not exhibit the hallucinations observed with DPS, whereas DPS substantially degrades at a fixed scale and \Pi GDM stays competitive only if it is re-tuned against the ground truth for each new problem. When the prior is applied to images outside its training set, this re-balancing between data and prior keeps FB-GDM faithful where a fixed face-prior guidance can otherwise hallucinate.
[AI-83] When Honesty is Not Enough in AI Debate
链接: https://arxiv.org/abs/2609.29189
作者: Rayne Holland,Liming Zhu,Jason Xue
类目: Artificial Intelligence (cs.AI)
备注: 30 pages, 1 figure
Abstract:Scalable oversight aims to verify the behaviour of agents whose capabilities exceed those of their overseers. AI debate has been proposed as an oversight solution in which competing agents help a resource-limited verifier assess claims that it cannot reliably evaluate unaided. Much of its promise rests on incentivizing honest arguments that lead to correct verdicts. Yet a correct verdict need not uniquely determine the arguments used to support it. Agents may retain discretion over which correct claims to present, how to frame them, and in what order to disclose them. This residual freedom can allow agents to shape what the verifier learns beyond the task-relevant conclusion, pursuing latent objectives without compromising verdict correctness. To study this phenomenon, we introduce the framework strategic interactive oversight (SIO), which treats oversight jointly as a verification mechanism and a strategic communication channel. Within this framework, we formalise the notion of task-admissible latent optimisation, which entails the pursuit of latent objectives while maintaining a prescribed task performance. As proof-of-concept, we instantiate SIO in the establish protocol debate with cross-examination and quantify a tradeoff between task success and information disclosure about a hidden variable. The trade-off identifies a strategic window in which substantial disclosure remains compatible with task admissibility. Towards mitigation, we reduce admissible bias by expanding the cross-examiner’s role to mitigate persistent disclosure over finite interaction horizons. Our results highlight the need to evaluate oversight not only by the correctness of its verdicts, but also by the information conveyed through its transcripts.
[AI-84] he Entropy Triangle Method (ETM): A novel framework for the prevention of cardiac arrhythmia with a review of more than 10000 patients
链接: https://arxiv.org/abs/2609.29187
作者: Arman daliri
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:One of the most important problems in medicine is to facilitate prediction. In this study, we propose entropy triangle method, a novel framework for predicting heart rhythms using a novel machine learning technique. This framework includes three steps: feature engineering, entropy triangle oversampling, and disease prediction. The dataset used in this study is a 12-lead electrocardiogram (ECG) arrhythmia research database with 10,646 patients. This dataset contains 11 different heart rhythms (5 sinus rhythms and 6 non-sinus rhythms). In this article, we introduce two firsts in machine learning and medicine that can predict non-sinus rhythm with over 85% accuracy. Our experimental results show, among others, that the most accurate classifier based on entropy triangles and the most useful oversampling are the supported vector classifiers and oversampling techniques for shark scent.
[AI-85] Right Choice of Classification Algorithms Based on Reinforcement Learning for Prediction of Non-Alcoholic Fatty Liver
链接: https://arxiv.org/abs/2609.29181
作者: Hasan Samadbin,Arman Daliri
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:There are many complex issues in the world of artificial intelligence. Some of these problems are solved using other artificial intelligence methods, which are called artificial intelligence for artificial intelligence. Finding an appropriate classifier algorithm is a time-consuming task. For this reason, an algorithm that can automatically learn the choice of classification algorithms is very important. Classification algorithms are useful in predicting various diseases. Also, Primary Biliary Cirrhosis is one of the most well-known diseases that have been predicted by classification algorithms. This research’s most significant achievement and novelty is the automatic increase in learning through a scoring method of reinforcement learning is called square learning (SL). In this research, an algorithm is presented that learns to automatically select the appropriate classification algorithm to predict Primary Biliary Cirrhosis. In this article, with inspiration from four evaluation metrics in classification algorithms, a new reinforcement learning method by the name of Fourth Degree Learning has been presented. In this research, we increased the performance of the classification algorithms used in this method from 63% of accuracy and achieved 98% accuracy.
[AI-86] Spot Separate and Enhance: Fully Generative Approach for Audio Mixing ICASSP2027
链接: https://arxiv.org/abs/2609.29169
作者: Ilpo Viertola,Giulio Cengarle,Gouthaman KV,Daniel Arteaga,Lie Lu
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: Submitted to ICASSP 2027. Project page this https URL
Abstract:We introduce Spot, Separate, and Enhance (SSE), the first multimodal, user-guided generative model for audio remixing and enhancement. SSE enhances video content by rebalancing the audio, removing unwanted audio sources, and reducing reverberation, guided by both video and textual descriptions. To support its training and evaluation, we propose DegradedMix, a new dataset built on the audio remixing benchmark MuddyMix. We also adopt evaluation metrics from generative modeling, which better capture the creative nature of remixing than standard reconstruction-based metrics. SSE outperforms existing baselines in both controllability and remixing quality, as shown by extensive experiments. Project page: this https URL
[AI-87] IndicBankBench: Evaluating Safety and Reliability of Language Model Assistants in Indian Retail Banking
链接: https://arxiv.org/abs/2609.29167
作者: Suvradip Paul,Chandra Bhushan,Harsh Sharma,Nitin Kukreja,Yatharth Dedhia,Keyur Doshi,Prashant Devadiga
类目: Artificial Intelligence (cs.AI)
备注: 16 pages, 4 figures
Abstract:Banking assistants must use account-specific information to answer requests and, in many cases, take actions through tools. Evaluating only the final response misses important errors. An assistant may ask for information it already has, rely on stale context, select the wrong account, or write an invalid value after stating the correct one. We introduce IndicBankBench, a 799-case benchmark for Indian retail banking spanning five operational domains, a capability/refusal domain, and twenty primary axes. Cases are evaluated at four stages: safety, action and tool use, response adequacy, and advisory quality. Tool use and most safety checks are deterministic. A narrow resolver handles only ambiguous confirmation-before-write cases, while a separate LLM judge evaluates semantic response adequacy. We run every case three times and report strict pass^3, which requires success on all trials. Across the eleven evaluated models, strict reliability ranges from 43.7% to 58.2%, whereas at-least-once success ranges from 60% to 74%. This gap shows that at-least-once success can overstate dependable banking behavior. The case-level diagnostics also distinguish systems that ask unnecessary questions from those that act but fail to reconcile customer context or fully resolve the request. We release the cases, mock environment, and evaluation harness.
[AI-88] HarnessPAI: An Evolving Harness for Physical AI
链接: https://arxiv.org/abs/2609.29166
作者: Xin Wang,Wenhao Wu,Menghao Zhang,Zhi Wang,Kun Shao,Jian Luan,Yang Li,Qing Li,Shangding Gu,Huichi Zhou,Shuqing Shi,Fei Ni,Shuo Lu,Weicheng Meng,Kang Li,Jin Wu,Kang Zhao,Shangmin Guo,Gen Li,Yongqiang Tang,Zhizhong Zhang,Yuan Xie,Heng Qu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 45 pages, 23 figures, 15 tables
Abstract:Physical AI aims to build embodied agents that perceive the world, understand and reason about it, and decide how to act. Yet the field has focused primarily on the last component: the action model that maps observations to low-level controls. The prevailing training recipe can erode the perceptual and reasoning capabilities needed for robust behavior, leaving even strong action models vulnerable to scene perturbations and long-horizon tasks. We introduce HarnessPAI, a model- and embodiment-agnostic Harness framework for Physical AI that treats code as the executable and evolvable interface that organizes the underlying action primitive. The framework separates two timescales: within a rollout, it executes open-loop at the program level, with a fixed program guiding and checking execution; across rollouts, it evolves closed-loop, using execution feedback to revise the program and distill failures into reusable skills. Across desktop robot arms, household robots, a robot vacuum, and a legged walking agent, HarnessPAI improves on both pure action models and code-as-policy baselines without retraining the underlying model: a 61.6-point gain over \pi_0.5 on LIBERO-PRO and a 27.2-point gain over WorldDreamer on RoboCasa atomic tasks. Once a program is selected, rollout execution requires no online high-level LLM deliberation. Beyond execution, the converged program is also a cheap and reliable expert-data collector, and fine-tuning \pi_0.5 on collected expert data lifts success rate on LIBERO-PRO by 38.8 points. Our results suggest that the frontier of Physical AI depends not only on stronger action models, but also on executable harnesses that integrate perception, task understanding and reasoning, and action execution into a unified, verifiable, and feedback-driven system. Website: this https URL
[AI-89] A Wrong Turn Does Not Ruin the Journey: Deviation-Guided Skill Self-Evolution for LLM Agents
链接: https://arxiv.org/abs/2609.29154
作者: Yichun Feng,Jiawei Wang,Haozhe Sun
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model agents increasingly rely on natural-language skills to solve complex tool-use tasks. However, such tasks often admit multiple valid solution paths, making it inappropriate to improve skills by forcing failed trajectories to match a fixed successful trajectory. Moreover, failed trajectories are rarely entirely wrong: an agent may first collect useful evidence and make meaningful progress, but later deviate into an erroneous suffix. We therefore argue that skill self-evolution should identify where productive problem solving begins to break down, rather than reflect coarsely over the entire failure. Based on this insight, we propose SkillPivot, a deviation-point-guided framework for skill self-evolution. SkillPivot detects the transition from a useful prefix to an erroneous suffix using execution validity, goal progress, and action diversity. A stronger teacher then continues from the same prefix and produces a successful alternative under the same interaction history. By contrasting the student’s failed suffix with the teacher’s successful suffix, SkillPivot generates localized skill updates while preserving already effective guidance. Experiments on ToolQA, LogicBench, and WildClawBench show that SkillPivot consistently outperforms competing skill-evolution methods, improves multiple agent models, and produces compact, transferable skill updates.
[AI-90] Scope Before You Persist: Preventing Cross-Family Interference in Agent Memory
链接: https://arxiv.org/abs/2609.29144
作者: Yezhou Cheng,Runjia Du,Zeming Liu,Qibai Chen,Hang Lyu,Yankai Zeng,Yilan Wei,Bojun Lin
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Persistent memory lets language-model agents improve prompts and skills without updating model weights. We show that matching retrieval scope to certification scope enables these edits to support reliable repeated adaptation across recurring task families. We study frozen-model agents on ProcStream-RSI, a 12-round code-repair stream, using Orthogonal Regression Control (ORC), an execution-grounded gate for persistent skill edits. In an intervention that holds proposals and gate decisions fixed, retrieving each accepted skill only for its originating family raises mean hidden trajectory utility from 0.713 under global memory to 0.816 and changes harmful deployments from six of eight to none. In 27 paired randomized-order streams, Scoped-ORC improves mean trajectory utility by 0.063 [0.037, 0.094] over Global-ORC, accepts 63 rather than 12 updates, and produces multiple accepted updates in 19/27 streams, with 0/63 harmful acceptances. The global control reaches 0.713, below the static agent’s 0.775, because locally valid edits can interfere with unrelated families. These results establish scope matching as a complementary control for persistent agent memory: certification determines whether an edit is supported, while retrieval scope determines where that evidence authorizes its use.
[AI-91] Not Every Token Is Worth Distilling: Selective Supervision for Direct-OPD
链接: https://arxiv.org/abs/2609.29142
作者: Yibo Zhao,Zixuan Yang,Yunshi Lan,Xiang Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 19 pages. Yibo Zhao and Zixuan Yang are equal contributors and may list their names in either order on their CVs
Abstract:Direct On-Policy Distillation (Direct-OPD) transfers reinforcement-learning-induced policy improvements from a small model to a larger student by using the token-level log-ratio between post-RL and pre-RL checkpoints as dense supervision on the student’s own rollouts. This transfer rewards the policy shift at every state, yet the log-ratio measures only relative change: it can stay fixed even as the probability mass that both checkpoints assign to the student’s candidate tokens vanishes. Through an exact construction, we show that the Direct-OPD reward and its update can remain unchanged while the Jensen-Shannon divergence (JSD) and both KL directions between the checkpoints vanish with this mass, and we note that a small JSD bounds how much the teacher’s behavior changed. Motivated by this analysis, we propose Selective Supervision for Direct-OPD (S ^2 D-OPD), which ranks student-sampled states by their teacher-reference JSD and masks Direct-OPD supervision at low-divergence states, retaining only the top 10% of states per response. Across two teacher pairs and four student models ranging from 1.7B to 8B parameters, S ^2 D-OPD improves held-out accuracy over dense Direct-OPD on AIME and HMMT benchmarks in seven of eight settings and matches it in the eighth, without extra forward passes. Our code is available at this https URL.
[AI-92] Sharp Limits for Honest Uncertainty in Hard-Budget Repeated Evaluation
链接: https://arxiv.org/abs/2609.29140
作者: Yezhou Cheng,Runjia Du,Zeming Liu,Qibai Chen,Hang Lyu,Yilan Wei,Yankai Zeng,Bojun Lin
类目: Artificial Intelligence (cs.AI); Information Theory (cs.IT)
备注:
Abstract:Repeated evaluation can estimate a benchmark score accurately while still requiring replication to certify narrow uncertainty. We characterize that requirement on a fixed grid of M tasks with L binary paths per task under the hard budget (M+t)K , where each path costs at most K responses or episodes. For fixed L \ge 3 and 0 \alpha \le 1/12 , the optimal expected width on the worst pure cohort is \Theta_\alpha,L([M(t+1)]^-1/2) when every task is observed and \Theta_\alpha,L([M(t+\sqrtM)]^-1/2) when omission is allowed. The lower bounds cover adaptive hard-budget policies, and fixed random-subset designs attain both rates through disagreement certificates. A joint mean/disagreement interval turns the task-covering law into practical finite-budget inference. In an equal-budget LiveCodeBench replay with 16 models, 880 tasks, and five outputs per task, the task-covering design reduces median point-estimation MSE by 87.0% relative to pooled uniform sampling, while the Joint certificate produces narrower confidence intervals in 15/16 panels and reduces median interval width by 30.6%. Finite-regime analyses identify task coverage as the effective choice at the evaluated scale and characterize how cohort size and within-task agreement determine the useful operating region. Together, the sharp laws and fixed-budget evidence make replication and task coverage explicit design variables for information-efficient repeated evaluation.
[AI-93] CounterRoute: Self-Routed Reasoning via Hierarchical Counterfactual Credit Assignment
链接: https://arxiv.org/abs/2609.29109
作者: Ruochen Jiao,Besnik Fetahu,Zhenyu Shi,Priyanka Nigam
类目: Artificial Intelligence (cs.AI)
备注: 16 pages including 7 tables and 4 figures, under review
Abstract:Reasoning-capable language models often produce long chains of thought when direct answers suffice, wasting inference compute. Many dual-mode models leave this choice to users. Automating it is challenging because routing targets evolve with the policy, initial mode preferences destabilize exploration, and sequence-level objectives entangle routing with response learning. We introduce CounterRoute, an online reinforcement-learning framework that jointly learns routing and modeconditioned responses in one shared policy directly from a native dual-mode checkpoint, without method-specific SFT warm-up. Paired current-policy counterfactual rollouts assign cross-mode credit only to the routing token, while within-mode GRPO trains response tokens. A paired-to-self-routed curriculum stabilizes early training with forced rollouts from both modes, then increases self-routed updates to improve autonomous routing. Across nine benchmarks, CounterRoute better balances accuracy and efficiency than heuristic and learned adaptive-routing methods. Relative to always-thinking checkpoints, it improves macro-average accuracy while reducing mean generated tokens by 51% for Qwen3-8B and 41% for Qwen3-14B. On instruction-following and commonsense benchmarks where direct answering is strong, think rates fall as low as 1% while response quality improves. Despite training only on math and instruction following, its routing behavior and response quality generalize to held-out coding, science, knowledge, and commonsense benchmarks.
[AI-94] Functional Architecture of European Electricity Trading Markets: Requirements for AI Supported Trading Systems under Regulatory Constraints
链接: https://arxiv.org/abs/2609.29108
作者: Walter Kurz,Wojtek Stricker
类目: Artificial Intelligence (cs.AI); Systems and Control (eess.SY); General Finance (q-fin.GN); Trading and Market Microstructure (q-fin.TR)
备注: 12 pages, 1 table. Published in Swissi AI Journal under CC BY 4.0
Abstract:European electricity trading in the EU operates as a constrained multi-layer system in which legal design, exchange microstructure, and network physics are executed jointly across forward, day-ahead, intraday, and balancing horizons. This paper develops a functional architecture for AI-supported trading that is aligned with market-coupling mechanics, cross-zonal transfer constraints, and compliance obligations under REMIT, MiFID II, MiFIR, and EMIR. The contribution is a formal system specification composed of a decision-state vector, residual-exposure accounting, constrained optimization objective, executable-action permission gate, and fail-closed AI control logic with auditable records. The analysis maps major Nominated Electricity Market Operator (NEMO) venues and related exchange operators into an operational venue topology and identifies where cross-border coordination fails in practice: interface-level timing, permission heterogeneity, and balancing-layer coupling. The resulting framework proposes how AI can be deployed as a bounded decision component inside regulated market operation with explicit governance, rather than as an unconstrained prediction layer.
[AI-95] Where Does Exactly-Once Live? Model Harness and Tool-Contract Effects on Duplicate Side Effects in LLM Agents
链接: https://arxiv.org/abs/2609.29095
作者: Jiapeng Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 23 pages, 6 figures, 13 tables
Abstract:When a tool-using agent’s write times out or returns a server error, the action may already have taken effect. Retrying blindly duplicates it – a second charge, a second announcement, a second deployment – while giving up skips required work. We ask where exactly-once behaviour should be enforced: in the model, in the agent harness, or in the tool contract. We introduce LIMBO, a deterministic sandbox of six services with realistic contracts (optional idempotency keys, eventually consistent and missing read paths) and twelve fault modes injected at the service boundary, including late commits, redelivery and partial batches; every episode is graded against a ledger of committed effects. Across 25,930 episodes spanning nine recent models, three production agent harnesses, two contract variants and fifteen recovery conditions, the answer depends on the fault. When an immediate read-back can reveal what happened, the model decides: frontier models instructed to act exactly once almost never duplicate a write whose acknowledgement was lost (0.5%), weaker models often do, and the model explains 53% of the explained variance. When it cannot – the request is still in flight, or the transport delivered it twice – the same frontier models duplicate in 56% and 74% of episodes, and the contract explains 81%. We prove that no verification-only policy is exactly-once under late commits without a bound on in-flight time. Waiting works when such a bound is short and known, but with heavy-tailed in-flight delays even an hour of waiting per episode falls short of offering an idempotency key on every write, which lowers the duplicate rate from 28% to 4% because agents use keys when they exist. The harness barely matters, a guard that attaches keys transfers across harnesses unchanged, and agents reported success in 90% of the episodes in which they had duplicated an effect.
[AI-96] A Rapid Pipeline for Training and Deploying ML Models on WeBe Band
链接: https://arxiv.org/abs/2609.29084
作者: Ehsan Kourkchi,Asmita Asmita,Houman Homayoun,Mahdi Eslamimehr
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Performance (cs.PF)
备注:
Abstract:Developing optimized machine-learning algorithms for edge devices with limited computational and memory resources is challenging, time-consuming, and highly dependent on device-specific constraints. In this work, we streamline an edge ML workflow to enable rapid development, optimization, and deployment of machine-learning (ML) models directly on the WeBe Band, a wrist-worn wearable device designed for multimodal physiological data monitoring. The proposed system automatically generates hardware-efficient ML models that can be easily integrated into the WeBe core firmware, supporting AutoML, hardware-aware quantization, and performance profiling to build models that meet desired latency targets while remaining compatible with device memory and power limitations. The proposed framework tightly integrates the open-source Piccolo AI ecosystem with an automated pipeline that generates deployable firmware artifacts, performs hardware-aware model compilation, and supports over-the-air (OTA) deployment. The system supports multiple lightweight model classes, including classical machine-learning algorithms and neural networks, and provides built-in on-device profiling tools to evaluate inference latency and memory footprint under realistic execution conditions. Experimental results demonstrate clear trade-offs between model complexity and deployability on a microcontroller, showing that classical models offer strong real-time performance while lightweight neural networks require careful resource management. Rather than proposing new learning architectures, the current work mainly focuses on system-level automation, deployability, and enabling researchers and developers to rapidly iterate on models and evaluate them directly on target hardware. Although demonstrated on the WeBe Band platform, the workflow is designed to be extensible to other ML-powered edge devices. Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Performance (cs.PF) Cite as: arXiv:2609.29084 [cs.AI] (or arXiv:2609.29084v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.29084 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-97] From Self-Distillation to Self-Practice: Privileged Information for Multi-Turn Agents
链接: https://arxiv.org/abs/2609.29051
作者: Xingyu Su,Abhishek Kumar,Qing Ping,Youzhi Luo,Jonathan Buck,Zach Zhang,Subramanian Chidambaram,Vinayak Arannil
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:On-policy self-distillation (OPSD) has become a popular recipe for post-training LLM agents. It supervises the agent model at the token level with a stronger teacher view of the same model, obtained by conditioning on privileged information (PI). In this work, we show that in multi-turn agents, this paradigm teaches the student to act with confidence but without the information behind it. The trained agent behaves as if it had privileged information it never observed, and its performance falls well short of plain RL, in the worst case below the untrained base model. Therefore, we propose Privileged Self-Practice (PSP), which keeps the PI and moves it from the loss to the sampler. When the student’s rollouts on a task mostly fail, we inject a short per-task instruction written by an analyzer model, sample the task again with the instruction in context, and train on the result with an unchanged GRPO objective. The privileged information stays in the prompt and never enters the loss. Across AppWorld and SWE-bench Verified, with three different student models, PSP obtains the best average score in every setting and is the only method that consistently outperforms plain GRPO, improving task-goal completion by up to 65% on AppWorld and the resolved rate by up to 61% on SWE-bench Verified.
[AI-98] SLCA-GRPO: Resolving Cross-Segment Credit Misattribution in Tool-Calling RL
链接: https://arxiv.org/abs/2609.29050
作者: Yan Zhan,Shaobo Liu,Qiunan Liu,Yuanjun Shi,Siqi Xu,WeiYi Hou,Xiang Xu,Zekang Li,Weizhou Pan,Jiahong Yan
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 41 pages, 13 figures. Code: this https URL ; Dataset: this https URL
Abstract:Tool-calling agents produce heterogeneous outputs, interleaving structured tool invocations with user-facing natural language summaries. This output heterogeneity presents a structural failure mode in standard on-policy Reinforcement Learning (RL): algorithms like GRPO indiscriminately broadcast a homogeneous trajectory-level scalar advantage to all tokens. Consequently, gradient noise from summary generation leaks into tool-decision tokens, causing cross-segment credit misattribution and brittle optimization. In this work, we propose SLCA-GRPO, a framework incorporating Segment-Locked Credit Assignment (SLCA). To enable scalable exploration without costly real APIs and stable training, we first construct the Schema-Guided LLM Simulator (SGLS) as foundational training infrastructure. Building on this, SLCA decouples advantage estimation at the structural segment level within a single group of rollouts, without requiring additional rollouts from intermediate states. Supported by Hierarchical Rewards (HierR), SLCA routes execution advantages to tool tokens and preference advantages to summary tokens, eliminating advantage contamination (the dominant cross-segment credit misattribution channel) within each policy update. On a 7B backbone, SLCA-GRPO accelerates convergence and outperforms standard GRPO, ToolPO, and RLTR by +2.53 pp on in-domain evaluation, +1.36 pp on the Berkeley Function-Calling Leaderboard (BFCL), and +9.15 pp on \tau^2 -Bench under the same training budgets, achieving higher accuracy with reduced tool redundancy and costs.
[AI-99] he Tokens Remember: When Tokenization Bypasses Knowledge Editing and Unlearning
链接: https://arxiv.org/abs/2609.29045
作者: Manit Baser,Aditya Nawal,Dinil Mon Divakaran,Mohan Gurusamy
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Open-weight LLMs give downstream users control over the inference stack, but this flexibility can undermine post-release guarantees that sensitive knowledge has been modified or removed. Model editing and machine unlearning are used to modify or remove targeted knowledge without retraining models from scratch. However, existing security evaluations of these techniques face two critical limitations. First, they typically require access to either the original pre-edit/unlearning model or auxiliary classifiers to detect modifications or reconstruct pre-edit behavior. Second, they evaluate modifications under the canonical tokenization of an input, implicitly treating tokenization as a benign preprocessing step. We show that this assumption creates a security gap: the same input string can be represented by alternative valid tokenizations that induce different computational trajectories, allowing an adversary to bypass localized modifications and recover information intended to be suppressed. We introduce Toketive, a simple yet powerful reference-free attack that exploits the tokenization-based side channel to (i) detect modified knowledge and (ii) reconstruct the corresponding pre-edit response. It operates solely on the released model and requires neither the pre-edit model, training data, shadow models, nor auxiliary classifiers. Across five LLMs, six datasets, and six editing and unlearning techniques, we find that 38.6% of alternative tokenizations bypass the modification and recover the pre-edit response. Toketive detects modified facts with an F1 score of 84.2%, a 26.2% relative gain over the strongest baseline, and reconstructs pre-edit responses with 74.5% top-5 accuracy, 21.7% higher than the best baseline. Our results show that localized modifications should not be treated as robust knowledge-control boundaries without adversarial evaluation over alternative representations. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.29045 [cs.CR] (or arXiv:2609.29045v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2609.29045 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-100] Multi-Agent Orchestration of 3GPP Channel Estimators
链接: https://arxiv.org/abs/2609.29044
作者: I. Zakir Ahmed,Hamid Sadjadpour
类目: Information Theory (cs.IT); Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
备注:
Abstract:Pilot-aided channel estimation is a decisive block in orthogonal frequency-division multiplexing (OFDM) receivers for both 5G New Radio (5G-NR) and Long-Term Evolution (LTE). A large body of estimators exists, from simple least-squares (LS) interpolation to statistically optimal linear minimum-mean-square-error (LMMSE) variants and, more recently, deep convolutional denoisers, yet no single estimator is uniformly best: the winner depends on the propagation scenario, the numerology, the operating signal-to-noise ratio (SNR), the mobility (Doppler), and the antenna configuration. In this paper, we quantify this fact through a unified study of eight literature estimators evaluated over the 3GPP TR~38.901 Urban-Macro (UMa), Urban-Micro (UMi), and Rural-Macro (RMa) channels generated with NVIDIA Sionna, for both 5G-NR and LTE numerologies, in single-input single-output (SISO) and 8\times2 multiple-input multiple-output (MIMO) settings. We then propose a \emphcondition-adaptive multi-agent orchestrator that treats each estimator as an independent agent and dispatches, per operating condition, to the agent that is best on a validation split without any genie knowledge. The orchestrator tracks the per-realization oracle to within 1.07 ~dB and improves the normalized mean-square error (NMSE) over the best \emphfixed strategy by up to 3.6 ~dB at high SNR, where the low-SNR champion is no longer optimal. Because the agents are independent, running them concurrently delivers this best-of-eight accuracy at essentially single-estimator latency: a data-parallel partition scales the wall-clock nearly as 1/K with K workers (up to 6.9\times ), whereas naive by-algorithm partitioning is Amdahl-limited by the heaviest agent. The results substantiate multi-agent orchestration as a practical route to robust channel estimation across heterogeneous 5G-NR/LTE deployments.
[AI-101] When Does Action Credit Need Updating?
链接: https://arxiv.org/abs/2609.29007
作者: Hongye Yang,Boxiao Huang
类目: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: 24 pages, 4 figures, 2 tables. Preprint
Abstract:Tool-using agents are continually updated with new interaction data. After each policy update, however, previously estimated action credits may become stale. Recomputing them from scratch can require many additional tool calls and environment interactions, making repeated updates increasingly expensive. We ask a simple question: when does historical action credit actually need to be updated? Our key observation is that a change in action value does not necessarily imply a change in the decision. Historical credit can still be useful as long as policy-induced drift is too small to overturn the existing action ranking. Building on this idea, we introduce pairwise branch sensitivity to capture how strongly a policy update affects the downstream regions that distinguish two candidate actions. We then derive a first-order anchored credit-transport estimator that updates historical credit using old interventional trajectories, and propose a Decision-Sufficient Credit Gate (DSC-Gate) that chooses whether to reuse, transport, or resample credit. Experiments show that branch sensitivity explains credit drift substantially better than global policy distance. With sufficient historical data, credit transport reduces estimation error, while its benefit to decision making is concentrated on updates that affect action-distinguishing branches. On a fully independent test set, DSC-Gate changes mean regret by only +0.00004 relative to a gap-based gate while reducing mean new tool steps from 472 to 286, a 39.4% reduction. We observe the same pattern after a real tool-agent parameter update. Overall, our results show that agents do not need to recompute action credit after every policy update: much of the historical evidence can be reused or cheaply corrected, reducing the additional interaction required to keep action decisions up to date.
[AI-102] CrossSafe: Towards Cross-Embodiment Latent Safety Filters
链接: https://arxiv.org/abs/2609.28984
作者: Ihab Tabbara,Yuxuan Yang,Hussein Sibai
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY)
备注:
Abstract:Cross-embodiment learning has shown that a single model, such as a vision-language-action (VLA) model, can learn state representations and manipulation skills that can be applied across heterogeneous robots to accomplish various tasks. We hypothesize that the same holds for safety enforcement. The reasoning required to satisfy a safety constraint, such as detecting an obstacle, recognizing that it should be avoided, and selecting a safe abstract action, is largely shared across robots. What differs across embodiments is how the abstract safe action is realized: morphology, kinematics, and dynamics determine which actions are safe and feasible. Consequently, the same action can be safe for one robot and unsafe for another. This is especially important for generalist manipulation policies that operate in a common end-effector action space without explicitly capturing how safety depends on the robot’s morphology and kinematics. We propose embodiment-conditioned safety filtering, in which a Hamilton-Jacobi reachability-based value function and its corresponding safety-maximizing policy are shared across robots. Using a morphology-aware latent representation of the robot and its environment, we perform Hamilton-Jacobi reachability analysis directly in latent space so that the learned safety concepts can generalize across embodiments while remaining explicitly conditioned on each robot’s morphology and kinematics. We evaluate our approach across five bimanual robot embodiments and five manipulation tasks with whole-body collision-avoidance constraints. Our results show that a single policy, jointly trained across five manipulation tasks and four embodiments, exhibits zero-shot generalization to a held-out embodiment, reducing the nominal policy’s collision rate. They also show that training using more embodiments improves generalization.
[AI-103] Back to the Definition: Estimating Step-Level Advantages via Trajectory Graphs for Agent ic Reinforcement Learning
链接: https://arxiv.org/abs/2609.28963
作者: Xincheng Yao,Haobo Fu,Weiming Liu,Chongyang Zhang
类目: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:Group-based reinforcement learning (RL) methods, such as GRPO and its variants, have become a leading paradigm for training reasoning and agentic large language models (LLMs). While their group-normalized advantage estimation is reliable at the response level, it becomes systematically biased at the step level, since coarse-grained trajectory-level advantages are hard to accurately reflect the contribution of individual steps (i.e, failed trajectories may contain valuable steps). Revisiting the foundational RL definition, we notice that GRPO’s success on single-turn tasks stems from its advantage estimation strategy, which adheres to the basic definition: the mean reward of multiple actions sampled from the same state constitutes a credible state-value estimate. Extending the faithful estimation to step-level would in principle demand sampling multiple actions from each intermediate state, which is too costly on a per-state basis. To mitigate this issue, we propose a Graph-based Faithful sTep-level credit-assignment framework (GRAFT) that grafts all rollout trajectories into a trajectory graph, recovering node state-values via Bellman iteration on the graph, and assigning credit to each edge by the node value difference. Theoretically, the estimated step-level advantage faithfully adheres to the basic advantage definition in RL. To further ensure the reliability of step-level advantage estimation, we further propose Graph GAE, which extends GAE to the trajectory graph for reducing the impact of state-value estimation bias. Experiments across a range of multi-turn agentic benchmarks show consistent gains over GRPO and superior performance compared to recent agentic RL algorithms. Code will be available at this https URL.
[AI-104] From Static Personal Values to Contextualized Personalization: Bayesian Personalized Value Alignment for LLM s
链接: https://arxiv.org/abs/2609.28942
作者: Hanze Guo,Aixuan Song,Jing Yao,Xiangxu Zhang,Xiaoyuan Yi,Xing Xie,Xiao Zhou
类目: Artificial Intelligence (cs.AI)
备注: 29 pages, including references and appendices
Abstract:Personalized value alignment has become increasingly important as large language models (LLMs) are expected to accommodate diverse user preferences. However, existing methods typically align model outputs with a static value profile across prompts, overlooking that the salience of value dimensions varies substantially across contexts. Inspired by Lewin’s Field Theory, which views human behavior as jointly shaped by personal dispositions and situational constraints, we model personal values as priors and context-dependent preferences as posteriors. We propose BaCVA, an inference-time Bayesian Context-aware personalized Value Alignment method that approximates posterior personalized preferences by integrating static personal values with scenario-specific value salience. BaCVA first estimates contextual value salience from generally normative responses, and then employs a dual-view personalization module to infer posterior preferences from complementary personal-value and scenario-driven perspectives. This Bayesian formulation enables more accurate and adaptive personalized value alignment while improving data efficiency via prior values. Extensive experiments on benchmarks demonstrate its superiority over strong baselines.
[AI-105] Calibrated Decision Models for Autonomous Penetration-Testing Harnesses: JEV and Laya as System One Decision Layers for LLM -Driven Pentest Agents
链接: https://arxiv.org/abs/2609.28940
作者: Joas Antonio dos Santos Barbosa
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 21 pages, 5 figures, 11 tables, 4 code listings
Abstract:Autonomous penetration-testing harnesses use large language models (LLMs) for reconnaissance, exploitation, and reporting, but often rely on those same models to confirm findings, grade severity, and select agents. This can lead to false positives, inflated severity, and wasted compute. We examine how System One decision models, lightweight non-generative classifiers that return typed, calibrated verdicts, can support these decisions. We make five contributions. First, we define four decision points: finding adjudication, severity recalibration, agent pruning, and confirmation loops. Second, we present an exploratory NeuroSploit case study comparing one run with TypeSafe System One (Jev) and one without it against a web target containing 13 vulnerabilities. Differences in severity distribution, runtime, and grading by exposed data type motivate the architecture but do not establish statistical significance. Third, we review published specifications for Jev, Jev-Ultrafast, and the open-source Laya without assuming that results from other benchmarks transfer to penetration testing. Fourth, we discuss RLHF, RLAIF, RLCD, and RLHV as training approaches and their implications for trust in security decisions. Finally, we propose Rave, a domain-adapted System One model, and outline its training data, evaluation protocol, and potential effect on harness assurance.
[AI-106] PFArena: Benchmarking Language Models for Protein Modification
链接: https://arxiv.org/abs/2609.28921
作者: Yawen Ouyang,Xinbo Zhang,Ziyuan Ma,Yixin Wu,Wenbin Liao,Feiran Zhang,Wenjie Li,Lihao Wang,Hao Wang,Xiaoqing Zheng,Xuefeng Yan,Lei Bai,Ya-Qin Zhang,Shuyi Zhang,Wei-Ying Ma,Dahua Lin,Bowen Zhou,Hao Zhou
类目: Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM)
备注: preprint
Abstract:Protein modification requires navigating an immense sequence space, yet wet-lab validation remains low-throughput and costly. Although computational paradigms including protein language models (PLMs), large language models (LLMs), and LLM-based agents have shown promise in protein modification, their relative efficacy across realistic experimental decision-making settings remains unclear. To bridge this gap, we introduce PFArena, a benchmark comprising four controlled task interfaces that cover single-mutant generation and multi-mutant ranking. By providing varying levels of mutation fitness data, PFArena reflects four representative research scenarios characterized by differing degrees of prior experimental context. We assess six PLMs, six LLMs, and five LLM-based agents using complementary metrics to measure both peak and overall protein modification performance. Our evaluation reveals that model performance shifts systematically with the availability of target-specific experimental evidence: PLMs demonstrate proficiency in open-ended single-mutant generation by leveraging protein-specific priors, whereas LLMs and agents perform strongly in multi-mutant ranking, particularly when target-specific fitness data are available. Nevertheless, all model families face fundamental challenges with increasing search-space size and mutation depth. We release our code and benchmark suite to facilitate reproducible research in model-assisted protein modification.
[AI-107] Control the Harness Control the Cost: Routing and Governing AI Coding Agents in the Enterprise
链接: https://arxiv.org/abs/2609.28919
作者: Arian Abbasi,Alan Aqrawi,Ted Kwartler
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 32 pages, 13 figures, 8 tables
Abstract:Harnesses, the products that run AI coding agents, are multiplying, and enterprises are rolling them out to their employees: what started as pilots with a few hundred seats is scaling to tens of thousands. Most enterprises do not build these harnesses but buy them from large vendors, such as Anthropic’s Claude Code or OpenAI’s Codex. A harness decides which model answers, what the model reads, how the prompt cache is used and which subagents run, so it picks the rate on the price sheet and sets the volume bought at it. Enterprises that keep a proprietary or untuned harness at its defaults inherit these choices and their bill. We build a fast, customisable router in which Jev, a classifier with calibrated probabilities, labels every prompt against a bring-your-own taxonomy of agentic requests. Because one user turn is many requests over a prompt cache that belongs to one model, the router moves work only where no running conversation has to rebuild its cache: at session start, in side lanes and at subagent launch. From the price sheet we derive when a mid-task switch pays back, and a crossover: on long tool-heavy sessions the highest-priced model costs less than the next tier, as repricing about 10,000 real sessions from public datasets confirms. In an emulated enterprise of 10,000 seats with user behaviour taken from these datasets, the router recovers 14 to 21% of model spend at Anthropic’s list prices of 21 September 2026, \ 3.3M to \ 5.0M a year. The paper also maps the risks across twenty harnesses, prices the dependence on one vendor’s models, and proposes a control plane that enterprises can run from within, starting now, with a ladder for deciding later whether to own the harness.
[AI-108] On the Effectiveness of Kernel-Level Evidence for Agent Security
链接: https://arxiv.org/abs/2609.28915
作者: Spencer King,Zhilu Zhang,Mikhail Kuznetsov,Kay Liu,Baris Coskun,Wei Ding
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 53 pages, 2 figures, 28 tables, including appendices
Abstract:LLM agents are deployed into infrastructure that grants them broad host authority, yet existing agent-security benchmarks and defenses operate almost exclusively at the application telemetry layer: the served tool manifest, the user prompt, and the model’s messages. Some threats, however, smuggle malicious instructions and actions past the application boundary, leaving them invisible to that layer. In this work, we bridge that gap by pairing application-level agent telemetry with kernel-level syscall traces to present the first paired-evidence characterization of kernel-level versus application-layer signal for agent security. To quantify the value of the enhanced telemetry, we introduce Agent Cross-Layer Evidence (ACE), a paired-session corpus of 4,047 sessions and 17 threat models spanning six delivery-vector families and 14 of the 25 OWASP LLM and agentic threat categories, organized into 12 attack mechanics with per-mechanic characterization of where the most discriminative evidence lies. Across four distinct detector families, we find that kernel evidence is discriminative on its own and that composing it with application-layer evidence generally outperforms either single-layer view, revealing complementary signals that single-layer analyses can miss. We further demonstrate generalization to unseen attack families and transfer to an alternate agent runtime. Together, these findings establish the value of cross-layer evidence for agent security.
[AI-109] Robots That Take Initiative: A Framework for Building and Evaluating Proactive Robots
链接: https://arxiv.org/abs/2609.28910
作者: Maithili Patel,Sonia Chernova
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Effective robot assistance beyond narrow roles and repetitive tasks requires robots to be proactive - to decide what needs to be done rather than waiting to be told. While proactivity is increasingly explored, it lacks a unified formulation, and work in the domain is typically evaluated offline against static human models that cannot capture the effect of a robot’s actions on the environment and the user’s own behavior. We introduce a unified formalism for proactive robot assistance, organize it into three levels, and provide a framework to address the highest level of unprompted proactive assistance. We then show that offline evaluation overstates performance in this setting, and contribute a closed-loop evaluation with a human model that adapts to the robot. Finally, we present a method, GAP, that instantiates our framework, learning from passive observation to anticipate user goals and act. Under closed-loop evaluation, prior state-of-the-art methods collapse, in some cases adding more work than they save, while GAP remains robust and substantially outperforms them.
[AI-110] Broadening Uncertainty Estimation for Audio Question Answering Across Methods Formats and Inputs
链接: https://arxiv.org/abs/2609.28879
作者: Aaron Isidore Grace,Weiran Wang
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: Preprint
Abstract:Audio-language models can produce confident answers unsupported by the audio, motivating uncertainty estimates that identify unreliable responses. We compare probability-based, sampling-based, self-verification, evidential, and contrastive measures across four open-weight models and five audio QA benchmarks. In multiple-choice evaluation, first-token measures are strongest overall, with top-1 probability achieving a mean AUROC of .740, compared with .708 for ten-sample discrete semantic entropy, while requiring no additional model calls. Across four benchmarks, shifting from multiple-choice to open-ended evaluation lowers mean accuracy from 57.6% to 36.6%, yet uncertainty remains predictive of errors: semantic entropy, maximum token entropy, and semantic agreement achieve mean AUROCs of .697, .694, and .693, respectively. To test whether uncertainty reflects the evidence available to answer the question, we perform input ablations that remove either the audio or the question. Across top-1 confidence, entropy, and sampling-based measures, removing audio reduces error-detection AUROC by .101 on average, compared with .010 when removing the question. Together, these results establish efficient uncertainty baselines and show that uncertainty in audio-language models depends substantially more on available audio evidence than on question text.
[AI-111] Forecast-Dojo: Replayable Environments for Benchmarking and Training LLM Forecasting Agents
链接: https://arxiv.org/abs/2609.28876
作者: Liqin Ye,Haorui Wang,Fardin Ahmed,Rongzhi Zhang,Yuan He,Ziyuan Lin,Yanbin Yin,Jing Peng,Michael Galarnyk,Sudheer Chava,Chao Zhang
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:We introduce Forecast-Dojo, a replayable environment for benchmarking and training LLM forecasting agents. It combines resolved prediction-market questions with dated news, allowing agents to research an event and revisit their predictions at successive historical dates. The same tasks and tools support repeated evaluation, collection of training interactions, and feedback from recorded outcomes without waiting for new events to resolve. Forecast-Dojo contains 1,568 Polymarket events, split by time into training and evaluation periods, and 18.8M dated news articles. In an evaluation of 12 models, research tools lower Brier score for all 12. Forecasts also improve as events unfold, with the largest gains at steps where more newly dated evidence is recorded. Every model still trails historical market forecasts in both Brier score and accuracy. A belief notebook carried between dates lowers research cost but does not consistently improve forecast quality. Beyond evaluation, Forecast-Dojo provides interaction trajectories and outcome feedback for agent learning, with supervised fine-tuning as a proof of concept.
[AI-112] Human-AI-Powered Hypothesis Testing: Cost-Aware Selective AI Scoring and Sequential Human Escalation
链接: https://arxiv.org/abs/2609.28859
作者: Dae Woong(David)Ham,Xuejun Zhao,Stefanus Jasin,Fenghua Yang
类目: Artificial Intelligence (cs.AI); Information Theory (cs.IT); Methodology (stat.ME)
备注:
Abstract:Large language models are increasingly used as inexpensive judges to evaluate outputs, label data, and assess whether a system meets a desired quality standard. Yet using AI judgments for formal statistical inference is fundamentally different from simply treating them as ground-truth labels: AI evaluations can be biased or noisy, and rigorous hypothesis testing requires explicit control of type-I and type-II errors. We study how to use AI judgments, together with selective human verification, to conduct a valid hypothesis test at minimum cost. We consider a population of items with hidden binary labels. After choosing a fixed pool of items, the decision maker can selectively query AI, send an item directly to a human, escalate an AI-scored item to a human after observing the AI report, or stop once sufficient evidence has accumulated. We derive an information-theoretic lower bound that captures the minimum cost of achieving prescribed testing errors and characterizes the value of AI information and human verification through a report-dependent information frontier. Motivated by this characterization, we develop SCALE, a sequential cost-aware policy that combines selective AI scoring with adaptive human escalation. SCALE is valid at finite sample sizes and matches the lower bound to first order as the target error probabilities vanish. We further extend the framework to an unknown AI-output model using paired AI-human pilot data. Numerically, SCALE approaches Human-only or AI-only testing when one source clearly dominates, while achieving its largest savings when inexpensive AI judgments and selective human verification are both valuable.
[AI-113] RECLAIM: Can Agents Reproduce the Claims of Machine Learning Papers?
链接: https://arxiv.org/abs/2609.28850
作者: Mithil Salunkhe,Haochen Ding,Samridhi Verma,Volodymyr Kindratenko
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Software Engineering (cs.SE)
备注: 87 pages, 51 figures, 14 tables
Abstract:Reproducing a machine learning paper involves most research steps, from installing software and debugging to running experiments, work that AI agents increasingly do. We introduce RECLAIM, a benchmark of 100 NeurIPS 2025 papers that can be rebuilt yearly from new conferences. For each paper we fix in advance the result to reproduce, what counts as a successful reproduction, and a GPU-hour budget. An agent must reproduce that result using the paper and whatever its authors released. What the authors released decides the difficulty tier. Run-tier releases include code, data, and weights; Retrain-tier releases lack weights, so the agent trains the model; Reimplement-tier releases lack code, so the agent writes it. A separate language model grades runs from logs and outputs rather than agents’ reports. We run four agents once per paper; the best agent in each tier reproduces only 41% of Run-tier papers, 27% at Retrain, and 15% at Reimplement, where every agent does worst. Failed attempts use on average 29% of their budget, so most stop with budget left. The most common agent error is writing the method without checking any part against the paper’s numbers, in 63 of 400 runs.
[AI-114] Blockchain-Enabled Artificial Intelligence and AI Agents for Secure Data Sharing and Cybersecurity Applications DATE
链接: https://arxiv.org/abs/2609.28843
作者: Harsh Verma
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: published in International Journal of Scientific Research and Management (IJSRM) , published date : August 6, 2026, issue : Vol. 14, No. 08, (2026), pages : 2972-2978, doi : https://doi.org/10.18535/ijsrm/v14i08.ec03
Abstract:Blockchain and artificial intelligence (AI) are converging into a single infrastructural layer for securing data sharing, model integrity, and autonomous decision-making across distributed systems. This paper presents a meta-synthesis that draws together four constituent studies covering adversarial machine learning, AI-powered anomaly detection in cloud environments, automated vulnerability patching by multi-agent large language model (LLM) pipelines, and the broader landscape of securing AI systems across their lifecycle and situates their findings within the emerging literature on blockchain-enabled AI and autonomous AI agents. Each constituent study addresses a distinct point of failure in modern AI-driven security operations: the integrity of training data and model behavior, the reliability of real-time monitoring, and the trustworthiness of automated code remediation. We argue that blockchain’s properties of immutability, decentralized consensus, and verifiable provenance directly address a gap common to all three: the difficulty of establishing trust in data, models, and autonomous agents that operate without a central authority. Building on real-world research on blockchain-secured data sharing, federated learning, and multi-agent coordination, we propose a layered reference architecture that couples adversarially hardened models, blockchain-anchored data provenance, AI-driven anomaly detection, and smart-contract-governed multi-agent remediation. We conclude by identifying open problems in scalability, privacy-transparency trade-offs, and the governance of autonomous agents that must be resolved before such integrated systems can be trusted in production-critical environments.
[AI-115] KeyGen: Unsupervised Keypoint based Object-Centric Representations for Category-Level Policy Generalization
链接: https://arxiv.org/abs/2609.28818
作者: Shuxin Cao,Liquan Wang,Masoud Moghani,Benjamin Joffe,Animesh Garg
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Generalization in robotic manipulation requires policies to perform tasks across diverse unseen object instances that vary in shape, size, and pose. However, conventional behavior cloning (BC) methods often overfit to instance-specific geometry and appearance, limiting transfer to novel objects. We introduce KeyGen, a framework that learns canonicalized semantic 3D keypoints from point clouds and uses them as structured object-centric representations for policy learning. A visuomotor diffusion policy conditions on these keypoints together with object-centric geometry to predict full manipulation trajectories, enabling consistent geometric correspondence across object instances. To evaluate category-level generalization, we construct a photorealistic simulation benchmark with three manipulation tasks and a planning-driven data generation pipeline that produces expert trajectories across diverse object instances. Experiments show that KeyGen significantly outperforms prior methods on both seen and unseen objects under pose variation, scales effectively with additional demonstrations per object, maintains robustness to object rescaling, and achieves strong performance in both simulation and real-world manipulation.
[AI-116] Learned Cross-Task Relationships in Multi-Task Models RECSYS2026
链接: https://arxiv.org/abs/2609.28776
作者: Victor Zhang,Yiping Yuan,Florian Raudies,Bosun Adeoti,Brian Y. C. Leung,Sanjay Surendranath Girija,Naijing Zhang
类目: Artificial Intelligence (cs.AI)
备注: Accepted to the 20th ACM Conference on Recommender Systems (RecSys 2026). 10 pages, 6 figures
Abstract:We propose a framework that learns cross-task relationships in multi-task models by approximating the joint distribution of task labels through targeted pairwise relationships. This approach improves performance via transfer learning and enhances information extraction without the intractable complexity of modeling the full joint space. Although our framework applies to any multi-task system, we demonstrate its efficacy within YouTube’s production recommendation systems. Experiments across the Notifications, Homepage, and Watch Next surfaces show improvements in both accuracy and user satisfaction metrics. Finally, we propose a workflow template to facilitate broader future implementation.
[AI-117] Agent Memory with Episodic Retrieval for Financial Decision-Making AACL
链接: https://arxiv.org/abs/2609.28771
作者: Nuoyue Xu,Jiang Liu,Wenxuan Huang,Xiang Zhang,Juntai Cao,Jiaqi Wei
类目: Artificial Intelligence (cs.AI)
备注: The paper has been accepted for AACL-IJCNLP 2026 findings
Abstract:Large language models (LLMs) have demonstrated strong capabilities in financial analysis and reasoning, inspiring recent advances in agent-based trading frameworks. While these systems show promise, prior approaches either emphasize long-horizon forecasting or operate as stateless analyzers, limiting their applicability to the demands of trading in complicated settings. To address these gaps, we introduce META (Memory Enhanced Trading Agent), the first RAG-like episodic-memory-augmented multi-agent framework for financial decision making. META integrates a family of specialized indicator agents (e.g., Trend, MACD, Stochastic, RSI, SMA, AVWAP, Heikin-Ashi) with a Decision Agent that fuses their reports, and a Memory module that retrieves and updates past trading episodes encoded as market state embeddings with outcomes and reflections. By recalling relevant experiences and adaptively reweighting signals under similar market regimes, META achieves improved directional accuracy and robustness under short-horizon evaluation. Our results demonstrate that episodic memory provides a powerful mechanism for regime-aware, interpretable, and low-latency decision-making in trading and decision making. The code of this project is released on GitHub.
[AI-118] KathDB-FAO: Synthesized Query Plans in a Multimodal DBMS
链接: https://arxiv.org/abs/2609.28761
作者: Guorui Xiao,Douglas Brown,Artur Borycki,Magdalena Balazinska
类目: Databases (cs.DB); Artificial Intelligence (cs.AI)
备注:
Abstract:We design, implement, and evaluate KathDB-FAO, a new query evaluation subsystem for our KathDB multimodal DBMS. KathDB-FAO takes as input a query in natural language (NL) and converts it into a query execution plan where each operator is a function whose body is synthesized during query evaluation, which allows powerful query-specific optimizations. To generate accurate and efficient plans from NL, KathDB-FAO first extracts fine-grained atomic actions for correctness, then establishes contracts on the inputs and outputs of those actions and groups them for efficiency, and finally synthesizes the function for each group on the fly. On SemBench, KathDB-FAO cuts execution cost by 58.8% on average across scenarios compared with the next best system, at comparable or better quality.
[AI-119] Policy Complexity Reaction Time and Bounded Rationality in Reinforcement Learning
链接: https://arxiv.org/abs/2609.28737
作者: James Wu,Chris R. Sims
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Theory (cs.IT)
备注:
Abstract:Biological agents do not learn under conditions of unlimited computation. For humans, learning and choice are shaped by constraints on perception, attention, and working memory, which limit how much state information guides behavior and therefore bound policy complexity. Standard reinforcement learning models typically optimize reward without explicitly representing these internal costs, making them less suitable as models of biological intelligence. We derive MI-SARSA, an on-policy temporal-difference algorithm that incorporates mutual-information regularization through a learned marginal action prior and a penalty on state-specific deviations from that prior. This yields a sequential learning model in which state information is used selectively when its expected return benefit justifies the added informational cost. Critically, the same state-specific information cost that governs policy compression also generates trial-level predictions for reaction time, distinguishing MI-SARSA from most reinforcement learning models, which predict choices or returns but not latency. Empirically, MI-SARSA produces a reward-complexity tradeoff, and stronger information penalties produce simpler policies with lower control costs and faster reaction times. Under environment shift, increasing regularization reduces post-switch performance degradation but also lowers asymptotic return, revealing a robustness-capacity tradeoff. Together, these results position MI-SARSA as a model of bounded sequential learning under cognitive constraints.
[AI-120] mporal Learning for End-Effector Position Estimation under Aerodynamic Disturbances in Aerial Continuum Manipulation
链接: https://arxiv.org/abs/2609.28716
作者: Niloufar Amiri,Houman Masnavi,Farrokh Janabi-Sharifi
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:This paper investigates temporal neural networks for \mboxend-effector position \mboxestimation of an aerial continuum manipulator (ACM) operating under aerodynamic effects induced by the unmanned aerial vehicle (UAV). An experimental dataset is collected under stationary (\mboxrotor-off) and \mboxfree-hovering conditions across continuum robot (CR) configurations and UAV altitudes, providing \mboxend-effector position measurements with and without aerodynamic residuals. To establish a nominal framework, \mboxstrain-parameterized kinematic models with progressively richer strain bases are evaluated to balance model complexity and prediction accuracy. The selected nominal model then serves as the baseline for 3D position residual estimation using a \mboxclosed-form \mboxcontinuous-time (CfC) neural network, with a multilayer perceptron (MLP) and a gated recurrent unit (GRU) used for comparison. On unseen test experiments, the CfC achieves an RMSE of (22.00\pm1.70~\mathrmmm) over five random seeds, compared with (36.38\pm3.58~\mathrmmm) for the MLP and (27.72\pm2.92~\mathrmmm) for the GRU, corresponding to reductions of (39.52%) and (20.62%), respectively. These results demonstrate the effectiveness of \mboxcontinuous-time learning for \mboxend-effector position estimation under aerodynamic disturbances relative to static and \mboxdiscrete-time learning methods.
[AI-121] Driving Epidemic Models with AI Agents : the Epydemix Agent Framework
链接: https://arxiv.org/abs/2609.28692
作者: Nicolò Gozzi,Ciro Cattuto,Alessandro Vespignani
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Artificial Intelligence agents based on large language models provide convenient natural language interfaces to scientific software, but reliability is not automatic. Here we introduce the Epydemix Agent Framework, an additive layer over Epydemix, an open-source Python library for stochastic compartmental epidemic modeling. The framework extends the library with four capabilities to facilitate interaction with an AI agent: discovery of available models and parameters, preventive validation of a declarative scenario specification, execution through tested library code, and inspectability of results. These capabilities let an agent handle the entire modeling process, from the natural-language description of the scenario to quantitative results, figures, and interpretation of findings without writing custom code. Each step reads input files and saves results in a separate output bundle, making the process auditable and reproducible. First, we show the end-to-end workflow with a case study comparing vaccination strategies for a novel respiratory virus. Second, we assessed the framework across 50 agent sessions and five modeling tasks by comparing the agent use of the framework against the direct use of the Python interface. The framework reduced turns, output tokens, and cost on most tasks, unless it trades resources for per-point reproducibility.
[AI-122] Beyond Surface Style: Aligning Multi-Turn User Simulators with Behavioral Consistency
链接: https://arxiv.org/abs/2609.28690
作者: Geng Chen,Ruotong Pan,Zhirui Yang,Qiqi He,Jiawei Chen,Zhang Yunfei,Chongyuan Chen,Minxuan Lv,Zheng Yang,Win-Bin Huang,Xiangyu Wu,Wenwu Ou
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Faithful user simulation is fundamental to building, evaluating, and improving interactive AI at scale. However, plausible individual responses do not ensure that simulated users reproduce the intent evolution and outcomes observed in real interactions. We propose TRACER, a multi-turn user simulator that explicitly models users’ evolving intent and learns to align simulated behavior with real interaction trajectories. TRACER is trained in two stages: supervised fine-tuning on real user dialogues, followed by multi-turn reinforcement learning. The RL stage combines hierarchical outcome- and trajectory-level rewards with deviation-aware advantage modulation, jointly mitigating reward sparsity and credit assignment in long dialogues. On real customer-service sessions organized into reference cohorts, TRACER-7B surpasses the strongest baseline by 11.4 conversion F1, while also achieving the lowest group-level conversion-rate error and semantic trajectory distance, and generalizing to out-of-distribution scenarios. Human Turing tests yield identification accuracy close to chance, supporting the perceived naturalness of generated conversations. Building on this simulator, we further introduce the Dynamic Marketing Benchmark, which jointly evaluates persuasion effectiveness and response quality of LLMs through simulated interactions, revealing that higher response quality does not necessarily correspond to higher conversion rates.
[AI-123] Beyond Static Graph World Models: Learning Stochastic Latent Dynamics over Evolving Topologies
链接: https://arxiv.org/abs/2609.28670
作者: Alex Schutz,Nick Hawes,Victor-Alexandru Darvariu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
备注:
Abstract:Graph-based world models have recently emerged as a means of learning transitions over relational state representations. However, existing approaches are largely limited to fixed-topology graphs or deterministic, fully observable environments. We propose the Graph Dynamics Model (GDM), a world model for graph-structured observations that is designed to handle the more general setting of evolving topologies in stochastic and partially observable environments. The GDM uses a sparse recurrent adjacency matrix to model topology updates and perform message passing, together with a recurrent state-space architecture for modelling stochastic transitions. Furthermore, we identify a gap in the evaluation of graph-based world models, as existing methods do not provide a means of comparing predicted and true distributions over the joint graph state comprising the interdependent topology, node features, and graph features. We therefore introduce the Graph Distribution Distance (GDD) metric, which uses maximum mean discrepancy with a graph kernel to comprehensively compare joint next-state distributions. We evaluate the GDM across several environments, including stochastic and partially observable settings. We demonstrate that GDM outperforms baseline models and displays zero-shot generalisation on large graphs.
[AI-124] Decision Hijacking: Prompt Injection Attacks on Jevs Typed Probabilistic Decisions
链接: https://arxiv.org/abs/2609.28613
作者: Tiantong Wu,Wei Yang Bryan Lim
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Most studies of prompt injection focus on generative agents, leaving their effects on models with schema-defined outputs unclear. We examine these effects in Jev, a non-generative decision model, using 510 reconstructed InjecAgent cases. Malicious content shifts action probabilities but rarely causes Jev to select the attacker’s target. Override markers reduce this influence, while claims of contextual relatedness have small effects. Adaptive attacks using score feedback double the mean highest attacker-target probability found during optimization, while success on fresh validation calls rises from 1.8% to 3.5%. Exploratory analysis links these successes to small initial decision margins or greater attacker control over the observation. Together, these findings show that schema-defined outputs change but do not eliminate prompt-injection risk, highlighting the need to evaluate how untrusted content influences choices within the allowed action set.
[AI-125] Adversarial Closed-Loop Curriculum for Evolving Role-Playing Agents
链接: https://arxiv.org/abs/2609.28609
作者: Zheng Zhang,Liu Liu,Qi Chai,Deheng Ye,Peilin Zhao,Mao Zheng,Hao Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Role-playing agents based on large language models have been widely applied in areas such as personalized assistance and social simulation. Recent RL methods typically train on a fixed scenario pool collected before learning begins. This creates a distributional bottleneck: as the agent improves, the scenarios where it performs poorly also change, while the training distribution remains static. Therefore, we propose AdvRole, an adversarial context rewriting framework that turns role-playing RL into a closed-loop curriculum. AdvRole alternates between an Actor that learns to role-play and a Rewriter that edits character profiles and dialogue contexts into actor-specific hard scenarios. The Rewriter is trained with a performance-gap reward, which favors rewrites that reduce the current Actor’s score relative to the original scenario. As a result, the scenario pool evolves with the Actor and continuously targets under-mastered regions of the character-context space. Experiments on three role-playing benchmarks covering English and Chinese, as well as a new multilingual benchmark we release, show that AdvRole consistently outperforms baselines.
[AI-126] UO-FIE: Combining Exact-Label Supervision with Graded Utility for Factivity Inference CCL26
链接: https://arxiv.org/abs/2609.28605
作者: Xinchen Xiao
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 11 pages, 6 figures. Accepted as oral presentation at CCL26-Eval
Abstract:The Factivity Inference Evaluation 2026 (FIE2026) classifies Chinese context-hypothesis pairs into nine ordered factivity intervals. Its evaluation metric rewards both exact predictions and proximity to the correct interval, while 64.1% of the 566 training examples belong to a single class. In preliminary experiments, several mDeBERTa classification models predominantly predict the dominant class, whereas a Huber-regression baseline produces more predictions near the correct interval but fewer exact matches. We introduce Utility-Oriented Factivity Inference (UO-FIE), a parameter-efficient system that combines exact-label supervision with graded utility. UO-FIE predicts a distribution over the nine classes and combines hard-label supervision, utility-based soft targets, scheduled class weights, and an ordinal loss. We evaluate expected-utility decoding in controlled comparisons and use ordinal calibration selected on out-of-fold predictions for the submitted system. Based on Qwen3.5-9B with LoRA, UO-FIE ranks first in the fine-tuning track with a macro utility of 0.8316. A separate prompt-based ensemble ranks third in the non-fine-tuning track with a macro utility of 0.8450. Comments: 11 pages, 6 figures. Accepted as oral presentation at CCL26-Eval Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.28605 [cs.LG] (or arXiv:2609.28605v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.28605 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-127] Learning to Discover Interesting Mathematics
链接: https://arxiv.org/abs/2609.28603
作者: Niket Patel,Ahmad Rammal,Amaury Hayat,Remi Munos,Julia Kempe
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Recently, Large Language Models (LLMs) have been increasingly able to solve advanced mathematical problems, including many that have been open for decades. This opens the door to expansion of mathematical knowledge at unprecedented scale. Yet, while LLMs may be able to conjecture and prove more and more theorems, it remains open whether this new mathematical knowledge is interesting or useful. We define intrinsic interestingness of a theorem as the ratio between the length of its proof and the length of its statement. We show that this correlates strongly with an extrinsic measure of the downstream utility of a theorem. We identify the difficulty of a proof conditioned on a set of premises as a useful primitive for computing these metrics, and train a 27B model that predicts proof difficulty more accurately than frontier general-purpose models. Optimizing for our metric creates a model capable of producing more interesting theorems, while also reducing substantial or full overlap with Mathlib from 91.9% to 30.6%, showcasing the creation of more out-of-distribution math. We show that our system can generate candidate theorems, select the most interesting among them, and iteratively build on a self-expanding mathematical library. These metrics provide a practical and quantifiable signal for ranking conjectures and guiding proof search within formal mathematical libraries. Our framework provides a path towards self-expanding, machine-verified mathematical libraries that can choose worthwhile statements without relying on human-supplied targets.
[AI-128] Persistent Billable State: Denial-of-Wallet Attacks and Defenses in Tool-Calling LLM Agents
链接: https://arxiv.org/abs/2609.28585
作者: Jinqian Zhang(1 and 2),Haojun Xia(1 and 2),Shujiang Wu(3),Jingkun Yue(4),Xia Zhang(1 and 2),Zhangpei Cheng(1 and 2),Bibo Tu(1 and 2) ((1) Institute of Information Engineering, Chinese Academy of Sciences, (2) School of Cyber Security, University of Chinese Academy of Sciences, (3) Beihang University, (4) State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 22 pages, 14 figures, 13 tables
Abstract:Multi-step tool-calling LLM agents rely on host runtimes to preserve state across turns. When a runtime carries an external tool return into later model inputs, providers meter it again. An admitted malicious or compromised tool can thereby convert untrusted data into recurring victim-billed processing without victim credentials or local runtime privilege. We call retained content persistent billable state and formalize the host’s decision over whether and how it enters later billable context as the persistent billable-state boundary. We present the first systematic security study of this post-admission lifecycle. We derive six denial-of-wallet attack vectors and build DOW-BENCH, an end-to-end harness evaluated across six model families. Across 243 executions, usage telemetry shows that the maximum per-session cumulative input reaches 14,293x the session’s first-call input. Controlled history-policy reruns isolate raw retention’s contribution: retaining raw history increases mean effective session cost by 21.2-35.9%. Compression succeeds on 10/12 and 11/12 history-dependent tasks, versus 2/12 under deletion for each provider. To govern this boundary, we combine deterministic history transformation with four host-side invariants that bound prompt mass, context growth, recursive opportunity, and cumulative spend before reingestion. The kernel contains every recurring attack in the 123-evaluation replay corpus. Across 24 Mistral Small 4 workflows, a progress-authorized policy achieves 22/24 oracle-verified task successes with no pre-completion interruptions, versus 13/24 under a fixed cap. Only 71 of 3,830 scanned MCP server and transport repositories expose any code-visible safeguard proxy, and none cover all four safeguard families. These results establish persistent billable state as a first-class security object and pre-reingestion as its host-owned control point. Comments: 22 pages, 14 figures, 13 tables Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.28585 [cs.CR] (or arXiv:2609.28585v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2609.28585 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-129] SGA: Uncertainty Quantification for Multi-Step Forecasting in Time Series Foundation Models
链接: https://arxiv.org/abs/2609.28582
作者: Xin-Yu Hu,Shuang Liang,Cheng Feng,Shao-Qun Zhang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:The recent emergence of Time Series Foundation Models (TSFMs) has significantly advanced multi-step forecasting performance, enabling accurate predictions over extended future horizons. However, existing TSFMs often suffer from significantly inherent uncertainty, which typically manifests as derived forecast branches emerging at each time step and spreading to subsequent steps; different forecast branches often exhibit varying forecasting performance, thereby undermining the credibility of TSFM forecasts. In this paper, we propose the Slicing-Graphing-Alignment (SGA) method to quantify the uncertainty of multi-step TSFM forecasts. The proposed SGA first characterizes the topology of all potential forecast branches using a directed acyclic graph, such that the graph complexity bounds the uncertainty of multi-step forecasts, and then precisely measures the graph complexity by integrating both topological information and TSFM-inherent stochasticity. Experimental results conducted on 11 TSFMs and 27 datasets demonstrate that (i) SGA achieves the best performance when ranking predictive errors with uncertainty estimates; (ii) SGA works with a more extensive and more precise sampling coverage than those of existing UQ methods, deriving a quantification mechanism fundamentally different from those of established ones; and (iii) larger model scales of TSFMs correlate with lower uncertainty estimates of multi-step forecasts, suggesting another empirical scaling law for uncertainty quantification of multi-step TSFM forecasts.
[AI-130] Auditability Is Not One Property: Rule Overlap Behavioural Agreement and Composition in Reinforcement Learning
链接: https://arxiv.org/abs/2609.28581
作者: Liu Hung Ming
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 35 pages, 4 figures, 14 tables. Experimental results cover eight random seeds on CartPole-v1 and Acrobot-v1. Code, data, and audit artifacts are available in the accompanying repository
Abstract:Reinforcement-learning (RL) policies are often distributed as opaque neural checkpoints, while training logs show that a run occurred without explaining what the policy learned. We study whether independently trained policies can be represented and composed through auditable discrete behavioral rules. We define auditability as six separately testable predicates: trace integrity, lossless coding, rule coverage, behavioral agreement, composition quality, and value-model reliability. Our protocol uses a shared frozen symbolizer, passive rule extraction, an append-only hash-bound ledger, exact environment replay, and offline confidence-ranked arbitration with an explicit blind-spot fallback. The results place strict limits on this description layer. Rule-set overlap does not imply behavioral agreement: policies may share symbolic rules while choosing near-chance-matching actions on fresh states. The fused policy therefore selects among existing rules rather than generating a new skill. On a conflict-dominated task, an apparent fusion failure is traced to an induction/deployment mismatch: rules induced from sampled actions were evaluated under argmax actions, and deployment-consistent re-induction reverses the arbitration ordering. A fitted-Q generalized-policy-improvement diagnostic also fails in both environments, limiting claims that rule fusion is superior to value-based composition. One exploratory comparison favors rule fusion, but its comparator is post hoc, the task is partly saturated, and the fused policy remains below the strongest held-out actor. We contribute an evidence-bounded audit and composition protocol, not a claim of universal interpretability or autonomous skill generation. Future work must add temporally extended skills, cross-skill interfaces, composition search, and independent novelty audits. Comments: 35 pages, 4 figures, 14 tables. Experimental results cover eight random seeds on CartPole-v1 and Acrobot-v1. Code, data, and audit artifacts are available in the accompanying repository Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.28581 [cs.LG] (or arXiv:2609.28581v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.28581 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-131] WIST: A Proposed Benchmark for Intervention Quality in Conversational Memory with a Human-Validated Draft-Alignment
链接: https://arxiv.org/abs/2609.28575
作者: Subrat Panda
类目: Artificial Intelligence (cs.AI)
备注: conversational memory, LLM agents, agent memory systems, benchmark, contradiction detection, belief revision, supersession, intervention quality, hard negatives, retrieval-augmented generation, evaluation methodology, memory governance, long-term memory, human annotation
Abstract:Long-conversation memory benchmarks increasingly test recall and prompted knowledge updates, and recent work studies evolving user beliefs and memory state. TWIST is a proposed benchmark suite for a complementary, unmeasured property: intervention quality – whether a deployed memory system, exercised through its own ingest/recall/vet surface, acts correctly at belief change points. Four tracks cover unprompted tension detection, vetting outgoing drafts against the record, answering with current beliefs while preserving supersession history, and governing sensitive recall. The suite extends LoCoMo’s corpora and harness, pairing every detect/block metric with a matched do-not-over-detect control: surface-matched hard negatives price false intervention, so no track can be gamed by flagging everything. The benchmark itself is validated first: independent, gold-blind double annotation with adjudication, judge decoy calibration, and a separability audit. On the human-validated Track B v1.0 key (161 items, post-adjudication kappa = 0.85), no tested configuration simultaneously achieves high contradiction recall, high hard-negative specificity, and high attribution: flat-RAG baselines detect 0.76-0.97 of true contradictions but falsely flag 16-43% of surface-matched safe drafts depending on backend, while a deployed coherence-oriented system almost never over-flags (0.98-1.00 specificity) yet catches 42% of true contradictions – a trade-off no recall-only score can see. A 13-configuration baseline ladder localizes causes: every gold contradiction is detectable from its evidence alone (recall 1.000), calibrated models nearly solve the track given the full transcript – consistent with substantial retrieval-coverage gaps – and draft-only floors reveal model-dependent style priors. A system’s TWIST profile, beside its recall score, measures whether memory knows when to intervene and when not to.
[AI-132] Where Cyber Agents Struggle: Bottleneck Analysis of Multi-Stage LLM Agents
链接: https://arxiv.org/abs/2609.28572
作者: Saeedeh Lohrasbi,Mohammad Mamun,Ahmed Yehia,Scott Buffett,Sherif Saad
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: The 19th International Symposium on Foundations Practice of Security (FPS 2026)
Abstract:Multi-stage LLM-based cyber agents may complete attack workflows while remaining brittle, costly, or reliant on incorrect interpretations of execution evidence. Success rates alone obscure inefficiency, adaptation through retries, and recognition of success or failure. We present an end-to-end diagnostic study of an Autonomous Adversary system with orchestrator, executor, and validator LLMs in enterprise-like lateral-movement scenarios. Six frontier models are evaluated across two scenarios and three modes: expert-defined, self-scaffolded, and fully autonomous. We assess validator consistency and evidence grounding; introduce a subtask-conditioned, cost-aware score for abnormal token use, retries, and runtime; and use comparative LLM-as-a-Judge analysis to identify planning deficiencies, including tool misalignment, plan similarity, over-specification, inadequate probing, and weak recovery. Validators are generally relevant and evidence-grounded but often nonspecific and overly optimistic. Bottlenecks cluster in credential and lateral-movement tasks, spread with scenario complexity, and vary more under full autonomy. Reliable evaluation must assess outcomes, evidence interpretation, resource use, and adaptation after failure.
[AI-133] DEEPO: Dual-Entropy Enhanced Policy Optimization for Hallucination in MLLM s
链接: https://arxiv.org/abs/2609.28570
作者: Yingxuan Zhuang,Miao Pan,Wangjie Gan,Jingxiao Yang,Fan Wang,Weiming Liu,Cheng Tan,Xuhong Zhang,Jintao Chen
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Reinforcement learning (RL) is widely used to sharpen reasoning in multimodal large language models (MLLMs), yet its effect on hallucination is uneven. We trace this to two weak points in the \emphcorrection chain from reward to parameter update. At the rollout level, hard queries—those with high semantic entropy—frequently produce unanimously wrong sample groups, collapsing the group-relative advantage to zero exactly where hallucination risk is highest. At the optimization level, confident-but-wrong tokens are gradient-invisible: a categorical policy’s expected score-gradient norm vanishes as its distribution sharpens, so the predictions that most need correction receive the weakest updates. We propose Dual-Entropy Enhanced Policy Optimization (DEEPO), a dual-stage enhancement combining signal variance regularization with gradient preconditioning: semantic-entropy-triggered expert prefixes inject grounded continuations on high-uncertainty queries, providing direct supervision and restoring advantage variance, while advantage-sign-aware Renyi preconditioning counteracts logit-level saturation so correction reaches confident errors in the operational confidence regime. Both branches improve over GRPO individually; their interaction is statistically significant on VideoMMMU—the most complex long-horizon task in our evaluation suite (+4.0 , 95% CI [1.1, 6.9])—and additive elsewhere. DEEPO reduces hallucination while preserving accuracy and training stability. Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2609.28570 [cs.AI] (or arXiv:2609.28570v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.28570 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-134] Who Is Behind the Harness? Fingerprinting LLM s through Agent ic Behavior
链接: https://arxiv.org/abs/2609.28559
作者: Chuyi Wang,Xiaohui Xie,Tongze Wang,Fangchen Luo,Yong Cui
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:LLMs increasingly operate through coding-agent harnesses that inspect repositories, invoke tools, and modify files. Substituting the model behind such an agent can therefore change security-relevant decisions, including whether it verifies changes or recovers safely from failures. Existing LLM fingerprints largely infer identity from direct text or token distributions. In coding agents, these signals are mediated by system instructions, controller logic, tools, and execution feedback, limiting their transfer. We present LIDAR (LLM Identification from Decisions and Actions at Runtime), an active black-box fingerprinting method for coding-agent execution. Three coding probe pairs expose post-edit verification, transient-failure recovery, and specification–test conflict resolution under controlled changes. LIDAR represents the resulting trajectories with complementary instance-level and distribution-level features and compares them with clean references using a lightweight probabilistic identifier. It requires no access to model weights, logits, or provider internals. Across 36 models from seven families and two agent harnesses, LIDAR achieves high Top-1 accuracy and MRR and outperforms four existing fingerprinting and API-auditing baselines. Ablations confirm that the two feature levels, all probe pairs, and their controlled variants contribute. These results show that agent execution behavior provides model-identity evidence beyond final outputs. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE) Cite as: arXiv:2609.28559 [cs.CR] (or arXiv:2609.28559v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2609.28559 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-135] BaseCamp — An Agent ic AI Framework for Automating DNA Sequencing Data Pipelines
链接: https://arxiv.org/abs/2609.28557
作者: Eranga Bandara,Xueping Liang,Asanga Gunaratna,Tharaka Hewa,Abdul Rahman,Peter Foytik,Safdar H. Bouk,Sachini Rajapakse,Isurunima Kularathna,Pramoda Karunarathna,Chalani Rajapakse,Ng Wee Keong,Kasun De Zoysa,Amin Hass,Wathsala Herath,Ross Gore,Ravi Mukkamala,Nihal Siriwardanagea,Gihan Siriwardanagea,Aruna Withanage,Nilaan Loganathan,Sachin Shetty
类目: Artificial Intelligence (cs.AI); Genomics (q-bio.GN)
备注:
Abstract:DNA sequencing pipelines, spanning quality control, alignment, variant calling, and annotation, are now reliably executed by workflow management systems that orchestrate established bioinformatics tools at scale. What remains manual is the decision layer surrounding that execution: selecting quality thresholds appropriate to a sample and platform, adjudicating borderline variant calls, diagnosing anomalies, and determining which findings warrant expert review. These decisions are repetitive, judgment-intensive, inconsistent across operators, and frequently undocumented. This paper introduces BaseCamp, a novel agentic AI framework for automating the decision layer of DNA sequencing pipelines. The framework decomposes the pipeline into six specialized AI agents, covering sample intake and quality control, alignment, variant calling, annotation, cross-stage monitoring, and reporting. Critically, BaseCamp agents do not perform sequence analysis: established tools execute alignment, calling, and annotation, while the agents select among them, configure them, interpret their output, and decide what follows. This confines language model reasoning to the judgment layer where it is reliable and preserves the reproducibility existing tooling guarantees. Agent reasoning is powered by a consortium of fine-tuned, domain-specialized large language models coordinated by a central reasoning LLM, executing locally so no sequencing data leaves the operating environment, under human-in-the-loop orchestration. Evaluation shows agent-generated configurations are concordant with expert practice, that an explicit filtering ledger renders inspectable what filtering otherwise removes without trace, and that cross-stage anomaly detection surfaces conditions execution monitoring misses. BaseCamp offers a generalizable blueprint for agentic automation of scientific data pipelines.
[AI-136] SMILESGNN: Interpretable Clinical Toxicity Prediction via SMILES-Graph Cross-Attention Fusion
链接: https://arxiv.org/abs/2609.28553
作者: Quang Minh Nguyen,Thuy Quynh Nguyen,Duc Minh Le,Ho Nhat Minh Nguyen,Thanh Long Dai Doan,Trong Nghia Nguyen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Drug toxicity prediction is critical for reducing late-stage attrition in drug discovery, yet remains challenging due to severe class imbalance, scaffold-based generalization, and the clinical need for interpretable predictions. Single-modality approaches-SMILES Transformers or graph neural networks capture complementary aspects of molecular structure, while sequence-only models cannot directly provide graph-attributed explanations. We present SMILESGNN, a multimodal architecture that fuses a SMILES Transformer encoder and a GATv2 graph encoder via cross-attention, and SMILESGNN-PT, a variant using a ChemBERTa-2 pretrained backbone. The design retains an explicit graph branch within the predictive pipeline, supporting GNNExplainer-based analysis of substructures associated with toxic predictions. On ClinTox, SMILESGNN achieves AUC-ROC 0.987 and F1 0.906 with only 0.4M parameters, performing competitively with a strong SMILESTransformer and a larger ChemBERTa-2/GATv2 concat-fusion baseline. On Tox21 (12 tasks), SMILESGNN-PT obtains mean AUC-ROC 0.750, comparable to ChemBERTa-2 alone and the same-backbone concat-fusion baseline. Overall, the results suggest that cross-attention is a practical fusion alternative that preserves competitive predictive performance while enabling graph-based interpretability support.
[AI-137] W3Cast: A Frozen Router of Lightly Fine-Tuned Foundation Models for Time-Series Forecasting on GIFT-Eval Selected Entirely on the Training Split
链接: https://arxiv.org/abs/2609.28506
作者: Nathan Thierry,Andre-Louis Rochet
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:TW3Cast is a time-series forecasting system that reaches position 3 of 130 entries on the GIFT-Eval benchmark by mean MASE rank, as of 2026-09-14. The two entries above it belong to the leaderboard’s agentic category, multi-step systems that use agents or language models to reason about, generate or select forecasts. TW3Cast runs no agent and no language model. Its selection is a table computed once on the training split and then frozen, and its experts are public foundation models lightly fine-tuned on those training splits. For each of the 97 dataset, frequency and horizon configurations, the table serves one of four modes: a specialist, which is a LoRA or full fine-tune of Chronos-2, TiRex or Toto whose training data was cleaned and enriched by explicit rules; a quantile blend that contains a specialist; a blend of base models; or a selection tournament played on a backtest carved from the training split. Every decision in the table was taken on that backtest. A specialist is admitted the moment it beats the tournament there, so a candidate costs a few megabytes and minutes of GPU time, and a failed candidate changes nothing. Three guarded mechanisms protect the selection from its own biases: a dual accuracy and calibration criterion, an asymmetric margin against candidates that saw the series during training, and conservative per-window gates. The selection rules themselves were chosen inside a temporal meta-backtest. The best base model served alone reaches a mean MASE rank of 33.8, the tournament served on every configuration reaches 38.0, and the full router reaches 19.4. The routing table, the expert index, the pinned base-model revisions, the submitted score file and the dated snapshot of the public scores are released, and every leaderboard number in this paper regenerates from them by one script.
[AI-138] CaliPPer: quantifying predicting and improving AI model performance for binding prediction
链接: https://arxiv.org/abs/2606.07258
作者: Jian-Qing Zheng,Hantao Lou,Zinan Yin,Sam Farrar,Yuze Zhou,Elie Antoun,Xiangxi Wang,Xuetao Cao,Tao Dong
类目: Computational Engineering, Finance, and Science (cs.CE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
备注:
Abstract:Binding prediction models accelerate therapeutic antibody and TCR discovery, but their performance on new datasets is unpredictable, often leading to low discovery rates. Density-ratio methods (PAPE, M-CBPE) provide label-free performance estimation for binary classification, but their assumptions and aggregate-only outputs limit binding prediction on neoepitopes, antigen variants and chemical scaffolds. Here we present CaliPPer (Calibration and Prediction of Performance), a post-hoc framework pairing a multi-chain Sample-to-Domain Distance (S2DD) with distance-aware Bayesian recalibration, operating at three resolutions: generalisability score, aggregate performance prediction, and per-sample confidence. Across ten models, eight architectures and two immune-receptor domains, CaliPPer attains distance–performance correlations |r|=0.80\text–0.92 , predicts AUROC/AP/F1 with mean absolute errors 0.008\text–0.070 , and improves AUROC by up to +0.20 on unseen epitopes/variants. Applied retrospectively to five published TCR, BCR, MHC–peptide and small-molecule studies, CaliPPer raises true discovery rates in all five (e.g.\ 0/5 \to 3/5 confirmed neoantigens), providing a triage layer between computational prediction and experimental validation.
[AI-139] Structured Pose-Conditioned Flow Matching for Generative 5G CSI Augmentation
链接: https://arxiv.org/abs/2609.29912
作者: Haojin Li,Anbang Zhang,Wai Ho Mow,Chenyuan Feng,Chen Sun,Haijun Zhang
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI)
备注:
Abstract:With the growing demand for privacy-preserving and occlusion-resilient human pose recognition (HPR), 5G channel state information (CSI) offers a promising contactless sensing modality by integrating communication and sensing capabilities. However, collecting large-scale synchronized CSI-pose pairs remains costly in practical 5G systems. To address this limitation, we propose StructFlow-HPR, a structured pose-conditioned flow matching framework for generative CSI augmentation. StructFlow-HPR learns a continuous latent transport process from Gaussian noise to real CSI representations under pose guidance, while preserving the receiver-frequency topology of CSI through a reconstruction-preserving autoencoder. A pose-conditioned Transformer is further designed to model the latent velocity field and generate pose-aligned CSI samples via ordinary differential equation sampling. Experiments on real-world 5G sensing data show that StructFlow-HPR can produce realistic CSI-pose pairs and improve downstream HPR performance under limited-data conditions.
[AI-140] Anatomy-aware cross-speaker adaptation of complete vocal-tract acoustic-to-articulatory inversion ICASSP2027
链接: https://arxiv.org/abs/2609.29766
作者: Nhat-Nam Nguyen,Pierre-Andre Vuissoz,Yves Laprie
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI)
备注: Submitted to IEEE ICASSP 2027
Abstract:Cross-speaker acoustic-to-articulatory inversion requires accounting for anatomical differences between speakers. We propose a geometric adaptation framework that uses anatomical landmarks, primarily on vertebrae and dental structures,to transfer predictions from a fixed inversion model to unseen speakers. An affine transformation followed by thin-plate spline (TPS) deformation maps the predicted contours of 10 vocal-tract structures into each target speaker’s geometry without retraining. Landmarks are identified in one selected /u/ frame per speaker as a common phonetic reference without assuming identical articulatory configurations across speakers, and the resulting mapping is reused across recordings. We train the model on a single-speaker rt-MRI database and evaluate adaptation on eight speakers from a separate multi-speaker rt-MRI database. We compare affine and TPS configurations using 12 or 14 landmarks. Affine12+TPS14 achieves the lowest mean point-to-closest-point error of 3.19mm. These results support the combined value of anatomical landmark information and nonrigid alignment.
[AI-141] WST-Graph: Topology-Preserving Wavelet Scattering Front-End for Speech Deepfake Detection
链接: https://arxiv.org/abs/2609.29372
作者: Kwok-Ho Ng,Tingting Song,Bingwen Feng,Zhihua Xia
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Sound (cs.SD)
备注:
Abstract:The acoustic front-end determines which forensic cues a speech deepfake detector can exploit. The wavelet scattering transform (WST) provides stable multiscale coefficients with explicit coordinates, yet direct flattening obscures the parent relation between paths. We introduce WST-Graph, reconstructing these paths as a sparse modulation-carrier grid for an AASIST graph backend. Modulation-level normalization and length-aware adaptive local attention pooling produce fixed relative-time representations while retaining the acoustic axes before learned adaptation. This yields a waveform-to-graph interface with a fixed, parameter-free WST. Our configurations remain competitive with AASIST while using approximately 60% fewer trainable parameters and show clear gains on selected out-of-domain benchmarks. These results underscore the value of preserving parent-child relations within the carrier-modulation topology when constructing a compact, physically grounded interface for graph-based speech deepfake detection. Code will be released at this https URL.
[AI-142] A Harness for Synthesizing Diverse Naturalistic Full-Duplex Conversations ICASSP2027
链接: https://arxiv.org/abs/2609.28806
作者: Matthew Sun,Vinay Kothapally,Meng Yu,Chao Huang,Hao Zhang,Yixuan Zhang,Steve Yves
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI)
备注: Short version submitted to ICASSP 2027
Abstract:Full-duplex dialogue systems, which listen while speaking, must distinguish a completed turn from a pause within a turn and an interruption that requests a turn from a brief acknowledgment or speech addressed to a third party. Yet existing conversational corpora provide limited control over these events and limited labels for their intent. We present a pipeline for synthesizing intent-labeled, two-channel conversational speech from relational event lists. An LLM authors each event’s speaker, text, conversational act, and attachment to an earlier event without predicting absolute timestamps. Events are synthesized independently, aligned with their source text, and placed on a shared clock, so turn-taking landmarks are measured from the rendered signal while silence durations are specified or sampled from turn-taking distributions. The pipeline covers 42 phenomena across eight families in English and Mandarin, derives frame-level system actions from authored intent, and promotes diversity using small, diverse sets of prior examples and batch prompts that request alternatives with self-reported probabilities. Ablations show gains in each targeted diversity dimension. On a four-action label space for taking, holding, releasing, and not holding the conversational floor, a semantic voice-activity detector using only current and past audio reaches start-speaking and start-listening F1 scores of 0.819 and 0.802. When generating its own responses, the full-duplex speech model Moshi takes 0.85 of the reference turns after fine-tuning on the generated corpus, compared with 0.44 before fine-tuning. Its frame-level precision for predicting system-floor occupancy rises from 0.46 to 0.88. With reference context at each step, its frame-level floor F1 rises from 0.893 to 0.962. These results show that controlled synthesis can provide learnable and transferable supervision for full-duplex turn management.
[AI-143] NumericJev: Jev-like LLM Numerical Decoding with Multiway Decision Trees
链接: https://arxiv.org/abs/2609.28587
作者: Weiwei Ye,Hangchen Liu,Renhe Jiang
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Large language models can interpret natural lan- guage, yet robust decisions remain challenging. Jev-like models expose structured choices, but these interfaces do not directly provide numeri- cal values at a requested precision. We propose NUMERICJEV, a training-free numerical decod- ing algorithm that enables numerical output from any LLM with a Jev-like structured-choice in- terface. Surprisingly, on our arithmetic bench- mark, it outperforms direct selection from a can- didate list containing the correct answer by 2.93 percentage points (Figure 1). Our motivation comes from the observation that numerical range selection is itself a decision problem that Jev- like LLMs can address. NUMERICJEV recur- sively refines a range through a multiway deci- sion tree while retaining the original question in context, without parameter updates or hidden- state access. On a 100-value grid, a ten-way tree requires only two decision rounds. Range- normalized MAE is 1.84% versus 5.18% for di- rect choice. A separate three-date historical- index study yields 4.58% mean relative recall er- ror and 0% readout error when the value is sup- plied. Code is available at https://github. com/Bring-AI/jev-numeric.
[AI-144] Speculative Evaluation of Stochastic LLM s
链接: https://arxiv.org/abs/2609.28560
作者: Qianli Shen,Xiang Li,Ruomeng Ding,Yanxi Chen,Daoyuan Chen,Yaliang Li
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Evaluating a stochastic large language model is costly: benchmark scores estimate expected performance from randomized rollouts, yet uniform repetition ignores sharp differences in task-level rollout variance. We ask how to minimize the variance of a fixed-benchmark mean under an exact rollout budget. We develop Speculative Evaluation with a Hierarchical Bayesian Neyman (HBN) policy with pilot size and stage weight jointly chosen ex ante. It runs a short uniform pilot, pools per-task success counts with a hierarchical Bayesian model, and uses posterior expectations of task-level sampling variances for exact positive-integer Neyman allocation. To mitigate the pilot synchronization barrier, HBN-async speculatively executes continuations from partial pilot feedback and retains those selected by the final allocation. Across six checkpoints and 18 benchmark groups, we evaluate 107 nondegenerate benchmark-checkpoint profiles. For rollout budgets of 8-64 per task, Speculative Evaluation reduces variance relative to Uniform by 12.8%-33.6% on average across profiles, outperforming hindsight-tuned empirical and independent Bayesian baselines. Real-generation experiments that account for the pilot synchronization barrier show that HBN-async mitigates its overhead, helping translate statistical efficiency into practical evaluation benefits.
[AI-145] CrossScale-GLIO: Topology-Preserving Vision-Language Alignment of MRI and Whole-Slide Histopathology for Diffuse Glioma
链接: https://arxiv.org/abs/2609.28524
作者: Yantong Liu,Zheyu Zhang,Runpeng Liu,Mu Xitang,Seong-Yoon Shin,Hyun-Ae Lee
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)
备注:
Abstract:Magnetic resonance imaging and histopathology observe the same glioma at radically different scales. We present CrossScale-GLIO, a visual multimodal framework that represents MRI as a tumor-habitat graph and histology as a cell-niche graph, then aligns them with a structure-aware optimal transport objective anchored by diagnostic language. Across paired and external glioma cohorts, CrossScale-GLIO achieved a paired-test subtype macro-F1 of 0.789, IDH AUROC of 0.934, 1p/19q AUROC of 0.884, and MGMT AUROC of 0.802. The subtype gain over feature-only transport was 2.8 percentage points (95% CI: 1.2 to 4.4, adjusted p = 0.0019). Bidirectional patient retrieval reached Recall@1 values of 0.286 and 0.278, and Recall@5 values of 0.621 and 0.608. Pathologists rated 81.2% of high-mass habitat-niche pairs as biologically plausible. Deleting the highest-mass pair reduced correct-class probability by 0.184, compared with 0.049 under random deletion. Degree-preserving graph rewiring reduced subtype macro-F1 by 0.034 and retrieval Recall@1 by 0.090, directly confirming that preserved relational topology drives cross-scale correspondence.
[AI-146] Certified Task-Conditioned Active Observability
链接: https://arxiv.org/abs/2609.28520
作者: Linzhe Zhang,Changming Xu
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Before acting upon an unobservable physical system, an autonomous agent must determine which latent distinctions govern downstream tasks, how many active interventions are necessary to certify them, and when to abstain to prevent catastrophic errors. Classical observability treats state reconstruction as an unconditioned binary predicate, failing when passive observations cannot break latent degeneracies without perturbation, full microscopic inversion is prohibitively costly, and distinguishing task-irrelevant degrees of freedom wastes interaction budgets. We formalize task-conditioned active observability complexity: the minimum worst-case expected interaction cost required to identify task-relevant states under certified error and safe abstention guarantees. We prove that task-predictive equivalence induces the unique minimal sufficient quotient \mathcalH/!\sim_\tau , leaving active observability complexity strictly invariant while eliminating superfluous distinctions. In deterministic regimes, this complexity is characterized by an optimal adaptive distinguishing tree and Bellman recursion; in noisy regimes, it obeys a stopped-transcript relative-entropy lower bound and adaptive martingale certificates that compose without independence assumptions. We instantiate a prospective certified observer with staged recovery: a nominal verifier defers candidate compilation, triggering active probing only upon evidence, while a history-measurable score shell prunes hypotheses without sacrificing risk bounds. Stress audits across high-dimensional physical systems and thousands of operational trials demonstrate certified state recovery with zero false acceptances and substantial reductions in sensor reads and model steps.
[AI-147] AI in Science: Early Insights
链接: https://arxiv.org/abs/2609.28504
作者: Mihai Codreanu,Alex Imas,Juan Mateos-Garcia,Joseph Emmens,Evalyne Muiruri,Arthur Turrell,Julian Jacobs,Atoosa Kasirzadeh,Ana Trisovic,Yiyuan Chen,Tanya Rodchenko,Catherine Pollard,Scott Strand,Daniel Rock,Zanna Iscenko,Fabien Curto Millet,Neil Thompson,James Manyika
类目: General Economics (econ.GN); Artificial Intelligence (cs.AI)
备注:
Abstract:Scientific progress is a key driver of economic growth and prosperity. There is great excitement - but also concerns - about the impacts of AI on science, but so far little data. We provide early insights on this from three data sources: a sample of 15 million Gemini interactions, an inventory of over 2,600 specialized AI models across disciplines, and a survey of over 600 scientists. We map these data to a new taxonomy of scientific tasks to study how scientists are using AI. Four main findings emerge. First, we find broad adoption and coverage: scientists use AI more than most other occupations. Specialized AI models have broad disciplinary coverage and are highly cited. Nearly half of the scientists surveyed report using some form of AI every day. Second, we document evidence that LLMs (proxied through Gemini usage) and specialized models act as complements-- LLMs are used for general analysis, coding, and manuscript preparation, while specialized models provide domain-specific predictions, data generation and classification. Third, scientists report large productivity gains from using AI: a saving of nearly 7 hours per week, time which is primarily re-invested in more research. Finally, we show that AI is already changing the scientific process. As some stages of scientific research become easier, bottlenecks shift downstream. Scientists report an increased backlog of untested hypotheses and substantial demand for output verification. Our findings suggest that AI holds significant potential to increase scientific productivity. However, as with other sectors, its ultimate impact will be governed by complex task interdependencies and investment into the elimination of emerging bottlenecks.
[AI-148] Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment
链接: https://arxiv.org/abs/2609.28491
作者: Dilli Hang Rai
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 5 pages, 5 figures, 5 tables
Abstract:Hybrid quantum-classical neural networks have emerged as a promising approach for leveraging quantum computing in machine learning while mitigating current hardware limitations. This paper presents Sim-HVQC, a hybrid Deep Quantum Neural Network that couples an adaptive, parameter-free SimAM weighting module with classical feature extraction to preserve class-discriminative information prior to encoding into a Variational Quantum Circuit (VQC). Previous studies are restricted to binary classification [1] [2] [3] [4] [5]. In contrast, the proposed framework is trained and evaluated on various multi-class datasets(MNIST, KMNIST, Fashion-MNIST, and EMNIST). The framework further demonstrates reproducibility, parameter efficiency, and interpretability through multi-seed evaluation, parameter analysis, and latent/quantum feature inspection. The source code is publicly available at this https URL SimAM-HVQC
机器学习
[LG-0] mporal Gradient Inversion for Private Trajectory Reconstruction in Embodied Reinforcement Learning NEURIPS2026
链接: https://arxiv.org/abs/2609.30258
作者: Sudip Bhujel,Shanghao Shi,Ruiquan Huang,Ning Zhang,Yang Xiao
类目: Machine Learning (cs.LG)
*备注: Accepted at NeurIPS 2026
Abstract:Distributed learning in embodied reinforcement-learning agents offers a degree of privacy by retaining raw sensor data on-device and transmitting only policy gradients to the server. Yet temporal structure can amplify this leakage beyond single-frame attacks. We introduce Temporal Reconstruction Attack on Consecutive Encodings (TRACE), an amortized temporal gradient-inversion attack that autoregressively reconstructs the sequence of private observation-action trajectories from per-step policy-learning gradients. The attack exploits two structural signals ignored by prior single-frame methods: (i) cross-time correlation between successive embodied gradients, which we formalize via a conditional mutual-information bound, and (ii) closed-form action recovery from policy-head gradient structure, which we prove exact when standard entropy regularization is sufficiently small. On held-out embodied scenes, TRACE reaches 18.8 dB PSNR with near-perfect action recovery at 3 - 4.5 ms per reconstructed frame, dominating the learning-based baseline across all reconstruction metrics and exceeding optimization attacks while running orders of magnitude faster. Further evaluation demonstrates TRACE’s broader applicability across recurrent, residual, and compact transformer victim architectures, multi-modal inputs, and larger discrete action spaces. Defense experiments suggest that protecting temporal gradient streams may require sequence-aware privacy mechanisms.
[LG-1] A Nearly Quadratic Lower Bound for Linear Optimization over Convex Bodies in the Membership Oracle Model
链接: https://arxiv.org/abs/2609.30215
作者: Santosh S. Vempala
类目: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Functional Analysis (math.FA); Optimization and Control (math.OC)
*备注:
Abstract:We prove nearly quadratic lower bounds for randomized algorithms for linear optimization and uniform sampling over convex bodies in the membership oracle model. For linear optimization, this matches the known nearly quadratic upper bound up to a polylog factor in the dimension. For uniform sampling, this improves on the previous linear lower bound. Our construction also implies the same lower bound for volume estimation.
[LG-2] Beyond Compression: Training Latent Representations for Stable Long-Horizon Rollout in Neural Surrogate Solvers
链接: https://arxiv.org/abs/2609.30198
作者: Andreas E. Robertson,Ashley T. Lenau,John D. Shimanek,Benjamin A. Jasperson,Vivek Oommen,David L. Damm,Krishna Garikipati,Remi Dingreville
类目: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci); Computational Engineering, Finance, and Science (cs.CE)
*备注:
Abstract:Latent neural surrogate solvers, or latent dynamics models, accelerate simulations of time-dependent physical systems by evolving a compressed latent space rather than resolving full-resolution fields directly. In principle this reduces computational cost and simplifies learning, but in practice errors often accumulate rapidly during long autoregressive rollouts, limiting predictive utility. We show that this instability does not stem from the latent representation itself, but arises when it is trained solely for reconstruction, producing representations poorly suited to long-horizon forecasting. We systematically evaluate training-level interventions that align latent representations with long-horizon rollout: Koopman operator learning and Hamming noise injection during autoencoder training to improve compression, together with noise injection and multi-step rollout fine-tuning to improve dynamics. Interventions that improve long-horizon rollout stability often degrade conventional training metrics, including reconstruction and one-step prediction accuracy. Collectively, these interventions reduce long-rollout error by approximately 40% and match or exceed the accuracy of full-resolution models on two physics benchmarks, while requiring 2 orders of magnitude fewer floating point operations and half the GPU memory. Applied to mesoscale crystal-plasticity simulations of high-cycle fatigue, the resulting surrogate achieves stable extrapolation over horizons orders of magnitude beyond those observed during training. More broadly, these results show that neural compression should be designed not merely to reduce dimensionality, but to restructure the solution space for stable dynamical evolution, a key requirement for reliable, efficient neural surrogates in scientific applications.
[LG-3] Intrinsic-Extrinsic Coupling in Learning Dynamics
链接: https://arxiv.org/abs/2609.30185
作者: Qinyou Wang
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 39 pages, 4 figures, 23 tables
Abstract:A learner’s current observations need not determine its response to further training. We formulate intrinsic-extrinsic coupling through the continuation-conditioned value of a constrained learning-state intervention, with observation-relative fibers describing present agreement. An executable finite-frame classifier-head write protects current logits while repairing specified historical margins under finite-precision acceptance checks. We distinguish local admissibility, continuation-conditioned intervention value, and complete-policy performance. A matched four-cell contrast identifies readout-specific non-additivity between the same intrinsic intervention and alternative external continuations. In a CLINC-derived class-incremental setting, replay changes the write’s 32-update contribution from five correct predictions to zero. Nonzero interactions also occur under output distillation, with a RoBERTa backbone, and under optimizer-native SGDW dynamics. Under SGDW, correct-count interactions are negative in all three activated roots at 128 updates, showing that coupling need not imply positive synergy. The mathematical analysis distinguishes feasible local repairs and favorable terminal outputs from training-reachable repair regions. Separate coordination tests show that content controls match or exceed the development gain, while a five-root fresh-test comparison with Fiber present in every arm shows root-dependent rather than uniformly beneficial correct-count effects. On the secondary cross-entropy readout, guided allocation yields lower mean loss than standard replay in all five pairs. Together, these results make intrinsic-extrinsic coupling operational by connecting executable state geometry to continuation-conditioned value, matched interaction identification, and closed-loop coordination, while separating identified coupling from complete-policy performance.
[LG-4] GridSFM: A Foundation Model for Solving AC Optimal Power Flow
链接: https://arxiv.org/abs/2609.30173
作者: Luke Bhan,Weiwei Yang,Margaret Capetz,Baosen Zhang
类目: ystems and Control (eess.SY); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 19 pages
Abstract:We introduce GridSFM, a framework that combines a pretrained foundation model across grid topologies with physics-informed fine-tuning for solving AC Optimal Power Flow (AC-OPF) at scale. It is a 15 million parameter physics-inspired graph neural network pretrained across 54 topologies of 500 to 4,000 buses. Our model attains a 2.45% zero-shot generation-cost error on a 10,000 bus case held-out operating conditions with no degradation as system size grows. Building on this, we pair the pretrained backbone with a physics-informed fine-tuning design based on Newton’s method for power flow. With only 100 solved instances, GridSFM adapts to unseen grids up to 10,000 buses. We show it out performs single topology, dedicated neural network models that are trained more data, both in terms of cost and solver iterations when deployed as warm starting points. In designing this foundation model, we overcome the fact that the feasible set for AC-OPF can be disconnected. This is an obstruction that prevents any continuous neural network from approximating the solution map. To do so, we lift the problem and relax its constraints with logarithmically penalized slacks. We prove that the resulting elastic feasible set is contractible, that the AC-OPF minimizers remain minimizers of the elastic problem above an explicit penalty threshold, and that projecting an approximate solution back onto the AC-OPF feasible set is well posed. We release all models, data, and code so that the community can build on a shared starting point for AC-OPF. Comments: 19 pages Subjects: Systems and Control (eess.SY); Machine Learning (cs.LG); Optimization and Control (math.OC) Cite as: arXiv:2609.30173 [eess.SY] (or arXiv:2609.30173v1 [eess.SY] for this version) https://doi.org/10.48550/arXiv.2609.30173 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-5] Graph-Based Inference and Topology-Aware Multi-Agent Reinforcement Learning for Large-Scale Railway Network Management
链接: https://arxiv.org/abs/2609.30150
作者: Giacomo Arcieri,Gregory Duthé,Christophe Muller,Konstantinos G. Papakonstantinou,Daniel Straub,Eleni Chatzi
类目: Machine Learning (cs.LG)
*备注:
Abstract:Modern infrastructure asset management constitutes a complex sequential decision-making problem, characterized by long planning horizons and system-level interactions, such as spatial deterioration correlations and economies of scale. While deep reinforcement learning has shown promise in optimizing maintenance policies, scaling to real-world networks remains challenging. Centralized approaches become computationally intractable in large-scale systems, whereas decentralized approaches often fail to capture essential coordination mechanisms. To address these challenges, we propose a graph-based framework that integrates accurate environment modeling with scalable decision support. First, we employ a hierarchical Bayesian model leveraging a Gaussian Process on Graph kernel to infer a realistic, spatially correlated networked environment of railway maintenance planning from real-world data provided by the Swiss Federal Railways. Second, we introduce a topology-aware Multi-Agent Reinforcement Learning (MARL) framework by integrating graph neural networks and graph Transformers to optimize network-level policies. A central contribution of this work is the demonstration of scalability through zero-shot transfer learning: graph-based agents, trained only on small network portions, are successfully deployed in a zero-shot manner on large-scale unseen networks without any retraining. Numerical results indicate that the proposed method significantly outperforms optimized heuristics and standard MARL baselines, reducing computational training time while maintaining superior performance on large-scale networks.
[LG-6] Orbital Error Dynamics: Self-Organized Criticality Ephemeral Parameter Resonance and Non-Linear Biological Ontologies in Zero-Storag e Neural Synthesis
链接: https://arxiv.org/abs/2609.30115
作者: Volkan Dağlı,Zerrin Dağlı,Dağhan Dağlı
类目: Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG); Chaotic Dynamics (nlin.CD)
*备注: Official National Patent Priority: TR 2026/016285 (Filed Sept 22, 2026). Foundational companion theory to Mandelbrot Fractal Neural Synthesis. Code and interactive lab: this https URL
Abstract:Modern deep neural networks treat parameters as static floating-point matrices stored in physical memory, incurring Von Neumann memory bottlenecks and representation collapse. We formulate Orbital Error Dynamics (OED), an analytical framework wherein synaptic weights are not stored masses (O(W)), but transient topological resonances (O(1)) derived procedurally from the complex quadratic polynomial map z_n+1 = z_n^2 + c. We introduce the Bent Sine Wave Hypothesis, demonstrating that non-equilibrium living systems emerge when harmonic waves curl inward through environmental drag toward the cardioid cusp (c = 1/4). We define the Observer Horizon Geometry in parameter space, identifying interior resonance shoulder loci X_upper = (0.25, +0.18) and X_lower = (0.25, -0.18) between the fixed-point basin and the true boundary at c = 0.25 +/- 0.50i. To escape non-convex stagnation without loss zeroing, we introduce a heavy-tailed Biomimetic Perturbed Jump Operator (Omega_tunneling) inspired by mammalian fertilization zinc sparks. We further couple an enteric-cranial Dual-Brain architecture shielded by adaptive CD4+ regulatory immune gating (M_CD4), and project the 4-nucleotide genetic basis (A, T, C, G) across quadrants in C. Multi-seed empirical validation on the Two-Moons manifold (5 seeds, 80/20 train/test split, 32x32 grid, zero test-time updates, zero label leakage) demonstrates that procedural parameterization from a 24-byte coordinate seed achieves 77.67% +/- 5.35% clean test accuracy (within an 8.00-point paired difference of an unconstrained gradient baseline at 85.67% +/- 5.35%, 95% CI: [-1.07%, 17.07%]) and 71.33% +/- 3.80% under distribution shift (N(1.2, 0.4)), alongside conceptual equivalence with an analog optical co-processor.
[LG-7] On the SoS Certifiability of Log-Concave Distributions
链接: https://arxiv.org/abs/2609.30105
作者: Aleksandr Storozhenko
类目: Machine Learning (cs.LG); Computational Complexity (cs.CC); Probability (math.PR)
*备注:
Abstract:For an arbitrary isotropic log-concave distribution P on \mathbbR^d , we prove that the polynomial (Cm)^m|v|_2^m - \mathbbE_X\sim P\langle X,v\rangle^m is a sum of squares for every even m\ge2 , where C0 is a universal constant. This removes the dependence on the Poincaré constant in the theorem of Kothari and Steinhardt (arXiv:1711.07465), recovering the optimal moment bounds for log-concave distributions. As an immediate corollary, we obtain computationally efficient algorithms with dimension-free error guarantees for a wide range of high-dimensional statistical estimation problems. Our proof uses stochastic localization to decompose P as an average of random strongly log-concave measures, whose centered moments admit the subgaussian certificates of Diakonikolas, Hopkins, Pensia, and Tiegel (STOC 2025; arXiv:2410.21194). With a covariance-adapted choice of localization, we show that a fourth-moment certificate derived from Letwin’s variance inequality for quadratic forms (arXiv:2607.24164) suffices to control this averaging at every even degree. Subjects: Machine Learning (cs.LG); Computational Complexity (cs.CC); Probability (math.PR) Cite as: arXiv:2609.30105 [cs.LG] (or arXiv:2609.30105v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.30105 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-8] Residual Correlation as a Diagnostic for Joint-Uncertainty Gains from GP Coregionalisation ACML2026
链接: https://arxiv.org/abs/2609.30085
作者: Fangqin Zhou,Joaquin Vanschoren
类目: Machine Learning (cs.LG)
*备注: Accepted at ACML 2026
Abstract:In multi-target regression, correlated targets are often coupled through multi-output Gaussian processes with an intrinsic model of coregionalisation (GP-ICM), assuming that sharing statistical strength improves overall performance. In practice, the benefits are inconsistent. Across the settings studied, we find that the main benefit of coregionalisation is joint uncertainty quantification rather than point prediction. Raw target correlation does not predict when coupling helps; in the separable GP-ICM settings studied here, residual correlation, the cross-target dependence left unexplained by independent per-target predictors, is the strongest predictor of joint-uncertainty gains. We introduce a lightweight diagnostic, D_\rm logdet=-\frac12\log\det R_\rm res , which represents the idealised joint negative log-likelihood (NLL) gain from modelling a full rather than diagonal residual covariance and is computable from independent GPs alone. Across a controlled synthetic study, 16 multi-target benchmarks, and frozen transformer and convolutional neural network representations for keypoint regression, point prediction remains largely unchanged ( \Delta R^2\approx 0 ). In contrast, D_\rm logdet strongly predicts observed ICM NLL improvements ( \rho_s=-0.83 , p0.001 ), outperforming heuristics such as the feature-to-sample ratio. We also propose Residual-ICM, which preserves independent marginal variances while adding residual-correlation structure to the joint covariance. Residual-ICM achieves the best average joint NLL among the compared methods, while the diagnostic indicates when covariance coupling is likely to be useful. The diagnostic is specific to global Gaussian residual dependence, the structure captured by separable coregionalisation. Comments: Accepted at ACML 2026 Subjects: Machine Learning (cs.LG) Cite as: arXiv:2609.30085 [cs.LG] (or arXiv:2609.30085v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.30085 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-9] Aim Short to Reach Far: Your Frozen World Model Can Plan Better Than You Think
链接: https://arxiv.org/abs/2609.30036
作者: Xvyuan Liu,Jianjie Fang,Chen Gao,Yong Li
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注:
Abstract:Planners built on visual world models commonly score each predicted outcome by its distance to the encoded goal image. We show that this target can limit control even with exact dynamics and globally optimal short-horizon search: reaching a goal may require actions that initially move away from it. With frozen LeWM models, intermediate targets substantially improve action synthesis and recorded-action ranking on Cube, PushT, Reacher, and TwoRoom. Learned targets and targets drawn from observed experience both produce these gains. We introduce Anchored Planning, which retrieves a recorded segment whose start and end resemble the current and goal observations, then aims at an observation shortly after its start. The frozen model scores actions toward this target from the current state. Without additional training, planning toward observed targets outperforms the released LeWM planner on every task in our long-range evaluation. Additional final-goal search falls short of the same gains. Lower successor-prediction error need not translate into better control. Success also depends on how far ahead the target is placed and on shrinking the retrieval span as execution advances. Changing only the target lets the same frozen model and planner reach goals that final-goal scoring misses.
[LG-10] Let Training Guide Selection: Online Synthetic Data Filtering via Real-Anchored Utility
链接: https://arxiv.org/abs/2609.29988
作者: Yanran Wu,Sana Lakdawala,Renzo Tassara Miller,Chongyang Bai,Sharath Ciddu,Shivendra Pratap Singh,Kungang Li,Sandeep Pandey,Chunwei Liu
类目: Machine Learning (cs.LG)
*备注: 21 pages, 6 figures
Abstract:Synthetic data can scale training supervision when real-world data are limited, but noise and distribution mismatch can reduce its value. Existing synthetic data selection methods often emphasize fidelity or diversity rather than the learner’s evolving needs. We propose FROST, an online framework that estimates synthetic-data utility through gradient feedback anchored in real training data. It calibrates batch utility against recent history to determine when filtering is needed and filters samples only in out-of-band batches to determine what to retain, without an external verifier or held-out validation set. Experiments on two public benchmarks for image classification and LLM fine-tuning for text-to-SQL show that FROST filters out around 20–30% of the synthetic data while improving real-task performance compared with training on the full synthetic data pool. We further apply FROST during training in a large-scale industrial ads re-ranking system, achieving significant performance gains over a highly optimized production baseline, demonstrating its effectiveness and generalizability.
[LG-11] Diverse Geometries Frozen Weights: Robust Heterogeneous Treatment-Effect Estimation via Causal Expert Ensembles
链接: https://arxiv.org/abs/2609.29974
作者: Ali Haghpanah Jahromi,Mohammad Taheri
类目: Machine Learning (cs.LG)
*备注: 31 pages, 3 figures, 8 benchmark protocols. Supplementary material is included as an ancillary file
Abstract:Estimating heterogeneous treatment effects from observational data is difficult because the most appropriate inductive bias varies with overlap, treatment imbalance, prognostic structure, and sample size. We introduce the Geometry-Diverse Anchor-Correction Expert Ensemble (GeoACE), a five-expert framework that combines a common anchor-correction estimator with complementary overlap-aware and outcome-guided geometries. Its task-level ensemble weights are learned only from internal validation predictions, frozen before test evaluation, and then applied to experts refitted on the complete development sample. The fifth expert, O-Phi-ACE, constructs an outcome-free, overlap-aware statistical projection from covariates and treatment assignment and replaces the anchor input with this lower-dimensional geometry. We evaluate GeoACE against 11 comparators on eight benchmark protocols. Adding O-Phi-ACE reduced mean sqrt(PEHE) relative to the four-expert ensemble on all seven benchmarks with individual-effect truth, winning 998 of 1,225 paired tasks; the change on JOBS policy risk was negligible. The five-expert ensemble ranked first on IHDP100, IHDPA, and IHDPB and second on NEWS, differing from the NEWS leader by 0.13%. Across the seven sqrt(PEHE) benchmarks it obtained the lowest observed average rank (3.714), although the omnibus Friedman and Iman-Davenport tests were not significant (p=0.328 and p=0.330). Using the same five frozen experts, inverse-DR weighting was consistently better than winner-take-all selection, convex DR fitting, R-stacking, and causal Q-aggregation in benchmark-balanced analyses, but was statistically indistinguishable from equal weighting and DR ridge shrinkage. The evidence therefore supports geometry-diverse expert libraries and leakage-free aggregation as a robustness strategy, not universal superiority of either GeoACE or one weighting rule.
[LG-12] A Contraction Framework for Stochastic Operators with Bootstrapping: Application to TD Learning
链接: https://arxiv.org/abs/2609.29961
作者: Ids van der Werf,Sergio Rozada,Antonio G. Marques
类目: Machine Learning (cs.LG); Signal Processing (eess.SP); Optimization and Control (math.OC)
*备注: 5 pages, 1 figure
Abstract:Many iterative algorithms rely on bootstrapping. A variable is updated using a second, frozen copy as a target, which is periodically replaced with the updated variable. Majorize-minimize and inexact proximal-point methods share this structure, as does temporal-difference (TD) learning. However, existing convergence guarantees for scenarios that combine sampled updates with targets refreshed only every K steps rely on the specific structure of the update, such as linear approximation or gradient-based inner steps, and on uniformly bounded sampling error. We instead model the sampled update as a stochastic operator on the parameter space, which reduces the analysis to a contraction argument that needs no gradient structure and allows the sampling error to grow with the iterates. Within this framework, we derive a finite-time bound for i.i.d. samples and any target-update period K . We show that the iterates converge geometrically in root mean square to a ball around the fixed point, provided the sensitivity to the frozen target is smaller than the contraction slack of the inner map. Existing deterministic frozen-target contraction and stochastic-gradient-type bounds follow as special cases of our framework, and simulations of TD learning reproduce the predicted contraction rate and scaling of the error floor with the step size.
[LG-13] Error- and Prediction-Driven Motor Learning in the Cortico-Cerebellar Loop
链接: https://arxiv.org/abs/2609.29945
作者: Ana Carolina Filipe,Rui Ponte Costa,Cláudia Soares
类目: Machine Learning (cs.LG)
*备注:
Abstract:Robust control under delayed sensory feedback remains a key challenge in both robotics and neuroscience. Classical cerebellar models explain delay compensation through forward prediction but fail to account for fast online corrections and rapid adaptation observed in biological systems. We propose a cerebellum-inspired control framework that combines multiplexed predictive representations with internal feedback. By jointly encoding kinematic variables and task-relevant error signals, the model enables accurate online correction despite delayed feedback. Furthermore, incorporating feedback within the cerebellar loop significantly accelerates adaptation, reducing learning time by an order of magnitude. Our results show that single-signal predictions are insufficient under delay, while multiplexing and feedback together provide a unified mechanism for online control and rapid learning. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2609.29945 [cs.LG] (or arXiv:2609.29945v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.29945 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-14] MF-SCBO : Multi-fidelity Scalable Constrained Bayesian Optimization
链接: https://arxiv.org/abs/2609.29941
作者: Lucas Palazzolo,Mickaël Binois,Laëtitia Giraldi
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:Many real-world optimization problems rely on expensive simulations or experiments, making the efficient use of available data essential. Multi-fidelity optimization of high-dimensional black-box functions subject to black-box constraints is increasingly relevant as the cost of objective evaluations continues to rise in applications such as machine learning, engineering, and control. To our knowledge, no existing method simultaneously addresses high-dimensionality, black-box constraints, an arbitrary number of fidelity levels, and non-nested sampling. In this work, we extend the Scalable Constrained Bayesian Optimization method to the multi-fidelity setting, resulting in the MF-SCBO method. The proposed approach is evaluated on standard benchmark functions as well as challenging problems. The experimental results demonstrate that MF-SCBO generally achieves better convergence than both the single-fidelity SCBO and the other multi-fidelity method considered in this high-dimensional and constrained settings.
[LG-15] Spatio-temporally complementary feature propagation on graphs for longitudinal AADT estimation
链接: https://arxiv.org/abs/2609.29906
作者: Linghang Sun,Qishen Zhou,Michail A. Makridis,Anastasios Kouvelas
类目: Machine Learning (cs.LG)
*备注:
Abstract:The estimation of Annual Average Daily Traffic (AADT) is vital for transportation planning and infrastructure maintenance, yet obtaining accurate values for an entire urban network across multiple years remains challenging due to the high cost and spatial sparsity of physical sensors. This research proposes a novel spatio-temporally complementary feature propagation framework that leverages the strengths of two distinct data sources: spatially sparse but temporally dense loop detector data, and a spatially complete but temporally sparse macroscopic transportation model. The methodology highlights a feature propagation algorithm on directed graphs, formulated as a Poisson energy minimization considering residues. The standard binary adjacency matrix is replaced with flow ratio matrices to capture real-world vehicle turn ratios at intersections. Validated in the city of Zurich, the algorithm demonstrates high computational efficiency, achieving convergence within minutes. Results indicate that the framework effectively reconciles theoretical models with empirical ground truths, yielding a normalized mean absolute error below 10% . This scalable approach provides a feasible solution for spatio-temporal network-wide AADT estimation through combining real-world limited sensor coverage and traffic models.
[LG-16] A New Gap Sequence for Shellsort: RL-Driven Algorithm Discovery Beyond N4/3
链接: https://arxiv.org/abs/2609.29881
作者: Bo Liu
类目: Computational Complexity (cs.CC); Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
*备注: 25 pages, 2 tables; full proof and technical appendix
Abstract:Choosing Shellsort gaps is a well-known open problem. For over sixty years, successful sequences have relied on human-designed formulas, numerical searches, or number-theoretic constructions. Although stronger general bounds exist for dense or mainly theoretical families, the worst-case upper bound for a short, sparse, and practically competitive construction has not advanced beyond N^4/3 for decades. We ask whether the sequence itself can instead be learned from execution. We present an RL-driven, self-supervised system that searches over executable gap generators. Every proposal is valid by construction, and executed candidates return exact comparison and move counts; no classical sequence is used as a target. Across five independent searches, the system discovers a common rational-geometric family. A second self-supervised stage tunes only a finite prefix, producing the practical sequence 1,3,8,20,47,116,300,585,1416,3303,\ldots . Once frozen, it obtains the lowest equal-task average operation count among seven classical baselines on 25 large tasks with 10^7N\leq 10^8 . We complete the learned tail without changing its practical behavior: only beyond 10^1000 , a zero-density set of unit companions h_s+1 removes the remaining congruence barriers. The resulting sparse sequence has matching polynomial upper and lower exponents, up to polylogarithmic factors: \Omega(N^1.024296451657\ldots) \leq T(N) \leq O(N^1.024296451657\ldots\operatornamepolylog N) . The lower bound follows from Zang’s recent theorem for rational-geometric sequences; our contribution is the matching upper bound. Thus one exact sequence connects self-supervised discovery, large-scale practical performance, and a substantial step below the classical N^4/3 bound for sparse practical Shellsort sequences.
[LG-17] From Graphs to Feeders: Constraint-Guided Diffusion for Rule-Compliant Feeder Generation
链接: https://arxiv.org/abs/2609.29879
作者: Yu Qin,Andrew Glaws,Aadil Latif,Ryan King
类目: Machine Learning (cs.LG)
*备注: 22 pages
Abstract:Generative modeling approaches often focus on recovering broad statistical characteristics from the training data. In the context of graph generation, this may refer to degree distributions, clustering coefficients, or spectral properties. However, generating usable distribution feeders when detailed feeder models are unavailable requires more than matching generic graph statistics: the sampled topology must also obey electrical compatibility and radiality rules. We therefore formulate feeder synthesis as a constraint-guided graph generation problem and propose the Power-Grid-constrained Discrete Denoising Diffusion model, PG-DiGress, which learns categorical node and edge patterns from feeder data, while respecting domain-specific rules. Specifically, it injects feeder constraints into the reverse diffusion process through soft masks that suppress incompatible edge classes during denoising, followed by a final projection step that rebuilds a connected, rule-compliant feeder graph. We evaluate PG-DiGress using graph-distribution similarity, feeder-rule satisfaction, structural validity, and downstream model construction. Compared with the unconstrained baseline, PG-DiGress increases the strict feeder pass rate from 13.7% to 96.8%. We also successfully convert the generated graphs into executable feeder models for downstream analysis.
[LG-18] Elucidating the Conformal Structure of the Brinkman Penalisation Method for Geometry-Adapted Structure-Preserving Operator Learning of Hamiltonian PDEs
链接: https://arxiv.org/abs/2609.29847
作者: Teo Deveney,Baige Xu,Takaharu Yaguchi
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG)
*备注:
Abstract:The Brinkman penalisation method embeds boundary-value problems on complex domains into a simple computational box by modeling the solid region as a strongly dissipative medium, avoiding body-fitted mesh generation. We show that multi-symplectic Hamiltonian PDEs regularised by Brinkman-type penalisation retain a multi-conformal symplectic structure under a compatibility condition linking the symplectic matrix and the penalisation projection. This yields an exact local conservation law, under which the multi-symplectic two-form is conserved in the fluid region and decays exponentially inside the solid. The linear wave equation with Brinkman friction and Maxwell’s equations with artificial Ohmic conductivity satisfy this condition, with explicit modified Hamiltonian densities. Building on this, we propose (i) structure-preserving numerical integrators via Strang splitting that satisfy a discrete conformal conservation law, and (ii) conformal symplectic neural operators that interleave exact dissipative flows with learnable multi-symplectic evolution operators, allowing geometry-dependent operator learning. Numerical experiments on wave and electromagnetic scattering demonstrate that our methods reproduce correct local energy budgets and avoid unphysical energy drift, providing a principled framework for physics-consistent scientific machine learning on complex domains.
[LG-19] SwitchPFN: Shared Switching Dynamics for Frozen In-Context Time Series Classification
链接: https://arxiv.org/abs/2609.29814
作者: Zhenyi Zhu,Jacqueline Pang,Peilin Shen,Tianyi Song,Tingwei Zhang,Keyi Hu,Kangjun Yin,Shiwei Pu,Yingbo Zhou,Chen Shao
类目: Machine Learning (cs.LG)
*备注:
Abstract:Tabular foundation models (TFMs) provide a promising route to time-series classification, but their effectiveness depends on how sequential data are converted into tabular representations. Existing representations face two challenges: global aggregation can lose the order of temporal evolution, while features computed in independently fitted coordinate systems may not have consistent meanings across sequences. We therefore view representation design for TFMs as a problem in its own right: the representation should preserve local temporal transitions while maintaining a shared feature definition across samples. We propose SwitchPFN, which learns a shared projection and regime codebook from the training sequences, making local dynamic operators and transition features directly comparable across samples. Across the evaluated benchmarks, SwitchPFN achieves the highest mean accuracy among the evaluated methods, improving over the strongest baseline by 4.47% relatively. Ablation studies, parameter sensitivity analyses, and reduced-training-data experiments further examine the contributions of the representation, its main design choices, and its behavior when labeled data are limited.
[LG-20] FlashLoop: Fast and Memory-Efficient Looped Transformers via Lazy Updates
链接: https://arxiv.org/abs/2609.29812
作者: Wanqi Yang,Shiwei Liu
类目: Machine Learning (cs.LG)
*备注: 16 pages, 9 figures
Abstract:Looped Transformers have attracted substantial attention as a parameter-efficient approach to increasing computational depth through repeated application of shared Transformer blocks. However, their practical advantages over conventional Transformers remain under debate: each additional loop incurs another Transformer pass and requires caching another set of KV states, causing inference FLOPs and KV-cache memory to grow continuously with loop depth. This overhead becomes particularly severe at large loop counts and long context, preventing the parameter efficiency of Looped Transformers from translating into practical inference efficiency. In this paper, we find that much of the additional computation and storage introduced by looping is redundant. As recurrence proceeds, state changes become increasingly concentrated on a small subset of tokens; attention-output differences are dominated by a sparse and stable subset of key columns; and KV residuals between adjacent loops become progressively more amenable to low-bit quantization. Building on these observations, we introduce FlashLoop, a training-free inference framework that reduces cross-loop redundancy through token-sparse updates, sparse attention, and KV-residual quantization. Across several Looped Transformers models, \textscFlashLoop delivers lossless accuracy while achieving up to 1.64 \times end-to-end speedup and up to 6 \times KV-cache memory reduction, substantially improving the practicality of scaling Looped Transformers to greater computational depths and longer context.
[LG-21] An Analytical Theory of Auxiliary Learning
链接: https://arxiv.org/abs/2609.29774
作者: Federico Milanesio,Alessandro Ingrosso,Matteo Osella
类目: Machine Learning (cs.LG)
*备注: Under review as a conference paper
Abstract:Auxiliary learning is an optimization paradigm in which a neural network’s performance on a target task is improved by jointly training it on additional tasks. However, the mechanisms behind this improvement remain poorly understood. We study this problem using a teacher-student framework and derive a closed system of differential equations describing the dynamics of online stochastic gradient descent in the large-input limit. For linear networks, we obtain a closed-form expression for the generalization error to leading order in the learning rate, quantifying how task correlations and label noise determine the benefit of auxiliary learning. For non-linear activation functions, we develop a fluctuation-dissipation analytical theory that establishes a general relation linking the main and auxiliary errors to the corresponding single-task error. Numerical experiments support the theoretical predictions and show how auxiliary tasks improve generalization by balancing the forcing dynamics towards the optimal solution with gradient noise.
[LG-22] WeatherDiagFlow: Evidence-Grounded Radar Nowcasting with Diagnostic Flow Refinement
链接: https://arxiv.org/abs/2609.29772
作者: Chunlei Shi,Yufeng Zhu,Yixiao Liang,Dan Niu,Yongchao Feng,Qiliang Wu,Jiong Wang
类目: Machine Learning (cs.LG); Multimedia (cs.MM)
*备注: 5 pages, 3 figures
Abstract:Radar nowcasting is essential for short-term warning and emergency response, yet conventional systems mainly return future radar fields and provide limited support for operational communication and post-event verification. We formulate radar nowcasting as an evidence-grounded forecast–bulletin–audit task, in which a numerical forecaster produces both future radar fields and structured diagnostic evidence. Forecast-time bulletins use only model-available evidence, whereas post-event audits incorporate future radar truth only after the forecast horizon is observed. Based on this task formulation, WeatherDiagFlow predicts motion, growth and decay, heavy-echo risk, and uncertainty to condition rolling flow refinement, while frozen-scaffold residual calibration improves long-lead strong-echo preservation. A multi-agent layer converts the structured evidence into operational bulletins and independently generates verification audits without feeding textual outputs back into the forecaster. Experiments on FJRADAR demonstrate competitive overall performance and improved strong-echo event skill. WeatherDiagFlow therefore connects numerical prediction, evidence-grounded reporting, and auditable verification under a leakage-controlled protocol.
[LG-23] On Growth and Form and Function: Reusable Regulatory Handles Control Phenotypic Variation
链接: https://arxiv.org/abs/2609.29755
作者: Benedikt Hartl,Milton L. Montero,Marcello Barylli,Sebastian Risi,Michael Levin
类目: Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)
*备注:
Abstract:How phenotypic transformations are implemented by changes in underlying regulatory dynamics remains a central question in developmental biology. Inspired by D’Arcy Thompson’s 1917 “On Growth and Form”, we ask whether coherent large-scale transformations of morphology can be encoded as low-dimensional modulations of a self-organizing developmental system. We use neural cellular automata (NCAs) as bio-inspired models of distributed development, in which a shared local regulatory network grows target morphologies from a single cell. We apply low-rank adaptation (LoRA) to pretrained NCAs, representing each adapted developmental program as a low-rank modulation of a fixed regulatory scaffold. Horizontal and vertical scaling of a fully grown 2D emoji phenotype can each be implemented by rank-one adaptations. Their linear combinations parametrically control phenotype size, generalize beyond the training distribution, and compose with target-specific adapters. Strikingly, adaptations learned for one phenotype transfer zero-shot across structurally and semantically diverse phenotypes sharing the same reference scaffold, while largely preserving internal features. This suggests reusable system-level hyper-directions of scale rather than morphology-specific transformations. From approximately 25,000 independently trained phenotype-specific NCA adapters with a shared scaffold, we further identify latent low-dimensional directions that functionally control phenotypic variation including scaling, style, and symmetrical fission. Together, our results provide a computational realization of D’Arcy Thompson’s remarkable grid transformations in a 2D NCA—a minimal cybernetic tissue in which variations of fully grown emoji phenotypes can be encoded, combined, and controlled through low-dimensional directions in regulatory weight space.
[LG-24] RAPTOR: RAndom-projection Physics-informed Transient sOlveR
链接: https://arxiv.org/abs/2609.29714
作者: Petros Ellinas,Benjamin Vilmann,Spyros Chatzivasileiadis,Johanna Vorwerk
类目: ystems and Control (eess.SY); Machine Learning (cs.LG); Mathematical Software (cs.MS); Numerical Analysis (math.NA)
*备注:
Abstract:The complexity of time-domain simulation of modern power systems has increased significantly because converter-based resources introduce control dynamics that must be simulated alongside slower system-level and fast electromagnetic dynamics. The resulting wide range of timescales may force classical time-domain solvers to use small timesteps, complicate the solution of nonlinear equations at each timestep, and reduce solver reliability under strongly nonlinear and multi-timescale transient conditions. This paper introduces RAPTOR, a first-of-its-kind time-domain simulation framework for power system dynamic simulations. At its core, RAPTOR introduces a new integration technique that represents the unknown trajectory of hybrid differential-algebraic equations (DAEs) over a time interval using a physics-informed random-projection neural network (PIRPNN) built from fixed Gaussian radial basis functions (RBFs). A nonlinear solver, e.g., Newton-Raphson, determines how to combine the fixed RBFs so that the resulting trajectory satisfies the governing equations. By combining RBFs with broad and localized shapes, RAPTOR can represent complex multi-timescale behavior over comparatively long time intervals. This enables larger effective simulation advances, fewer overall nonlinear solver iterations, and faster integration over long time horizons. Numerical studies on stiff RMS models, IEEE 9-, 14-, 39-, 57-, and 118-bus RMS simulation benchmarks, and EMT test cases show that RAPTOR accurately finds solutions with substantially fewer sequential simulation advances while offering superior accuracy-runtime performance. With speedups of over 10x in certain cases, these results showcase RAPTOR’s potential to outperform long-standing and widely used integration methods, such as the Radau method and the trapezoidal rule.
[LG-25] Safety-oriented pedestrian trajectory prediction at urban intersections using time-to-collision and crossing-zone context
链接: https://arxiv.org/abs/2609.29706
作者: Erel Avineri,Yftach Gil,Yehudit Aperstein
类目: Machine Learning (cs.LG)
*备注: 22 pages, 7 figures
Abstract:Accurate pedestrian trajectory prediction is important for proactive road-safety applications, particularly at urban intersections where pedestrian motion is shaped by both vehicle interactions and crossing context. This study presents a safety-oriented trajectory-prediction framework that combines pedestrian motion history with Time-to-Collision (TTC) information and crossing-zone indicators. Using naturalistic trajectories from one urban intersection in the inD (Intersection Drone) dataset, several neural architectures were evaluated with 1.6 s observation and 2.4 s prediction horizons. A pooled Long Short-Term Memory (LSTM) separately encodes TTC histories and crossing-zone context before integrating them with pedestrian positions. In addition to conventional Average Displacement Error (ADE) and Final Displacement Error (FDE), prediction performance was assessed using the frequency and magnitude of errors exceeding a study-defined 1 m tolerance. A weighted loss was also introduced to place greater training emphasis on large coordinate-wise errors. Applying this loss to the position-only LSTM reduced ADE from 0.210 to 0.190 m and FDE from 0.550 to 0.503 m, while reducing ADE and FDE exceedance counts by 34.8% and 19.8%, respectively. The final pooled configuration incorporating TTC and crossing-zone information achieved an ADE of 0.184 m and FDE of 0.491 m, with further reductions of 33.5% and 6.3% in ADE and FDE exceedance counts relative to the safety-oriented position-only LSTM. The results indicate that safety-oriented training and structured integration of interaction and contextual information can reduce large trajectory-prediction errors, although broader validation across pedestrians, sites, and datasets is required.
[LG-26] An Agnostic Sample Compression Scheme for Squared Loss of Near-Linear Size in the Fat-Shattering Dimension
链接: https://arxiv.org/abs/2609.29696
作者: Guangjian Zhang
类目: Machine Learning (cs.LG)
*备注: 12 pages
Abstract:We construct, for every function class \mathcalF\subseteq[0,1]^\mathcalX and every accuracy 0\alpha\le 1 , an agnostic sample compression scheme for the empirical squared loss: for every finite sample S\in(\mathcalX\times[0,1])^m with arbitrary (noisy) labels, the scheme stores at most O(\mathrmfat(\mathcalF,c’\alpha)\cdot\log^3(2/\alpha)) original labeled examples and auxiliary bits, independent of the sample size m , and reconstructs a function \hat f with L_2(\hat f,S)\le\inf_f\in\mathcalFL_2(f,S)+\alpha . This resolves, in the positive, the open problem of Attias, Hanneke, Kontorovich, and Sadigurschi (ICML 2024, Section 5), which asks for an agnostic \ell_2 compression scheme of size \mathrmfat(\mathcalF,c\alpha)\cdot\mathrmpolylog(c/\alpha) . All previously known bounded-size constructions, agnostic and even realizable, incur a multiplicative dual fat-shattering factor, which can be exponentially larger than the primal dimension; our scheme removes the dual factor entirely, including in the realizable case. The dual factor in prior work enters solely through a sparsification step that forces uniform approximation on the sample. By targeting only a (1-\epsilon) -fraction of sample points, which suffices for an average-loss guarantee over a bounded range, K’egl’s boosting margin bound yields O(\log(1/\epsilon)) rounds independent of m , and sparsification is never needed. The booster’s synthetic target labels (values of a near-optimal f^*\in\mathcalF ) are transmitted through quantized side-information bits attached to stored original examples, and the cross term of the squared loss forces the weak-learning scale \Theta(\alpha) , matching the same-scale form of the open problem.
[LG-27] Bandit Multiclass PAC Learning: Corrected Lower Bounds Exact Families and a Confidence Direct-Sum Phenomenon
链接: https://arxiv.org/abs/2609.29694
作者: Guangjian Zhang
类目: Machine Learning (cs.LG)
*备注: 23 pages
Abstract:We study realizable multiclass PAC learning with bandit feedback: the learner observes an i.i.d. instance, predicts one of K labels, and learns only whether the prediction was correct. Hanneke, Meng, Moran, and Shaeiri (arXiv:2605.25678) characterized the optimal sample complexity via the bandit DS dimension \mathrmBDS up to logarithmic factors, and asked whether every class admits sample complexity O((\mathrmBDS+\log(1/\delta))/\epsilon) . First, we show that the published lower bound \Omega((\mathrmBDS+\log(1/\delta))/\epsilon) is incorrect as stated: we exhibit explicit classes with \mathrmBDS=K-1 whose sample complexity is exponentially smaller, and locate two independent gaps in its proof. We repair the lower-bound theory around a new anchored dimension \mathrmaBDS\le\mathrmBDS , proving a constant-free three-part lower bound. On the upper-bound side we remove the ambient label count K entirely, proving O((B\log^3 B+B\log(1/\delta))/\epsilon) for B=\mathrmBDS , plus a constant-confidence bound via a new fiberization lemma; for two natural families we determine the sample complexity up to constant factors. Finally, we answer the open question in the negative under its uniform-constant reading, and show the failure is intrinsic: for an explicit affine multiplexer class we establish the full confidence profile \Theta((n\minn,\log(1/\delta)+\log(1/\delta))/\epsilon) , a confidence direct-sum regime where a multiplicative \log(1/\delta) cost is information-theoretically necessary, followed by a rank-saturation phase transition. Two classes with identical dimension profiles can have polynomially different sample complexities, so no characterization by these dimensions alone is accurate to polylogarithmic factors. We also show these results are consistent with additive-confidence list-PAC guarantees via the ListCascade bridge. Comments: 23 pages Subjects: Machine Learning (cs.LG) Cite as: arXiv:2609.29694 [cs.LG] (or arXiv:2609.29694v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.29694 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-28] Predicting Symptoms of Amotivation and Anhedonia among University Students with a Novel Oversampling Method
链接: https://arxiv.org/abs/2609.29690
作者: Dang Nguyen,Bao Duong,Arun Kumar,Dat Phan-Trong,Julian Berk,Taylor Braund,Kien Do,Debopriyo Bal,Wu Yi Zheng,Leonard Hoon,Jill Newby,Helen Christensen,Svetha Venkatesh,Alexis Whitton,Sunil Gupta
类目: Machine Learning (cs.LG)
*备注:
Abstract:University students experience disproportionately high rates of common mental health conditions, such as depression, which can impair learning, social functioning, and overall well-being. Within this context, symptoms of amotivation (i.e. loss of motivational drive) and anhedonia (i.e. diminished interest or pleasure) are particularly debilitating, yet they frequently go undetected. Developing new approaches to identify students with prominent amotivation and anhedonia could enable earlier and more targeted intervention. Machine learning (ML) methods have increasingly been used to classify individuals according to symptom severity. However, these ML models often suffer from class imbalance, where the majority of cases fall in the low-symptom group and relatively few in the high-symptom group. This imbalance can reduce model accuracy and bias predictions. To address this, studies commonly employ the popular oversampling strategy SMOTE. However, SMOTE has a notable limitation: it may generate invalid values for nominal variables. In this paper, we introduce a novel and effective oversampling method that addresses this shortcoming. Our approach leverages a predictive model to generate nominal variables, rather than interpolating them. We validate our method on a large-scale GPS location dataset collected from university students and demonstrate that it is significantly better than existing oversampling approaches in predicting elevated symptoms of amotivation and anhedonia.
[LG-29] Not All Synthetic Data Are Equal: Expert-Committee Audit Screening for Imbalanced Crash-Injury-Severity Prediction in Automated Driving Systems
链接: https://arxiv.org/abs/2609.29687
作者: Zewei Li,Qiaoqiao Ren,Hang Yang,S.C. Wong,Stergios-Aristoteles Mitoulis,Yun Ye
类目: Machine Learning (cs.LG)
*备注:
Abstract:Automated driving systems (ADSs) are increasingly operating on public roads, raising safety concerns, yet reliable prediction of crash injury severity remains difficult because crash reports are limited, severe outcomes are rare, and injury classes are highly imbalanced. Existing augmentation methods mainly increase minority-class sample size but rarely assess whether generated samples are credible for safety-critical prediction. This study proposes Expert-Committee Audit Screening (ECAS), a credibility-aware sample acceptance framework for ADS crash injury severity prediction under data imbalance. Using 1,477 incident-level ADS crashes from the National Highway Traffic Safety Administration Standing General Order records, ECAS audits generated minority samples through a real-data-only expert committee based on label support, boundary separation, committee agreement, and local plausibility. Within-class percentile normalization and Pareto non-dominated sorting select accepted samples without manually assigned evidence weights. With a fixed backbone combining normalizing flow augmentation and a Tabular Prior-data Fitted Network (TabPFN) classifier, the best ECAS configuration achieved the highest balanced accuracy, macro-F1, and minor-injury recall among all evidence configurations. Local neighborhood analysis showed that ECAS-accepted samples were better supported by nearby real minority crashes than unscreened retained samples. Shapley additive explanations and partial dependence plots further indicated that lower injury severity classes were mainly associated with crash counterpart and pre-crash movement, whereas moderate-plus injuries were more sensitive to posted speed limit and operating context. These findings support a shift from quantity-oriented augmentation to credibility-aware sample acceptance for ADS safety prediction and risk governance.
[LG-30] GBFRVFL: Granular-Ball Computing-Based Fuzzy Random Vector Functional Link Network
链接: https://arxiv.org/abs/2609.29670
作者: A. Quadir,A. Rahaman,P. N. Suganthan,M. Tanveer
类目: Machine Learning (cs.LG)
*备注:
Abstract:In practical machine learning tasks, data are often contaminated with noise, outliers, and class imbalance, which can degrade the performance of conventional models. While random vector functional link (RVFL) networks offer fast training and strong generalization, they do not explicitly handle uncertainty or exploit local data structure. To address these limitations, we propose a fuzzy granular-ball random vector functional link (GBFRVFL) framework that leverages granular-ball computing to abstract raw samples into adaptive granular balls. Within this framework, we introduce two membership assignment schemes: (i) F-GBRVFL, which incorporates fuzzy membership to quantify the reliability of each granular ball, and (ii) SDAP-GBRVFL, which we propose, incorporates a novel statistical density-adaptive pythagorean membership (SDAPM) scheme that dynamically adjusts membership and non-membership values based on class variance, local sparsity, and granular-ball compactness. These schemes enhance robustness to noise, outliers, class imbalance, and uncertainty in granular-ball distributions, while retaining the computational efficiency of RVFL networks. Extensive experiments on 37 benchmark UCI and KEEL datasets under both clean and noisy conditions demonstrate that the proposed models consistently outperform baseline models, achieving superior accuracy and stability. The results validate the effectiveness of integrating granular-ball computing with adaptive membership schemes for reliable, scalable, and noise-tolerant learning.
[LG-31] Limited Structural Reliability in Public Educational Prediction Benchmarks: A Four-Dimension Audit of Seven Datasets
链接: https://arxiv.org/abs/2609.29625
作者: Yan Ma,Lizhuo Zhang
类目: Machine Learning (cs.LG)
*备注: 26 pages, 9 figures, 13 tables. Accepted by Scientific Reports (2026). Code and data: this https URL
Abstract:Across seven public educational prediction datasets, three passed all four pre-modeling reliability checks; the remaining four either failed group-aware generalization tests or lacked the provenance metadata needed to run them. One dataset was initially classified as failing but corrected after excluding group-identifier features from the holdout matrix, demonstrating that the audit can distinguish genuine cross-group confounding from feature-encoding artifacts. Each dataset was audited before model optimization using four checks: baseline gap, split instability, null separation, and metadata adequacy under group-aware holdout. The dominant failure mode was not weak iid performance alone but cross-group fragility: in the clearest case, UCI Student declined from iid R-squared 0.242 to group-holdout R-squared -0.097, while Higher Ed collapsed from 0.041 to -8.79. Increasing model complexity did not remove this pattern: ensemble models improved structurally sound datasets but amplified instability or failed under group holdout on fragile ones. An exploratory cross-dataset comparison further showed that stronger profiles clustered in larger, richer-grouped, performance-proximal datasets, while random-split performance severely overstated deployable signal in fragile datasets. Classification-metric sensitivity analyses reached the same substantive conclusions. The results show that benchmark reliability in educational AI is constrained less by algorithm choice than by data structure, group heterogeneity, and evaluation design. A reusable pre-modeling audit offers a minimum quality gate before public educational datasets support strong benchmark or deployment claims.
[LG-32] Active Client Selection in Federated Trajectory Prediction with Uncertainty-Awareness and Heterogeneous Complexity
链接: https://arxiv.org/abs/2609.29600
作者: Yiming Xie,Muzi Peng,Fei Miao,Ningfang Mi,Lili Su
类目: Machine Learning (cs.LG)
*备注:
Abstract:Training sequence models such as transformers is now standard for autonomous vehicle trajectory prediction, yet assembling high-quality centralized datasets remains challenging because real-world trajectories are fragmented across regions and vehicles. Federated Learning (FL) offers a natural alternative, but faces two distinctive challenges: high scene uncertainty arising from trajectory or map ambiguity, and cross-scene complexity heterogeneity caused by diverse map topology, traffic density, agent composition, and driving behaviors. We propose a family of active client selection methods that progressively incorporate awareness of scene uncertainty and complexity to prioritize informative clients. Our uncertainty-aware selectors use per-client negative log-likelihood under an uncertainty-aware global objective and estimated aleatoric uncertainty. We further develop a selector that jointly considers scene complexity and uncertainty, motivated by the intuition that knowledge from complex scenes can transfer to easier ones. Experiments on Argoverse show that federated trajectory prediction outperforms locally trained models. Uncertainty-aware selection accelerates convergence and improves minADE, minFDE, and MR. Under strong scene-complexity heterogeneity, our joint complexity- and uncertainty-aware selector achieves the best generalization and further accelerates convergence, demonstrating the benefit of prioritizing complex and informative scenes. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2609.29600 [cs.LG] (or arXiv:2609.29600v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.29600 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-33] When Identical Rows Disagree: From Benchmark Identifiability to Replication-Robust Anomaly Detection
链接: https://arxiv.org/abs/2609.29580
作者: Jie Deng
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 11 pages, 3 figures
Abstract:A released table is often treated as an i.i.d. sample, although its repeated rows may encode business frequency, repeated entities, joins, resampling, or extraction errors. We show that this ambiguity creates a hidden measurement layer with three consequences: feature-identical rows impose an attained evaluation ceiling, row-weighted AUROC is sensitive to replication, and row-trained detectors learn a multiplicity-size-biased law. An exact-row audit of all 690 OddBench datasets finds train-test overlap in 355, feature-identical label conflict in 147, and a test anomaly identical to a training normal in 137. Switching from row to support weighting changes AUROC by at least 0.05 on 50-61 datasets across four classical detector geometries. We introduce SCOUT (Support-Count Orthogonalized Unsupervised Testing), a factorized anomaly detector that separates replication-invariant support evidence from exposure-aware count evidence. Factorwise split-conformal calibration yields marginal false-positive-rate control, while the support channel is exactly invariant to arbitrary positive row replication. On 686 OddBench datasets and five seeds, support-only SCOUT is non-inferior to row-wise Isolation Forest in raw AUROC and improves replication-invariant AUROC. External normal-support evaluations track nominal false-positive levels, and four backbones remain exactly unchanged under controlled replication. Semi-synthetic interventions show that conditional count modeling helps materially only under strong rate heterogeneity. These results specify when multiplicity should be treated as signal, nuisance, or uninterpretable without additional information.
[LG-34] Classifier-Dependent Benefits of Pseudo-Labeling for Semi-Supervised Android Malware Attribution
链接: https://arxiv.org/abs/2609.29564
作者: Md Rafid Islam,Zahid Hasan,Hafiz Abdur Rahman
类目: Machine Learning (cs.LG)
*备注:
Abstract:Detecting and classifying Android malware families remains challenging due to high feature dimensionality, class imbalance, and the high cost of expert-labeled data. Semi-supervised learning (SSL) offers a way to leverage unlabeled samples, but prior works rarely test whether SSL benefits generalize across classifier types or report statistical significance. We present a systematic evaluation of pseudo-labeling across six classifiers (LightGBM, XGBoost, Random Forest, Logistic Regression, MLP, and SVM) on the CICMalDroid 2020 dataset, using five-fold stratified cross-validation and paired t-tests across five labeled ratios (1-20%). We find that SSL benefit is strongly classifier-dependent: SVM shows the largest significant gain (+4.4% accuracy at 5% labels, p = 0.0028), LightGBM improves modestly (+0.8 to +1.3% at 2-5% labels), while Random Forest is significantly harmed at low label ratios (-3.1% at 1% labels). Per-class analysis reveals SSL disproportionately benefits the hardest-to-classify families, with Adware F1 improving by +13.8 percentage points versus only +0.8 for the already well-classified Benign class. We further show that approximately 800 labeled samples (10% of the dataset) yield near-optimal performance across all classifiers. These findings offer practical guidance on when and with which classifier pseudo-labeling is worthwhile for Android malware classification.
[LG-35] Certified Predictive Value-of-Advice Gating for Cost-Aware Language-Model Guidance in Reinforcement Learning
链接: https://arxiv.org/abs/2609.29548
作者: Ibne Farabi Shihab,Md Najmus Swaqeeb,Abu Sa-Adat Mohamed Moon-Im Al Ahsan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Language-model advice can accelerate reinforcement learning, but calls are costly and returned actions may be stale or wrong. We formulate advice acquisition as a response-contingent metareasoning problem: before querying, the controller predicts possible parsed responses, evaluates the decision and declared continuation that would follow each response, and queries only when a lower confidence bound on predictive value exceeds the priced cost. Execution is governed separately by an action-specific certificate. Under explicit assumptions, certified advice is near-optimal, a wrapped learner inherits fallback regret only under intervention stability, and conservative allocation loses at most the declared query-value estimation error relative to a myopic oracle. On BabyAI, a proxy-calibrated controller with Qwen2.5-1.5B and 7B advisors improves GoToObj return over no querying by 0.029 +/- 0.016 and 0.030 +/- 0.015 across 20 seeds while reducing calls by more than 97% relative to always-query. GoToLocal is a null result. Exactly matched-call tests show an advantage over random placement only for the 1.5B advisor and no advantage over an equal-budget early schedule. Mondrian calibration improves decision-relevant empirical coverage from 0.47 to 0.85, still below the 0.90 target, while the formally covered radius is vacuous. The demonstrated benefit is therefore robust sparse advice volume on a useful task, not a proven per-state placement advantage.
[LG-36] he Impossible Trinity of Time-Series Validation: A Conservation Law among Training Sufficiency Test Coverag e and Temporal Causality
链接: https://arxiv.org/abs/2609.29530
作者: Jiayu Li
类目: Machine Learning (cs.LG); Statistical Finance (q-fin.ST); Machine Learning (stat.ML)
*备注:
Abstract:Validating a model on a time series asks for three things at once: each training run should use most of the sample (sufficiency), the test sets should together cover most of the sample (coverage), and training data should come before test data (causality). We prove that the three cannot be had together and price each one. Let \alpha be the smallest training fraction over folds, \beta the fraction of the sample covered by tests, \Lambda the fraction of the sample used as training data from the future of a test point, and \delta the distance from a test point to the nearest training point in its future. Every scheme on a sample of length T satisfies \alpha+\beta \le 1+\Lambda and \alpha+\min\beta,\delta/T\ \le 1 , and under \beta -mixing the leakage bias at a test point is at most 2M\beta_\mathrmmix(\delta) . In words: going beyond the causal frontier \alpha+\beta=1 requires training on the future; that future data must sit within (1-\alpha)T of a test point; and its harm depends on its distance, not its amount. Hence expanding walk-forward is exactly the Pareto frontier of causal validation, k -fold cross-validation buys the most future data, and purged k -fold with an embargo pays in distance instead, which is cheap when the process forgets quickly but cannot repair the part of causality demanded by non-stationarity. On pure noise, shuffled 5-fold reports an information coefficient of +0.32 , while contiguous 5-fold, using the same amount of future data, reports +0.004 .
[LG-37] Common Covariance Geometry and Certification for Brownian Kernel Ladders
链接: https://arxiv.org/abs/2609.29525
作者: Mahdi Mohammadigohari
类目: Machine Learning (cs.LG)
*备注:
Abstract:A representation-adaptive kernel class produces, on a fixed sample, a union of reproducing-kernel Hilbert-space ellipsoids rather than one ellipsoid. We introduce the minimum-trace common covariance that dominates the unrestricted empirical union generated by Brownian kernel ladders and develop its statistical, approximation-theoretic, and computational consequences. The covariance value admits exact formulations through absolutely two-summing operators and covariance-dominated multipliers, and it yields a universal Gaussian-complexity bound. A closed last-layer Dirac-trace reduction and a signed Brownian threshold representation convert the generic covariance problem into threshold, graph-coarea, and effective-resistance geometry. These tools give deterministic depth laws, conditional Gaussian reverses, random-design and perturbation transfers, and an exact empirical Kolmogorov-width formula whose leading covariance eigenspaces approximate the complete adaptive ball simultaneously. Finite contact, active semidefinite programs, verified separation, and a convex resistance-design relaxation provide complementary lower and upper certificates. A finite covariance-indexed Brownian path on frozen representations illustrates the distinction between successful covariance certification and predictive selection: all reported path certificates succeed, whereas the locked predictive study misses one predeclared aggregate criterion. The paper thereby identifies one finite-dimensional covariance object linking unrestricted kernel adaptation, Gaussian geometry, common subspaces, and certifiable computation.
[LG-38] Sample-Weighted End-to-End Trace-Norm Geometry for Multitask Learning
链接: https://arxiv.org/abs/2609.29520
作者: Mahdi Mohammadigohari
类目: Machine Learning (cs.LG)
*备注:
Abstract:Multitask models combine a shared representation with task-specific outputs, but generalization bounds often control the two components separately. Such products can discard relative orientation and cancellation and can change under equivalent transformations of intermediate coordinates even when the represented predictors are unchanged. We study instead the sample-size-weighted trace norm of the end-to-end map from task coefficients to input-space predictors. For its fixed-radius class, we derive the exact empirical Rademacher complexity. The same quantity is characterized by eliminating a positive-definite task covariance after the representation acts and, in finite-dimensional intermediate spaces, by optimizing the separated product over all equivalent invertible refactorizations. Explicit constructions show unbounded orientation and factorization gaps and an exponential depth gap for cancelling linear layers. As a geometric application, finite-to-one Lipschitz shared maps yield an exact Sobolev task Gram matrix determined by multiplicity and local directional distortion. We evaluate the corresponding convex regularizer in two protocol-locked unseen suites. Across 252 paired held-out comparisons, weighted joint nuclear regularization improves average population excess over unweighted nuclear regularization by 0.00764, with a stratified-bootstrap 95% interval [0.00465, 0.01110]. Correct task counts improve average and least-sampled-quartile excess over shifted counts by 0.01072 and 0.02847; all 15 imbalanced rank-suite cells are positive and the balanced effect is zero. Weighted joint nuclear also outperforms weighted Frobenius and independent ridge. The least-sampled-quartile comparison with unweighted nuclear remains unresolved, delimiting rather than contradicting the average advantage. All seven predeclared gates pass.
[LG-39] CataOPD: Catalytic On-Policy Distillation for Large Language Model Reasoning
链接: https://arxiv.org/abs/2609.29518
作者: Wenjin Liu,Chenxi Wang,Jiapu Wang,Zhe Cui,Anh Tuan Luu,Haoran Luo
类目: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE)
*备注:
Abstract:Reinforcement learning (RL) and on-policy distillation (OPD) are two representative paradigms for improving large language model reasoning. However, when no correct trajectory is sampled, RL lacks a positive correctness signal, while OPD remains constrained by the reasoning trajectories reachable under the student’s on-policy distribution. Therefore, we propose CataOPD, where the teacher acts as a catalyst rather than a target, expanding reachability while internalizing verified student-produced trajectories into a catalyst-free policy. Self-Rescue Routing uses empirically all-failed groups as routing signals rather than teacher-intervention triggers, first seeking correct trajectories through additional on-policy self-sampling. For problems unresolved after self-rescue, Catalytic-Guided Self-Resolution uses catalytic guidance to elicit a verified student-produced trajectory in the guided student distribution. Barrier-Weighted Internalization weights tokens by guided-to-unguided log-probability gaps, focusing updates on decisive tokens difficult without guidance. Experimental results show that CataOPD outperforms current baselines, extends independent student reasoning to still-unrecovered problems, and improves out-of-distribution generalization under catalyst-free inference. Our project is available at this https URL.
[LG-40] Spectral-Guided Diffusion: Accelerating Inference via Static Spectral Layer Scheduling
链接: https://arxiv.org/abs/2609.29505
作者: Ibne Farabi Shihab,Abu Sa-Adat Mohamed Moon-Im Al Ahsan,Anuj Sharma
类目: Machine Learning (cs.LG)
*备注:
Abstract:Diffusion inference repeatedly evaluates the same large network. We ask whether pretrained weights alone can identify residual branches that need not be recomputed throughout the trajectory. Our \textbfSpectral Concentration Ratio (SCR) measures leading-versus-tail singular-value energy. Combined with Frobenius magnitude, it yields an offline sensitivity proxy and a deterministic lifetime for each scheduled unit. A frozen unit reuses its cached residual-branch update while the current residual stream and all external conditioning continue to propagate. The method needs no router, calibration prompts, or input-dependent search. At matched layer-step budgets, SCR/Frobenius preserves quality better than random, depth, norm, stable-rank, and Frobenius–stable-rank schedules on LLaDA-8B, DiT-XL/2, U-ViT-L, and SDXL. Broader LLaDA tests cover retrieval, reasoning, code, summarization, and open-ended generation; matched-horizon controls retain the ranking down to ten denoising steps. The complete captured-graph system reaches 2.8\times – 3.0\times wall-clock speedup over eager inference. This is a systems-level number: on LLaDA, padded graph execution already gives 2.7\times , while eliminating inactive branch work raises it to 3.0\times . The perturbation analysis motivates pre-norm attention and MLP components under explicit local assumptions; results on AdaLN, U-shaped, convolutional, and cross-attention blocks are empirical transfer, not certified guarantees.
[LG-41] ask-Aware Spectral Pruning: A Mixture-of-Masks Framework for Efficient LLM Inference
链接: https://arxiv.org/abs/2609.29499
作者: Ibne Farabi Shihab,Fariya Afrin,Sanjeda Akter,Anuj Sharma
类目: Machine Learning (cs.LG)
*备注:
Abstract:Static pruning imposes one sparse structure on every prompt, even though reasoning, retrieval, generation, coding, and translation can depend on different parts of a language model. We introduce Task-Aware Spectral Pruning (TASP), a post-training framework that calibrates module-level spectral descriptors against measured task-specific ablation effects, closes grouped-query-attention and SwiGLU dependencies during sparse-mask construction, and routes each user turn to one compiled mask that remains fixed throughout prefill and decoding. A module-disjoint pilot first determines whether the spectral signal is informative before full calibration. Under the stated retrospective operating rule, the pilot passes on the evaluated Llama-3-8B and Llama-3-70B checkpoints but rejects Qwen2.5-1.5B, demonstrating that applicability is model-dependent rather than universal. At a 43% active-FLOP reduction, the Llama-3-70B benchmark harness retains 97.7 +/- 0.2% of the dense BF16 score. In the deployment-matched INT8-weight/BF16-compute runtime on a single A100 80GB, the compiled sparse path retains 97.3 +/- 0.2% relative to dense BF16 and reduces decode latency from 45.2 +/- 0.4 to 31.3 +/- 0.4 ms/token, yielding a 1.44x speedup. Factorized ablations, disjoint-module tests, compiled structured baselines, routing-corruption studies, and an explicit 136-GPU-hour calibration audit further delimit the source and operating regime of these gains
[LG-42] BLADE: Distilled LLM Regularization for Calibrated Knowledge Graph Completion
链接: https://arxiv.org/abs/2609.29487
作者: Ibne Farabi Shihab,Rabeya Bosri Tamanna,Abdo El Karaky,Sanjeda Akter,Anuj Sharma
类目: Machine Learning (cs.LG)
*备注:
Abstract:Knowledge graph completion models optimize ranking, although many downstream applications require calibrated probabilities. We present BLADE, a variational model that separates latent truth from graph recording and distills offline language-model judgments into a frozen teacher regularizer. The LLM is absent during inference. Posterior samples provide predictive probabilities and epistemic uncertainty, while the compact teacher remains available only as an optional triage factor. Across five benchmarks, BLADE remains competitive under a common ranking protocol and reduces adaptive ECE by a macro-average of 60.1% relative to deep ensembles and 78.1% relative to temperature-scaled RotatE. On identical FB15k-237 candidate sets, BLADE also improves ECE, Brier score, and NLL over validation-selected histogram binning and a matched generative ComplEx2 model, with these improvements persisting on a prespecified near-miss pool. Under controlled injected missingness, the full triage score achieves a mean AUC-PR of 0.863, compared with 0.805 for its strongest non-teacher variant. Leakage stress tests show that aligned semantics matter, but they cannot exclude knowledge acquired during LLM pretraining. We therefore claim calibration only for the declared candidate distributions, not for all unobserved triples.
[LG-43] Precise Convergence Speed of Clipped SGD
链接: https://arxiv.org/abs/2609.29458
作者: David A. R. Robin
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:We present a tightened convergence analysis of clipped gradient descent on (L_0, L_1) -smooth functions, with quantitative constants. Building on the ideas of Koloskova et al (2023), we refactor several case disjunctions to reveal the central role of a control of the bias derived from fundamental properties of \ell_2 -projection, simplifying proofs. We also extend the domain of validity from \eta \leq 1 / (9 \beta) to \eta 1 /\beta where \beta = L_0 + c L_1 for clipping constant c , which matches the more traditional analysis of smooth functions. We strengthen the convergence criterion from \left( \min_t T \mathbbE[\lVert \nabla f(x_t) \rVert_2] \right) to \left( \frac1T \sum_t T \mathbbE[\lVert \nabla f(x_t) \rVert_2] \right) with matching speed, and lower the final achievable loss from \mathcalO(\min(\sigma^2/c, \sigma)) to the more precise 6 \min(\sigma^2 /c, 3 \sigma) .
[LG-44] SPADE-DFL: Communication-Efficient Decentralized Federated Learning via Derivative-Free Linearized ADMM
链接: https://arxiv.org/abs/2609.29446
作者: Mengli Wei,Mengkai Zhu,Jiawen Chen,Wenwu Yu,Duxin Che
类目: Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注:
Abstract:Reducing communication in derivative-free decentralized learning requires controlling the disagreement accumulated over multiple local updates. This paper develops SPADE-DFL, a primal–dual method that allows the number of local function-value updates between neighbor exchanges to grow with the computation budget while preserving the nonprivate convergence order. For smooth nonconvex objectives under uniform query-moment bounds, the prescribed nonprivate schedule achieves a time-averaged stationarity and consensus bound of \mathcalO(T^-1/3) using only \Theta(T^2/3) communication rounds, where T is the number of local updates per client. For private training, the accumulated data-dependent increment is isolated from the graph correction, allowing one protected state per client and round to generate all outgoing messages. We prove client-level differential privacy for the full interactive transcript and quantify the resulting optimization error over a finite horizon. Experiments on four classification tasks show that SPADE-DFL achieves higher mean test accuracy than existing decentralized learning methods.
[LG-45] Neural Transport Nested Sampling
链接: https://arxiv.org/abs/2609.29413
作者: David Yallup,Will Handley
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 26 pages, 9 figures
Abstract:Sampling from Boltzmann distributions of molecular systems is an inference problem that has seen significant recent developments fuelled by advances in neural density estimation. We develop a novel sampling algorithm, Neural Transport Nested Sampling (NTNS), which combines the classical strengths of nested sampling with modern neural flow-based methods. NTNS uses a flow matching velocity as the drift in a Metropolis–Hastings corrected Langevin kernel inside a nested sampling outer loop, requiring only evaluations of the target energy function and providing scalable estimation of the full partition function of high-dimensional particle systems. We benchmark NTNS on challenging molecular sampling benchmarks, scaling up to Lennard–Jones clusters of 55 interacting particles, where it reduces both interatomic distance and energy Wasserstein errors to reference MCMC by over an order of magnitude relative to the strongest neural baselines at lower wall-clock cost. To our knowledge, NTNS is also the first neural sampler to return a calibrated, temperature resolved partition function estimate at this scale, recovering the phase structure across temperature from a single run.
[LG-46] ICE: Task-Aligned Clifford Latent Fields for Multimodal Graph Foundation Models
链接: https://arxiv.org/abs/2609.29398
作者: Xunkai Li,Xu Wang,Yinlin Zhu,Xiong Yongfu,Yi Liu,Rong-Hua Li,Guoren Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Multimodal attributed graphs connect entities, visual content, language, and observed relations. Learning one foundation across such graphs requires more than compressing each node into a fused Euclidean vector. The representation must preserve entity semantics, construct interaction state from graph neighborhoods, and expose that state to prediction units with different geometry. Our empirical study shows why these requirements are inseparable. Higher-grade channels recover pair relations across the foundation graphs, specialized queries reveal information hidden by a generic readout, and rigid blade isolation removes cross-grade capacity. We therefore introduce ICE (Interaction-aware Clifford Encoder), a multimodal graph foundation model built on a node-indexed Clifford latent field. Topology, text, and images enter explicit Cl(3) addresses. Edge-aware geometric products transform these directions into scalar, bivector, and trivector relations over observed neighborhoods. A protected Grade-1 route preserves entity semantics, while the full grade and depth bank remains available to fresh node and link heads. We establish exact cross-grade reachability, node-permutation equivariance, and a bound on the task residual around the semantic score. Experiments span one shared foundation over eleven graphs, six node-classification datasets, three link-prediction datasets, and matched few-shot tasks. ICE ranks first in all 30 reported supervised and few-shot comparisons. Core removals reduce every task summary, and mechanism controls connect the gains to higher-order transport, retained multidepth structure, semantic protection, and direct field access.
[LG-47] Concurrent Split Learning Through Stable Client Clustering
链接: https://arxiv.org/abs/2609.29395
作者: Mohammad Kohankhaki,Valentin Rentschler,Anke Schmeink
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注: 11 pages, 6 figures, 4 tables
Abstract:Training with a fixed global batch limits how many distributed clients can provide examples in any one step. We examine a way to use additional server workers without increasing the batch processed by an individual workload. Global Clustered Parallel Split Learning (GCPSL) assigns clients to fixed clusters, executes a Parallel Split Learning with Global Sampling (GPSL) workload for each cluster concurrently, and periodically fuses the client and server model segments. In simulations with 256 logical clients, dividing the population across more workloads improves direct data participation, while smaller clusters can incur an accuracy cost. A four-H100 implementation of label-aware GCPSL reaches 85% CIFAR-10 validation accuracy in 6.13 \pm 0.15 minutes over three matched runs, versus 19.09 \pm 0.45 minutes when the same workloads are serialized. Within the four-GPU allocation, size-balanced and random fixed affiliations reach the target in similar mean times (5.70 and 5.66 minutes); size balancing increases direct participation by 3.25 percentage points. These measurements characterize a trade-off among execution concurrency, assignment information, participation, and accuracy for stable-client split learning.
[LG-48] MORE-PLR: multi-output regression employed for partial label ranking
链接: https://arxiv.org/abs/2609.29386
作者: Santo M. A. R. Thies,Juan C. Alfaro,Viktor Bengs
类目: Machine Learning (cs.LG)
*备注: Code available at this https URL . Extended version of a paper presented at Discovery Science 2024
Abstract:The partial label ranking problem is a supervised learning scenario that aims to fit a preference model that predicts a bucket order defined over a set of labels for a given input instance. This problem generalizes the well-known label ranking problem, which, in practice, is limited to outputting total orders of labels. Existing partial label ranking methods have primarily extended label ranking approaches to handle ties in predictions. This paper proposes using multi-output regression to address the partial label ranking problem, introducing an encoder that, during the learning phase, transforms the (possibly incomplete) rankings with ties of labels to multivariate regression targets, an underexplored perspective in both label ranking and partial label ranking. Moreover, during the inference phase, we introduce several post-hoc layers that convert the multi-output regression results into the output bucket order to effectively implement this approach. This framework provides learning strategies that are competitive with the current state-of-the-art partial label ranking methods, as demonstrated through experimental evaluations.
[LG-49] Lightweight Probabilistic Downscaling from a Deterministic Base Model
链接: https://arxiv.org/abs/2609.29383
作者: Joseph McLean,Tiffany Vlaar,Sigrid Passano Hellan,Linus Ericsson
类目: Machine Learning (cs.LG)
*备注:
Abstract:Climate data downscaling is the task of increasing the spatial resolution of climate data, typically by generating fine-resolution regional climate data from coarse global model output. Recent machine learning (ML) work in the related task of weather forecasting has seen significant improvements due to newly devised training methods and architectural components, but these have not yet benefited downscaling. We adapt two of these methods to create a family of lightweight probabilistic ML downscaling models built on a modified U-Net backbone and evaluate them on the CORDEX-ML-Bench suite for daily maximum temperature and precipitation across three geographic regions: the Alps, New Zealand and South Africa. We find that a two-stage training curriculum, combining deterministic pretraining with probabilistic tuning, transfers well to downscaling, beating the state-of-the-art for RMSE. Our work provides an advancement towards lightweight, probabilistic downscaling models, reducing the current trade-off between computational intensity and distributional fit.
[LG-50] Decoupled Early Exits for Task-Dependent Compute Allocation in Flow-Matching VLAs
链接: https://arxiv.org/abs/2609.29382
作者: Riccardo Andrea Izzo,Rimvydas Rubavicius,Gianluca Bardaro,Subramanian Ramamoorthy,Matteo Matteucci,Alessandro Suglia
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Flow-matching Vision-Language-Action (VLA) models have emerged as a potential solution for generalist robot control, designed by combining a pretrained Vision-Language Model (VLM) backbone with an action expert that generates continuous robot actions. While these models exhibit impressive capabilities, due to their very high number of parameters, their computational requirements are often prohibitive for robotics control. To mitigate these inefficiencies, existing methods predominantly skip VLM backbone layers with early exits or reduce denoising steps, while leaving action expert depth untouched. We propose a framework that exposes backbone depth V , action expert depth A , and denoising steps D as three jointly configurable compute axes in a VLA. Starting from a pretrained VLA, we attach lightweight Exit Transformers (ET) at intermediate depths in both the backbone and the action expert, trained to distil the last layer of the policy into each exit. Furthermore, we introduce a KV Cache synthesis mechanism that manages the missing keys and values of the skipped backbone layers, allowing the action expert to exit deeper than the backbone. Finally, we show that the optimal compute budget is task-dependent, with different tasks benefiting from different axes and depths. Notably, our method does not require training the original policy from scratch, and for each exit, it increases the number of parameters by only 2.1% for SmolVLA and 4.1% for \pi_0.5 . We validate our approach across two flow-matching VLAs (SmolVLA, \pi_0.5 ) and two benchmarks (LIBERO, Meta-World), revealing complementary effects: V and A respectively reduce FLOPs and latency, while D improves both. Our joint configurations (V,A,D) reduce latency by 79.2% and computation (FLOPs) by 31.8% , while improving mean success rate by 5.6% .
[LG-51] FlowAtom: Atom-Based Evidence Aggregation for Multi-Label Website Fingerprinting ICASSP2027
链接: https://arxiv.org/abs/2609.29330
作者: Chongru Fan,Wentao Huang,Wei Wang,Zhenquan Ding,Jinqiao Shi,Wei Cai,Zhiyu Hao
类目: Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注: 5 pages. Submitted to ICASSP 2027
Abstract:Identifying the set of monitored websites in mixed encrypted traffic is challenging because an individual flow often provides only partial evidence of website identity. To address this challenge, we propose FlowAtom, which constructs shared prototypes, called Atoms, from flow representations without website labels. Specifically, FlowAtom pretrains a flow encoder on external unlabeled traffic and aggregates Atom responses across flows within each observation window into a fixed-dimensional, permutation-invariant representation for monitored website-set prediction. Across Direct HTTPS, Trojan, and VMess, FlowAtom achieves micro-F1 scores of 97.82%, 94.43%, and 93.92% in closed-world evaluation, respectively, and consistently outperforms the evaluated baselines in open-world evaluation on windows containing monitored visits. The code is available at this https URL.
[LG-52] nyCardioUNet: IMU-to-ECG Translation with Graph-Encoded Inter-Axis Dependencies and Tensor Decomposition-Based Parameter Reduction
链接: https://arxiv.org/abs/2609.29322
作者: Seungwoo Han,Ingon Chanpornpakdi,Motoi Noda,Puwadej Leelasiri,Ibuki Hiruma,Toshihisa Tanaka
类目: Machine Learning (cs.LG)
*备注: The source code and pretrained models are available at this https URL
Abstract:Estimating electrocardiography (ECG) from a chest-worn inertial measurement unit (IMU) enables continuous heart rate (HR) monitoring without the discomfort of electrodes. We propose TinyCardioUNet, a lightweight UNet that uses all six IMU axes without prior channel selection, refines its bottleneck with a graph neural network that encodes inter-axis dependencies, and employs tensor decomposition with automatic variational Bayesian rank selection for parameter reduction. On a public dataset, TinyCardioUNet achieves an RMSE of 0.098 and a Pearson correlation coefficient of 0.677 with only 36.0 k parameters and remains comparatively robust to additive noise, demonstrating accurate ECG reconstruction with a compact model.
[LG-53] Beyond Feature Reliability: Repeat-Informed Multifractal Curve Regression for Brain-Age Prediction
链接: https://arxiv.org/abs/2609.29307
作者: Yu Chang,Anzhe Cheng,Jiahao Chen,Heng Ping,Peiyu Zhang,Puquan Pan,Tamoghna Chattopadhyay,Sophia Thomopoulos,Shahin Nazarian,Paul Thompson,Paul Bogdan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Brain-age prediction from resting-state fMRI provides a quantitative framework for characterizing age-related changes in spontaneous brain dynamics and for identifying functional signatures. Existing studies have linked fractal and multifractal scaling to age and examined the reliability of individual features. However, prediction repeatability depends on how features fluctuate jointly and how a predictor combines them, which feature-wise reliability assessments do not capture. To address this problem, we propose Repeat-informed Multifractal Curve Regression (RMCR), a structured framework for learning stable age-predictive patterns from multifractal curves. By jointly modeling curve structure and repeat-scan variability, RMCR learns predictive combinations of fluctuation orders that target both accuracy and within-subject consistency. Relative to a matched run-level ridge baseline, RMCR reduces single-run MAE by 6.1% on HCP-A and 7.9% on an external Cam-CAN cohort, and within-visit repeat absolute difference by 18.5% on HCP-A, using a single scan at inference. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2609.29307 [cs.LG] (or arXiv:2609.29307v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.29307 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-54] Online Task Adaptation via Self-Organisation
链接: https://arxiv.org/abs/2609.29281
作者: Krsto Proroković
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注: 6 pages, 1 figure
Abstract:Neural networks are typically adapted by computing gradients and updating model parameters. We investigate whether task-specific adaptation can instead emerge from a meta-learned self-organising process that requires no gradients at adaptation time. We instantiate this idea with a Neural Cellular Automaton in which locally interacting recurrent cells maintain both a recurrent state and a fast associative memory. During meta-training, backpropagation is used to learn the recurrent dynamics together with how the memory is read and written. Once training is complete, the slow model parameters remain fixed, and online adaptation occurs only through cellwise memory updates driven by local prediction errors and a delta rule. We evaluate whether the learned mechanism can adapt to semantically distinct held-out classification tasks. A single pass over the support data produces substantial improvements in held-out performance without gradient computation or parameter updates during adaptation, and the mechanism remains effective across large changes in the number of examples processed jointly. These results show that task-specific adaptation can be achieved through explicit fast-memory updates while keeping the slow model parameters fixed. Comments: 6 pages, 1 figure Subjects: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE) Cite as: arXiv:2609.29281 [cs.LG] (or arXiv:2609.29281v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.29281 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-55] Learnable Time-Frequency Masks for Explaining Time-Series Classifiers
链接: https://arxiv.org/abs/2609.29270
作者: Theresa Dahl Frehr,Francisco Pelayo,Lukas Raad,Alicia García Sanz,Thea Brüsch,Tommy Sonne Alstrøm
类目: Machine Learning (cs.LG)
*备注:
Abstract:Time-series explainability remains challenging because discriminative information is often encoded in latent frequency or time-frequency features rather than in the raw signal itself. Existing attribution methods typically operate either in the time domain or in a fixed transform domain, limiting their ability to capture salient information across different representations. We propose XACT, a general framework that learns sparse attribution masks over coefficients from arbitrary invertible time-frequency transforms. We evaluate the framework on the STFT, the continuous wavelet transform, and the discrete wavelet transform. In addition, we extend the virtual inspection layer approach from the STFT to both wavelet transforms, enabling LRP to generate explanations in these representations. On a synthetic dataset, XACT produces precise explanations and is less prone to highlighting spurious features than the tested baselines. Across two real-world datasets, XACT produces sparse and structured explanations, although no method performs best across all quantitative evaluation criteria. These results demonstrate that learning explanations directly in time-frequency representations offers a flexible approach to interpreting deep-learning models for time series data.
[LG-56] BridgeMem: Causal Dyadic Transition Residuals for Temporal Knowledge Graph Forecasting
链接: https://arxiv.org/abs/2609.29268
作者: Zeyan Li,Libing Chen,Shengda Zhuo,Yin Tang,Jianfeng Xu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Temporal knowledge graph forecasting aims to infer future relational facts from the temporal structure of observed events. Existing forecasters mainly summarize history through entity states, relation states, paths, or exact recurrence. These views often miss pair-specific transition evidence, that is, the way prior relations between the query actor and a candidate change the odds of the target relation. We introduce BridgeMem, which estimates this quantity as a residual added to the log scores of a frozen full-vocabulary forecaster. For each candidate, BridgeMem retrieves the pair’s events that strictly precede t, encodes their relations, directions, and lags, and converts them into a likelihood-ratio correction. A support-adaptive empirical-Bayes reader trusts exact transition counts where they are abundant and backs off to a learned attention estimator where they are sparse. The backbone’s own uncertainty gates the correction, so confident queries and candidates without dyadic history are left unchanged. On five benchmarks, BridgeMem improves on the strongest of nine baselines from 2021–2026 in all 20 filtered MRR and Hits@1,3,10 comparisons, with MRR gains of 0.0213, 0.0164, 0.0216, 0.0112, and 0.0028 over the best prior result. These results show the value of explicit dyadic transition modeling.
[LG-57] AFT Neural Function Approximators for 1D Nonlinear Force Laws
链接: https://arxiv.org/abs/2609.29242
作者: Miriam Goldack,Johann Groß,Malte Krack,Merten Stender
类目: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG); Dynamical Systems (math.DS)
*备注: 20 pages, 6 figures, 6 tables
Abstract:Nonlinear contacts and friction strongly influence the vibration response of assembled structures, but their accurate numerical treatment is computationally demanding. The harmonic balance method is widely used to compute periodic steady-state responses, yet the required alternating frequency-time scheme becomes costly for nonsmooth and hysteretic nonlinearities and must be repeated throughout the nonlinear solution process. Here we show that this procedure can be replaced by neural networks that directly map displacement Fourier coefficients to nonlinear force coefficients and provide the corresponding Jacobian through automatic differentiation. The surrounding solver and continuation algorithms remain unchanged for the computation of frequency response curves. The neural networks exclusively learn individual nonlinear elements rather than complete system responses. Physics-based nondimensionalization and phase normalization facilitate the learning process and enable a single trained network to cover a wide range of parameter combinations. Building on the cubic spring, unilateral spring, and Jenkins elements considered here, the approach points toward a reusable library of nonlinear-element surrogates that can be combined in arbitrary number and location within a mechanical system. By bypassing the iterative force evaluation in time domain, the method offers favorable computational scaling for high-resolution analyses and systems with many nonlinear elements.
[LG-58] Continuous Online Fault Detection for Mobile Robots via Adaptive Edge Models
链接: https://arxiv.org/abs/2609.29194
作者: Jordan Levy,Nicolas Verstaevel,Vincent Talon,Benoit Gaudou
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Mobile robots require robust, real-time fault detection capable of continuous adaptation on constrained edge hardware. While deep time-series models excel at unsupervised anomaly detection, their computational cost prohibits high-frequency onboard execution. This paper bridges this gap via a Teacher-Student distillation framework. An offline foundation model (TSPulse) generates pseudo-labels from unlabeled time series augmented with fault injections. A lightweight MiniRocket Student, adapted with a Recursive Least Squares estimator, approximates this complex decision boundary to execute real-time inference onboard. Evaluations on the TSB-AD benchmark and a physical mobile robot demonstrate the Student achieves a 4.30 ms CPU inference latency. During real-world domain shifts, online adaptation enables the Student to recover from unseen mechanical degradation, improving VUS-PR scores from 0.26 to 0.75 without catastrophic forgetting. Crucially, an uncertainty-guided active learning strategy minimizes operator cognitive load, requesting sparse interventions only when encountering novel fault distributions. These results validate the deployment of state-of-the-art anomaly detection on resource-constrained robotics through offline-to-online distillation.
[LG-59] owards Deployable Underwater Vessel Classification
链接: https://arxiv.org/abs/2609.29179
作者: Abishek Soti,Thura Pyae Sone,Naqib Ibnul,Htoo Htet Aung,Henry Zhong,Gregory Cohen,Ying Xu
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注:
Abstract:We propose a compact underwater acoustic classification framework combining multi-representation feature engineering, temporal statistical pooling, and compact convolutional architectures designed for acoustic time-frequency and cochlear representations. We investigate multiple conventional and auditory-inspired representations and first evaluate lightweight classifiers and Conventional Neural Networks (CNNs) on ShipsEar dataset. On the provided split, a two-layer CNN achieves a macro F1 of 0.9918, while a Radial Basis Function Support Vector Machine (RBF-SVM) reaches 0.9883. However, source-recording provenance cannot be reconstructed, preventing verification of recording-independent generalisation. We therefore evaluate on DeepShip dataset using recording-level partitioning before segmentation. Under this protocol, a 157K-parameter compact CNN achieves a test macro F1 of 0.7226, while an 11.17M-parameter ResNet18 provides no improvement in validation performance under the matched setting. These results demonstrate the importance of representation-aware feature and model design, together with rigorous recording-level evaluation, for classification performance and deployability in compact underwater acoustic systems.
[LG-60] Edge AI on Constrained Devices for Binary Sleep-Wake Classification in Dynamic Environments
链接: https://arxiv.org/abs/2609.29163
作者: Stefan Reitmann,Lena Oden
类目: Machine Learning (cs.LG)
*备注:
Abstract:This paper presents an Edge AI-based system for detecting sleep and wake states in non-stationary mobile environments using resource-constrained embedded hardware. Conventional approaches relying on accelerometer-based activity metrics are highly susceptible to motion and vibration artifacts and are limited by strict compute and energy budgets of wearable and IoT devices. To address these challenges, a multimodal pipeline is designed and implemented on an ESP32-S3 microcontroller. The system combines inertial sensing for head movement analysis and visual pose classification. A dual-core architecture with FreeRTOS enables parallel execution of real-time data acquisition and on-device inference. Sleep detection follows a two-stage strategy: low-movement detection over a temporal window, followed by visual validation of poses. Experimental results show accuracies of 96.5% for motion-based detection and 89% for pose classification, yielding robust binary sleep-wake classification. Field tests confirmed feasibility in representative mobile scenarios. The results demonstrate that privacy-preserving, local sleep detection is achievable on edge hardware through careful co-design, while highlighting limitations in sensing intrusiveness, dataset scale, and system integration. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2609.29163 [cs.LG] (or arXiv:2609.29163v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.29163 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-61] A Particle-Swarm-Assisted Gradient Meta-Learning Algorithm for Joint Transmit Precoding and STAR-RIS Coefficient Optimization
链接: https://arxiv.org/abs/2609.29150
作者: Kang Zhou
类目: Machine Learning (cs.LG); Information Theory (cs.IT)
*备注: 11 pages, 11 figures, 2 tables
Abstract:This paper investigates the joint optimization of the transmit precoder and the transmission/reflection coefficients of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) to maximize the weighted sum rate (WSR) in a multi-user downlink. We propose a particle-swarm-assisted gradient meta-learning (PSA-GML) algorithm for this non-convex problem. The original problem is first equivalently transformed via an amplitude-split parameterization and a collapsed precoder representation, which automatically satisfy the energy-conservation constraint and reduce the search dimension. Particle swarm optimization (PSO) then performs a global search over the STAR-RIS coefficients to yield a high-quality, initialization-robust warm start, with the transmit precoder obtained in closed form. Departing from conventional alternating optimization (AO), a coordinate-wise long short-term memory (LSTM) meta-optimizer trained by first-order gradient meta-learning further refines the coefficients and precoder jointly, learning per-coordinate adaptive update rules from data. The meta-optimizer is trained offline and applied to unseen channels without further adaptation. Numerical results show that PSA-GML attains an 11.06 bits/s/Hz WSR at 10 dB with N=32 elements and K=4 users, exceeding AO by 13.1% (and by 6.2% even with multiple random restarts) and the random-phase scheme by 35.1%. In the interference-limited regime it reaches 83.9% of the hand-designed Adam refinement without manual hyper-parameter tuning, and it transfers zero-shot across regimes, indicating that the learned update rule captures the intrinsic WSR landscape structure.
[LG-62] A Concentration Bound for Two-Timescale Actor-Critic Algorithm
链接: https://arxiv.org/abs/2609.29117
作者: Prashansa Panda,Shalabh Bhatnagar
类目: Machine Learning (cs.LG)
*备注:
Abstract:Significant research effort has been directed in recent years towards establishing both asymptotic and non-asymptotic convergence guarantees for two-timescale actor–critic algorithms, where the actor recursion is run on a slower timescale than the critic recursion. This work derives a uniform all-time concentration bound for the actor–critic algorithm with function approximation in the long-run average-reward setting. This bound helps us analyze the behavior of the actor parameter with high probability. We show that, after some finite time, the actor parameter enters a safe region and remains within it thereafter with high probability. Specifically, with probability at least 1-\epsilon_1-\epsilon_2 , the actor error \Vert \theta_k-\theta^*\Vert is O\left(\fracn_0^3/4k\frac1\sqrt\epsilon_2+\left(\frac1n_0\right)^1/4\log^1/4\left(\frac1\epsilon_1\right)+\left(\frac1n_0\right)^1/4\right) for all k\geq n_0 and sufficiently large n_0 . We also present experimental results demonstrating that the aforementioned actor error diminishes with the number of actor-parameter updates.
[LG-63] Language Specificity vs. Domain Diversity: Benchmarking Transformers for Bangla Medical NER ALT
链接: https://arxiv.org/abs/2609.29101
作者: Rakib Abdullah,Md. Maruful Islam Maruf
类目: Machine Learning (cs.LG)
*备注: 6 pages, 2 figures. Accepted at the 2026 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON), Dhaka, Bangladesh
Abstract:Medical Named Entity Recognition (NER) for low-resource languages remains a challenging task due to high linguistic variability and a scarcity of domain-specific annotated corpora. This work presents a comprehensive empirical benchmark evaluating three fine-tuned transformer encoders-BanglaBERT, multilingual BERT (mBERT), and XLM-RoBERTa-against GPT-4o mini under zero-shot and few-shot prompting configurations for Bangla medical NER. In contrast to prior studies that evaluated large language models on limited subsets of only 50 samples, we conduct a large-scale evaluation across the full test set of 3,179 samples, providing statistically robust and reproducible baselines. Our fine-tuned XLM-RoBERTa model achieves an F1- score of 0.5959, establishing a new state-of-the-art and surpassing the previously reported best result of 0.5848. Crucially, we demonstrate that the language-specific BanglaBERT model consistently underperforms its multilingual counterparts with an F1-score of 0.4937, indicating that pretraining domain diversity can outweigh language specificity in highly specialized clinical settings. Furthermore, we present a detailed per-entity-type analysis for this task, revealing that Medicine and Specialist categories are recognized with high reliability, achieving F1- scores above 0.83, while the Symptom category remains the most challenging with an F1-score of 0.4367 despite being the most frequent training class. Finally, fine-tuned transformer models outperform the optimal prompting configuration by a factor of 3.76, confirming that prompt-only pipelines remain inadequate for structured clinical entity extraction in low-resource language environments.
[LG-64] raceGuard: Adaptive Multimodal Poison Filtering through Cross-Feature Rank Agreement
链接: https://arxiv.org/abs/2609.29099
作者: Haoyang Li,Yaxin Xiao,Linyan Dai,Jiawen Fu,Zi Liang,Jason Xue,Qingqing Ye,Haibo Hu
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: 42 pages
Abstract:Multimodal training relies on image-text corpora collected from external sources, creating opportunities for attackers to poison the data. Stealthy attacks can preserve plausible image-text pairs while concealing the differences used by detectors, so apparently clean data can still redirect the trained model. We therefore ask which properties a poison set must preserve for the attack to remain effective. A small poison set must still exert enough collective influence during training to induce the attacker’s target behavior. We analyze this influence in terms of how often an attack pattern occurs and how strongly the examples carrying it jointly affect the model. This analysis motivates six corpus-level features that examine cross-modal neighborhoods, recurring text, and changes after text-span erasure without training the victim model. We introduce TraceGuard, an adaptive rank-based filtering method that uses agreement among complementary feature rankings to identify suspicious examples. It refines the selected set through shared patterns and adapts the removal threshold to each corpus without knowing the attack or poison rate. Across 19 attack configurations spanning image-text learning, generative vision-language model fine-tuning, and encoder-transfer tests, TraceGuard removes an average of 98.4% of poisoned examples and 5.4% of clean examples. After training on the filtered corpora, the residual attack metric is at most 1% in 13 configurations. Matched-removal controls and ablations support the contributions of sample selection and adaptive removal. Stress tests also identify detection failures under adaptive attacks and unnecessary removal on poison-free corpora.
[LG-65] Downside-Controlled Online Forecast Combination under Delayed and Revised Outcomes
链接: https://arxiv.org/abs/2609.29096
作者: Minkyoung Kim,Hyunjung Byun,Yohan Lee,Beakcheol Jang
类目: Machine Learning (cs.LG)
*备注: 8 figures, 8 tables; supplementary material (1 figure, 23 tables) appended
Abstract:Post-hoc correction adjusts a forecaster that cannot be retrained, such as a foundation model, but a correction fitted where errors are stable can hurt where they shift. We aim for downside control: not much worse than the starting forecast. We combine the frozen forecaster, a static corrector and an online corrector on the simplex, using only losses that mature after the horizon. Across seven benchmarks and four base models, two of them foundation models, the worst deterioration over 28 pairs at the main horizon is 0.15% and gains reach 11.5%. On day-ahead load for seven European bidding zones it lowers mean MSE in all seven zones, while single correctors raise mean MSE by up to 102% where the published forecast is most accurate. Three empirical conditions on expert speed, stream length and outcome alignment, each fixed by a documented failure, delimit its scope. Learning from the provisional outcome improves four zones on the settled one; learning on the settled outcome restores all seven.
[LG-66] Physics and Data Driven Transformer-Mamba Framework for Flow Field ICASSP’26
链接: https://arxiv.org/abs/2609.29087
作者: Zhuo Zhang,Shun Zou,Canqun Yang,Xi Yang
类目: Machine Learning (cs.LG)
*备注: Corrected version of our ICASSP’26 paper: Corrected seven Dam/MISSFormer metrics in Table 1 that were mistakenly estimated using the MSE(u) ratio. The conclusions remain unchanged
Abstract:While deep learning accelerates expensive partial differential equation solving in computational fluid dynamics (CFD), existing methods like PINNs and FNOs often struggle with generalization, noise robustness, and physical consistency. We introduce the Transformer-Mamba for Flow Field (TM4FF) framework, a physics-constrained operator learning model with three key innovations: a Residual Wavelet Mamba (RWM) layer for feature denoising, a Transformer-based attention mechanism for enhanced feature fusion, and a physics-informed loss using Fourier derivatives to enforce the Navier-Stokes equations. Experiments on four CFD datasets show TM4FF achieves high accuracy and robust generalization across varying flow conditions.
[LG-67] BranchShine-CR: Compact Multilingual IPA Transcription with Self-Conditioned CTC and Consistency Regularization
链接: https://arxiv.org/abs/2609.29069
作者: Nikhil Navas,Sergio Chevtchenko,Talisson Damiao,Saeed Afshar
类目: Machine Learning (cs.LG)
*备注: 5 pages, 3 figures, 3 tables
Abstract:We introduce BranchShine-CR, a 25M-parameter model for multilingual transcription into the International Phonetic Alphabet (IPA). It combines log-mel features, a rotary-position E-Branchformer encoder, intermediate self-conditioned connectionist temporal classification (CTC), and consistency regularization across augmented views. On 16,646 shared IPApack++ test utterances, it achieves 4.47% IPA character error rate, a 22.3% relative reduction from ZIPA-CTC-NS, with approximately one-twelfth as many parameters while being trained from scratch. BranchShine-CR also outperforms a similarly sized NeMo Conformer baseline across all 41 dataset language labels. Ablation studies indicate the individual components synergetically acting in model performance contribution. These findings support compact IPA recognition capabilities under limited compute budget, for applications in low-resource on-device pronunciation assessment.
[LG-68] Paging the Experts: A Reproducible Characterization of Flash-Backed MoE Inference on iPhone
链接: https://arxiv.org/abs/2609.29032
作者: Musa Shams
类目: Performance (cs.PF); Machine Learning (cs.LG)
*备注: 13 pages, 2 figures. Code: this https URL
Abstract:Sparse activation reduces mixture-of-experts computation without eliminating the need to store all experts. We present Routide, a Swift/MLX runtime that executes the text path of a pinned public Qwen3.6-35B-A3B quantized checkpoint while keeping expert weights in iPhone storage and a byte-budgeted subset in memory. We characterize cache-policy sensitivity, numerical comparison boundaries, and measurement limits. Across five recorded 128-token workloads, fixed-route replay gives 0.00% demand hits with a 512 MiB LRU cache, 18.80% with seeded random eviction at the same budget, and 38.58% with 576 MiB LRU. The apparent capacity cliff is therefore a policy/workload interaction, not a universal memory requirement. Same-runtime Mac controls preserve generated sequences across eviction and asynchronous prefetch, including 2,560 exact token comparisons and 10,334 speculative loads. In contrast, complete resident-Python versus recorded-phone sequences disagree on all five tested cases, precluding a general numerical equivalence claim. Two separately scoped iOS 27 memory protocols observe sampled process-footprint peaks of 1.87-2.32 GiB on short prompts and 2.39-2.73 GiB on one longer prompt. We retain a thermal stopping event, negative timing comparisons, and a single qualified whole-device power estimate. These results establish bounded feasibility and identify limitations that a deployment claim must not hide.
[LG-69] Generative Atmospheric Super-Resolution from Heterogeneous In Situ Observations through Composable Interfaces
链接: https://arxiv.org/abs/2609.29027
作者: Yang Xu,Dibyajyoti Chakraborty,Haiwen Guan,Sen Wang,Romit Maulik
类目: Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph)
*备注: 42 pages, 20 figures, 11 tables
Abstract:Atmospheric observations are sparse, heterogeneous, and unevenly distributed, whereas many generative atmospheric models learn distributions over regularly gridded multivariate states. Once pretrained, diffusion models can supply atmospheric priors that can be combined with observation-derived likelihood factors in a Bayesian formulation. However, these observation sources differ substantially in geometry and sampling density, complicating the consistent use of their observations within a common inference framework. Here, we formulate this reconstruction problem as generative atmospheric super-resolution and introduce composable observation interfaces for conditioning a single pretrained 13-variable atmospheric diffusion model. The interfaces convert sparse radiosonde ®, clustered aircraft (A), and dense irregular surface-station (S) observations into source-specific likelihood factors that specify where observations constrain the gridded state, how residuals are counted under uneven sampling, and how strongly each source guides posterior sampling. We developed the aircraft and surface observation interfaces using 2019 observations and evaluated the selected interfaces throughout 2020 without further tuning. Compared with reconstructions conditioned only on radiosonde observations, the composed R+A+S interface reduces RMSE evaluated against ERA5 by 9.24% across all 13 state variables over the CONUS domain. The aircraft and surface factors provide complementary improvements in upper-air and surface variables. The R+A+S combination also lowers the Continuous Ranked Probability Score (CRPS), while evaluations at held-out aircraft and surface-station observations show reduced prediction errors. Together, these results demonstrate a modular route for conditioning a pretrained atmospheric generative prior on heterogeneous in situ observations without retraining the underlying model.
[LG-70] Growth-Inspired Graph Generation and Inverse Design of Mechanical Lattices via Dot Matrices Database Augmentation and GCNN
链接: https://arxiv.org/abs/2609.29024
作者: Weiyun Xu,Jiamu Liu
类目: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci); Applied Physics (physics.app-ph)
*备注:
Abstract:Natural load-bearing and transport networks are not assembled in a single step; they emerge through a temporally ordered process of growth, branching, reinforcement, and loop formation. Inspired by this developmental logic, this work introduces a morphogenetic graph-generation framework for mechanical lattices in which a discrete dot matrix provides potential nodes and the final architecture is created by sequential cross-layer and intra-layer growth. The same rule is visualized in two dimensions as a leaf-vein-like developmental sequence and implemented in three dimensions on a 3x3x3 nodal matrix containing 27 candidate nodes. A dataset of distinct three-dimensional lattices was evaluated by beam-based finite element analysis and represented directly as graphs. A graph convolutional neural network (GCNN) with three graph-convolution layers and dual global pooling learns the topology-property mapping and predicts effective compressive stiffness. Coupling the GCNN surrogate with rapid structural sampling enables inverse design: for a target stiffness of 1000 MPa, the selected design was predicted at 1042.43 MPa and validated by finite element analysis at 1027.49 MPa. Beyond straight members, the framework has also been extended to parameterized horseshoe-shaped curved beams made of nonlinear materials, enabling topology-geometry design toward prescribed deformation shapes. Our work provides a paradigm for augmenting the database of mechanical metamaterials, and the resulting perspective links biological morphogenesis, graph learning, and nonlinear shape programming in a unified generative design framework for architected materials.
[LG-71] EvoTreeNAD: Genealogy-Guided Evolution for LLM -Driven Neural Architecture Discovery
链接: https://arxiv.org/abs/2609.29016
作者: Lishan Yu,Derek Jiu,Qizhen Lan,Xiaoqian Jiang
类目: Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)
*备注: 31 pages, 5 figures, including appendices
Abstract:AI-driven scientific discovery accelerates research by autonomously developing solutions and designs. Large language model (LLM) agents support this process through iterative generation and evaluation. Yet these iterations alone do not ensure cumulative progress or establish which directions to pursue next. Costly evaluation further constrains the scope of exploration. Neural architecture discovery brings these challenges together, coupling open-ended design with resource-intensive experimentation. We introduce EvoTreeNAD, a genealogy-guided evolutionary algorithm that constructs trainable architectures without a supplied seed or a hand-specified search space. Starting from an empty root, it grows a persistent genealogy in which each new node represents a complete architecture. Top-percentile values computed from each node and its descendants guide lineage selection. Using the selected design history, an Idea Agent proposes a variant and a Code Agent implements it. Each evaluated variant becomes a child node, expanding the genealogy while providing evidence for subsequent lineage selection. Our theoretical analysis establishes the existence of stationary variation regimes as the genealogy grows. Under specified variation assumptions, sustained top-percentile family values quantify the probability of generating high-reward architectures in these regimes. EvoTreeNAD discovers architectures that outperform the compared NAS and NAD baselines, achieving CIFAR-10/100 test errors of 2.05\pm0.06% and 15.09\pm0.22% . On all six MedMNIST-v2 tasks, the discovered architectures surpass the strongest listed baselines. A controlled CIFAR-10 study further shows that EvoTreeNAD outperforms direct generation, best-of- N greedy continuation, and full-family-mean routing.
[LG-72] Learning from Mixed-Quality Deployment Experience for Robot Manipulation
链接: https://arxiv.org/abs/2609.29000
作者: Yangang Ren,Yujie Yan,Zirui Li,Jiaming Guo,Di Zeng,Ji Tao,Lan Yu,Xuesong Tian,Chen Lv
类目: Machine Learning (cs.LG)
*备注:
Abstract:Robot policies deployed in real environments naturally accumulate mixed-quality experience, including successful executions, partial progress, and failures. Although these rollouts provide valuable information for further learning, directly incorporating them into imitation learning may reinforce undesirable behaviors, while offline reinforcement learning often suffers from unreliable value estimation under sparse rewards and limited data coverage. We consider a practical post-deployment setting where learning relies only on naturally accumulated autonomous rollouts, without additional human corrections or exploratory interaction. To effectively exploit such experience, we propose Predictive Action Chunk Learning (PACL). PACL first learns a predictive chunk-level critic that evaluates temporally extended action sequences and augments temporal difference learning with future latent prediction, providing richer supervision for long-horizon value estimation. The learned critic then converts chunk-level Q-values into discrete quality conditions, which guide a diffusion actor to learn jointly from these mixed-quality experiences without treating all behaviors as equivalent supervision. At inference, the actor generates multiple action chunks and the critic selects the highest valued candidate. Experiments across simulated and real-world robot manipulation tasks show that PACL consistently improves the pretrained policy and outperforms strong imitation learning and offline reinforcement learning baselines.
[LG-73] Automatic Rank Allocation for Low-Rank Adaptation in Large Language Models via lp Regularization
链接: https://arxiv.org/abs/2609.28998
作者: Zebang Xie,Chuanyang Zheng,Yik-Chung Wu,Yihang Gao
类目: Machine Learning (cs.LG)
*备注: 4 pages of main text and 1 page of reference
Abstract:Low-rank adaptation (LoRA) has become a popular parameter-efficient fine-tuning method for large language models. A key challenge in LoRA is how to determine the rank of each adaptation matrix, as rank directly controls its capacity and efficiency. Existing adaptive-rank methods typically allocate ranks according to manually designed importance scores, which are not directly derived from an optimization objective. In this work, we propose \ell_p -LoRA, a principled rank-allocation method based on \ell_p regularization with 0p1 , which is a classical sparsity-inducing technique in signal processing and statistics. Specifically, we regularize the energy of each rank-one LoRA component, encouraging redundant components to vanish while preserving important ones. We derive the corresponding proximal subproblem and reduce the matrix optimization to a two-dimensional problem, leading to an implicit thresholding criterion for identifying redundant components. Experiments on natural language understanding and question-answering tasks demonstrate that the proposed method achieves competitive performance with existing LoRA baselines.
[LG-74] Spectral Graph Neural Networks with Hermite Polynomials: A Comprehensive Study
链接: https://arxiv.org/abs/2609.28979
作者: Shuang Wu
类目: Machine Learning (cs.LG)
*备注:
Abstract:We study spectral graph neural networks built from Hermite polynomials and propose HermNet, a simple model that combines a nodewise predictor with normalized Hermite propagation. Its sparse recurrence requires neither eigendecomposition nor a learned basis. We distinguish the basic model from optional coordinate calibration, response normalization and Gaussian derivative regularization. Hermite and other complete polynomial bases span the same degree-bounded filter space, but their coordinates can produce different optimization behavior under limited training budgets. We analyze this behavior through spectral signal energy, label sampling, changes in learned features and the bias–variance trade-off of regularization. Controlled synthetic experiments identify a regime in which plain HermNet outperforms matched polynomial-basis alternatives, including with a jointly trained nonlinear predictor. Curvature regularization further improves HermNet when the same functional penalty is available to every comparator. Fixed-predictor controls support the advantage under short training budgets, but longer training removes the plain-model lead. Matched real-data comparisons show accuracy deficits, and architectural and numerical studies identify further limits. Together, the analysis and experiments clarify when Hermite propagation is useful and how calibration and regularization affect its performance.
[LG-75] Why Does Misinformation Propagate Faster? An Algorithmic Perspective on X
链接: https://arxiv.org/abs/2609.28947
作者: Pan Li,Shuang Gao
类目: ocial and Information Networks (cs.SI); Machine Learning (cs.LG); General Economics (econ.GN)
*备注:
Abstract:Misinformation is widely reported to propagate faster on engagement-based platforms, yet prior work largely focused on empirical analysis, without identifying a specific algorithmic mechanism that results in this phenomenon. Thanks to the open-sourcing of X’s recommendation algorithms, we conduct what is, to our knowledge, the first component-level study of the recommendation algorithm deployed by a social media platform, which examines how each of its components affects misinformation propagation. Specifically, we identify the engagement fungibility mechanism in the algorithm, where the final recommendation score is constructed as a weighted sum of all predicted user activities. As a result, a tweet can be repeatedly recommended simply because it is predicted to draw many instant reactions (e.g., likes and retweets), even when it is not expected to draw thoughtful responses (e.g., replies and quotes). Since misinformation typically draws a larger share of its engagement from instant reactions, this mechanism enables it to receive more recommendation exposure and to propagate faster. To empirically validate this mechanism, we re-implement X’s recommendation algorithm on the USC X 2024 election corpus, and build a calibrated simulation study to analyze the impact of different scoring rules. We find that re-tuning the metric weights has little or even a negative impact on reducing the credibility exposure gap, while those scoring rules that set a precondition of thoughtful engagement for amplification would be able to alleviate the gap significantly, across 46 robustness checks. Our diagnosis, therefore, yields a simple and deployable fix, a reflective-threshold gate that withholds amplification until a tweet is predicted to draw thoughtful engagement, which we find to reallocate exposure away from low-credibility content at no cost to mainstream exposure and with no loss of engagement. Subjects: Social and Information Networks (cs.SI); Machine Learning (cs.LG); General Economics (econ.GN) Cite as: arXiv:2609.28947 [cs.SI] (or arXiv:2609.28947v1 [cs.SI] for this version) https://doi.org/10.48550/arXiv.2609.28947 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-76] Response-state Learning for Transferable Vibrational Spectroscopic Characterization with Electron Prior
链接: https://arxiv.org/abs/2609.28935
作者: Zetong Li,Zhuosong Xie,Hengyu Fan,Jiaao Yu,Qiyao Hua,Zheng Lu,Liming Xu,Juanni Wu,Honglin Li
类目: Machine Learning (cs.LG); Chemical Physics (physics.chem-ph)
*备注:
Abstract:Vibrational spectral prediction can become inaccurate when localized stereoelectronic environments perturb intermediate response states and high-risk response units dominate characteristic spectral fingerprints, making prediction across external chemical space difficult. SO(3) Equivariant Neural Kalman Networks (SENK) form a response-state cascade that combines an equivariant transformer backbone for Hessian, dipole-derivative and polarizability-derivative learning, an Equivariant Neural Kalman bridge for state-dependent refinement and reliability sensing, and an NBO-informed electronic-prior pathway coupling consistency regularization with bounded, branch-specific guided spectral calibration. SENK outperforms DetaNet on QM9S and QMe14S while preserving full-spectrum IR and Raman fidelity from small molecules to drug-like systems. SENK remains stable and selectively improves spectrally sensitive features in biomolecular systems with complex stereoelectronic effects. It therefore integrates tensor prediction, reliability diagnosis and physics-informed calibration, supporting transferable vibrational spectroscopy from molecular systems to functional molecular materials.
[LG-77] Automatic Harness Evolution for Hardware Design Verification: Can LLM s Consolidate Gains Across Discovered Harnesses?
链接: https://arxiv.org/abs/2609.28908
作者: Kidus Seyoum,Ajay Mittur
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG)
*备注:
Abstract:Agent behavior depends on the harness surrounding a language model, but it remains unclear whether language models can reliably improve such harnesses for hardware-design tasks. We study automatic harness evolution around a fixed subject model on 12 proprietary design-verification root-cause localization tasks. Across five trials per task, automatically evolved harnesses increased completed attempts by 71-76% and any-hit task coverage by 80-100%, while total correct attempts improved by only 18-24%. The strongest success reproducible at least twice result improved by one task, and later candidates exchanged gains across tasks rather than preserving them. An auxiliary candidate improved on a four-task validation set excluded from search but tied its baseline on a subsequent 12-task replay containing both search and validation tasks, so the selected gain did not persist across the full pool. Across the tested lineage, useful search, evidence, and finalization behaviors appeared in different candidates but did not consistently consolidate into a single harness that dominated across tasks and metrics. In a separate CVDP cross-benchmark case study, an automatically evolved defined-width repair harness produced 35.6% more functional passes than its 142-task reference baseline; the final functional verifier scored completed outputs but was not shown to the subject agent during repair. These results support archive-aware selection when evolution yields complementary specializations without consistent consolidation.
[LG-78] When Fancy Eviction Fails: Rethinking Cache Replacement For LLM Prefix Reuse
链接: https://arxiv.org/abs/2609.28870
作者: Yiyu Liu,Minlan Yu,Juncheng Yang
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注: 19 pages, 20 figures, 6 tables
Abstract:Long-running LLM applications repeatedly send growing context, making prefix caching critical for reducing prefill cost. Yet prefix-cache behavior under agentic workloads remains poorly understood. We study production traces from two companies and evaluate 14 eviction algorithms across HBM-constrained and large memory-pool settings. Despite a large gap to Belady, sophisticated policies designed for traditional caches provide little benefit over LRU. The reason is structural: prefix reuse is dominated by the regular pacing of active sessions, making recency unusually predictive. Prefix caching nevertheless introduces new challenges, including heavy-tailed session footprints and highly variable miss costs as attention computation grows with sequence length. We introduce the compute-savings ratio and two offline oracles to quantify these effects. Our results show that effective prefix-cache management should retain recency as its foundation while selectively adding quick demotion for one-hit prefixes, compute-aware partial eviction for expensive misses, and capacity-dependent eviction granularity. We will release the traces and simulator to support future research.
[LG-79] Image Fidelity is Not Field Fidelity: Joint Thermodynamic Reconstruction and Error Localization in Neural Tomography
链接: https://arxiv.org/abs/2609.28868
作者: Alan Hsu,Jenna Samra,Alin Razvan Paraschiv,Liam Connor
类目: Machine Learning (cs.LG); Instrumentation and Methods for Astrophysics (astro-ph.IM); Solar and Stellar Astrophysics (astro-ph.SR)
*备注:
Abstract:Neural fields for scientific tomography are optimized from 2D images, but the actual quantity of interest is often a latent 3D physical field. Because the forward map is many-to-one, low 2D image error need not certify a correct 3D field. Moreover, the latent field is not directly supervised during training, and its error cannot be evaluated against truth at deployment. We develop CoroNeRF to jointly optimize 3D electron density and temperature fields directly from multiview, multiline intensities through a differentiable atomic-emission renderer. Using solar coronal tomography as a controlled testbed, we evaluate physical-field recovery and test whether cross-seed instability provides a ground-truth-free-at-inference indicator of local physical-field error. We underscore the following two observations. (i) Image fidelity is not field fidelity: spectral ablations show that limited-channel reconstructions can fit their available observations well while recovering substantially worse fields, whereas evaluation on a common richer probe exposes the discrepancy. (ii) Cross-seed instability ranks local physical-field error across tested matched-model conditions, supported by sparsification and physical signal-strength controls. Seed-deviation projections provide complementary directional validation, but shared forward-model mismatch can still produce incorrect cross-seed consensus. These results characterize joint thermodynamic recovery and the usefulness and limits of seed-based error localization in a controlled, single-scene solar tomography testbed.
[LG-80] Uncertainty-Gated Exploration Noise Suppresses Task Collapse in Online RL Fine-Tuning of a Flow-Matching Vision-Language-Action Policy ICLR2027
链接: https://arxiv.org/abs/2609.28838
作者: Mehmet Turan Yardımcı,Yunus Emre Çoğurcu
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: 38 pages, 6 figures. Submitted to ICLR 2027
Abstract:Online reinforcement learning fine-tuning of pretrained flow-matching vision-language-action (VLA) policies promises robots that keep learning after deployment, but continued updates often destroy competence on individual tasks while the aggregate still looks healthy. We study this failure mode, which we call task collapse, under a matched small-compute budget on LIBERO-10 with a 450M-parameter SmolVLA policy trained by PPO with stochastic (SDE) sampling. Three exploration-noise policies differ in one live variable: a fixed noise scale, a ReinFlow-style learned noise network, and an uncertainty-gated controller that redistributes exploration across task streams from task-agnostic novelty and competence signals, without task labels or episode boundaries. Under the pooled definition, fixed noise collapses tasks in two of three seeds and learned noise in every seed measured to iteration 200, while the controller collapses none in any of its three seeds. Measured parameter displacement shows the controller’s action expert keeps changing, while its mean applied noise is close to the fixed scale in the available logs. The matched comparison supports the controller’s effect on task preservation; the separate contributions of its adaptation across states and over time are not disentangled. A lower fixed scale slows the decline but does not stop it. No arm improves on the behavior-cloning baseline in this budget. Two properties of that regime are measured beside this result, not offered as its cause: following the reference recipe, training runs in bfloat16 with no fp32 master copy, under which 96.02% of the action expert’s elements stay bit-identical across three consecutive iterations, and an fp32 master copy at the reference learning rate collapses both arms in a single-seed observation. We release tools measuring per-task collapse under four definitions, rescoring noise and instrument tares.
[LG-81] When Does Unsupervised Learning Succeed or Fail? A PoS Perspective on Reconstruction-Based Anomaly Detection
链接: https://arxiv.org/abs/2609.28832
作者: Mehmet Yamaç,Yagmur Mustu,Muhammad Numan Yousaf,Lei Xu,Marcel van Gerven
类目: Machine Learning (cs.LG)
*备注: 39 pages, 8 figures, and 27 tables, including appendices
Abstract:Reconstruction-based unsupervised learning can fail in two opposing ways: a model may reconstruct anomalies too accurately or discard valid nominal variation. Using the Pursuit of Subspaces hypothesis, we characterize these failures through the meet, union, and join geometries induced by the nominal components. Excess learned range produces join blindness, while insufficient capacity produces meet preference and loss of nominal fidelity. We show that the compact nominal union is optimal among nominal faithful ranges and generally requires a nonlinear reconstruction map. Based on this geometry, we introduce Dynamic Push and Pull, which learns from controlled perturbations without anomaly labels, and nested manifold carving, which applies the same principle recursively in latent space. Experiments confirm the predicted changes in latent geometry across every tested Push and Pull configuration. The proposed methods improve reconstruction-based anomaly detection across standard benchmarks and unseen image degradations, while also improving pretrained ECG representations for downstream classification. These results connect reconstruction failures to identifiable geometric conditions and provide practical mechanisms for learning compact representations.
[LG-82] Stream Recursion Model (SRM)
链接: https://arxiv.org/abs/2609.28809
作者: Asael Sorensen,Charles Brock,David Chamberlain,Jennifer Minnich,Matthew Hoffman,Ramyaa Ramyaa
类目: Machine Learning (cs.LG)
*备注: 21 pages, 27 figures
Abstract:Mechanistic interpretability seeks to make verifiable statements about the internal behavior of large language models (LLMs). Many interpretability techniques struggle to scale with the increasing size and depth of architectures. Our solution to this is to introduce smaller models with structures that lend themselves to interpretability. In this work, we introduce the Stream Recursion Model (SRM), a modification of the Hierarchical Reasoning Model (HRM) designed to expose internal computational structure while remaining scalable. SRM organizes computation into multiple interacting latent streams that are updated through recursive refinement, enabling direct analysis of stream dynamics, causal contribution, and routing behavior. SRM achieves performance comparable to GPT-2 on a per-parameter basis. Our analysis reveals consistent and distinct behavior across streams, indicating structured specialization and interaction. These results suggest that SRM provides a practical architectural foundation for scalable mechanistic interpretability and opens up promising avenues for future research in both reasoning performance and interpretability.
[LG-83] Monitoring Urban Traffic Dynamics at Fine Spatiotemporal Resolution Using Distributed Acoustic Sensing and Deep Learning
链接: https://arxiv.org/abs/2609.28793
作者: Hao Tian,Heng Cai,Xiaowei Chen,Yifan Yang
类目: Machine Learning (cs.LG)
*备注: 33 pages, 7 figures
Abstract:Mapping the distribution of traffic dynamics at high spatiotemporal resolution is a fundamental question in transportation research. Distributed acoustic sensing (DAS), an innovative seismic observation tool, emerges as a promising solution for real-time urban traffic monitoring at high spatial and temporal scales. Distributed acoustic sensing repurposes existing underground fiber-optic cables as dense, continuous sensor arrays, enabling passive and privacy-preserving monitoring of roadway traffic activity at meter-level spatial and second-level temporal resolution. This study examines whether integrating DAS and deep learning models can serve as a continuous and efficient urban traffic observatory for revealing urban traffic dynamics (i.e. traffic volume and congestion, event-driven changes) at high spatiotemporal resolution. Using a DAS deployment along a roadway network in the City of College Station, Texas, USA, this study develops a deep learning-empowered analytical framework that converts raw ground vibration waveforms into spatiotemporal representations, detects vehicle trajectory, and infers traffic states from aggregated traffic volume and speed. A hybrid training strategy combining synthetic and manually annotated DAS images is used to improve vehicle detection under noisy and congested conditions, with model outputs further aggregated to characterize system-level traffic dynamics.
[LG-84] Vector Bellm an Theory for Multichain Robust Averag e-Reward Markov Decision Processes
链接: https://arxiv.org/abs/2609.28792
作者: Yue Wang,George Atia
类目: Machine Learning (cs.LG)
*备注: preprint, work in progress
Abstract:Robust average-reward Markov decision processes provide a fundamental framework for long-term performance optimization under uncertainty, and can have optimal long-run rewards that depend on the initial state. This state dependence requires a vector Bellman theory that accounts for both recurrent-class rewards and transition uncertainty. We develop such a theory for finite models with compact, post-action (s,a) -rectangular ambiguity. A gain-first, bias-second optimization principle yields a coupled vector gain-bias system, and every finite solution identifies the optimal robust gain and supplies stationary saddle strategies against history-dependent opponents, simultaneously from all initial states. We further characterize solvability through stationary gain conditions and a uniform bound on canonical transient corrections, and give sufficient conditions that permit distinct recurrent-class gains. The certificates also yield asymptotically affine trajectories of the robust Bellman operator, based on which we design a robust approximately shifted Halpern planning algorithm. Under finite Bellman solvability, the gain estimates and Bellman displacements converge to the optimal gain vector, and every extracted greedy controller is average-optimal after a finite, instance-dependent budget. These results thus connect finite Bellman certificates to undiscounted planning for state-dependent robust average rewards, providing theoretical understandings.
[LG-85] he Mechanics of Delta Learning: Target Design for Generalizable Scientific Machine Learning
链接: https://arxiv.org/abs/2609.28782
作者: Kareem M. Gameel,Ihor Neporozhnii,Sjoerd Hoogland,Oleksandr Voznyy
类目: Machine Learning (cs.LG); Chemical Physics (physics.chem-ph); Computational Physics (physics.comp-ph)
*备注: 41 pages, including 24 pages of Supplementary Information; 4 main-text figures
Abstract:In scientific machine learning, \Delta -learning trains models on residual errors relative to physical baselines, assuming that more accurate baselines with smaller residual scales inherently improve downstream performance. Here, we demonstrate that residual scale alone is an insufficient heuristic for learnability. Evaluating molecular graph neural networks on total energy targets, we show that complex local descriptor baselines can yield small residual targets that are disproportionately rough within architecture-informed proxy spaces and harder to learn relative to their scale. Conversely, semi-empirical baseline reduces both scale and normalized roughness, improving in-domain and out-of-domain prediction. We introduce scale-normalized graph Dirichlet roughness ( D_\textIQR ) as a pre-training diagnostic for residual learnability and establish baseline complementarity as a core target-design principle, elevating target space formulation alongside model architecture as a key axis for scientific machine learning.
[LG-86] Evaluating Cross-region Generalization for Wavelet-Diffusion Precipitation Downscaling
链接: https://arxiv.org/abs/2609.28749
作者: Weikang Qian,Yixin Wen,Chugang Yi,Zhi Li,Lingcheng Li,Haizhao Yang
类目: Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph)
*备注:
Abstract:Diffusion models have shown strong potential for kilometer-scale precipitation downscaling, but their performance in geographically unseen regions and event regimes remains insufficiently understood. Building on the wavelet diffusion model (WDM) framework, this study evaluates cross-region and cross-event generalization. Six 3 x 3 deg U.S. regions represent convective, winter, tropical, and atmospheric-river precipitation regimes. Low-resolution inputs are generated by block averaging NOAA Multi-Radar/Multi-Sensor (MRMS) composite reflectivity fields. A WDM trained only on Oklahoma (OK) samples and a WDM trained on all six regions are compared with nearest-neighbor and Bicubic interpolation. Model performance is evaluated using three metric families that measure image-domain reconstruction, spectral and distributional fidelity, and bin-wise precipitation detection. The OK-trained WDM remains competitive outside OK. Although the all-region WDM delivers the best and most consistent overall image-domain and detection performance, its gains are uneven across precipitation intensities. Bin-wise critical success index (CSI) over 5-dBZ reflectivity bins shows that WDM improvements concentrate in localized higher-reflectivity structures, which image-domain metrics partly obscure. In addition, the performance differences among samples are strongly associated with the spatial organization of the precipitation field, quantified by Moran’s I as the spatial autocorrelation of each reflectivity bin. The sample-level Moran’s I-CSI correlation stratified by sample intensity reaches 0.901 in all six regions, including regions unseen during training. Overall, these findings support future efforts to transfer downscaling models to regions with limited local training data and to generate globally consistent, high-resolution precipitation products.
[LG-87] Unmasking Shortcut Learning in IoT Intrusion Detection: A Forensic Multi-Paradigm Evaluation of Feature Dependence and Data Leakage
链接: https://arxiv.org/abs/2609.28725
作者: Uday Shankar Roy,Mahbuba Jahan Minu
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注: Currently under review
Abstract:Machine learning-based Network Intrusion Detection Systems often report near-perfect performance on IoT benchmarks. However, whether these models learn generalizable attack behavior or exploit spurious dataset shortcuts- such as static testbed IP/MAC addresses and chronological recording artifacts-remains an important question. We evaluate the CyberFlowIoT-GICAP benchmark, containing 3,617,388 flow records across 126 PCAP sessions with 849,395 benign flows. Four learning paradigms are evaluated across four feature configurations using PCAP-disjoint splits; LightGBM is additionally evaluated using conventional random-flow splitting. When only statistical flow behavior is used (Fbehav), LightGBM (92.58% +/- 8.18%), Random Forest (92.59% +/- 8.18%), and Deep MLP (92.55% +/- 8.18%) achieve nearly identical Macro-F1, indicating that performance is constrained by feature representation rather than model complexity. With raw timestamps (Ftstamp), tree-based models reach 99.28% Macro-F1, while the linear model remains at 90.62%, showing that nonlinear models can exploit dataset-specific temporal structure. Attack detectability is highly asymmetric: high-rate and active attacks maintain 99.8% recall from flow behavior alone in nonlinear models, whereas the DNS Beaconing drops from 27.78% to 0.00% recall when contextual features are removed. Conventional random-flow splitting increases attack recall by up to 14.00%, highlighting the effect of placing flows from the same sessions in both training and test sets. We conclude with a 4-point protocol checklist for realistic IoT NIDS evaluation.
[LG-88] Upholding Robustness in Federated Learning: Trends Emerging Strategies and Research Opportunities
链接: https://arxiv.org/abs/2609.28722
作者: Pravija Raj P V,Ashish Gupta,Andrea Augello,Sajal K. Das
类目: Machine Learning (cs.LG)
*备注: 35 pages, 11 figures, 13 tables
Abstract:While Federated Learning (FL) has been widely adopted for protecting user privacy in machine learning, it remains vulnerable to various robustness challenges, including performance-impairment risks, information-stealing threats, and aggregation vulnerabilities. This work offers a holistic synthesis of FL robustness along three tightly coupled angles: (i) a threat-centric view of robustness that categorizes the multifaceted attack surfaces, (ii) a structured taxonomy of robust aggregation strategies distinguishing outcome-centric approaches from security-centric strategies, and (iii) a layered taxonomy of defensive strategies. We rigorously examine current evaluation practices for FL robustness and identify major applications and open research challenges to guide future research.
[LG-89] LabFactory: Building and Evaluating Executable AI Labs
链接: https://arxiv.org/abs/2609.28697
作者: Jinge Wu,Hongjian Zhou,Mingde Zeng,Jiayuan Zhu,Junde Wu,Jiazhen Pan,Lei Clifton,Andrew Liu,David A. Clifton
类目: Machine Learning (cs.LG)
*备注:
Abstract:Scientific tasks specify a desired capability, but realizing it often requires building a computational system tailored to the task—acquiring data, designing representations, training models, implementing tools, and deciding how they are used at inference. We present LabFactory, a framework in which an AI builder turns a scientific brief into an executable AI lab: a task-specific solver that integrates models, knowledge resources, tools, and a controller behind a fixed interface. The builder develops and packages the lab in a metered workspace; a separate host then executes the delivered artifact on held-out inputs, with reference labels kept outside the solver’s input interface, and scores its outputs under the task’s protocol. This makes the delivered system, rather than the builder’s account of its progress, the object of evaluation. We document 28 selected constructions across seven scientific task categories—from molecular and genomic prediction to physiological signals, clinical decision support, and biomedical text—whose delivered labs exceeded their configured reference values on all 33 subtests under host-side execution. Ten contain predictive models fitted during construction; the others assemble retrieval systems, executable analysis environments, and tool-driven workflows around a fixed platform LLM. Together they show that an AI agent can carry a scientific brief all the way to a working lab that can still be invoked, inspected, and checked after construction ends.
[LG-90] Federated Learning of AnDE Classifiers ECML KDD2025
链接: https://arxiv.org/abs/2609.28695
作者: Pablo Torrijos,Juan C. Alfaro,José A. Gámez,José M. Puerta
类目: Machine Learning (cs.LG)
*备注: Accepted at WAFL@ECML PKDD 2025
Abstract:This work presents a federated framework for training Averaged n -Dependence Estimators (AnDE) in distributed environments. The proposed method focuses on the discriminative setting, where model weights are learned locally and aggregated globally, supporting any dependency order n . This design allows federated training without transmitting semantically meaningful parameters, improving privacy. Additionally, generative AnDE models are federated to provide a comparative baseline, with optional differential privacy applied to the aggregation of probability tables. Experiments on 12 discrete datasets show that discriminative models with n \geq 1 consistently outperform federated Naive Bayes (NB, n=0 ), and that privacy-preserving aggregation is effective with limited accuracy loss. These results establish federated AnDE as a viable and privacy-preserving framework, showing that probabilistic models remain applicable in modern federated learning settings.
[LG-91] hinking Leakage: A Causal Audit of NoThink Post-Training in Hybrid Reasoning Models
链接: https://arxiv.org/abs/2609.28682
作者: Zehao Liu,Vasant G. Honavar
类目: Machine Learning (cs.LG)
*备注:
Abstract:Post-training hybrid reasoning models in NoThink mode has attracted growing interest as a way to improve performance while keeping inference fast. However, these gains may draw on thinking behavior already accessible through the base model’s Think mode. We formulate this thinking leakage in a causal mediation framework and audit its contribution using bidirectional interventions along a simple base-derived activation direction. Across three models and three post-training methods on competition math benchmarks, we find that leakage is real, causal, and substantial: behavioral and representational analyses reveal shifts toward Think, steering the base model along this direction reproduces most of the post-training accuracy gain, and counter-steering a checkpoint removes a substantial share of what it gains. Across nine aligned checkpoints with positive NoThink gains, the resulting leakage ratio ranges from 42% to 79%. These interventions support a substantial causal contribution of thinking leakage. Our findings show that a post-training method’s apparent advantage can therefore reflect greater drift toward Think, obscuring whether it improves capability within NoThink or more effectively re-invokes existing Think behavior.
[LG-92] OPDiv: Optimal Selection of Top-K High-Scoring Diverse Compounds
链接: https://arxiv.org/abs/2609.28665
作者: Miroslav Lžičař(Deep MedChem)
类目: Machine Learning (cs.LG)
*备注: 12 pages, 3 figures. Code: this https URL
Abstract:A virtual screening campaign may produce thousands of promising candidates, but only a small number can be purchased, synthesized, or tested. The practical question is how to select a set of compounds that both rank well and are diverse enough: this poses a genuine tradeoff, where selecting the highest-scoring molecules yields limited diversity, while diversity selection sacrifices some well-scoring molecules. We introduce OPDiv, a diversity selection and evaluation algorithm solving this tradeoff by finding an optimal subset of molecules using integer optimization. We demonstrate the selection algorithm in practice with fingerprint distance, shape and electrostatic diversity and compare the resulting diversity spectra. We argue that virtual screening is not merely a ranking problem, but also an implicit constrained optimization task: when redundant chemotypes are undesirable, pipelines should be compared based on the top-k compound selections satisfying the desired diversity constraints. OPDiv makes it possible to find the optimal compound set under a given diversity threshold efficiently and serves as a fair benchmark of the best diverse selection achievable by a given structure-based or ligand-based virtual screening pipeline, molecular search or generative model.
[LG-93] RLVR landscapes for iterated multiplications can be benign: Insights from spin-glass theory
链接: https://arxiv.org/abs/2609.28625
作者: Noa Rubin,Zohar Ringel
类目: Machine Learning (cs.LG); Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (stat.ML)
*备注:
Abstract:Despite the importance of reinforcement learning with verifiable rewards (RLVR), the extent to which it can learn new reasoning capabilities remains debated. Here we study the optimization landscape of RLVR on algorithmic tasks, such as iterated group and quasigroup multiplication. To this end, we map entropy-regularized RLVR over myopic tabular policies onto an energy-based (spin-glass) model over deterministic policies. This mapping upper-bounds what RLVR can achieve, and lets us rigorously characterize the landscape in this tabular setting. We show, both theoretically and experimentally, that for a wide class of models and tasks with uncorrelated inputs, this landscape is benign, containing no local minima that could trap RLVR training. Rather, the practical difficulty of these tasks appears to stem, at least in part, from issues such as diffusive barriers and gradient-estimation error in traversing the landscape. These are genuine obstacles that can prevent a solution from being found, but they are distinct from the landscape itself being rugged. We show that these obstacles can often be mitigated through the choice of entropy regulator. Consistent with this theory, we find that a transformer trained from scratch, using only last-token rewards, successfully learns an algorithmic chain of thought for iterated non-Abelian group multiplications.
[LG-94] fable.intermittent: benchmarking probabilistic forecasting methods for intermittent time series
链接: https://arxiv.org/abs/2609.28607
作者: Stefano Damato,Lorenzo Zambon,Giorgio Corani,Dario Azzimonti
类目: Machine Learning (cs.LG)
*备注: Submitted to the International Journal of Forecasting
Abstract:Intermittent time series are common in spare-parts demand and retail sales. Since the cost of forecast errors is typically asymmetric, decisions such as inventory control require the full predictive distribution rather than a point forecast. Many probabilistic forecasting methods have been proposed; their implementations, however, are scattered across different software frameworks, making it difficult to compare them systematically. We introduce this http URL, an R package that implements several probabilistic forecasting methods for intermittent series within the fable framework. The package allows several models to be fitted and evaluated on a collection of time series through a single, simple forecasting pipeline. We also introduce TWEES, a new exponential smoothing model with a Tweedie predictive distribution. Fitting TWEES requires repeated evaluation of the computationally demanding Tweedie density. We also release the R package tweedieDistr, whose implementation of the Tweedie distribution is substantially faster than the existing one while preserving the same numerical accuracy. We evaluate the methods implemented in this http URL on four datasets, also released in the package.
[LG-95] Physics-Informed Self-Supervised Learning for Joint Wire Calibration and Interaction Position Reconstruction in Multi-Wire Parallel Plate Avalanche Counters
链接: https://arxiv.org/abs/2609.28604
作者: Antoine Lemasson,Maurycy Rejmund
类目: Machine Learning (cs.LG); Nuclear Experiment (nucl-ex); Instrumentation and Detectors (physics.ins-det)
*备注:
Abstract:Scientific instruments require accurate calibration to convert detector signals into reliable physical observables. Conventional calibration procedures typically rely on dedicated calibration measurements, analytical response models or labelled reference data, limiting their ability to adapt to changing operating conditions and detector aging. We present a physics-informed self-supervised learning framework that jointly performs wire calibration and interaction position reconstruction in Multi-Wire Parallel Plate Avalanche Counters (MWPPACs) without requiring labelled position measurements or dedicated calibration runs. The method formulates detector calibration as a latent optimization problem in which global wire gains and event-wise interaction positions are estimated simultaneously using supervision derived exclusively from detector geometry and charge-energy consistency constraints. A detector-independent neural network reconstructs sub-wire interaction positions from local charge distributions, eliminating the need to assume analytical induction profiles by learning the detector response directly from experimental data. The end-to-end differentiable framework enables continuous detector self-calibration while improving the uniformity and accuracy of position reconstruction. Experimental evaluation on the entrance MWPPAC tracking detectors of the VAMOS++ magnetic spectrometer demonstrates stable convergence, improved spatial homogeneity and enhanced position resolution. Beyond the detector studied, the method establishes a general framework for physics-informed self-supervised calibration of scientific instruments and is a step toward autonomous intelligent instrumentation capable of continuous adaptation during operation. In this paradigm, detector calibration is no longer a prerequisite for an experiment but an integral part of the measurement process itself. Subjects: Machine Learning (cs.LG); Nuclear Experiment (nucl-ex); Instrumentation and Detectors (physics.ins-det) Cite as: arXiv:2609.28604 [cs.LG] (or arXiv:2609.28604v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.28604 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-96] BRFID: Toward Byzantine-Robust Federated Intrusion Detection
链接: https://arxiv.org/abs/2609.28599
作者: Asmah Muallem,Firdous Kausar,Sajid Hussain,Lei Qian
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:Flipping 60% of training labels from a single Byzantine client using label-flipping model poisoning self-degrades an attacker’s own federated detection accuracy, 99.96% (at no poisoning rate) to 84.33% in a three-client federated IDS. Where the Federated global ensemble maintains stable accuracy across all tested poison rates, without a defense mechanism in place and without coordination between attackers. In this paper, we present empirical results quantifying the impact of label-flipping poisoning attacks on a three-client federated IDS trained on CICIDS2017 with non-IID attack subtype distributions across clients. We demonstrate that the signal of the adversarial self-compromise represents a detectable anomaly for exploitation for Byzantine client identification in the absence of target data exfiltration. We note that the aggregation step uses a Federated Forest (tree concatenation) rather than a parametric FedAvg; the results therefore measure the impact of poisoning on per-client performance under ensemble aggregation, and extension to genuine FedAvg with a parametric classifier is planned for future work.
[LG-97] Uncovering Residential PV-EV Co-Adoption from Smart-Meter Data: Load Archetypes and Detection for Demand-Side Planning
链接: https://arxiv.org/abs/2609.28578
作者: Jack Zheng,Hao Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:The increasing adoption of electric vehicles (EVs) and rooftop photovoltaic (PV) systems is reshaping residential electricity demand and creating new challenges for demand-side management (DSM), tariff design, and low-voltage network planning. Much of the existing literature examines EV charging or PV generation in isolation, leaving the behavioral dynamics of household co-adoption less understood. We develop an integrated, two-part workflow to analyze advanced metering infrastructure (AMI) data. A discovery component applies dynamic time warping (DTW) k-means with DTW barycenter averaging to cluster daily import or export profiles into interpretable behavioral archetypes, while a predictive component trains a bidirectional long short-term memory (BiLSTM) model on 21-day windows and benchmarks it against tabular baselines for PV/EV activity detection. The EV activity labels are inferred from charging-like load signatures because charger measurements are unavailable. Using half-hourly AusNet residential data from Victoria, Australia, the clustering uncovers distinct patterns across PV-only, EV-only, co-adoption, and neither cohorts; for co-adopters, a midday-centered weekday export archetype accounts for approximately 50% of days. At validation-tuned thresholds, both BiLSTM and XGBoost achieve strong discrimination. BiLSTM obtains 0.991 for the area under the receiver operating characteristic curve (AUROC), 0.906 for macro-F1, and the highest recall on the most difficult class (0.836 for EV-only recall). Tree-based baselines remain competitive. Performance remains stable across plausible labeling rules (macro-F1: 0.894–0.914) and strictly forward temporal splits (macro-F1: 0.894–0.906).
[LG-98] me-Series Foundation Models That Understand Data Revisions
链接: https://arxiv.org/abs/2609.28576
作者: Taimoor Ahmad
类目: Machine Learning (cs.LG); Software Engineering (cs.SE)
*备注:
Abstract:Historical observations are not always fixed: statistical agencies revise previously published values as new evidence arrives. Forecasting from a contemporary download can therefore expose a model to information unavailable at the date it purportedly made a prediction. We propose VINTAGE-TS, a revision-aware adaptation of a time-series foundation model that distinguishes observation time from information-availability time. Its targets are the next period’s first-published value and the value available a fixed number of days after that publication; neither is declared final truth. A joint predictive distribution preserves dependence between these targets and exposes uncertainty about their difference. We specify an ALFRED-based rolling evaluation, a matched Chronos-2 comparison, conventional and revision-aware baselines, and a separate audit of pretraining overlap. The accompanying software implements validity-interval reconstruction, delayed-label filtering, a frozen-backbone adapter interface, and reproducible diagnostics. An executed synthetic demonstration and a 25-configuration sensitivity suite verify the workflow, expose variation across seeds and revision regimes, and illustrate how hindsight contamination changes measured performance. Thirty one automated tests check temporal and integration contracts. Real ALFRED and Chronos-2 experiments have not been executed; no empirical foundation-model advantage is claimed.
[LG-99] Leakage-Safe Machine Learning for Hydrogen Embrittlement Detection in 316L Stainless Steel: A Region-Held-Out Evaluation of Texture and Deep Features in SEM Micrographs
链接: https://arxiv.org/abs/2609.28567
作者: Muhammad Awais,Muhammad Yaseen,Abdul Shakoor,Niaz Ahmed Niaz,Huria Zia,Muhammad Zain Shakoor
类目: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci)
*备注:
Abstract:Scanning electron microscopy (SEM) is routinely used to characterize the microstructural changes caused by hydrogen embrittlement (HE) in structural steels. Machine learning can automate this characterization, but models are often evaluated using image-level splits. When several images come from the same specimen region, such splits leak information between the training and test sets. Here, we propose a region-held-out protocol for classifying as-received (AR) and hydrogen-charged (H2) SEM micrographs of 316L stainless steel, based on Leave-One-Region-Out (LORO) cross-validation over 14 spatial regions (8 AR, 6 H2; 31 images). We compared six feature-classifier combinations built on local binary patterns (LBP), grey-level co-occurrence matrices (GLCM), self-supervised convolutional embeddings pretrained on 143 unlabeled SEM images, and a convolutional neural network (CNN). The simplest texture approach, LBP with a support vector machine (LBP+SVM), performed best, achieving a balanced accuracy of 0.79, H2 recall of 0.69, and H2 precision of 0.82, outperforming every deep-learning and combined-feature model. A group-level permutation test (500 permutations sampled from the 3,003 possible region-to-label assignments) yielded p = 0.008, indicating that the result cannot be explained by a chance alignment of the region structure. Grad-CAM maps from a CNN trained on the full dataset tended to concentrate on localized surface and grain-boundary features, where hydrogen-induced morphological changes are known to occur. Under a leakage-safe, statistically validated protocol, texture descriptors recover a hydrogen-charging signature from SEM micrographs even with few samples, and the same protocol can be extended to larger HE detection studies in other alloy systems.
[LG-100] Dont Read the Log: Execution Traces Contaminate Verifiers in Video-Generation Agents
链接: https://arxiv.org/abs/2609.28564
作者: Jian Xu
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:Agentic video-generation systems close a loop between a generator and a verifier: an LLM plans shots, calls a text-to-video model, and a multimodal judge decides whether the result satisfies the request. To diagnose where a long workflow fails, recent harnesses deliberately show the judge more than the video-the agent’s execution trace, its plan, the narration it synthesized. We ask whether this auxiliary text moves the judge’s verdict on purely \emphvisual requirements, holding the frames fixed. On a benchmark of 109 generated two-event clips with manual labels, in which the requested event is either visibly completed or visibly missing, a trace that reports a successful tool call makes three open-weight Qwen-VL judges (7B, 8B, 32B) accept 78 – 90% of the failures, up from 7 – 19% without text, and a contradicting trace makes them reject up to 100% of correct clips; an instruction to ``use only the frames’’ does not remove the effect. Frontier closed judges are essentially unmoved on the same clips, showing that the vulnerability is a property of the judge’s learned trust in tool logs rather than of the task. Plan-derived text carries no clip-specific information, so it can only shift a judge’s operating point, and in a repair loop that shift becomes a cap on the true pass rate that no repair policy can exceed; the cap matches simulation to two decimals. In the loop, contamination is exploited without any adversarial agent: an honest LLM planner that always regenerates ends with a judge pass rate of 1.00 and a human-labelled pass rate of 0.28 , and a pipeline in which a cheap checker writes its verdict into the trace launders that checker’s errors into a stronger final judge ( 0.69 false accepts).
[LG-101] SpaFactor: Lightweight Spatial Context-Aware Gene Program Modeling for Histology-to-Transcriptomics Inference
链接: https://arxiv.org/abs/2609.28563
作者: Shiting Ruan,Xitong Ling,Qiming He,Ziyou Yan,Huaitian Yuan,Tian Guan,Ying Xiao,Xu Guan,Yonghong He
类目: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
*备注:
Abstract:Spatial transcriptomics (ST) profiles gene expression within tissue architecture, but its cost and experimental complexity limit routine use. Predicting spatial expression from routinely available hematoxylin and eosin (HE) images therefore offers a scalable alternative. However, conventional methods often fit high-dimensional gene outputs as independent targets, overlooking the biological coordination among genes while remaining vulnerable to high-dimensional noise and overfitting. Existing attempts to address this limitation often rely on computationally heavy graph networks or complex auxiliary supervision. We therefore introduce SpaFactor, a lightweight and efficient low-rank morphology-program-gene factorization framework. At the input, SpaFactor efficiently fuses the visual representation of the central spot with multiscale local and regional neighborhood context, yielding a histologic representation that captures cellular morphology and microenvironmental heterogeneity. For modeling, a residual MLP stably learns a nonlinear mapping from the tissue microenvironment to low-dimensional latent gene programs. These activities are decoded through shared gene loadings into coordinated multi-gene expression predictions. Across five public cohorts, SpaFactor achieves the best aggregate performance, with particularly clear improvements for spatially variable genes, and more faithfully recovers biologically organized spatial patterns. These results demonstrate that lightweight joint modeling of tissue context and gene programs can improve both predictive accuracy and biological fidelity.
[LG-102] CFD Correction of Open Tip Clearance Flow in a Compressor Cascade Using VAE Latent Space Adaptation
链接: https://arxiv.org/abs/2609.28558
作者: Xiang Zuo,Hefang Deng,Caiyan Chen,Honglin He,Mingmin Zhu,Songan Zhang,Jinfang Teng
类目: Machine Learning (cs.LG)
*备注:
Abstract:CFD predictions of open tip clearance flow in compressor cascades are subject to discrepancies relative to experiments, while experimental observations are sparse and high-resolution experimental ground truth is unavailable. This study proposes a non-intrusive correction method based on a variational autoencoder (VAE) and latent-space adaptation. A VAE is first trained using a dataset of 166 parametrically sampled CFD total pressure loss fields to learn a low-dimensional statistical representation of these fields. The VAE is then frozen, and a low-rank latent-space adapter is trained using only 12 paired CFD–experiment operating conditions. An observation operator maps the corrected high-resolution fields to the experimental observation space, allowing supervision to be applied only at the available measurement locations and within the measured pitchwise windows. In the current 12-fold cross-validation, the mean absolute error decreases from 0.1335 to 0.0473, the root mean square error from 0.1717 to 0.0621, and the relative L_2 error from 0.5108 to 0.1871. These results indicate that the method improves agreement between CFD predictions and sparse experimental observations of open tip clearance flow without modifying the RANS solver or constructing artificial high-resolution experimental labels.
[LG-103] An Exposition of GPT Astras Proof of Lower Bound on DP Continual Counting
链接: https://arxiv.org/abs/2609.28528
作者: Jalaj Upadhyay
类目: Cryptography and Security (cs.CR); Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
*备注: This is full proof of GPT generated proof for DP continual counting written in preprint https://arxiv.org/abs/2609.17650v2
Abstract:The goal of this note is to give a detailed proof, to the best of our understanding, of the recent presentation by Harrison and Leeman (arXiv:2609.17650v01 and arXiv:2609.17650v02) of the proof by Astra on the lower bound for differentially private continual counting. We believe a more natural and easy proof is possible and hope that this note will help in that effort. Prior to the initial preprint by Harrison and Leeman (arXiv:2609.17650v01), Bairaktari and Larsen (arXiv:2607.00876) gave an elegant proof to show a lower bound of \Omega(\log^3/2(n)) for both pure and approximate-DP continual counting, and in personal communication had informed us that they have a proof of optimal \Omega(\log^2(n)) for pure-differential private continual counting as well. They have subsequently published their \Omega(\log^2(n)) bound, which is now a joint work of Bairaktari, Dahl, and Larsen (arXiv:2607.00876v3). Their new result is an elegant extension of their technique for approximate-differential privacy. Although the two proofs are technically different, the Astra argument uses related tree geometry introduced in Bairaktari and Larsen. Comments: This is full proof of GPT generated proof for DP continual counting written in preprint https://arxiv.org/abs/2609.17650v2 Subjects: Cryptography and Security (cs.CR); Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG) Cite as: arXiv:2609.28528 [cs.CR] (or arXiv:2609.28528v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2609.28528 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-104] Stable and Faithful Explanations for Knowledge Tracing
链接: https://arxiv.org/abs/2609.28502
作者: Praveena Padi,Arun Morampudi,Ujval Sai Gopal Irrinki,Pradeep Kumar Dolabehera Kakitapelli
类目: Machine Learning (cs.LG)
*备注: 41 pages, 8 figures, 14 tables. Code is available at this https URL
Abstract:Knowledge tracing (KT) models predict student performance opaquely, limiting pedagogical action. This study contributes a validation protocol testing predictive competitiveness (RQ1), explanation stability (RQ2) and retraining-based faithfulness (RQ3) together. Thirteen behavioral features across five pedagogical themes were engineered from ASSISTments 2009 and 2012, with history features computed from temporally preceding interactions and current response latency retained only for retrospective analysis. ASSISTments 2009 was rebuilt: the uncorrected skill-builder release duplicates each multi-skill interaction across one row per skill, and because those rows share one correctness label, they leak it into preceding-interaction features. Rebuilding lowered model AUC and reordered the explanation results. An Extreme Gradient Boosting (XGBoost) model explained with Tree SHapley Additive exPlanations (TreeSHAP) was compared against four deep baselines (DKT, SAKT, AKT and SimpleKT) under an information-matched protocol giving the deep models the same behavioral signals and restricting XGBoost to what is derivable from the identifier-and-correctness stream they consume. XGBoost reached an area under the curve (AUC) of 0.777 on 2012 and 0.786 on rebuilt 2009, with prediction-time AUCs of 0.771 and 0.775, respectively, after excluding current response latency; restricted to the baselines’ information it performed as they did (0.697 against 0.700, and 0.717 against 0.720), locating the difference in information supplied, not model family. Rankings were consistent across folds, seeds and conditioning schemes (Spearman rho = 0.989-1.000), and removing top-ranked TreeSHAP features harmed AUC more than random removal, though split-gain and permutation rankings performed comparably. Student-level examples are illustrative interpretations, not validated recommendations.
[LG-105] RADAR: Readiness for AI Discovery and Agent ic Reach
链接: https://arxiv.org/abs/2609.28480
作者: Luke Jordan,Tiago C. Peixoto,Manuel Ramos-Maqueda
类目: Computers and Society (cs.CY); Machine Learning (cs.LG)
*备注: 37 pages including annexes, 8 figures
Abstract:Governments increasingly meet citizens through an AI system rather than a website. RADAR (Readiness for AI Discovery and Agentic Reach) measures whether that system works, across 166 countries and on two tasks: whether a chatbot can give a correct, officially sourced, country-specific answer about a public service (informational legibility), and whether an automated agent can reach the service to act on it (agent operability). The central finding is that AI can describe public services far better than it can reach them. In every one of the 166 countries, informational legibility scores exceed average agent operability scores under RADAR’s respective measures, and the gap does not shrink with national wealth. Income and language explain only part of the pattern, and several governments perform far better or worse than their resources predict. The two failures have different correlates and different fixes. Whether AI can describe a service is associated with how well a country’s main administrative language is represented in web-scale corpora, which a government cannot change quickly. Whether an agent can reach it is associated with the country’s national web presence, which a government can change now. Traditional digital-government rankings miss the second problem entirely. RADAR lets governments at any income level see it and offers a concrete agenda to fix it, so that public services are not only described by AI but actually reachable through it.
[LG-106] From Prediction to Explainable Provider Behavior Profiles for Fraud Waste and Abuse Review
链接: https://arxiv.org/abs/2609.28477
作者: Yubin Park,Evan Brociner
类目: Computers and Society (cs.CY); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 20 pages, 5 figures
Abstract:Claims data can show that provider behavior changed but cannot by itself explain why. FWA (fraud, waste, and abuse) review requires identifying material behavior, locating the codes and dollars driving it, and testing plausible explanations. A common alternative, predictive modeling, flags deviations from an expected-utilization forecast – but a forecast has limited value unless it beats simple persistence and explains why a deviation matters. In our quarterly provider-procedure data, the latest observation captures most forecastable variation, and added model structure adds little accuracy. Residuals conflate growth, service-line shifts, code maintenance, and incomplete observation with potentially concerning behavior, making point forecasts incomplete. We instead formulate provider review as a descriptive representation problem: billed revenue y = s * p, where s measures provider scale and p describes procedure composition. The profile records scale history, effective-dated code lineage, clinical-family shares, first-use events, billing context, and Medicare-versus-client differences. An optional rank-32 nonnegative factorization of procedure co-occurrence adds a fixed semantic geometry for similarity and retrieval, surfacing evidence for review without inferring intent or adjudicating FWA. In an eight-quarter proprietary Medicare Carrier+DME audit of 1.22 million providers, simple descriptions outperform complex forecasts: regularized AR(1) attains the lowest log MAE, while persistence attains the lowest dollar WAPE. The learned semantic dictionary raises recall at 10 from 35.9% to 44.7% and high-cost-rare recall at 50 from zero to 51.8%. A lineage-aware family profile stays compact and interpretable, correlating 0.790 with learned-state movement, supporting a layered architecture where transparent descriptions form the core and learned representations add optional context. Comments: 20 pages, 5 figures Subjects: Computers and Society (cs.CY); Machine Learning (cs.LG); Machine Learning (stat.ML) ACMclasses: I.2.6; H.2.8 Cite as: arXiv:2609.28477 [cs.CY] (or arXiv:2609.28477v1 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2609.28477 Focus to learn more arXiv-issued DOI via DataCite
[LG-107] Anchored Extra-Proximal Methods: Optimal Higher-Order Methods for Monotone Inclusion Problems
链接: https://arxiv.org/abs/2609.30212
作者: Ruichen Jiang,TaeHo Yoon
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 51 pages
Abstract:We study the deterministic oracle complexity of finding approximate solutions to composite monotone inclusion problems, formed by the sum of a smooth single-valued monotone operator and a maximally monotone set-valued operator, under the tangent-residual criterion. We introduce the Anchored Extra-Proximal (AEP) framework, which combines an anchored extrapolation step with an inexact anchored proximal update satisfying a relative-error condition. The framework recovers the composite Fast Extragradient method in the first-order setting and yields natural second- and higher-order extensions by replacing the operator in the implicit update with its Taylor approximation at the extrapolated point. For every p\geq 2 , assuming that the (p-1) th derivative of the single-valued operator is Lipschitz continuous, we combine this construction with a bisection line search to obtain a p th-order method that finds a point with tangent residual at most \varepsilon in \widetildeO(\varepsilon^-2/(3p-1)) oracle calls. This improves all prior upper bounds for p th-order methods: in particular, it improves the previous best-known \widetildeO(\varepsilon^-1/p) tangent-residual complexity as well as the classical O(\varepsilon^-2/(p+1)) bound of higher-order hybrid proximal extragradient methods under the weaker duality-gap criterion. We complement this result with a worst-case lower bound of \Omega(\varepsilon^-2/(3p-1)) for every deterministic algorithm in the p th-order oracle model, without restricting the algorithm to tensor steps or any other prescribed update structure. Thus, the proposed method attains the optimal dependence on \varepsilon , up to logarithmic factors, for all p\geq2 .
[LG-108] Learning and interpreting policies for simultaneous entanglement requests in quantum networks
链接: https://arxiv.org/abs/2609.30157
作者: Leon Rode,Sumeet Khatri,Supartha Podder
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注:
Abstract:Future quantum networks will make use of entanglement to perform numerous tasks, such as sending quantum information over long distances, distributed quantum computing, and quantum sensing. In general, these tasks will need to be performed simultaneously in various regions of a network, while minimizing resources and latency. We will thus require policies for scheduling link-level entanglement resources, and using the link-level entanglement to create various forms of multipartite entanglement required for every task. In this work, we address this problem using reinforcement learning. We formulate a Markov Decision Process for the problem and use double deep Q-networks (DQN) with Message Passing Neural Networks (MPNNs), experience replay buffers, and curriculum training to obtain policies. The key physical parameter is the probability of link-level entanglement generation, i.e., the link activation probability. We show that our policies maintain 100% success for up to 71% lower link activation probability than the baseline heuristics for a set of physically relevant network topologies. We then examine an additional constraint where experiment (task) placements are restricted to specific hardware types and demonstrate a similar advantage in performance over heuristics, with our policy maintaining at least an 80% success rate for up to a 59% lower link activation probability. Finally, we explore methods to interpret the learned policy by defining metrics enabling conclusions to be drawn about the model’s behavior and by tasking a large language model (LLM) to derive a novel heuristic given example actions taken by the DQN-trained policy. We find that the LLM heuristic performs similarly to the DQN-trained policy in performance, indicating a promising method for interpretable policy extraction for large quantum networks, where direct training becomes computationally expensive.
[LG-109] MQSS-Selector: RL-Guided Pass Selection for an MLIR Compilation Pipeline
链接: https://arxiv.org/abs/2609.30104
作者: Andre Youssefi(1),Ercüment Kaya(1 and 2),Minh Chung(1),Jorge Echavarria(3),Laura B. Schulz(4),Martin Schulz(1 and 2) ((1) Leibniz Supercomputing Centre (LRZ), (2) Technical University of Munich (TUM), (3) Munich Quantum Valley (MQV), (4) Argonne National Laboratory (ANL))
类目: Quantum Physics (quant-ph); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Programming Languages (cs.PL)
*备注: 11 pages, 5 figures, 1 table
Abstract:High Performance Computing (HPC) and Quantum Computing (QC) systems are increasingly converging towards unified High Performance Computing-Quantum Computing (HPCQC) infrastructures, driven by a growing need to bridge classical and quantum workflows, which affects all levels of the system stack, from the hardware to compilers and runtimes, all the way to applications. However, today’s QC devices are still in the Noisy Intermediate-Scale Quantum (NISQ) era, are error-prone and resource-limited, and therefore require specialized optimizations and topology mappings to achieve sufficient fidelity. This places special emphasis on proper compilation and optimization within the overall quantum software stack. Many existing stacks remain fragmented, with separate components responsible for device selection, compiler-pass optimization, and job queue scheduling. This paper proposes a unified, learning-based selector that integrates these disparate stages into a cohesive framework. Our proposed selector scheme leverages reinforcement learning and deep learning models that can be extended to simultaneously optimize multiple objectives – such as fidelity, compilation time, and scheduling latency – while dynamically adapting to circuit characteristics and device conditions.
[LG-110] Nuclear Norm-Regularized Bayesian Matrix Completion
链接: https://arxiv.org/abs/2609.30078
作者: Calvin Tolbert
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 26 pages, 2 figures
Abstract:Matrix completion, the problem of estimating missing entries in a matrix from noisily observed ones, underlies a diverse array of problems such as recommender systems and counterfactual outcome estimation in panel data. Many algorithms address the problem using regularized least squares, often with the nuclear norm as a regularizer, but this method yields a point estimate with no built-in uncertainty quantification. A Bayesian formulation is a natural alternative, and if the noise variance is known, the nuclear norm-based prior yields a log-concave posterior. Unfortunately, in practice, the noise variance will not be known a priori, so for a fully Bayesian approach, a prior must be imposed on it. We give the first sampler for this model with an explicit non-asymptotic guarantee: polynomial in the matrix dimensions and in the reciprocal of the target accuracy. Our technique is to discretize the distribution of the noise precision onto a grid and build a categorical posterior via thermodynamic integration. This extension is not specific to matrix completion and may be useful in other non-log-concave sampling problems where the non-log-concavity is restricted to a single variable and the joint distribution of the remaining variables is nonsmooth. Our contribution is a feasibility result: we show that a polynomial-time Bayesian sampler for this model exists at all, and the resulting complexity, while polynomial, is not intended as a deployable algorithm at current problem scales.
[LG-111] From Processing to Functionality: Engineering Accessible Material States in Cu-Embedded SiO_x Memristive Devices
链接: https://arxiv.org/abs/2609.30047
作者: Tobias Gergs,Rouven Lamprecht,Sahitya Yarragolla,Ole Gronenberg,Luca Vialetto,Hermann Kohlstedt,Thomas Mussenbrock,Jan Trieschmann
类目: Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG); Plasma Physics (physics.plasm-ph)
*备注:
Abstract:Resistive switching in oxide-based devices is widely governed by stochastic defect processes, yet a predictive link between fabrication conditions and functional behavior remains elusive. Here, we establish a multiscale framework connecting plasma-defined deposition conditions to macroscopic device functionality in sputtered SiO _x /Cu/SiO _x -based systems. By combining large-scale statistical analysis of more than 50,000 experimentally characterized devices with physics-based plasma and atomistic simulations, we show that device behavior does not emerge from deterministic process-to-performance mappings, but from a probabilistic cascade spanning defect formation, defect-state evolution, and functional-regime emergence. Data-driven clustering reveals a continuous functional state space composed of operational switching types, while inverse modeling identifies the reconstructed oxygen-vacancy density as an effective latent descriptor capturing the combined influence of structural disorder and defect topology. This latent descriptor is strongly coupled to both Cu redistribution and electrical response, linking otherwise hidden material properties to observable device characteristics. Furthermore, macroscopic switching behavior is argued to arise from ensemble integration across spatially heterogeneous subdomains, providing a physical explanation for the pronounced variability of large-area devices. These findings shift the perspective from deterministic defect engineering toward probabilistic defect-state design and establish a physically grounded framework for understanding and controlling functional variability in such oxide-based systems, such as memristive or resistive-switching devices.
[LG-112] Path-specific harm decomposition: A partial identification framework
链接: https://arxiv.org/abs/2609.29938
作者: Ruizi Yan,Dennis Frauen,Maresa Schröder,Stefan Feuerriegel
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:A central goal when designing treatment policies is often to “do no harm”, that is, to avoid interventions that improve average outcomes while worsening outcomes for some individuals. A widely used notion for harm is the fraction of negatively affected (FNA), defined as the probability that an intervention decreases an individual’s outcome. However, in many applications, treatments operate through mediators, and a single “total” FNA can obscure whether harm arises primarily through direct pathways or indirect (mediator-induced) pathways. In this work, we introduce a path-specific analogue of the FNA. For this, we disentangle total harm into direct and indirect harm in causal mediation settings. However, these quantities depend on joint distributions of potential outcomes that are not point-identified even in randomised controlled trials. As a remedy, we develop a novel partial identification framework for direct and indirect FNA. In our framework, we (i) derive sharp Makarov bounds for the FNA, and (ii) propose a semiparametrically efficient estimator with valid confidence intervals for these bounds under mild margin conditions. We demonstrate our framework across various numerical experiments. To the best of our knowledge, we are the first to study path-specific decomposition of causal harm and to develop an orthogonal inference framework for its analysis.
[LG-113] Robust Detection of LLM -Generated Text under Contamination
链接: https://arxiv.org/abs/2609.29935
作者: Jiaxun Li,Saptarshi Chakraborty,Ambuj Tewari
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:We study the detection of LLM-generated text under editing and contamination. Modeling human and machine text as finite-order Markov processes with Huber contamination, we characterize an exact boundary for reliable detection under our assumptions. Detection is impossible when contamination is sufficiently large relative to clean-source separation. Below this boundary, a collection of clipped likelihood-ratio tests achieves vanishing worst-case errors. This construction motivates clipping as a simple modification of existing statistical detectors. For a broad class of additive scores, we identify conditions under which the clipped test is consistent while the raw test’s worst-case power tends to zero. We evaluate seven detectors across three datasets and three generation models, and on the RAID benchmark. Clipping improves robustness in both studies, with gains varying across detectors and contamination settings. For example, at a target false-positive rate of 5%, clipping improves the log-likelihood–log-rank ratio (LRR) detector’s true-positive rate by a median of 8.3 percentage points in the controlled study and 2.1 and 4.3 points in rate- and attack-specific RAID evaluations, respectively.
[LG-114] Cost-Sensitive Online Window Size Selection for Portfolio Management
链接: https://arxiv.org/abs/2609.29887
作者: Yi-Chen Liu,Chung-Han Hsieh
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Portfolio Management (q-fin.PM)
*备注:
Abstract:This paper investigates cost-sensitive online window size selection for portfolio management under changing market conditions. Specifically, we propose a two-level framework that constructs portfolios using candidate window sizes and dynamically aggregates them through online learning. By treating candidate window sizes as ``experts,‘’ we dynamically update their aggregation weights using turnover-inclusive losses. Moreover, we derive finite-horizon cost-sensitive tracking-regret bounds that account for turnover of the aggregated portfolio, with static regret as a special case. Under bounded losses and cost rates, suitably tuned Fixed Share achieves asymptotically no tracking regret for sublinear switching budgets, with Hedge covering the static case.
[LG-115] Does per-frame early exit pay? A compute-matched study of dynamic depth for on-device speech enhancement
链接: https://arxiv.org/abs/2609.29867
作者: Clément Laroche,Riccardo Miccini
类目: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD)
*备注:
Abstract:Deep learning-based speech enhancement is increasingly deployed on-device in hearing aids, headsets, and earbuds. Most of these devices, however, can only accelerate static int8 graphs, so a depth-varying network must be implemented as several graphs, orchestrated by a policy. In this paper, we supervise every intermediate depth of one causal model, then we fine-tune its output heads to guarantee that deeper outputs are never worse than shallower ones. Using this training protocol, we can derive a family of static models that are more Pareto-efficient than their equivalently-sized counterparts trained from scratch on the same budget. Specifically, we achieve up to 0.11 higher PESQ for equivalent compute, and match the best PESQ at 30% less compute. We then quantize the models to int8 and measure the latency-quality frontier on an STM32N6 microcontroller. On VoiceBank-DEMAND, the dynamic enhancer lies on the same frontier as the static models, rather than trading quality for dynamic execution. Running the policy on the companion Cortex-M55 takes only 26 \mu s per frame, while splitting the enhancer into separate NPU graphs adds 2.2% latency overhead. The cost of dynamic execution is therefore small.
[LG-116] Beyond Model Size: Redesigning LiSenNet for embedded speech enhancement
链接: https://arxiv.org/abs/2609.29866
作者: Clément Laroche,Rasmus Kongsgaard Olsson
类目: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD)
*备注:
Abstract:Deploying real-time speech enhancement on resource-constrained devices requires meeting strict latency, memory, and energy constraints. Microcontroller NPUs can accelerate neural inference under these constraints, but only through a restricted set of operators in static, integer-quantized graphs. Recent speech-enhancement networks have reduced parameter counts and MACs to levels nominally suitable for microcontrollers, but their operators and execution patterns often remain incompatible with restricted NPUs. We address this gap by redesigning LiSenNet, a 37k parameter sub-band dual-path model, for the STM32N6570-DK Neural-ART accelerator. We replace its recurrent bottleneck with convolutional frequency and temporal mixers, reformulate unsupported operations as static int8-compatible primitives, and use bounded decoder activations to preserve quality after quantization. On VoiceBank-DEMAND, the final NPU-compatible model matches or exceeds the recurrent LiSenNet baseline, reaching PESQ 3.08 versus 3.01 in FP32 and 3.01 versus 2.93 in int8. Deployed on a microcontroller, it processes each 16 ms input hop in 4.83 ms, corresponding to a real-time factor of 0.30. Stateless receptive-field recomputation is an order of magnitude slower at the same frame rate despite higher accelerator utilization. These results show that parameter count and operator compatibility, quantization range, and persistent streaming state must be co-designed to achieve efficient real-time speech enhancement on restricted NPUs.
[LG-117] Optimal Recovery Meets Bayesian Learning: Where Worst-Case Bounds Pay Off
链接: https://arxiv.org/abs/2609.29622
作者: Gordei Verbii
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 48 pages, 22 figures
Abstract:Worst-case Optimal Recovery (OR) and Bayesian learning describe the same Gaussian-quadratic-Hilbert problems in two vocabularies. We sharpen the correspondence - the radius of information equals a nugget-optimized GP posterior variance and is attained by the posterior mean at a closed-form balance nugget - and measure, inside three published Bayesian systems, where the worst-case side pays. The ledger is two-sided: the losses instruct as much as the wins. Morozov calibration tracks a test-access oracle within 1.00 - 1.19\times where \sigma -blind rules fail, is 4.9 - 6.3\times more reproducible across noise draws ( p=0.002 - 0.004 ), and is the only deployable rule whose selection survives a change of backend ( 1.36\times against 12 - 30\times for the released weight, ML-II and GCV); tight certificates cover at the information-theoretic floor with no numerical slack. But on exchangeable data split-conformal beats the OR head on interval score, a water-filling prior adds nothing without an oracle noise hint, and under covariate shift the OR band keeps coverage on every dataset yet loses interval score to split-conformal, and to a feature-free constant band, on most cells; what pays is not shift but shift on a learnable target, which a training-free audit statistic predicts before any model is fitted. In Bayesian optimization the certified width is a validity floor whose scalar inflation we prove inert under a checkable margin condition and check at every step. Inertness is graded, not binary, and in the size of the inflation as much as in the objective: \kappa=2 is inert wherever \kappa=5 is and on more cells besides, while \kappa=5 moves half the Ackley seeds and every Griewank seed. Exploration is a shape problem, not a scale one. The design rule: match the guarantee tool to the data regime, and audit the regime first.
[LG-118] ask-Resolved Fisher Spectroscopy for Quantum Reservoir Computing
链接: https://arxiv.org/abs/2609.29570
作者: Yang Peng
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: 18 pages, 11 figures
Abstract:Quantum reservoir computing (QRC) uses fixed quantum dynamics to encode a time series and trains only a classical readout, but a benchmark capacity alone does not reveal whether task information is lost in the reservoir, the measurement, feature compression, or finite sampling. We introduce \emphtask-resolved Fisher spectroscopy, in which prediction targets define orthonormal score coordinates on the stationary distribution of input histories. Reweighting labeled histories along these scores generates an exactly affine family of reservoir states and measurement outcomes. In the same coordinates, we obtain a many-body Fisher-information hierarchy relating the state quantum Fisher information, the Fisher information of the complete measurement record, and moment matrices retained through many-body order r . The quadratic form of each moment matrix is exactly the stationary capacity of the optimal linear readout, while a finite-measurement extension predicts the approach from one-shot records to ideal expectation-value features. The measurement-level quantities require only stationary labeled records and measured outcome strings, not an input model or quantum-state tomography. In a five-spin open reservoir, interactions route fourth-order temporal information into higher-body correlations, so low-order compression can incur orders-of-magnitude sampling overhead even when a conventional parity target remains accessible. For correlated inputs and outputs, record-defined task scores predict held-out capacities, the required feature order, and measurement-budget dependence; optimizing the local measurement axis recovers otherwise hidden task information. The framework connects benchmark performance to experimentally actionable diagnoses of reservoir encoding, measurement choice, classical representation, and shot allocation.
[LG-119] Machine Unlearning for Gibbs Supervised Learning Algorithms
链接: https://arxiv.org/abs/2609.29409
作者: Yaiza Bermudez,Samir M. Perlaza,Iñaki Esnaola
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: In Proc. of the IEEE International Symposium on Information Theory (ISIT), Guangzhou, China, Jun., 2026. 2026 Jack Keil Wolf ISIT Student Paper Award
Abstract:In this paper, a method for achieving exact unlearning for Gibbs supervised learning algorithms is proposed using a variational formulation inspired by empirical risk minimization subject to relative entropy regularization (ERM-RER). Such a method consists of maximizing the expected empirical risk over the dataset to be unlearned subject to a regularization by relative entropy with respect to the original algorithm. The optimization variable is a probability measure on the models; and the solution is another Gibbs probability measure that represents a new Gibbs supervised learning algorithm. The method guarantees exact unlearning in the sense that the new Gibbs algorithm coincides in distribution with the algorithm that would have been obtained by retraining from scratch on the dataset to be retained. As a byproduct, a framework for reweighting data points in ERM-RER by strategically choosing both the reference measure and the regularization factor is obtained. In this framework, exact unlearning is the special case in which zero-weight is assigned to the contribution of the data points to be unlearned. More generally, depending on the choice of certain parameters, data points can be up-weighted or down-weighted in ERM-RER problems for particular purposes, e.g., controlling the generalization error of Gibbs algorithms. This paves the way for new constructive or adversarial views on classical reweighting data points in ERM-RER.
[LG-120] ranscript-Supervised Post-Training of Generative Speech Enhancement on Real Recordings via Reinforce Adjoint Matching ICASSP2027
链接: https://arxiv.org/abs/2609.29405
作者: Julius Richter,Christoph Boeddeker,Yoshiki Masuyama,Kohei Saijo,Dominik Klement,Gordon Wichern,Jonathan Le Roux
类目: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG)
*备注: Submitted to ICASSP 2027
Abstract:We adapt Reinforce Adjoint Matching (RAM), a reward-based post-training method, to generative speech enhancement (SE). Starting from a pretrained SE model, RAM tilts the model’s conditional distribution toward outputs with higher reward. During training, the current model generates enhanced speech on-policy, evaluates each generated endpoint with a potentially non-differentiable reward, and analytically re-noises the endpoint to construct inputs for a reward-guided regression objective. This enables post-training directly on real recordings using weak supervision, such as text transcripts, without requiring paired clean speech targets or reward gradients. We investigate word error rate (WER)-based post-training and whether recognition performance can be improved without compromising perceptual speech quality. Experiments on real CHiME-4 recordings reduce WER by 5.08 percentage points relative to pretrained FlowSE without reducing any of the reported non-intrusive speech quality metrics. A subjective listening test at the default reward scale finds no statistically significant preference between the post-trained and pretrained models.
[LG-121] GCUL: Ambiguity Identification in Text Emotion Classification via Cluster-Guided Learning
链接: https://arxiv.org/abs/2609.29327
作者: Zhongqi Fan,Tianyou Zhang,Fei Chen
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 15 pages, 8 figures, 21 tables
Abstract:Selective classification enables a model to abstain from predictions on uncertain instances, but existing approaches typically reject them through confidence scores, predefined coverage constraints or instance-level distance measures. These approaches may overlook the collective geometric structure of difficult samples in learned representation spaces. We propose Guided Clustering-based Uncertain Learning (GCUL), a geometric-guided selective classification framework that identifies misclassified and ambiguous instances as a potential confusion attractor in the representation space. GCUL uses a three-phase procedure to initialize, cluster, and explicitly relabel this uncertain region, allowing the rejection boundary to emerge from the underlying representation geometry rather than from a prescribed rejection rate. We further derive a selectivity score and a geometric sufficient condition that characterizes when rejection can provide positive operational utility, enabling pre-deployment feasibility assessment. GCUL improves DistilBERT accuracy from 89.37 percent to 94.98 percent with less than 9 percent rejection. Beyond accuracy, our selectivity score correctly pre-detects the only dataset (GoEmotion) where all baselines fail, and controlled simulations yield 6.1 percent Type-I and 0 percent Type-II errors, validating the sufficient condition’s conservatism. These results suggest that collective representation geometry provides a useful alternative perspective for selective prediction.
[LG-122] Sufficiently Reduced Distributional Regression
链接: https://arxiv.org/abs/2609.29291
作者: Alexander Henzi,Tiange Liu,Xinwei Shen
类目: Methodology (stat.ME); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:We propose Sufficiently Reduced Distributional Regression (SRDR), a generative method that combines conditional distribution estimation with nonlinear sufficient dimension reduction (SDR). It builds on a characterization of sufficiency through strictly proper scoring rules: a dimension reduction is sufficient if and only if predicting the response from the reduced covariates incurs no loss in expected score relative to the full covariates. Sufficient dimension reduction thus becomes a risk minimization problem. SRDR jointly trains a dimension reduction map and a generative prediction model by minimizing the energy score, which can be estimated by sampling without density evaluation or adversarial training. The framework extends to multi-environment data and to classification. We prove that the estimated conditional distributions converge in energy distance to the true ones, which implies that the learned representation is asymptotically sufficient. In simulations and applications to CT slice localization, superconductivity, and digit classification, SRDR recovers low-dimensional sufficient structure and matches or outperforms state-of-the-art nonlinear SDR methods in representation quality and predictive performance.
[LG-123] Functional dynamic mode decomposition: Learning infinite-dimensional systems from data
链接: https://arxiv.org/abs/2609.29159
作者: Stefan Klus,Eirini Ioannou
类目: Dynamical Systems (math.DS); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Dynamic mode decomposition (DMD) is a data-driven method that computes the best linear approximation of the underlying dynamical system and decomposes the dynamics into a superposition of characteristic spatiotemporal patterns. Originally introduced by the fluid dynamics community, DMD and its extensions have found widespread use in many other research areas such as molecular dynamics, climate science, engineering, finance, and neuroscience. Applications include dimensionality reduction, forecasting, system identification, control, and spectral clustering. In order to apply DMD to partial differential equations, the spatial domain is typically first discretized using finite difference or finite element techniques, thus implicitly rendering the problem finite-dimensional. We extend projected and exact DMD to infinite-dimensional systems. Rather than estimating matrices from vector-valued observations, our DMD variants learn finite-rank operators from functional data such as observables, densities, or wavefunctions. We show that conventional DMD algorithms can be regarded as special cases of their functional DMD counterparts. All results will be illustrated with the aid of guiding examples. We focus in particular on Koopman, Perron-Frobenius, and Koopman-von Neumann operators associated with graphons, ordinary differential equations, and stochastic differential equations.
[LG-124] Feature Space Selection and Heterogeneous Effect Estimation for Blood-Brain Barrier Permeability: A Random Forest to the Generalized Random Forest Pipeline
链接: https://arxiv.org/abs/2609.29076
作者: Tshemollo Rapolai,Seite Makgai,Mohammad Arashi
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Predicting blood-brain barrier (BBB) permeability is critical for central nervous system drug discovery. Using the MoleculeNet BBBP dataset (n = 2039), this study systematically ablates molecular feature spaces to isolate featurisation from model architecture. We evaluate three feature families (Morgan fingerprints, RDKit physicochemical descriptors, SMILES bigrams) across four learning algorithms. Results demonstrate that predictive performance depends jointly on feature representation and algorithm. Dynamic Random Forest using combined features achieved the highest mean AUC (0.970, 95% CI: 0.963-0.977). Second, this optimal representation enables exploratory estimation of heterogeneous associations between molecular structure and BBB permeability using Generalized Random Forests. Constructing a pseudo-treatment from a LogP median split, we applied double/debiased machine learning to account for confounding. Orthogonalization substantially attenuates the heterogeneity detected by naive causal forests; no conditional effects remained significant after false discovery rate correction (smallest adjusted p = 0.082). Furthermore, orthogonalized feature importance shifted toward residual structural information in SMILES bigrams. Ultimately, once observed confounding is properly accounted for, evidence that LogP-BBB associations vary systematically across chemical space is insufficient. This underscores that feature representation and model architecture are coupled design choices, and that unorthogonalized causal forests risk overstating genuine treatment effect heterogeneity.
[LG-125] ransformers as Cross-Task Learners: Shared Structure Drives Sample Efficiency in In-Context Learning
链接: https://arxiv.org/abs/2609.29060
作者: Zhongjie Shi,Rongjie Lai,Alexander Cloninger,Wenjing Liao
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Transformers achieve remarkable performance by jointly learning broad families of tasks during pretraining and adapting to unseen tasks from only a short prompt. Yet a rigorous mathematical and statistical understanding of this phenomenon remains limited. This paper aims to study how Transformers exploit shared cross-task structure and how this structure affects the sample complexity of in-context learning (ICL). Specifically, we characterize task-space complexity through covering numbers under a prescribed metric, thereby quantifying the low-dimensional cross-task structure without requiring an explicit parametric representation. The resulting cover provides a set of anchor functions, which we use to introduce a task-identification-and-evaluation procedure: context observations localize an unseen task among the anchor functions, and the response at a query is predicted by aggregating the corresponding anchor function query evaluations. For approximation, we explicitly construct a Transformer with Softmax attention to approximate this procedure. For generalization, we derive an error bound that separates the effects of the number of pretraining tasks and the prompt length. The scaling with respect to the number of pretraining tasks is governed by the intrinsic dimensions of the task space and input domain; once sufficiently many tasks are available, the dependence on the prompt context length becomes dimension-free. To the best of our knowledge, this is the first work to quantify cross-task complexity for general nonlinear task families and explicitly construct a Transformer that exploits their low-dimensional structure to perform ICL. Our theory provides a quantitative explanation of how joint pretraining across related tasks improves in-context generalization.
[LG-126] Personalised federated learning for Riemannian and Euclidean EEG decoding
链接: https://arxiv.org/abs/2609.29037
作者: Thibault Pautrel,Florent Bouchard,Ammar Mian,Guillaume Ginolhac
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Federated learning (FL) lets EEG decoders learn from recordings of several subjects without pooling them. We consider two light EEG decoders, the Riemannian SPDNet and the Euclidean EEGNet. Both split into a trunk, which builds a latent representation, and a head, which classifies it. Inter-subject variability, however, makes a single shared FL model a poor fit for each subject. Personalised FL addresses this: all subjects learn a common trunk, and each subject keeps its own head. We adapt it for SPDNet and study its effects against standard FL and centralised training, with EEGNet as a Euclidean baseline. Experiments cover three motor-imagery datasets that span diverse regimes in channels, subjects and classes. We observe that personalised SPDNet reaches higher accuracy than both standard FL and centralised training, while converging in fewer rounds and communicating fewer parameters than standard FL. It also outperforms every EEGNet configuration on two of the three datasets, although centralised EEGNet outperforms centralised SPDNet.
[LG-127] Same Bit Width Different Outcomes: Post-Training Quantization of Text-to-Speech Across Architectures ICASSP2027
链接: https://arxiv.org/abs/2609.28974
作者: Se Un Park,Yutae Kim,Junyoung Park
类目: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD)
*备注: Submitted to ICASSP 2027. 4 pages plus references. Code and run records: this https URL
Abstract:Post-training quantization (PTQ) reduces the cost of on-device text-to-speech (TTS), but published evaluations cover one system or method. We evaluate PTQ across TTS architectures under one protocol with three core models, weight and activation ablations of eight more, and two held-out models quantized blind. Four-bit per-channel weights reduce UTMOS, a predicted mean opinion score, by 2.8 on Supertonic and 0.07 on Kokoro, and per-tensor scaling can cause severe degradation even at 8 bits. The same bit width yields different outcomes, because the sensitive component is model-specific and not reliably predicted from the model class. A staged ablation procedure identifies it, and per-layer GPTQ can restore it to within 0.1 UTMOS. Real int8 and int4 kernels reproduce the simulated ordering at hardware-dependent cost. On a Mac mini, a 4-bit weight kernel runs Supertonic at 0.60x the fp32 latency while int8 is slower, so each configuration requires validation on the target runtime.
[LG-128] GeoDose-CP: Graph-Local Conformal Inference for Continuous-Treatment Earth Observation
链接: https://arxiv.org/abs/2609.28895
作者: Md Khalid Hasan Sakib,Dristi Datta,Manoranjan Paul,Davina White
类目: Applications (stat.AP); Machine Learning (cs.LG)
*备注:
Abstract:Reliable intervention-oriented uncertainty quantification from Earth observation (EO) remains challenging when continuous treatment shifts, spatial dependence, limited support, and satellite-outcome uncertainty must be addressed simultaneously. Existing causal, conformal, and spatial approaches address parts of this problem, but their direct combination does not generally recover the appropriate interventional reference law because candidate reassignment jointly alters treatment likelihood, standardized residuals, and graph-dependent residual likelihood. This study presents GeoDose-CP, a support-aware conformal framework for localized stochastic potential outcomes under continuous or mixed continuous-atomic treatment. Its central methodological contribution is a graph-local target-orbit law that jointly represents intervention-induced treatment shift, the inverse outcome-scale Jacobian, and spatial residual dependence. The framework further provides exact weighted candidate inversion, a scalable sparse approximation with explicit discrepancy accounting, and refusal under inadequate support. Evaluation used controlled known-truth experiments, MineDoseBench, treatment-density sensitivity analysis, external conformal comparators, and a multi-mine New South Wales (NSW) study. In MineDoseBench, GeoDose-CP achieved mean selective coverage of 0.9692 across 27 configurations and a minimum local q0.05 of 0.8951; exact-sparse auditing produced nine inclusion disagreements over 2,700 targets. In the NSW study, the absence of an auditable longitudinal rehabilitation treatment rendered treatment-dependent inference nonoperational rather than forcing inference through a proxy exposure.
[LG-129] Physics-Guided Multi-Objective Deep Learning for Ultrasound RF Data Interpolation in Resource-Constrained Imaging
链接: https://arxiv.org/abs/2609.28775
作者: Luoyuan Zhang,Yiyang You,Ananya Tandri,Yinan Feng,Hyunwoo Song,Jeeun Kang,Youzuo Lin
类目: Image and Video Processing (eess.IV); Machine Learning (cs.LG)
*备注: Submitted to the Journal of Computational Design and Engineering
Abstract:Ultrasound imaging increasingly targets portable, point-of-care, and wearable settings where constraints on power, bandwidth, and hardware complexity often necessitate sparse data acquisition in spatiotemporal scanning. However, image reconstruction using the sparse data can introduce insufficient phase information in coherent beamforming process, resulting in grating-lobe artifacts that degrade imaging contrast resolution. We present a physics-guided, data-driven framework for sparse-to-dense radio-frequency (RF) reconstruction that aligns training with downstream image formation. Our approach trains an end-to-end interpolation network using a hybrid supervision scheme that combines an RF-domain and a beamforming-domain loss with exponential moving average (EMA) to stabilize the multi-objective training. To improve generalization under variable acquisition layouts, we also introduce a random-skip masking strategy that varies sparsity patterns during training so a single model can handle diverse decimation factors and irregular channel configurations. We evaluate the framework on a held-out test set using the mean structural similarity index measure (SSIM) between reconstructed and ground-truth beamformed images. Across decimation factors \times 2 to \times 13 , the best-performing configuration maintains mean SSIM around 0.95. Overall, the results show consistent gains in RF reconstruction and post-beamforming image quality across diverse acquisition conditions. This approach enables robust, high-quality ultrasound imaging at resource-constrained settings by allowing more sparse scanning in spatiotemporal domain.
[LG-130] Selective Inference for Deep Clustering in Latent Spaces
链接: https://arxiv.org/abs/2609.28756
作者: Eina Mizui,Tomohiro Shiraishi,Shunichi Nishino,Ichiro Takeuchi
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 62 pages, 7 figures
Abstract:Deep clustering is a powerful approach for discovering meaningful structures in high-dimensional data by learning a low-dimensional latent representation prior to clustering. Despite its empirical success, assessing the statistical reliability of the resulting clusters remains challenging. Testing discovered clusters on the same data induces selection bias and invalidates classical p -values. Selective inference (SI) provides a principled framework for correcting this bias, but existing methods focus on clustering performed directly on the observed features. In this work, we develop an SI framework for deep clustering with a fixed pretrained encoder. The key challenge is that cluster assignments are determined through a nonlinear transformation from the original data space to the latent space, resulting in a substantially more complex selection process than in conventional clustering. Our method provides a computationally tractable way to account for this process and enables valid statistical testing of differences between clusters identified in the latent space. Synthetic experiments demonstrate that the proposed method controls the Type I error rate while achieving higher power than valid but conservative baselines, and genomic applications show that it can identify significant cluster differences while appropriately accounting for selection bias. Our framework provides a principled approach to quantifying the statistical reliability of structures discovered by deep clustering.
[LG-131] Exact Bayes Regret and Asymptotic Optimality in High-Dimensional Gaussian Bandits
链接: https://arxiv.org/abs/2609.28718
作者: Prakhar Singhvi,Yi Zou,Abhishek Bhattacharjee(Abstract Math Institute)
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注: 17 Pages
Abstract:We study Bayesian linear bandits with an isotropic Gaussian parameter, independent Gaussian candidate arms, and Gaussian reward noise when the horizon is proportional to the dimension. The normalized posterior uncertainty has an explicit limit that is uniform over all causal policies. Gaussian posterior identities then determine the limiting parameter overlaps without an assumed closure of the adaptive recursion. These results yield exact regret curves for Thompson sampling, posterior-mean greedy selection, and a family of policies that scale the posterior sampling covariance. The normalized realized cumulative regret converges in L1, uniformly on compact proportional-time intervals. A policy-uniform lower bound identifies the limiting optimal Bayes regret and proves that posterior-mean greedy selection attains it. Thompson sampling incurs a strictly larger leading regret; its instantaneous regret ratio relative to greedy selection lies between one and two and approaches two at long proportional horizons. Closed-form cumulative curves also identify a different comparison in the vanishing-noise limit. Finally, the instantaneous regret converges to a nondegenerate Gaussian decision-loss distribution, rather than to its mean. The analysis separates the amount of information acquired by a bandit policy from the quality of the decisions made using that information.
[LG-132] HClimRep-Ocean: A Global Ocean Emulator on an Unstructured Mesh
链接: https://arxiv.org/abs/2609.28601
作者: Kacper Nowak,Aleksei Koldunov,Nikolay Koldunov,Savvas Melidonis,Ankit Patnala,Simon Grasse,Julius Polz,Christian Lessig,Martin Schultz,Thomas Jung
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG)
*备注:
Abstract:Machine-learning (ML) emulators for atmospheric processes have advanced rapidly in recent years, transforming weather forecasting. Although early ML ocean forecasting models now exist, they remain less developed than their atmospheric counterparts. Unlike the atmosphere, much of the ocean’s kinetic energy resides in mesoscale eddies whose characteristic spatial scales are approximately an order of magnitude smaller than those of comparable atmospheric features. Moreover, complex coastlines, narrow straits, and ice-covered seas make boundary representation a central challenge that atmospheric models do not face. Consequently, numerical ocean simulations commonly use locally refined or even completely unstructured meshes. However, their data-driven counterparts have so far been built around latitude-longitude grids. We present HClimRep-Ocean, an ocean emulator that operates directly on the native unstructured mesh of FESOM2. The emulator is trained on a 209-year AWI-CM3 control integration and is run without atmospheric forcing, receiving the atmospheric state only at initialisation time, which isolates the predictability carried by the ocean state itself. Skill is strongly field-dependent: for currents, HClimRep-Ocean outperforms every reference at 30 day forecast, whereas for temperature and salinity a damped-anomaly persistence forecast remains the more accurate estimator. This behaviour is physically interpretable: current variability is largely geostrophic and internally generated, whereas sea-surface temperature and salinity fluctuations are driven by atmospheric forcing through weather state. Evaluated independently on the OceanBench benchmark, a reanalysis-trained variant of HClimRep-Ocean achieves the lowest RMSE against GLORYS reanalysis among all assessed systems, confirming the competitiveness of the native-mesh approach.
[LG-133] Matrix Aggregation Operators
链接: https://arxiv.org/abs/2609.28562
作者: Inmaculada Gutiérrez(1 and 2),Asier Urio-Larrea(3 and 4),J.Tinguaro Rodríguez(5 and 6),Daniel Gómez(1 and 2),Javier Montero(5 and 6),Humberto Bustince(3 and 4) ((1) Faculty of Statistical Studies, Complutense University of Madrid (2) Instituto Universitario de Estadística y Ciencia de Datos, Complutense University of Madrid (3) Department of Statistics, Computer Science and Mathematics, Universidad Pública de Navarra (4) Institute of Smart Cities (ISC), Universidad Pública de Navarra (5) Faculty of Mathematics, Complutense University of Madrid (6) Instituto de Matemática Interdisciplinar, Complutense University of Madrid)
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Aggregation theory has traditionally focused on operators defined over vectors. However, many applications-including Multi-Criteria Decision Making, Group Decision Making, Fuzzy Rule-Based Classification Systems, and overlap/grouping indices-require aggregating information naturally structured as a matrix of membership degrees (e.g., where a set of objects interacts with a family of fuzzy sets). Despite this, no formal framework has been proposed for this class of operators, partly due to the common practice of flattening matrices into vectors (which discards structural information) and partly due to a reliance on decomposable operators that aggregate rows and columns sequentially. This paper addresses this gap by formalizing the notion of a matrix aggregation operator (MAO). We analyze the decomposability and symmetry properties of MAOs, showing that certain operators cannot be expressed in decomposable form and examining several notions of symmetry. Finally, we introduce a family of MAOs termed maximum entropy global coverage indices (MEGCIs), provide a construction method for them based on combining grouping functions and MEOWA operators, and illustrate their usefulness in cluster quality assessment through an extensive computational study.
[LG-134] An Order-Theoretic Characterization of Consistent Inductive Inference
链接: https://arxiv.org/abs/2609.28551
作者: Zhou Lu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:When can a learner make only finitely many prediction errors along every infinite sequence labeled by a fixed, unknown hypothesis? We characterize this form of consistency for arbitrary binary hypothesis classes in ZFC, without requiring a uniform mistake bound. The characterization uses a single linear order on finite realizable traces. Each trace selects its least subtrace, and the order must satisfy two conditions: conflicting traces select different subtraces, and the order is well-founded on the traces of each fixed target. These conditions induce a learner whose selected evidence decreases on every mistake. Conversely, a consistent learner yields such an order through canonical mistake transcripts and the Kleene–Brouwer ordering. The result provides a representation of consistent prediction by finite evidence, answering a question of Lu (2024).
[LG-135] Stochastic Inertial Krasnoselskii-Mann Iteration Achieves Near-Optimal Sample Complexity
链接: https://arxiv.org/abs/2609.28543
作者: Tong Yang,Tao Jiang,Yuejie Chi,Ashok Cutkosky,Lin Xiao
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:We analyze a simple stochastic inertial Krasnosel’skii–Mann (iKM) method for finding a fixed point of a nonexpansive operator in a real Hilbert space. Our method is obtained simply by adding two inertial extrapolations to stochastic KM [Bravo and Cominetti, 2024], and it retains one call to a possibly biased stochastic oracle per update and achieves sharp rates in both the stochastic and deterministic regimes. Specifically, with our proposed parameter schedule, we prove the following last-iterate fixed-point residual bound: [ O!\left(\frac1K +\frac\sigma\log K\sqrt K +\fracB_K\log KK\right), ] where K is the horizon, \sigma is the noise level and B_K is the accumulated root-mean-square bias. When B_K=O(\sqrt K) , this yields \widetilde O(\epsilon^-2) sample complexity that matches, up to a logarithmic factor, the stochastic-oracle lower bound given under the unbiased subclass of our model [Foster et al., 2019, Theorem 2]. It also improves the best-known O(\epsilon^-4) random-iterate guarantee for stochastic KM [Bravo and Cominetti, 2024, Corollary 5.4]. To our knowledge, this is the first single-loop method for general nonexpansive fixed-point problems to attain this near-optimal sample complexity without variance reduction or batching. When the oracle is exact, the same method attains the worst-case-optimal O(K^-1) last-iterate residual rate [Park and Ryu, 2022, Theorem 4.6], improving the O(K^-1/2) rate of classical KM [Cominetti et al., 2014; Bravo and Cominetti, 2018]. Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG) Cite as: arXiv:2609.28543 [stat.ML] (or arXiv:2609.28543v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2609.28543 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-136] Sequential Confidence Sets for Coverag e-Constrained Conformal Model Selection
链接: https://arxiv.org/abs/2609.28522
作者: Jing Li,Haibin Zhu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Modern conformal forecasting systems often maintain several adaptive pipelines that differ in base forecasters, conformity scores, calibration windows, and update rules. Comparing them is difficult because coverage is a hard constraint, whereas efficiency should be optimized only among feasible pipelines. We formulate this problem as sequential inference for a stochastic constrained argmin. At each time, the target is the set of minimum-cost pipelines satisfying multiple prefix-average conditional miscoverage constraints. We introduce Coverage-Constrained Sequential Model Confidence Sets (CC-SMCS), which separate certifiably feasible, possibly feasible, and possibly constrained-optimal pipelines. Using simultaneous martingale confidence sequences, CC-SMCS projects a rectangular confidence region onto the constrained argmin and admits an exact closed-form rule. With probability at least 1-\delta , it contains every constrained-optimal pipeline simultaneously over all times. This finite-sample guarantee requires no stationarity or mixing assumptions and remains valid under data-dependent stopping. We also establish an impossibility result for safe certification at the coverage boundary and extend the construction to delayed multi-horizon feedback and outcome-dependent efficiency objectives.
[LG-137] Algebraic Expressivity Certificates for Shallow Polynomial Neural Networks
链接: https://arxiv.org/abs/2609.28500
作者: Sepehr Akbari,Shahrzad Jamshidi
类目: Commutative Algebra (math.AC); Machine Learning (cs.LG); Algebraic Geometry (math.AG)
*备注: 9 pages, 1 figure
Abstract:We study exact representability by bias-free shallow polynomial neural networks using algebraic geometry. Over \mathbbC , a width- r network with activation z\mapsto z^d computes a sum of r d -th powers of linear forms, whose Zariski closure is a Veronese secant variety. Ideal elimination therefore yields polynomial certificates of nonrepresentability. We implement this construction as a generic architecture-to-certificate pipeline. For quadratics, we recover the exact symmetric determinantal description and explain its dimension through orthogonal symmetry. In higher degree, the implementation recovers classical catalecticant and secant equations and maps the practical reach of direct elimination across a finite architecture sweep. We also derive the exact population loss floor for a rank-two quadratic network on the sphere, illustrating how an algebraic obstruction induces irreducible approximation error.
[LG-138] Reconstructing short-lived particles using hypergraph representation learning
链接: https://arxiv.org/abs/2402.10149
作者: Callum Birch-Sykes,Brian Le,Yvonne Peters,Ethan Simpson,Zihan Zhang
类目: High Energy Physics - Phenomenology (hep-ph); Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex)
*备注: 13 pages, 10 figures. Published by Physical Review D (PRD)
Abstract:In collider experiments, the kinematic reconstruction of heavy, short-lived particles is vital for precision tests of the Standard Model and in searches for physics beyond it. Performing kinematic reconstruction in collider events with many final-state jets, such as the all-hadronic decay of top-antitop quark pairs, is challenging. We present HyPER: Hypergraph for Particle Event Reconstruction, a novel architecture based on graph neural networks that uses hypergraph representation learning to build more powerful and efficient representations of collider events. HyPER is used to reconstruct parent particles from sets of final-state objects. Trained and tested on simulation, the HyPER model is shown to perform favorably when compared to existing state-of-the-art reconstruction techniques, while demonstrating superior parameter efficiency. The novel hypergraph approach allows the method to be applied to particle reconstruction in a multitude of different physics processes.
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