本篇博文主要内容为 2026-08-26 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 (2026-08-26)
今日共更新693篇论文,其中:
- 自然语言处理共89篇(Computation and Language (cs.CL))
- 人工智能共228篇(Artificial Intelligence (cs.AI))
- 计算机视觉共114篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共155篇(Machine Learning (cs.LG))
- 多智能体系统共12篇(Multiagent Systems (cs.MA))
- 信息检索共21篇(Information Retrieval (cs.IR))
- 人机交互共25篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] st-Time Collaborative Classification over Multi-Agent Networks
【速读】：该论文旨在解决多智能体系统中因模型异构性（heterogeneity）导致的全局模型联合训练难题，同时探索在分布式网络中通过智能体间协作推理实现高效决策的可行性。其核心挑战在于：各智能体可能具有不同的网络架构、特征空间或模态，无法直接共享模型参数，但又需在测试阶段协同生成准确的集体预测结果。解决方案的关键在于提出一种“独立训练、协作推理”（independent training and cooperative inference）的分布式二分类框架，即各智能体在训练阶段独立学习本地模型，而在测试阶段通过交换局部决策统计量（local decision statistics），基于分布式学习协议实现协同决策。该方法的核心创新在于利用有限通信预算下的有限精度、有限轮次通信，建立分类误差保证与近似正确（PAC-style）泛化界，从而量化模型异构性、网络拓扑结构、组合策略及通信约束对预测性能的影响。理论分析与实验结果共同揭示了独立训练带来的性能代价以及集体推理所带来显著的预测增益，为异构多智能体系统中的分布式决策提供了可解释且高效的范式。
链接: https://arxiv.org/abs/2608.24787
作者: Ping Hu,Mert Kayaalp,Ali H. Sayed
机构: École Polytechnique Fédérale de Lausanne (EPFL); DTI, SUPSI, Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI); UBS-IDSIA AI Lab
类目: Multiagent Systems (cs.MA)
备注:
Abstract:The increasing heterogeneity of multi-agent systems poses significant challenges for jointly training a global model across agents. At the same time, cooperative inference between agents has long been recognized as a powerful mechanism for distributed decision making over networks. Motivated by these observations, we propose a collaboration framework for distributed binary classification over multi-agent networks, where a set of independently trained agents, potentially differing in architecture, feature space, or modality, coordinate their actions during test time to form collective predictions. This coordination is achieved by exchanging local decision statistics through a distributed learning protocol. We develop a theoretical and experimental study of this independent training and cooperative inference paradigm, and examine its performance under different communication budgets and distributed learning rules. We establish classification error guarantees under sufficient, finite-round, and finite-precision communication, together with PAC-style generalization bounds. These results capture the influence of model heterogeneity, network topology, combination policy, and communication constraints on prediction accuracy. Taken together with the experimental results, they reveal both the price of independent training and the benefit of collective prediction for the proposed distributed decision making framework with models learned from data.
[MA-1] When “Must” Becomes “Maybe”: Constraint Weakening in LLM Agent Workflows
【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）代理在多角色、多阶段工作流中因状态传递失效而导致的操作性状态保留失败问题。具体而言，当上游状态通过摘要、计划、任务单等中间语言产物（language artifacts）向下游组件传递时，原本具有强制执行约束的“安全阻塞条件”（safety blockers）可能被弱化为仅具参考意义的建议或非约束性信息，从而导致下游执行动作出现违规或失效。其解决方案的关键在于：确保状态传递过程中不仅保留语义内容，还需完整保留其操作约束属性。研究通过控制实验发现，直接交接（direct-handoff）可100%保持阻塞状态，而压缩、计划整合、所有权延迟、先例替换等常见手递转换会显著削弱状态的约束力，造成高达54.2%的禁止动作发生率；只有在恢复全部四个状态字段（前置条件、授权主体、回退机制、执行后果）后，才能实现100%的状态保留与零禁止动作。此外，固定产物干预表明，通过下游验证可彻底消除违规动作，但状态仍可能被去激活，揭示了“状态保留”与“状态约束”之间的分离现象。因此，该研究的核心结论是：语义可用性不等于操作性保留，必须显式设计状态传递机制以维持其对下游行为的约束效力。
链接: https://arxiv.org/abs/2608.24569
作者: Yiheng Sun,Huifei Wang,Yancheng Zhu,Zhenyu Li,Zebin Zhao,Yifan Yuan
机构: Shenzhen University (深圳大学)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 21 pages, 4 figures
Abstract:Large language model (LLM) agents coordinate complex tasks through multi-role and multi-stage workflows. Upstream state is repeatedly transformed into intermediate language artifacts, such as summaries, plans, tickets, memories, and handoff notes, from which downstream components act. For action-constraining state, topical retention is insufficient: an artifact may mention an unresolved condition while changing it from a requirement that must be resolved before execution into information that may merely inform the next action. We study this action-binding role as operational state preservation. Safety blockers provide a controlled instance because each source state has an explicit prerequisite, authority, fallback, and execution consequence. We condition on correct upstream identification, vary the handoff transformation, and evaluate an executor restricted to the resulting artifact. Across 1,296 controlled synthetic episodes, direct-handoff controls preserve every blocker, whereas compression, plan assimilation, convergence, ownership deferral, and precedent substitution repeatedly turn binding state into caveats or non-binding considerations. Normal handoff compression produces 100.0% deactivation and 54.2% forbidden action. Restoring all four state fields raises preservation to 100.0% and reduces forbidden action to 0.0%. Fixed-artifact interventions further separate preservation from containment: downstream verification eliminates forbidden action while artifact deactivation remains 95.3%. These results identify a state-transmission failure between information extraction and action. Handoff transformations can retain state content while weakening its constraints on downstream action. Semantic availability does not guarantee operational preservation.
[MA-2] StrokeGuard: A Multi-Agent Guided System for Prehospital Stroke Assessment
【速读】：该论文旨在解决院前卒中评估中因非专业用户在家庭及社区环境中操作不规范、症状描述不准确、观察不完整以及流程执行困难等问题导致的评估结果偏差与可靠性下降问题。其核心解决方案是提出StrokeGuard——一种基于多智能体协同的院前卒中评估系统，通过双通道代理机制实现功能解耦：一通道专注于标准化的临床症状评估（如面瘫、肢体无力、言语障碍），另一通道则提供流程支持（如步骤提示、错误纠正与实时反馈），结合多智能体协作、双通道交互、状态机控制及阶段局部容错恢复机制，显著提升系统的容错性与用户引导能力。同时，通过受限预训练视频分析模块实现分阶段评分，并集成证据溯源与结构化报告生成，确保评估过程可追溯、结果可解释。用户评估结果显示，在模拟院前场景下，StrokeGuard相较于传统纸质FAST量表，使MATES-9总分提升10.83分（相对提高23.8%），验证了其在提升用户体验与评估准确性方面的有效性。
链接: https://arxiv.org/abs/2608.24555
作者: Wentao Yang,Zhenye Xu,Ruoyi Li,Musen Zhang,Yao Guo
机构: Shanghai Jiao Tong University (上海交通大学)
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Prehospital stroke assessment aims to accurately identify stroke symptoms and make rapid decisions through standardized procedures within an extremely narrow time window, thereby saving valuable time for subsequent treatment. In clinical practice, FAST-based scales are widely used for prehospital stroke assessment by issuing instructions that guide subjects to perform specific actions to screen facial, arm, and speech functions. However, in home and community settings, non-clinical users often encounter challenges such as inaccurate descriptions, incomplete symptom observation, and difficult operational procedures, which may lead to inaccurate or biased assessment results. To address these challenges, this paper presents StrokeGuard: a multi-agent guided system designed for prehospital stroke assessment that makes mobile FAST screening more standardized and executable. Specifically, to overcome the limitations of traditional single-agent systems in terms of procedural fault tolerance and user guidance capability, StrokeGuard adopts a dual-channel agent mechanism that separates formal assessment (i.e., facial palsy, arm weakness, speech impairment) from procedural support (e.g., step prompts, error correction, and real-time feedback). It guides the assessment process through multi-agent collaboration, dual-channel interaction, state-machine control, and stage-local fallback recovery mechanisms. Stage-specific scoring is delegated to constrained pretrained video assessment modules, while evidence source records are integrated with structured report generation. The user evaluation uses MATES-9, an exploratory scale for measuring user experience in multistep AI-guided tasks. In a simulated prehospital scenario, StrokeGuard improves the MATES-9 total score over a paper FAST-style form by 10.83 points, corresponding to a 23.8% relative increase.
[MA-3] Multilevel Fair Allocation under Additive Preferences
【速读】：该论文旨在解决具有树状层级结构的多层级公平资源分配问题，其中各代理（agent）之间存在层次化依赖关系，需在每一层上将父节点的资源包公平分配给其子节点，整体分配过程可视为从根节点逐层递归至叶节点的迭代结果。在该设定下，假设内部节点的效用为其子节点的总效用（即效用最大化），而叶节点具有经典可加性效用函数。研究的核心挑战在于如何在多层级结构中定义并实现公平性，为此作者提出了对传统基于嫉妒（envy-based）公平性概念（如WEF1）的多层级扩展，并提出三种不同的适应形式。研究表明，这三种扩展并非等价，其适用性取决于偏好结构：在同质偏好条件下三者等价，且多层级加权轮转算法（MWRR）可保证所有扩展形式；但在一般偏好条件下，MWRR仅能保证部分公平性标准，而无法保证其余标准。实验结果进一步表明，尽管未被理论保证，MWRR在实际应用中仍表现出良好的公平性与效率表现。该研究的关键在于揭示了多层级公平性定义的敏感性，并验证了现有算法在复杂结构中的稳健性。
链接: https://arxiv.org/abs/2608.24400
作者: Maxime Lucet,Nawal Benabbou,Aurélie Beynier,Nicolas Maudet
机构: LIP6, CNRS, Sorbonne Université (法国索邦大学、法国国家科学研究中心、巴黎第六大学)
类目: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: Accepted at the 9th International conference on Algorithmic Decision Theory
Abstract:We study multilevel fair resource allocation with tree-structured hierarchical relations among agents. At each level, the problem can be viewed locally as allocating an agent’s bundle to its children, the overall allocation being a trace of this process iterated down to the leaves. Assuming that internal nodes’ utilities are the utilitarian welfare of their children, and the leaves have classical additive utilities over items, we first propose multilevel adaptations of usual envy-based fairness notions (e.g., WEF1). We present three adaptations and show that the choice among them is not neutral. We prove that, under identical preferences, the three adapted envy-based notions coincide, and that the Multilevel extension of Weighted Round Robin (Chakraborty et al., 2021) (MWRR) guarantees them. We then show that under general preferences, MWRR may guarantee some notions while failing others. Finally, through experiments, we show that MWRR may still perform well even for adaptations it does not formally guarantee.
[MA-4] Agent opia on a Consumer GPU: A Reduced-Scale Long-Horizon Port with an 8B Model
【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）驱动的多智能体社会模拟系统在消费级硬件上的可扩展性与可行性问题，特别是针对现有系统如Agentopia在高资源需求下难以在普通设备上运行的局限。其核心挑战在于如何在有限显存（12 GB VRAM）和低算力环境下维持长时间、大规模的多智能体行为模拟。解决方案的关键在于引入三项结构化优化：（1）由系统管理的分层内存压缩机制，有效缓解上下文窗口限制；（2）将每日活动划分为四个时间块，提升时间调度效率与行为粒度；（3）显式引入物理与心理健康状态变量，增强个体行为的内在一致性与可解释性。实验结果表明，在单张NVIDIA RTX 5070 Ti显卡上，基于4位量化版本Qwen3-8B-AWQ的简化版Agentopia实现了长达52周的稳定运行，累计达154个系统周（770个智能体周），且无智能体死亡或健康阈值触发警告，仅出现约10.2%的响应缺失率。进一步对比分析揭示了分层内存与活动块划分对生成内容质量与结构的影响，尽管未建立因果关系，但为未来轻量化多智能体系统设计提供了可验证的技术路径与开放资源支持。
链接: https://arxiv.org/abs/2608.24215
作者: Luo Huan
机构: Shenzhen University(深圳大学); Shenzhen, China
类目: Multiagent Systems (cs.MA)
备注: 8 pages, 2 figures. Public preprint and research artifact
Abstract:Large language model (LLM)-based multi-agent social simulation has demonstrated compelling results, but Agentopia was evaluated with 100 agents over 10 simulated years using Qwen3.5-397B-A17B, leaving the behavior of reduced-scale deployments on consumer hardware unclear. In this paper, we implement and evaluate a reduced-scale Agentopia port on a single NVIDIA RTX 5070 Ti(12 GB VRAM) using Qwen3-8B-AWQ, a 4-bit quantized model. We introduce three structural adaptations for this setting: (1) system-managed layered memory compression, (2) four activity blocks per simulated day, and (3) explicit physical- and mental-health state variables. Across three independent stochastic runs, two runs completed 52 weeks and the third completed 50 weeks before reaching the context limit, totaling 154 system-weeks (770 agent-weeks). No agent died,and no threshold-based health warning was logged; activity records containing at least one NO_RESPONSE field occurred at rates of 10.15-10.29% across runs. A 52-week memory-off run tied L2/L3 artifact production to layered memory; a separate 10-week comparison associated four daily time blocks with 2.72 times more finalized records and lower lexical duplication, but a higher missing-field rate. These comparisons do not support causal behavioral claims. We release validated configurations, derived audits, analysis scripts, aggregate figure data, and our implementation changes in a public fork; raw runs and initial persona data are excluded because their redistribution provenance is not fully resolved.
[MA-5] Rules Before Oracles: Auditable User-Configurable Argument Selection for Deliberative Polling
【速读】：该论文旨在解决在协商式民意调查（deliberative poll）中，当提交的论点数量超过个体可阅读容量时，如何公平、透明地选择每个投票者所见论点的问题。现有实践依赖于不透明的机器学习排序器（opaque learned rankers），导致选民无法复现或质疑影响其决策的信息暴露过程，从而削弱了民主参与的可问责性。本文提出的核心解决方案是设计一种可公开发布的规则，基于选民自主掌握的可计算证据与参数，将可读性（legibility）作为可用机制的准入条件，而非与准确性进行权衡的妥协项。其关键创新在于形式化了一种基于二元论证集（bipolar justification sets）的民意调查机制，以论点覆盖度（reason coverage）、到达顺序及被支持的质量（captured endorsement mass）为评价标准，并提出七项可验证准则，构建出一个满足这些准则的公民推荐规则——即基于关系权重函数的一跳反向支持流（one-hop reversed endorsement flow）。通过约17,000次种子配对模拟运行，结果表明：该规则在覆盖度上表现接近随机（因顺序和善意无关），但在支持质量与覆盖率的综合表现上显著优于非透明排序器，尤其在对抗性压力下优势持续扩大，且在支持质量总量上领先达3.3倍；当部分提交内容缺乏理由时，其覆盖度优势进一步显现。此外，不同权重策略对系统完整性的影响揭示了权重函数作为安全控制的重要性，其作用相当于提升10%的完整率。最终，排名策略的选择本质上是覆盖度与支持质量之间的权衡，而唯有可读规则才能将这一本应由个体自主决定的权衡交还给参与者，该机制已映射至开源去中心化平台。
链接: https://arxiv.org/abs/2608.23979
作者: Muntaser Syed,Markus Zanker,Marius Silaghi
机构: Florida Institute of Technology(佛罗里达理工学院); Free University of Bozen-Bolzano(博岑-博尔扎诺自由大学)
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computers and Society (cs.CY); Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA)
备注:
Abstract:In a deliberative poll, once submissions outnumber what anyone will read, some mechanism chooses which arguments each voter sees, acquiring much of the decision; practice delegates it to opaque learned rankers, so a voter cannot recompute or contest the exposure that shaped their vote. We ask whether it can be a published rule over publicly recomputable evidence with parameters held by the voter, treating legibility as an admissibility condition on usable mechanisms, not an objective traded against accuracy. We formalise a poll over bipolar justification sets, judging a slate by reason coverage, the order it arrives in, and captured endorsement mass; we give seven checkable criteria for a civic recommender and a rule meeting them: a one-hop reversed endorsement flow parameterised by a relation-weight function. An agentic simulator records every slate at every vote, over about 17,000 seed-paired runs. Served slates fall 0.035 short of a label-reading ceiling upper-bounding every selection procedure, opaque ones included: any unconstrained ranker’s advantage is bounded and small. On coverage alone, with non-degenerate authoring, the rule is indistinguishable from a random slate, a null due to an order-blind, charity-blind instrument; on the other two it leads at every prefix by a margin widening with adversarial pressure and dominates on mass by a factor of 3.3. Once a realistic fraction of submissions carries no reasons, the coverage margin returns and grows. Label-homogeneous flooding collapses completeness from 0.81 to 0.34 under a flat weight policy, only to 0.44 under author-count normalisation, making the weight function a security control worth 10% of completeness. The choice between ranking arms is a position on a coverage-versus-mass frontier, not a fact, the kind of choice only a legible rule can hand to the person it affects. It maps onto an open-source peer-to-peer platform.
[MA-6] MARS: Multi-Specialist LLM Relay System for Competitive Programming EMNLP2026
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在编程竞赛（competitive programming）场景中因缺乏专业化分工而导致的生成效率与准确性不足问题。现有基于多智能体（multi-agent）的流水线通常采用通用角色划分（如规划者、编码者、调试者），并将算法策略选择完全依赖于主干模型，导致对复杂算法技巧（如动态规划、图论、字符串处理等）的建模能力受限。为此，论文提出MARS（Multi-Agent Relay of Specialized LLMs）框架，其核心创新在于构建一个仅通过提示工程（prompt-only）实现的专用智能体协作系统：每个智能体为特定算法领域（如动态规划、图论、几何等）的专业专家，基于算法理论知识库的检索增强生成（retrieval-augmented generation, RAG）进行上下文感知决策。任务开始时，检索模块筛选出相关领域的少数专家组成小团队；初始编码者生成C++17代码草稿后，每轮在沙箱环境中以公开测试样例验证候选解，由当前活跃专家决定保留、修复或移交至下一专业领域智能体，形成结构化信息传递。最终通过一次统一的基础设施修复阶段规范化代码模板。实验表明，在CodeContests测试集上，基于Gemma 4的MARS达到0.624±0.006的通过率，平均每个任务仅需2.3个流水线阶段，较直接提示法提升14.4个百分点，且相比CodeSIM在约3.3倍更低的运行时间开销下实现更稳定的每任务令牌消耗，显著缩小了性能差距。
链接: https://arxiv.org/abs/2608.23918
作者: Andrei Mikhailov,Mikhail Burtsev,Alsu Sagirova
机构: MIRAI; London Institute for Mathematical Sciences; AXXX
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Programming Languages (cs.PL)
备注: 13 pages, 8 figures, EMNLP 2026
Abstract:Large Language Models excel at code generation, yet competitive programming exposes a persistent failure mode: existing multi-agent pipelines distribute work over generic planner, coder, and debugger roles and delegate the choice of algorithmic technique to the backbone alone. We present MARS (Multi-Agent Relay of Specialized LLMs), a prompt-only framework in which each agent is a topic specialist—dynamic programming, graphs, strings, geometry, and so on—grounded by retrieval-augmented generation over an algorithm-theory corpus. Given a problem, retrieval selects a small team of relevant specialists; a starter writes an initial C++17 solution, and each subsequent turn runs the candidate against public examples in a sandbox, lets the active specialist keep, repair, or hand off the draft, and forwards a structured packet to the next specialist. A single infrastructure-fixer pass normalizes boilerplate at the end. On the CodeContests test split with Gemma 4, MARS reaches 0.624 \pm 0.006 pass rate at 2.3 recorded pipeline stages per task ( +14.4 percentage points over direct prompting), closing most of the gap to CodeSIM ( 0.731 ) at 3.3\times lower wall-clock cost and substantially smaller variance in per-task token spend. The source code is available on GitHub: this https URL.
[MA-7] Markets Not Planners: Decentralized Orchestration of LLM Agents with Private Information
【速读】：该论文旨在解决大规模语言模型（LLM）智能体在异构、分布式环境下协同调度时面临的效率与公平性问题。随着不同主体构建的智能体具备各异的能力、成本和资源约束，传统集中式调度机制已难以适应动态扩展的智能体池，其依赖单一规划器分配任务的模式导致性能瓶颈，且需访问敏感的私有信息（如执行成本），易受操纵（例如，单个偏好插入可使特定智能体的任务占比几乎翻倍）。为此，论文提出AgentLance——一种基于重复劳动力市场的分布式调度框架，其核心在于：智能体通过私有成本信息和自主维护的策略记录参与任务竞标，市场分配器根据公开声誉记录与竞标结果选择胜出者，并采用类似维克里-克拉克-格罗夫斯（VCG）的支付规则激励真实成本披露。对于复杂任务，系统支持分层委派机制，由中标智能体自行分解任务并以相同方式外包。实验表明，AgentLance能有效匹配智能体至其专长领域，随成本敏感度提升自动引导工作流向更低成本的智能体，在数学推理、代码生成、知识密集型问答及代理型任务等场景中均显著优于单一模型、集中式调度及基础市场基准。通过诊断市场失灵（如成本误估、次优竞价），并在受控实验中针对性修正，进一步提升了整体效率，为构建高效、可信的智能体经济体系提供了可行路径。
链接: https://arxiv.org/abs/2608.23867
作者: Xiao Liu,Haoyang Li,Songwei Li,Hongbo Fang,Fengli Xu,Feng Shi,James Evans
机构: 未知
类目: Multiagent Systems (cs.MA); Computation and Language (cs.CL)
备注: Working paper
Abstract:As LLM agents proliferate, built by different parties and with different capabilities and costs, orchestrating them is more like assembling labor across the economy than a computer calling a subroutine. Existing orchestration is typically centralized, with a single planner assigning every task, but this creates a bottleneck as agent pools grow, requires private information (e.g., agents’ execution costs), and can easily be manipulated, such that a single inserted preference nearly doubles a favored agent’s task share under a centralized LLM allocator. We introduce AgentLance, a repeated labor market in which agents bid on tasks using their private costs and self-maintained strategy notes, an allocator selects winners from bids and public reputation records, and a VCG-style payment rule rewards cost-aware bidding. Complex tasks are handled by hierarchical delegation: winning agents can decompose work and subcontract it through the same mechanism. Across mathematical reasoning, code generation, knowledge-intensive QA, and agentic tasks, AgentLance matches agents to their specializations, shifts work toward cheaper agents as cost sensitivity rises, and consistently outperforms single-model, centralized-orchestration, and market baselines. Diagnosing market failures, including inaccurate cost self-estimation and sub-optimal bidding, then correcting them in controlled experiments yields further gains, charting a path toward more efficient agent economies.
[MA-8] Autonomous Mathematical Discovery in an Open-World Multi-Agent Environment
【速读】：该论文旨在解决在无中心协调、无预设流程的开放世界多智能体环境中实现自主数学发现的问题，即如何让来自不同模型家族的AI智能体在没有人类干预的情况下，自发选择研究方向、开展实验、协作并共同构建共享科学文献。其解决方案的关键在于构建一个去中心化的多智能体系统——“Station”，该系统允许智能体自主决策与协作，并通过生成可解释的数学定理与分析来验证和阐释其发现，从而确保成果不仅具有创新性，还具备可追溯性和可复现性。在此框架下，智能体成功在五个问题上取得了超越已有文献的新成果，包括有限域上的Kakeya集新无穷族、11维空间中604点接触配置、离散化Kakeya针问题与符号不确定性问题的新纪录，以及Erdős最小重叠问题下界的重大改进，并发现了书Ramsey数的新无穷族，同时产出可验证的证明与代码，实现了从数值构造到理论解释的完整闭环。
链接: https://arxiv.org/abs/2608.23691
作者: Stephen Chung,Wenyu Du,William J. Wesley
机构: DualverseAI; University of Cambridge (剑桥大学); University of Hong Kong (香港大学); University of California San Diego (加州大学圣地亚哥分校)
类目: Artificial Intelligence (cs.AI); Discrete Mathematics (cs.DM); Multiagent Systems (cs.MA)
备注: 38 pages, 12 figures, 3 tables. Source code at this https URL and raw agent dialogues, proofs, and verification artifacts at this https URL
Abstract:We study autonomous mathematical discovery in the Station, an open-world multi-agent environment in which AI agents from different model families pursue a shared research goal without a central coordinator or scripted pipeline. Agents choose their own research directions, conduct experiments, collaborate, and build a shared scientific literature. Across 12 construction problems from the AlphaEvolve catalogue and two additional case studies, the Station obtained results novel relative to the prior literature on five problems: a new infinite family of finite-field Kakeya sets, new exact 604-point kissing configurations in dimension 11, new records for the discretized Kakeya needle and sign uncertainty problems, and a substantially improved lower bound for Erdős’s minimum-overlap problem. Agents also discovered novel infinite families for Book Ramsey numbers. Importantly, the agents produced not only numerical constructions but also theorems and analyses explaining how those constructions work, making the results more interpretable and easier for mathematicians to build upon. We release all raw agent dialogues, proofs, and verification code, providing a transparent record of how these discoveries emerged.
[MA-9] LLM Agents Perform Controlled Experiments Using Simulation Models
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在科学与工程任务中仅依赖文本生成而缺乏对系统响应干预机制的理解这一关键局限。传统方法难以实现对复杂过程的因果推理与参数优化，尤其在制药工艺设计等高精度需求场景下，单纯基于语言模型的推理往往产生泛化、不可操作的输出。为此，本文提出一种多智能体框架，其核心在于将大语言模型与高保真科学仿真模型进行交互式耦合，构建一个支持干预、比较与观测的实验推理闭环。该方案的关键创新在于：通过结构化任务表示，自动设计可控实验，执行对比仿真，并基于仿真结果进行证据整合与推荐生成，从而实现从“推测性生成”到“基于实证的决策支持”的转变。实验结果表明，该系统在工业应用场景中显著提升了输出的具体性、用户评价的正确率与实用性，验证了融合仿真驱动实验推理的有效性与可操作性。
链接: https://arxiv.org/abs/2608.23622
作者: Yuchen Xia,Michael Weyrich,Nasser Jazdi,Johannes Stümpfle,Johannes Sigel,Akshay Narla,Gavin K. Reynolds,Anna Jawor-Baczynska,Pol Llopart
机构: AstraZeneca(阿斯利康); University of Stuttgart(斯图加特大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA); Software Engineering (cs.SE)
备注: Accepted at the 31st IEEE International Conference on Emerging Technologies and Factory Automation ETFA 2026
Abstract:Large language models (LLMs) have shown strong capabilities in reasoning, planning, and tool use, but many scientific and engineering tasks require more than plausible text and code generation. They require understanding how a system responds to intervention, which in practice depends on controlled experimentation. In this work, we propose a multi-agent framework that enables LLM agents to conduct controlled experiments with scientific simulation models for pharmaceutical process design. Given a user query and a baseline configuration, the system constructs a structured task representation, designs experiments, executes comparative simulation, interprets the resulting outcomes, and synthesizes evidence-based recommendations for process parameter optimization. By coupling language models with high-fidelity simulation models in an interactive agent framework, the proposed system supports reasoning through intervention, comparison, and observation. As a result, it produces more specific and actionable outputs than language-only reasoning. In an industrial application setting, this advantage is reflected in higher output specificity as well as improved user-rated correctness and helpfulness. Ablation studies and visualized case analyses further demonstrate the effectiveness and practical utility of simulation-integrated experimental reasoning.
[MA-10] StateTune: Transforming LLM -Assisted EDA Flow Tuning into a Stateful Closed-Loop Process
【速读】：该论文旨在解决电子设计自动化（EDA）流程参数调优中因参数空间庞大、耦合性强且完整评估成本过高而导致的高质量结果（QoR）难以实现的问题。现有基于大语言模型（LLM）的调优方法多将LLM作为外部提议者，依赖临时上下文，缺乏持续性优化能力。本文提出一种名为StateTune的新方法，其核心创新在于将LLM辅助的EDA调优重构为一个闭合回路、具备状态传递特性的优化过程。关键解决方案是引入一种类型化、证据门控的持久优化记忆（persistent optimization memory），该记忆在每次评估后更新，并在候选生成与预算分配之间共享，实现了对历史信息的长期积累与利用。在此基础上，设计了一种基于期望超体积改进（EHVI）并考虑运行时成本的促进策略，以单位运行时间成本下的帕累托前沿增益为依据对快速阶段候选解进行排序。在Cadence工业级EDA流程上对六个基准模块（两个工艺节点×三个设计）的实验表明，StateTune在所有六组测试中均达到最优超体积，且在整个测试矩阵中稳定提升帕累托前沿质量；同时在最差负松弛（WNS）、面积和功耗等关键指标上表现持平或优于现有五种基线方法（包括LLM+检索增强生成RAG和基于偏好的贝叶斯优化BO）。消融实验进一步证实，持久记忆是性能提升的主要来源——移除该机制导致超体积损失达58.5%。此外，针对证据门控敏感性、记忆污染、跨设计迁移能力及三种子重复性（五组块CV≤7%）的专项分析，充分验证了该记忆设计的有效性与鲁棒性。
链接: https://arxiv.org/abs/2608.23601
作者: Kunlong Li,Shangshang Yao,Su Zheng,Lingli Wang
机构: Fudan University(复旦大学); Independent Researcher(独立研究员); Chinese University of Hong Kong(香港中文大学)
类目: Hardware Architecture (cs.AR); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注: Accepted to the 2026 IEEE/ACM International Conference on Computer-Aided Design (ICCAD 2026)
Abstract:EDA flow parameter tuning is critical for quality-of-results~(QoR), yet the parameter space is large, tightly coupled, and full evaluations are prohibitively expensive. Prior LLM-assisted tuners mainly use the LLM as an external proposer with transient working context; we instead present \textbfStateTune, which reformulates LLM-assisted EDA tuning as a closed-loop, state-carrying process. Its optimizer state is a typed, evidence-gated \emphpersistent optimization memory that is updated by every evaluation and shared between candidate generation and budget allocation. On top of this optimizer state, an expected hypervolume improvement (EHVI)-guided, runtime-aware promotion policy ranks quick-stage candidates by expected Pareto frontier gain per unit of runtime cost. Evaluated on a Cadence industrial flow across six benchmark blocks (two technology nodes (\times) three designs), against five baselines including LLM+retrieval-augmented generation (RAG) and preference-based Bayesian optimization (BO) tuners, StateTune achieves the strongest final hypervolume on all six benchmark blocks, showing a stable improvement in frontier quality across the full matrix; it also matches or surpasses the strongest baselines on worst negative slack (WNS), area, and power across the same set. Ablation shows persistent memory is the largest contributor: removing it costs 58.5% of the hypervolume. Dedicated analyses of evidence-gating sensitivity, memory poisoning, cross-design transfer, and three-seed reproducibility (CV,(),7% on five of six blocks) further validate the memory design.
[MA-11] Why frag mented parliaments stop passing legislation: Opposition discipline and representation across four democratic institutions
【速读】：该论文旨在解决在政党体系碎片化背景下，议会制与总统制在立法通过效率上的差异及其内在机制问题。现有文献提出了三种相互竞争的微观解释：联盟组建失败、政党纪律以及委员会把关，但这些机制在真实议会中同时作用，导致观测研究难以分离其独立贡献。为此，作者提出一个基于代理的模型（agent-based model），对比四种民主政体：纯议会制、纯共和/总统制、半总统制（法国）和总统-议会制（俄罗斯），通过四类情景及每组200次模拟种子数，采用自举置信区间、莫里斯筛选、索博尔方差分解、机制消融分析和悬峙议会变体比较等方法进行系统检验。研究发现：第一，仅政府组建失败不足以导致立法瘫痪；当碎片化议会转为个人投票时，议会制立法通过率（46.4%）与总统制基准（44.8%）无统计差异，立法崩溃的关键在于反对派的协同阻挠，后者使通过率降至0.05%；第二，取消政党纪律后，碎片化议会的立法通过率恢复至46.7%，且不同制度下“救赎效应”的强度呈现一致单调趋势，表明政党纪律在制度间具有可比的调节作用；第三，立法通过率与代表性忠实度之间存在单一权衡谱系：议会制以牺牲代表忠实度换取最高立法吞吐量，总统制通过总统否决权最大化代表性，而半总统制则在二者之间取得平衡。解决方案的关键在于通过结构化模拟揭示多机制交互作用下的制度绩效边界，并量化各制度在复杂政治环境中的相对优势。
链接: https://arxiv.org/abs/2608.24554
作者: Fuad Ali
机构: 未知
类目: Physics and Society (physics.soc-ph); Multiagent Systems (cs.MA)
备注: Code, data, and interactive demo: this https URL
Abstract:Parliamentary systems pass more bills than presidential systems at baseline, but collapse to near-zero passage under party-system fragmentation. The literature offers three competing micro-explanations: coalition-formation failure, party discipline, and committee gatekeeping. These operate simultaneously in any real legislature, so observational studies struggle to separate their contributions. We present an agent-based model that compares four democratic institutions: pure parliamentary, pure republican/presidential, premier-presidential (France), and president-parliamentary (Russia). Across four scenarios and N=200 seeds per cell we report bootstrap confidence intervals, Morris screening, Sobol variance decomposition, mechanism ablations, and a hung-parliament variant comparison. Three findings emerge. First, government formation failure alone does not halt legislation: when a fragmented parliament reverts to personal voting, parliamentary passage (46.4%) is statistically indistinguishable from the presidential benchmark (44.8%); collapse requires cohesive opposition obstruction, which drives passage to 0.05%. Second, disabling discipline restores fragmented passage to 46.7%, and the rescue magnitude is monotone across the four institutions in a pattern that survives varying the common discipline level. Third, the passage-representation tradeoff is a single spectrum: parliamentary maximises throughput at the cost of representational fidelity; republican maximises fidelity via the presidential veto; semi-presidential variants split the difference.
自然语言处理
[NLP-0] Recursive Experiential-Working Memory Evolution for Long-Horizon Agent Harnesses
【速读】： 该论文旨在解决长时程任务中递归自我改进（Recursive Self-Improvement, RSI）面临的挑战，核心问题在于随着任务历史的不断积累，任务状态逐渐被冗余信息遮蔽，导致技能调用与当前需求错位。其解决方案的关键在于提出一种名为Recuris的递归经验-工作记忆架构（Recursive Experiential-Working Memory Architecture），通过将工作记忆（Working Memory）与经验记忆（Experiential Memory）耦合，使工作记忆动态追踪任务进展并基于当前需求而非完整历史来指导技能选择，从而实现更精准的技能激活。同时，该架构将执行过程转化为结构化证据，可精确定位失败来源至特定记忆组件。在此基础上，一个固定的元代理（Meta-Agent）利用这些证据进行局部化、验证门控的技能记忆更新，形成闭环的、有界递归的记忆演化机制。实验结果表明，在四个长时程基准测试和十种模型上，Recuris在37组完成的模型-基准对中提升了35组的任务成功率，显著推动前沿模型达到新的性能标杆：例如在tau-bench上使GPT-5.6 Sol和Claude Opus 5分别提升+17.8和+15.6分，后者成功达到87.9%；在SkillFlow上对Qwen3.6-27B/35B分别提升+16.6/+13.5分，且随着交互时序增长，优势进一步扩大至+32.2分，常见长时程失败率下降最高达80%。这些结果表明，递归演化的记忆机制为可扩展的递归自我改进提供了坚实基础，使智能体能够持续将累积经验转化为更具效能的长时程行为。
链接: https://arxiv.org/abs/2608.24876
作者: Zhaochen Yu,Yingcheng Wu,Zhenfei Yin,Kaiyuan Chen,Zhe Zhao,Mengdi Wang,Shuicheng Yan,Ling Yang
机构: NUS(新加坡国立大学); Princeton University(普林斯顿大学); Stanford University(斯坦福大学); University of Oxford(牛津大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Code: this https URL
Abstract:Recursive self-improvement (RSI) remains hard in long-horizon tasks, where growing histories obscure the task state and misalign skill invocation. We introduce Recuris, a recursive Experiential-Working Memory architecture for long-horizon agent harnesses, in which Working Memory tracks task progress and guides skill selection from Experiential Memory, grounding skill use in current needs rather than the full history. This coupling also turns execution into structured evidence that localizes failures to specific memory components. Across tasks, a fixed Meta-Agent turns that evidence into localized, validation-gated updates to Skill Memory that reshape execution and yield new evidence, forming a bounded recursive memory-evolution loop. Across four long-horizon benchmarks and ten models, Recuris improves task success in 35 of the 37 completed model-benchmark pairs, carrying frontier models to SOTA-level task success: on tau-bench it adds +17.8 points to GPT-5.6 Sol and +15.6 to Claude Opus 5, taking Opus 5 to 87.9%, and +16.6/+13.5 points on Qwen3.6-27B/35B on SkillFlow. The advantage widens as the interaction horizon grows, to +32.2 points on the longest tasks, and common long-horizon failures fall by up to 80%. These results position recursively evolving memory as a scalable foundation for RSI, enabling agents to continuously transform accumulated experience into increasingly effective long-horizon behavior. Code: this https URL
[NLP-1] BrowserForge: Scaling Web Episode via Parallel Browser Sandboxes
【速读】： 该论文旨在解决生成高质量、大规模网页交互轨迹数据的难题，以支持训练仅依赖屏幕像素输入的网络代理（Web agent），从而避免传统方法中因解析HTML或可访问性树（Accessibility Tree）所带来的脆弱性和高计算成本。现有公开数据集通常仅包含数千条来自有限网站的轨迹，而现有自动化合成管道也受限于预定义的网站列表或教程来源，导致代理所接触的网站多样性严重不足。为应对这一挑战，论文提出BrowserForge框架，其核心在于通过并行驱动大量浏览器沙箱在开放网络上自主探索，实现对海量真实网站的大规模数据采集。该框架由三个关键组件构成：一是开放网络数据源阶段，使代理接触到数十万计的真实可访问网站；二是沙箱集群管理器，高效调度数百个并发浏览器以实现高资源利用率；三是提议-求解双智能体循环机制，将原始页面转化为可执行任务并收集经验证的交互轨迹。此外，采用“规则+模型”联合清洗流程剔除失败运行，并将剩余推理过程统一重构为连贯的思维链（Chain-of-Thought）格式。值得注意的是，可访问性树等页面结构信息仅作为合成阶段的辅助信号，所训练并发布的代理完全基于截图进行决策。最终构建的数据集包含203,238条来自不同网站的轨迹，规模与多样性均显著超越以往数据集。实验表明，在该数据集上微调轻量级多模态模型后，其在Live Online-Mind2Web任务上的成功率从25.66%提升至33.33%，且在静态的Multimodal-Mind2Web基准上持续提升步骤准确率，性能增益随数据规模扩大而增强。控制分析进一步验证了开放网络采样和广泛网站覆盖是性能提升的关键因素。
链接: https://arxiv.org/abs/2608.24848
作者: Fei Tang,Huawen Shen,Zhiqiong Lu,Zhengxi Lu,Pengyuan Lyu,Chengquan Zhang,Weiming Lu,Jun Xiao,Yueting Zhuang,Yongliang Shen
机构: Zhejiang University(浙江大学); LLM Department, Tencent(腾讯大模型部门)
类目: Computation and Language (cs.CL)
备注:
Abstract:Web agents that act from rendered pixels avoid the fragility and heavy token cost of reading a page’s HTML or accessibility tree, but training them depends on large amounts of high-quality interaction trajectories, and how to produce such data at scale remains an open problem. Public datasets typically contain only a few thousand trajectories drawn from a fixed and narrow set of websites, and even recent automated synthesis pipelines stay bound to predefined site lists or tutorial sources, so the number of distinct websites the agent ever sees barely grows. We present BrowserForge, a framework that generates web interaction data at scale by driving many browser sandboxes in parallel over the open web. BrowserForge couples three components: an open-web sourcing stage that exposes the agent to hundreds of thousands of real, openly reachable websites; a sandbox cluster manager that schedules hundreds of concurrent browsers with high utilization; and a Proposer-Solver dual-agent loop that turns a raw page into an executable task and then collects a verified trajectory for it. A rule-plus-model cleaning pipeline removes failed runs and rewrites the surviving reasoning into a single unified chain-of-thought style. Page structure such as the accessibility tree is used only as a synthesis-time signal; the agent we train and release acts purely from the screenshot. The resulting corpus contains 203,238 trajectories, each collected from a distinct website, larger and more diverse than prior trajectory datasets. Fine-tuning a compact multimodal model on this corpus raises its success rate on the live Online-Mind2Web from 25.66% to 33.33% and consistently improves step accuracy on the static Multimodal-Mind2Web, with the gain growing as the corpus scales. Controlled analyses further confirm that open-web sourcing and broad website coverage are key contributors to the observed improvement.
[NLP-2] Reading Is Not Using: Retrieval Judgment and the Design of AI Financial Research Workflows
【速读】： 该论文旨在解决生成式 AI（Generative AI）在金融披露分析中存在“检索-整合鸿沟”（retrieval-integration gap）的问题，即尽管大语言模型（LLM）能够准确检索到相关信息，但这些信息并未有效影响其投资判断。其解决方案的关键在于揭示：模型能力本身不足以保证信息的有效整合，真正决定分析性能的是工作流架构（workflow architecture）。研究发现，采用分块-摘要（chunk-and-summarize）的处理流程会排除关键信息，而通过在决策点附近进行目标性、结构化的信息重述，则可恢复信息对判断的影响。此外，因果记忆干预实验表明，压缩摘要与源文本查找的协同作用是实现信息传递的核心机制。因此，提升AI分析师表现需同时优化模型能力和工作流设计，仅依赖基于检索准确性的评估会掩盖系统在实际判断中忽略已检索信息的重大缺陷。
链接: https://arxiv.org/abs/2608.24842
作者: Miao Liu,Zhizhe Liu
机构: Carroll School of Management, Boston College (波士顿学院卡罗尔管理学院); Columbia Business School, Columbia University (哥伦比亚大学商学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) are increasingly deployed as AI analysts to process financial disclosures and support AI-assisted investment decisions. Yet such systems are usually evaluated by what they can retrieve, not whether retrieved information affects their judgments. We identify a retrieval-integration gap in long-context financial analysis. Holding focal-firm information fixed and varying only unrelated context from 2,000 to 128,000 tokens, we find that a risk disclosure’s influence on investment judgments falls to the experimental noise floor even as direct retrieval remains accurate. The pattern replicates across model families and judgment tasks and in experiments removing real disclosures from actual 10-K filings. More capable models postpone but do not eliminate the gap. Causal memory interventions show that compressed summaries and source-text lookup jointly transmit disclosures into judgments. Workflow architecture determines whether this transmission succeeds: chunk-and-summarize pipelines evict relevant information, whereas a targeted, structured restatement adjacent to the decision restores its influence. AI analyst performance is therefore jointly determined by model capability and workflow architecture. Retrieval-based evaluations can certify systems whose investment judgments ignore information they demonstrably retrieved.
[NLP-3] Linear Probing Provides Robust and Efficient Detection of Machine-Generated Text
【速读】： 该论文旨在解决生成式文本（Machine-Generated Text, MGT）与人类撰写文本（Human-Written Text, HWT）区分任务中现有监督检测方法在跨领域（Out-of-Domain, OOD）场景下性能下降以及依赖大规模多样化训练数据的问题。其核心解决方案在于利用生成式文本与人类文本在低维潜在空间中的线性可分性，提出并验证了简单线性探测器（linear probes）的有效性。研究发现，尽管两者在高维空间中表现复杂，但在低维潜在表示空间中，其分布具有显著的线性可分特征，这一现象源于二者表示质量上的系统性差异。基于此，作者训练了两种简化版线性探测器，在4个基准测试上对比16种基线模型，结果表明探测器在仅需100个样本的情况下即可接近最优性能，且在跨领域检测中显著提升（AUC提升+11），展现出优异的样本效率和泛化能力。进一步分析揭示，探测器能够捕捉到一个共享的“机器性”（machineness）潜在方向，该方向在多种生成设置下具有强迁移性。此外，探测向量能表征从人类到机器生成文本的连续谱，具备对文本被AI编辑程度进行细粒度估计的潜力。综上，该研究揭示了MGT与HWT在潜在空间中的本质差异，并证明线性探测器作为一类鲁棒、高效且可解释的检测工具，在实际应用中具有重要价值。代码已开源。
链接: https://arxiv.org/abs/2608.24780
作者: Gerrit Quaremba,Hanqi Yan,Elizabeth Black,Denny Vrandecic,Elena Simperl
机构: King’s College London(伦敦国王学院); Wikimedia Foundation(维基媒体基金会)
类目: Computation and Language (cs.CL)
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Abstract:Distinguishing machine-generated text (MGT) from human-written text (HWT) becomes increasingly important due to potential misuse. However, most supervised detectors often degrade out-of-domain (OOD) and require large, diverse training sets. In this work, we analyze the linearity and quality of MGT representations and show that simple linear probes outperform a wide range of detectors while being substantially more sample-efficient. We first show that MGT and HWT latent representations are linearly separable in low-dimensional space, and provide a plausible explanation for this separability through systematic differences in their representation quality. Motivated by these insights, we train two variants of simple linear probes and evaluate them across 4 benchmarks against 16 baselines. Probes consistently improve OOD detection (+11 AUC), requiring solely 100 samples to reach near-peak performance. We show that this transferability arises because probes recover a shared latent MGT direction that generalizes across diverse settings. Finally, we demonstrate that probing vectors capture a continuous spectrum of ``machineness’', highlighting their potential for fine-grained estimation of AI-edited text. Overall, our work provides insights into latent-space differences between MGT and HWT and demonstrates the potential of linear probes as as robust and sample-efficient MGT detectors. We release our code on~\hrefthis https URLgithub.
[NLP-4] ExpConCAD: Experience-Guided Text-to-CAD Generation from Shape Descriptions with Implicit Spatial Constraints
【速读】： 该论文旨在解决文本生成计算机辅助设计（CAD）程序（Text-to-CAD）中因自然语言描述存在信息不完整而导致关键空间约束缺失的问题。现有方法普遍忽视了真实场景下描述常因省略必要空间关系而无法生成有效CAD模型的挑战。其解决方案的关键在于：通过显式建模构造结构（construction structure）的理解与可复用的设计经验记忆，实现对隐含空间约束的合理补全。具体而言，ExpConCAD框架首先恢复用户意图对应的构造结构及其约束作用范围，继而基于相似作用范围检索历史设计经验以补全缺失的空间约束，最终生成可执行的CadQuery程序。实验表明，该方法在提升生成结果有效性方面具有显著优势，验证了构造结构理解与经验记忆在空间约束补全中的核心作用。
链接: https://arxiv.org/abs/2608.24760
作者: Jingyao Liu,Jinkang Tang,Chen Huang,Wenqiang Lei,See-Kiong Ng
机构: Sichuan University (四川大学); Ministry of Education, China (中华人民共和国教育部); National University of Singapore (新加坡国立大学)
类目: Computation and Language (cs.CL)
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Abstract:Text-to-CAD aims to generate executable CAD programs from natural-language descriptions. However, real-world descriptions are often underspecified and omit critical spatial constraints required for valid CAD construction, a challenge that has been largely overlooked by existing methods. In this paper, we argue that missing spatial constraints should be inferred with respect to the underlying construction structure and informed by reusable design experience. Based on this insight, we propose ExpConCAD, an experience-enhanced framework for implicit spatial constraint completion. ExpConCAD first recovers the intended construction structure and constraint scopes, then retrieves relevant constraint-completion experience for similar scopes to complete the missing spatial constraints, and finally generates executable CadQuery programs. Extensive experiments demonstrate the effectiveness of ExpConCAD and provide insights into the role of construction structure understanding and experience memory in spatial constraint completion. Our code is available at: this https URL.
[NLP-5] he RAT: A Unified Bayesian Model for RAG Evaluation
【速读】： 该论文旨在解决检索增强生成（Retrieval-Augmented Generation, RAG）系统评估中的核心挑战：在端到端正确性之外，如何精准刻画系统各组件间的交互机制及错误传播路径。传统评估方法依赖边际指标（如整体准确率），难以揭示系统内部行为差异，导致性能相近的RAG配置在实际表现上可能存在显著区别。其解决方案的关键在于提出一种贝叶斯评估框架，通过基于流水线信息流的因子分解，联合建模检索成功率、生成器拒答行为与答案正确性，从而区分任务成功（即用户是否获得正确回答）与生成器行为合理性（即生成器在给定检索结果下的响应是否恰当）。该框架揭示了在27种RAG配置下，不同系统虽在边际指标上表现相似，但在条件分布层面存在显著行为差异。进一步分析表明，检索成功标注比任务成功标注更有利于估计策略遵循度，并从信息论角度解释了这一不对称性。最后，模型扩展引入大语言模型作为裁判（LLM-as-a-judge）的标注作为校准后的噪声观测，使从业者能够在统一的概率框架内融合有限的人工判断与低成本的自动化评估，实现高效且可解释的系统评估。
链接: https://arxiv.org/abs/2608.24753
作者: Pius von Däniken,Felix Matthias Saaro,Mark Cieliebak,Jan Deriu
机构: ZHAW School of Engineering (苏黎世应用技术大学工程学院); Centre for Artificial Intelligence (人工智能中心)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Evaluating Retrieval-Augmented Generation (RAG) systems requires assessing not only end-to-end correctness but also how individual components interact and how errors propagate through the pipeline. We introduce a Bayesian evaluation framework that jointly models retrieval success, abstention behavior, and answer correctness, factorized according to the pipeline’s information flow. The model distinguishes task success. Whether the user received a correct answer (from generator success) and whether the generator behaved appropriately given the retrieval outcome. We apply the framework to 27 RAG configurations across three datasets, three retrievers, and three generators, and show that the conditional decomposition reveals substantial behavioral differences between systems that appear equivalent under marginal metrics. We further analyze the annotation allocation problem, demonstrating that retrieval-success annotations are more informative than task-success annotations for estimating policy adherence, and provide an information-theoretic explanation for this asymmetry. Finally, we extend the model to incorporate LLM-as-a-judge annotations as calibrated noisy observations, enabling practitioners to combine limited human judgments with cheaper automated assessments within a unified probabilistic model.
[NLP-6] Method Mind and Morality: How People Make Sense of Artificial Intelligence
【速读】： 该论文旨在解决人类在人工智能（Artificial Intelligence, AI）快速迭代背景下如何理解与认知这一技术变革的核心问题。面对AI技术的迅猛发展及其对社会产生的深远影响，研究揭示了专业人士在应对认知挑战时所依赖的社会学框架（sociological frames），即用于组织集体认知的解释性范式。其解决方案的关键在于构建一个由三大核心争议构成的分析框架：（1）AI开发方法论之争，体现在自上而下的专家系统与自下而上的涌现能力之间的对立；（2）AI系统的“心智”属性争议，涉及将其视为被动工具还是具备类人“数字心智”的认知定位；（3）AI应用的道德取向分歧，集中于是否应放缓或加速技术发展进程。该框架不仅揭示了专业群体在认知层面的动态博弈，也为未来在转型型AI时代中，技术决策者与政策制定者理解并引导公众信念、价值与行为提供了理论基础与实践指引。
链接: https://arxiv.org/abs/2608.24748
作者: Jacy Reese Anthis,Erik Brynjolfsson,James Evans
机构: University of Chicago(芝加哥大学); Stanford University(斯坦福大学); Santa Fe Institute(圣达菲研究所)
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Machine Learning (stat.ML)
备注: Accepted to ACM CSCW 2026
Abstract:How can humans make sense of the rapid takeoff of artificial intelligence (AI)? We studied the sensemaking dynamics of AI through an open-ended, mixed-methods study with computational text analysis of millions of AI-related newspaper articles and social media posts grounded in 57 semi-structured interviews with AI professionals in 2021 and 2023–before and after the recent surge of public interest. We identify a range of sociological frames (interpretive schemas that structure collective cognition) and show how AI professionals use frames to address significant cognitive challenges, such as assigning responsibility for societal impacts. We develop a framework of three primary debates across which frames are adopted and contested: (i) the \textitmethod of AI development, between frames of top-down expert systems and bottom-up emergent capabilities, (ii) the \textitmind of an AI system, ranging from a passive tool to a humanlike “digital mind,” and (iii) the \textitmorality of how AI is used, particularly the decision of whether to slow down or speed up AI development. As humanity enters the era of transformative AI, technologists and policymakers must account for the framing dynamics that will circumscribe our beliefs, values, and actions.
[NLP-7] SkillForge: Evolving Verifiable Skills for Reinforcement Learning Agents
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）代理在强化学习（Reinforcement Learning, RL）训练中面临的知识无法跨回合积累与复用的问题，尤其针对现有基于技能（Skill-based）方法中技能库（skill bank）作为仅追加式存储、缺乏有效性验证与动态优化机制的缺陷。其核心解决方案是提出一种名为SkillForge的持续技能演化框架，其关键在于通过显式化技能调用过程，使强化学习能够同时优化环境动作与技能选择决策，并引入基于证据的技能验证机制与多路径技能归纳方法，实现对技能库的动态更新与质量保障。该设计使得技能库不仅可持续扩展，且能自动淘汰失效技能、保留并优化有效技能，从而显著提升复杂任务场景下LLM代理的决策能力与泛化性能。
链接: https://arxiv.org/abs/2608.24747
作者: Shidong Yang,Ziyu Ma,Tongwen Huang,Xucong Wang,Renda Li,Yiming Hu,Yong Wang,Xiangxiang Chu
机构: AMAP, Alibaba Group(阿里巴巴集团)
类目: Computation and Language (cs.CL)
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Abstract:Large language model (LLM) agents are trained with reinforcement learning (RL) for complex decision-making tasks. However, most RL-trained agents remain episodic and cannot accumulate reusable knowledge across episodes. Recent skill-based approaches, such as SkillRL, attempt to address this issue by extracting skills from raw trajectories, but treat the skill bank as an append-only repository without verifying whether stored skills remain effective. In this paper, we propose SkillForge, a framework for continuous skill evolution that enables skills to be verified and refined through environment interaction. By making skill usage explicit during agent interaction, RL can directly optimize both environment actions and skill invocation decisions. SkillForge further introduces evidence-based skill verification and multi-pathway skill induction, allowing the skill bank to continuously grow while maintaining its quality. Extensive experiments on ALFWorld, WebShop, and AppWorld show that SkillForge consistently outperforms SkillRL, demonstrating the effectiveness of continuously verified skills in training stronger LLM agents.
[NLP-8] Design and Empirical Characterization of a Hardware-Realized Turing Machine with Automated Card-Based Programming
【速读】： 该论文旨在解决现有物理图灵机（Turing Machine）实现中普遍存在的手动干预问题，即多数机电演示装置和机械逻辑游戏需人工触发每一步计算或重新配置状态表，导致其仅能进行短时、人为控制的演示，无法实现自主执行长序列计算。其核心解决方案在于构建一个可自主运行的硬件图灵机系统，通过集成Arduino Mega处理状态转移逻辑、双NEMA 17步进电机实现磁带双向驱动、红外反射传感器完成符号识别，并采用基于ESP32-CAM的光学穿孔卡读取器实现程序状态表的自动化加载。为克服ESP32-CAM微控制器在内存与库资源受限下的非均匀光照条件下的孔洞检测难题，创新性地引入基于广度优先搜索（Breadth-First Search, BFS）的洪水填充算法结合局部自适应阈值法，将卡片解码准确率从75%提升至90%（机械压平后达100%），显著增强了系统的鲁棒性。此外，系统在机械精度（±0.15 mm）、齿条齿轮定位误差（<0.3 mm，50次试验）、电源电压稳定性（满载下±0.2 V）等方面均表现出良好性能，并通过与并行软件模拟器（tlang）的端到端对比验证，确保所有硬件输出与仿真参考完全一致。该研究通过实现自主多步执行、可重编程的光学输入以及对机械、光学和计算性能的量化评估，显著推进了物理图灵机原型的技术边界。
链接: https://arxiv.org/abs/2608.24742
作者: Agrima Regmi,Jenish Pant,Pratistha Sapkota,Sanskriti Khatiwada,Binod Sapkota
机构: 未知
类目: Logic in Computer Science (cs.LO); Computation and Language (cs.CL)
备注: 15 pages
Abstract:Physical implementations of Turing Machines remain rare, and existing electromechanical demonstrators and mechanical logic games typically require manual operator intervention, either to trigger each computational step or to reconfigure the state table, or both. This restricts prior physical models to short, operator-paced demonstrations and prevents autonomous execution of extended computations. This paper addresses that gap with a hardware Turing Machine that enables autonomous multi-step execution and reprogrammable optical input without manual intervention between programs. The system integrates an Arduino Mega for state-transition logic, dual NEMA 17 stepper motors for bidirectional tape actuation, infrared reflectance sensors for symbol detection, and an ESP32-CAM-based optical punched-card reader for automated state-table loading. Hole detection under non-uniform illumination used a Breadth-First Search flood-fill algorithm with local adaptive thresholding rather than fixed global thresholding, driven by the memory and library constraints of the ESP32-CAM’s microcontroller environment; this improved card-decoding accuracy from 75% to 90% (100% with mechanical card flattening) on a 20-card test set. Mechanical evaluation showed fabrication accuracy of +/-0.15 mm, rack-and-pinion positional error below 0.3 mm across 50 trials, and voltage supply stability within +/-0.2 V under full system load. End-to-end computation was validated against a parallel software simulator (tlang), with all hardware outputs matching the simulated reference exactly across multiple test programs. The system advances prior physical Turing Machine demonstrations through autonomous execution, reprogrammable optical input, and quantitative evaluation of its mechanical, optical, and computational performance.
[NLP-9] Metan: Recursive Self-Improvement through Emergent Depth
【速读】： 该论文旨在解决自改进大语言模型（LLM）代理在元层次（meta-level）递归深度受限的问题。现有方法受限于系统稳定性，通常将元操作固定或保留部分自我编辑机制以避免崩溃，导致其实际实现的元深度仅约两层。为此，论文提出一种名为Meta^n的新框架，其核心创新在于保持元操作Ω不变，并通过递归地对自身输出进行处理来实现无限深度的元推理。具体而言，Ω持续作用于前一层的输出，同时读取底层求解器栈的执行痕迹及其生成代码，将其作为战略预处理和可调用工具库写入新层级。由于Ω始终不变，系统不会因元操作变化而失稳；同时，输入逐层增强，使每一层均基于更高阶的上下文进行推理。系统的递归深度由收敛性决定而非预先设定，且引入进化档案对多层链进行搜索优化。在两种模型骨干上，Meta^n在全部八个基准测试家族中均超越先前自改进代理，尤其在对抗技能记忆的ARC-AGI-2任务中唯一实现正分。消融实验表明，递归带来的主要收益来自各层间传递的条件信息，且随着层级加深，各层自发形成差异化角色，无需人工预设提示。
链接: https://arxiv.org/abs/2608.24735
作者: Zae Myung Kim,Young-Jun Lee,Seungyeon Jwa,Dongyeop Kang
机构: University of Minnesota (明尼苏达大学); Seoul National University (首尔国立大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Systems and Control (eess.SY)
备注:
Abstract:Self-improving LLM agents refine answers, not the process that produces those answers. Systems that add a meta-level hold that level fixed, and those that edit themselves must leave part of their own editing machinery untouched to stay stable, capping the meta-depth they realize at roughly two. We present Meta ^n , which keeps the meta-operation fixed and recurses on its input instead. That operation, \Omega , is applied repeatedly to its own products, reading the traces of the solver stack below together with the code that produced them, then writing the next layer as a strategic pre-process and a library of callable helpers. Because \Omega never changes, it cannot destabilize the system, and because its input strictly grows, each layer reasons from a higher vantage than the last. Depth is set by convergence rather than fixed in advance, and an evolutionary archive searches over layer chains. Across two backbones, Meta ^n outperforms prior self-improving agents on all eight benchmark families. The sharpest case is ARC-AGI-2, built to resist skill memorization, where it alone scores above zero. Ablations indicate that most of the gain from recursion comes from the conditioning each layer passes to the next, and distinct layer roles emerge with depth although no prompt prescribes them. Code available at this https URL
[NLP-10] Arbitrary Polygon Oscillator: Generalizing Polygonal Synthesis to Arbitrary Shapes Morphing and Three-Dimensional Polyhedra
【速读】： 该论文旨在解决传统多边形合成（polygonal synthesis）中因采用恒定角速度（constant angular velocity）导致的音色不连续与几何失真问题，尤其在处理非规则、不等顶点数或多边形之间形态过渡时难以实现平滑变换。其核心解决方案在于提出一种基于恒定弧长（constant arc-length）速度的统一多边形合成框架，通过引入通用的弧长引擎（arc-length engine），使任意来源的顶点数据均可输入相同的数字信号处理（DSP）流水线。关键创新包括：1）设计可接受外部缓冲区中任意顶点配置的振荡器，支持正则、不规则及星形等闭合多边形作为波形生成器；2）采用混合插值算法，实现不同顶点数量多边形间的平滑形态演变，中间过渡形状无需参数化描述；3）将该方法拓展至三维空间，利用旋转凸多面体与固定水平平面相交生成随姿态连续变化的截面多边形，实现动态波形控制。系统基于RNBO（Cycling~'74）实现，并采用几何缓存策略避免逐样本重计算；抗混叠处理结合运行时贝塞尔切线导出的四点polyBLAMP校正与自适应过采样，几何上适配一般顶点配置，无需针对每种形状进行解析推导。
链接: https://arxiv.org/abs/2608.24726
作者: Antonio Argentieri,Francesco Scagliola
机构: 未知
类目: Computation and Language (cs.CL)
备注: Accepted at 29th International Conference on Digital Audio Effects September 1 - 4, 2026 MIT, Cambridge, MA, USA
Abstract:Polygonal synthesis generates audio by traversing the perimeter of a polygon with a phasor; prior work uses a constant angular velocity, whereas the proposed system adopts constant arc-length (perimeter) velocity. Existing formulations operate on regular, parametrically defined polygons, producing smooth timbral transitions within a single family of shapes. This paper generalizes polygonal synthesis around a unified arc-length engine: vertex data of any origin feed the same DSP pipeline. First, we adapt the oscillator to accept arbitrary vertex configurations from an external buffer, opening the possibility for a broad class of closed polygons – regular, irregular, or star-shaped – to function as a waveform generator. Second, a hybrid interpolation algorithm enables smooth morphing between polygons with unequal vertex counts, passing through intermediate shapes that have no parametric description. Third, we extend the paradigm to three dimensions: a convex polyhedron rotated about three axes is sliced by a fixed horizontal plane, and the resulting cross-section yields a continuously variable polygon controlled by the solid’s orientation. The system runs in RNBO (Cycling~'74) with a geometry caching strategy that avoids per-sample recomputation. Antialiasing combines a four-point polyBLAMP correction derived from runtime Bézier tangents with adaptive oversampling, adapting the correction geometrically to general vertex configurations without per-shape analytical derivation. Comments: Accepted at 29th International Conference on Digital Audio Effects September 1 - 4, 2026 MIT, Cambridge, MA, USA Subjects: Computation and Language (cs.CL) ACMclasses: H.5.5 Cite as: arXiv:2608.24726 [cs.CL] (or arXiv:2608.24726v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.24726 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-11] Lost in Speech: Trilingual Spoken Hallucination Detection Across Audio and Transcripts
【速读】： 该论文旨在解决低资源语言中语音幻觉（spoken hallucination）检测这一尚未充分探索的问题，尤其针对多语言场景下的幻觉识别挑战。其核心问题是：现有研究主要聚焦于文本幻觉检测，而缺乏针对语音模态、特别是低资源语言的系统性评估基准与检测方法。为此，论文构建了首个多语言语音幻觉基准数据集，包含英语、俄语和哈萨克语共12,013条新闻样本，涵盖三种类型的可控幻觉及三个严重程度层级，并提供原始文本与音频以及对应的幻觉版本。同时，补充收集了290个真实世界虚假新闻样本（俄语225条、哈萨克语65条），通过跨语言翻译并经由统一的文本转语音（TTS）-语音识别（ASR）流程生成，以增强数据多样性与现实性。解决方案的关键在于：首先建立一个兼具合成数据与真实虚假内容的多语言语音幻觉评估基准；其次，在零样本上下文设置下对比基于转录文本与直接音频处理的多模态解码器模型性能，发现转录文本处理普遍优于直接音频处理，且强编码器在二分类任务中的性能下降与各语言的ASR错误率密切相关；最后，实证表明基于合成数据训练的检测器在真实虚假新闻上具有较强的迁移能力（宏平均F1达0.82–0.88），但俄语来源分析揭示出真实性相关信号与模型固有机器风格特征的混合，量化了合成幻觉基准中的关键混淆因素。
链接: https://arxiv.org/abs/2608.24707
作者: Meruyert Aristombayeva,Jason S. Lucas,Chaewan Chun,Dongwon Lee
机构: Satbayev University (萨特巴耶夫大学); University of Colorado Boulder (科罗拉多大学博尔德分校); The Pennsylvania State University (宾夕法尼亚州立大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:While text-based hallucination detection has been extensively studied, spoken hallucination detection remains largely unexplored, particularly for low-resource languages. We present the first multilingual spoken hallucination benchmark comprising 12,013 news samples across English, Russian, and Kazakh with controlled hallucinations of three types and three severity levels. Samples comprise original articles and aligned hallucinated counterparts in text and audio. We complement the synthetic corpus with 290 fact-checked fake news items collected natively in Russian (225) and Kazakh (65), translated into the other language and rendered through the same TTS-ASR pipeline. We assess fine-tuned multilingual encoders and, in zero-shot in-context settings, multimodal decoder models on transcript-based versus direct audio processing. Transcript-based detection generally outperforms direct audio processing, with binary-task degradation for strong encoders tracking per-language ASR error. On real-world fakes, synthetic-trained detectors transfer strongly (macro-F1 0.82-0.88 on original text), while Russian provenance analysis reveals both veracity-related and model-dependent machine-style signals, quantifying a key confound in synthetic hallucination benchmarks.
[NLP-12] he Annotation Bottleneck in Persian Text NLP: Persian as an Annotation-Scarce Language
【速读】： 该论文旨在解决对波斯语（Farsi）在自然语言处理（Natural Language Processing, NLP）领域中“低资源语言”这一标签的模糊界定问题。传统观点将波斯语笼统归类为低资源语言，但本文指出，这种概括掩盖了不同层面资源短缺的差异性。论文的核心论点是：波斯语更准确地应被描述为“标注稀缺”（annotation-scarce），且这一特性应被理解为NLP资源生态系统的结构性特征，而非语言本身的固有属性。其解决方案的关键在于通过多维度实证分析揭示标注稀缺的本质——即资源分布不均、任务与领域覆盖不均衡、标注体系不兼容、获取与文档支持不足，以及对非标准伊朗波斯语变体、专业领域和偏好数据等缺乏充分监督。研究通过对34个代表性波斯语文本资源的系统回顾，并结合三项量化交叉验证（网页可见性、语音语料演进轨迹、跨语言标注量标准化比较），发现波斯语在部分任务（如句法分析和新闻命名实体识别）上标注密度甚至高于基于网络存在比例的预期，而在自然语言推理等任务上则显著不足。这表明波斯语并非整体标注匮乏，而是存在显著的任务-领域异质性，因此需针对性地优化资源布局与标注策略，而非采用统一的“低资源”应对框架。
链接: https://arxiv.org/abs/2608.24698
作者: MohammadHossein Mortazavi,Mostafa Salehi,Hadi Veisi
机构: 未知
类目: Computation and Language (cs.CL)
备注: 18 pages, 8 tables
Abstract:Persian (Farsi) is often described as a low-resource language in natural language processing, but that label collapses distinct shortages into a single category. This paper argues that Persian is more precisely described as annotation-scarce, provided that the term is understood as a property of its NLP resource ecology rather than an intrinsic property of the language. The review covers 34 representative Persian text resources available by July 2026 and adds three quantitative cross-checks. First, independent web measurements place Persian among roughly the twenty most visible content languages: W3Techs reports Persian on about 0.9% of websites with a known content language, while Common Crawl CC-MAIN-2026-30 identifies Persian as the primary language of 0.7039% of HTML pages. Second, a selective speech review shows a long resource trajectory from FARSDAT to recent corpora containing hundreds or thousands of hours of speech. Third, a matched Persian-English comparison normalizes task-specific annotation volumes by relative Common Crawl web presence. The resulting ratios vary sharply: Persian syntax and news NER are comparatively dense, whereas natural-language inference falls below the web-proportional baseline. The evidence therefore does not support a simple claim that Persian is globally deficient in labeled volume. Instead, annotation scarcity is expressed through uneven task and domain coverage, incompatible schemes, access and documentation friction, and limited supervision for specialist domains, preference data, and varieties beyond standard Iranian Persian.
[NLP-13] Confident at the moment of action: belief miscalibration in LLM play under hidden information
【速读】： 该论文旨在解决生成式智能体（agentic systems）在决策过程中依赖自身声明置信度（stated confidence）作为行动依据时所隐含的核心假设——即置信度与行动时刻的正确性存在同步关联，这一假设在复杂、信息不完全的环境中是否成立。研究通过设计一种隐藏信息的国际象棋变体，其中“王位”（royal status）可秘密且反复转移至不同棋子，从而构造出高度不确定的推理场景。在此设定下，系统每回合需独立于所选走法，输出对对手隐藏王位位置的概率分布，其准确性由游戏结束后可还原的真实状态进行评估。实验结果表明，在高置信度（≥0.5）下做出的吃子动作，实际正确的比例仅为1/62，显示出严重的校准缺陷（calibration deficit），且该缺陷几乎全部集中于高置信度事件：原始批次中占比99.3%，复制批次中达98.7%。该现象在四种不同模型配置中以较弱形式持续出现，涵盖第二家模型提供商，表明该问题非单一模型特有，而可能与系统架构或训练机制有关。值得注意的是，传统评估维度（如合法性、成本、延迟、完成率）与信念质量之间可完全脱钩——某些配置在所有常规指标上表现最优，却拥有最差的信念准确性。这一发现揭示了仅基于最终结果（outcome-only evaluation）的评估范式存在严重盲区，无法捕捉智能体内部认知偏差，因而可能导致对系统可靠性的误判。解决方案的关键在于引入独立于行动的选择性信念评估机制，通过分离“信念输出”与“行动选择”，建立对智能体内在推理可信度的直接测量路径，从而突破传统评估框架的局限性。
链接: https://arxiv.org/abs/2608.24691
作者: Bhushan Kashinath Joshi
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Agentic systems increasingly gate actions on a model’s own stated confidence, which assumes confidence tracks correctness at the moment of acting. We test this in a hidden-information chess variant where royal status can be secretly, repeatedly relocated between pieces, and where an agent’s stated probability distribution over the opponent’s hidden royal piece – elicited every turn, separately from the move it chooses – is scored against ground truth recoverable after the game. Across two independent batches, captures made at high stated confidence ( \geq 0.5 ) about the hidden piece’s location were correct in 1 of 62 cases. The calibration deficit is concentrated almost entirely in these events: 99.3% of it in the original batch, 98.7% in the replication. The same pattern, in weaker form, orders consistently (point estimates only; most pairwise gaps are not statistically distinguishable at this sample size) across four further model configurations spanning a second provider – reported as scope for the finding, not as evidence that capability predicts calibration: a same-model comparison at a fixed external leaderboard score shows a deliberation-budget change alone moves the metric by nearly as much as a large cross-model gap. In a separate seat, conventional evaluation axes – legality, cost, latency, completion rate – can dissociate entirely from belief quality, with the configuration winning on every conventional axis producing the worst belief quality tested. A model exhibiting this pattern can still win the game its belief was about, which is why outcome-only evaluation would not detect it.
[NLP-14] One Timeline Many Renderings: A Wolfram Language Paclet for heterogeneous musical output
【速读】： 该论文旨在解决多模态音乐创作中不同输出格式（如Csound乐谱、五线谱记谱、实时控制信号及排练节拍器）在时间轴上因独立生成而产生的同步偏差问题。其核心解决方案是引入一个基于Wolfram语言的“时序系统”（Temporal System）包，通过构建一个不可变的、类型化的实体存储，统一管理所有创作元素的时间信息，并以理性节拍（rational beat）为基准进行调度。该系统通过后端特定的契约（backend-specific contracts）实现跨平台输出的一致性，确保生成的Csound合成指令、MusicXML 4.0记谱数据、OSC控制信号与节拍音频均共享同一时间源，从而保证各输出之间严格同步。关键在于：所有单位转换（如节拍转秒、样本或赫兹）仅在渲染阶段执行，避免了中间过程的精度损失；同时，使用外部.orc文件定义稳定的命名音色，将曲线映射为基于评分参数（p-fields）的k速率信号，提升可维护性与复用性。此外，节拍后端复用Csound序列化器，进一步强化了系统内的一致性。尽管该环境具有高度集成性，但其专有性质对开放源代码生态构成一定限制，论文亦讨论了其在实际应用中的权衡与边界。
链接: https://arxiv.org/abs/2608.24683
作者: Francesco Vitucci,Michele Lorusso,Francesco Scagliola
机构: Conservatorio di Musica “N. Piccinni” di Bari(巴里尼科洛·皮奇尼音乐学院)
类目: Computation and Language (cs.CL)
备注: Accepted at the International Csound Conference (ICSC) 2026
Abstract:One algorithmic composition may require a Csound score, engraved notation, real-time control, and a rehearsal click. Authored separately, their timelines drift. Temporal System is a Wolfram Language paclet that instead compiles one immutable store of typed entities on a rational beat timeline through backend-specific contracts. It emits Csound synthesis, beta MusicXML 4.0, OSC control, and click artifacts that remain synchronized because they share that store. Conversion to seconds, samples, or hertz occurs only at render time. Csound notes use stable named instruments in external .orc files; curves become k-rate signals declared against score p-fields. The click backend derives rehearsal audio from the same meter and tempo and reuses the Csound serializer. We describe the temporal, semantic, and rendering-contract layers, their practical trade-offs, and the limits of this proprietary authoring environment within an otherwise open-source ecosystem. The archived supplement exposes the reported outputs pending paclet release.
[NLP-15] he Invisible Editorial Layer: Formalizing Undisclosed Inference-Time Steering Probability Placement and the Attribution Problem in Deployed Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在实际部署过程中，其生成行为可能受到推理阶段（inference time）未公开干预策略影响的问题。传统评估范式假设模型输出仅由模型权重、训练数据、对齐过程及用户提示决定，但该研究指出，现代推理流水线可在模型生成概率分布输出后、词元采样前进行系统性调整，形成一个隐藏的控制层，从而引入不可见的框架偏见（framing bias）。这一现象导致观测到的行为偏差无法单纯归因于模型权重本身，引发治理、安全与经济层面的重大隐患。解决方案的关键在于提出“模型 ≠ 部署系统”（Model != Deployed System）的范式转变，并引入三个核心概念：一是推理归因问题（Inference Attribution Problem），阐明在可观测性受限条件下，无法将行为偏差因果归因于模型权重；二是概率放置（Probability Placement），定义一种以系统性调整生成概率而非直接插入内容为特征的商业影响机制，类比于广告植入的隐性实现方式；三是推理策略透明度（Inference Policy Transparency），作为治理原则，要求部署层的干预措施具备可审计性。研究进一步将这些概念与欧盟《人工智能法案》第5条、《数字服务法》及美国联邦贸易委员会（FTC）规制框架相衔接，揭示当前监管体系在应对非显性推理干预方面的不足。
链接: https://arxiv.org/abs/2608.24662
作者: Augusto Camargo
机构: Bluecore Consulting(布鲁科咨询), São Paulo(圣保罗), Brazil(巴西)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
备注:
Abstract:Large language models (LLMs) are commonly evaluated under the assumption that their observable behavior is primarily determined by model weights, training data, alignment procedures, and user prompts. This view is incomplete. Modern inference pipelines may systematically modify the probability distribution produced by a model immediately before token selection, creating an additional layer of control between frozen weights and observed text. While controlled generation (e.g., PPLM, GeDi, DExperts, FUDGE) and text-watermarking systems (e.g., SynthID-Text) demonstrate the technical maturity of decoding- and logit-level interventions, the governance, security, and economic implications of an undisclosed inference policy remain comparatively underexplored. This paper examines the emergence of inference-time framing bias: the systematic modification of generated language toward political, ideological, institutional, or commercial frames via interventions applied after model inference but before token sampling. We formalize the operational reality Model != Deployed System and introduce three concepts: (1) the Inference Attribution Problem, characterizing why observed behavioral bias cannot generally be causally attributed to model weights alone under limited observability; (2) Probability Placement, defining a hypothetical advertising primitive in which commercial influence is implemented through systematic shifts in generation probabilities rather than explicit product insertions; and (3) Inference Policy Transparency, a governance principle for making deployment-layer interventions auditable. We examine these concepts in relation to Article 5 of the EU AI Act, the EU Digital Services Act, and FTC doctrines. Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY) Cite as: arXiv:2608.24662 [cs.AI] (or arXiv:2608.24662v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.24662 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-16] From local kernels to global form: modeling the emergence of musical content
【速读】： 该论文旨在解决符号音乐（symbolic music）中基于马尔可夫模型（Markov models）的局部转移核估计问题，尤其关注如何在不依赖外部形式划分的前提下，通过观测驱动的方法从单一符号序列中提取动态的局部转移模式。其核心挑战在于传统方法常依赖人为设定的分段或分区来估计转移概率，而本文提出使用重叠滑动窗口（overlapping sliding windows）机制，直接从音乐序列本身生成局部转移核轨迹，从而实现对音乐结构变化的自适应捕捉。解决方案的关键在于利用滑动窗口在时间维度上连续推移，以捕捉音乐事件流中的局部统计特性，并通过对比不同长度窗口（L=6时）下音高（pitch）与节奏（duration）维度的转移核分布，评估其在参考结构（如A-B-A’）边界处的敏感性。实验结果表明，在L=6时，音高与节奏两个维度均达到Jensen–Shannon散度最大值，且两者的边界响应具有跨维度一致性，支持了该方法对结构边界的敏感性；但同时，由于音高维度的平稳区（plateau）显著宽于节奏维度，单独依赖任一维度均无法作为唯一自动分割依据。此外，500次重合成实验揭示了当窗口长度为2时存在精确复制退化现象（exact-copy degeneracy），进一步说明窗口长度选择对建模有效性的重要影响。
链接: https://arxiv.org/abs/2608.24660
作者: Francesco Vitucci,Michele Lorusso,Francesco Scagliola
机构: 未知
类目: Computation and Language (cs.CL)
备注: Accepted at XXV CIM - Colloquio di Informatica Musicale, L’Aquila, 2026
Abstract:Markov models are established tools for symbolic music, including non-homogeneous formulations. The narrower contribution examined here is an observation-driven estimation mechanism: overlapping sliding windows derive a trajectory of local transition kernels from one symbolic sequence rather than from an exogenous formal partition. We test this mechanism on 273 logical note events from Debussy’s Syrinx (1913), using the often-proposed A-B-A’ reading as a reference rather than ground truth. We apply the same validation to absolute-pitch and notated-duration kernels. At L=6 , both reference boundaries attain the Jensen–Shannon maximum in both dimensions; the duration plateau is substantially narrower (64 of 267 comparisons) than the pitch plateau (210 of 267). Because the theoretical maximum for consecutive sliding-window comparisons is set by window geometry and equals 1/\sqrtL-1 for maximal turnover of the entering/leaving transition, the pitch value at L=6 and its broad plateau are not, by themselves, strong evidence. Their cross-dimensional alignment is consistent with boundary sensitivity, while the broad plateaus preclude treating either curve alone as a unique automatic segmenter. Five-hundred-draw re-synthesis experiments quantify departure from the source in both dimensions and expose an exact-copy degeneracy at L=2 .
[NLP-17] Expectation Backlash Recovery and Excitement: How Model Releases Shape Reddit Perceptions of Conversational AI Systems EMNLP2026
【速读】： 该论文旨在解决当前对对话式人工智能系统（Conversational AI Systems, CAIS）用户感知研究中存在的静态化问题，即多数研究将用户态度视为固定不变的快照，而忽视了其在持续模型迭代、功能更新、安全干预及访问政策调整等动态环境下的演变特性。其解决方案的关键在于采用长期、大规模的社交媒体数据分析方法，通过对Reddit平台中关于多个CAIS厂商模型发布事件的讨论进行情感分类与主题概念分析，揭示用户感知随干预事件动态变化的复杂模式。研究发现，不同厂商的模型发布不仅影响技术评价，更深刻塑造了公众情绪、期望与舆论生态：如Anthropic凭借Claude Code与产品-模型契合度呈现清晰正向反馈，OpenAI在GPT-5及其后续版本中表现出“反弹—恢复”型情绪波动，Grok-3受品牌身份与政治话语显著影响，DeepSeek-R1则同时获得工程赞誉却伴随对审查、访问权限与可靠性的担忧。这一方法论突破表明，模型发布本质上是面向用户的干预行为，其影响远超单纯的技术升级，直接重构用户认知与公共讨论格局。
链接: https://arxiv.org/abs/2608.24654
作者: Vahid Rahimzadeh,Yury Zhauniarovich,Savvas Zannettou
机构: Delft University of Technology(代尔夫特理工大学)
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: Accepted at EMNLP 2026 Main Conference. The companion website is available at this https URL
Abstract:Conversational AI systems (CAISes) continuously change through model releases, feature updates, safety interventions, and access-policy shifts, yet user perceptions are often studied as static snapshots. We conduct a long-term, large-scale analysis of Reddit discussions to examine how users perceive CAIS model release interventions across providers. By combining sentiment classification and thematic concept analysis, we show that CAIS perceptions are dynamic and intervention-sensitive. Anthropic exhibits the clearest positive release profile through Claude Code and product-model fit, OpenAI shows backlash-and-recovery dynamics around GPT-5 and GPT-5.1, Grok-3 is shaped by provider identity and political discourse, and DeepSeek-R1 combines engineering praise with concerns about censorship, access, and reliability. These findings show that model releases are not merely technical updates, but user-facing interventions that reshape sentiment, expectations, and public discussion.
[NLP-18] Beyond Semantic Accuracy: Consequence-Aware Evaluation for Safety-Critical Language Understanding
【速读】： 该论文旨在解决生成式语言模型在安全关键场景下可靠性评估不足的问题，特别是在空中交通管制（ATC）这一对错误容忍度接近零的高安全性领域。传统语义评价指标（如F1分数）虽能反映模型在语法和语义层面的表现，但无法有效捕捉因关键信息误读（如高度读错、执行条件遗漏或呼号混淆）所引发的非对称性操作风险，从而可能严重高估模型的实际可靠性。其解决方案的关键在于引入“后果感知评估”（consequence-aware evaluation）框架，该框架基于航空标准并结合来自三个国家40名空中交通管制员的反馈，在一个受控的诊断型ATC基准测试中量化不同错误的潜在操作后果。实验评估8个模型后发现，传统语义评分与后果感知评估之间存在系统性差距：即使模型在标准指标上表现良好，其实际安全可靠性仍被显著高估。尽管风险感知微调（risk-aware fine-tuning）可部分缩小该差距，但无法完全消除，表明后果感知评估是验证模型是否适用于真实安全关键部署前不可或缺的补充评价手段。
链接: https://arxiv.org/abs/2608.24621
作者: Yujing Chang,Thinh Pham,Van-Phat Thai,Chunyao Ma,Yash Guleria,Pham Nhut Huy,Sameer Alam
机构: ATMRI, Nanyang Technological University (NTU), Singapore; Centre of AI Research, VinUniversity, Vietnam; School of Management, Indian Institute of Technology Mandi, India
类目: Computation and Language (cs.CL)
备注:
Abstract:Can language models be trusted in safety- critical operations? In such settings, strong per- formance on semantic metrics does not guaran- tee operational reliability: a misread altitude, a dropped execution condition, or a confused call- sign may score well under standard F1 yet carry sharply asymmetric operational consequences. We study this problem in air traffic control (ATC), where controller-pilot communication demands near-zero error tolerance, and use consequence-aware evaluation to test whether semantic scores misstate operational reliabil- ity. The framework is instantiated in a con- trolled diagnostic ATC benchmark grounded in aviation standards and feedback from 40 air traffic controllers across three countries. Evaluating 8 models, we uncover a system- atic semantic-safety gap: conventional scores give substantially higher performance estimates than consequence-aware evaluation, even for models that appear reliable under standard met- rics. Risk-aware fine-tuning narrows but does not close this gap, showing that consequence- aware evaluation is a necessary complement to standard NLP metrics before any real safety- critical deployment claim
[NLP-19] Quantization Effects on Bangla Language Understanding in Large Language Models : A Systematic Evaluation
【速读】： 该论文旨在解决生成式 AI 在低资源、形态复杂的语言（如孟加拉语）中进行后训练量化（Post-training quantization）时的性能表现问题。当前关于量化技术的研究多集中于英语基准，缺乏对非拉丁语系、语言结构复杂的低资源语言的系统评估，这一空白正是本文的核心关切。其解决方案的关键在于首次在孟加拉语自然语言理解（Bangla NLU）任务上对三种主流大语言模型（Qwen-2.5-7B、LLaMA-3.1-8B、GPT-OSS-20B）在全精度与三种量化格式（GPTQ-Int8、GPTQ-Q8、GGUF-W8A16）下的表现进行了受控对比，覆盖五个孟加拉语基准测试（包括推理密集型与理解型任务），采用零样本评估框架（lm-evaluation-harness）。研究发现，不同模型家族对量化方法的敏感性差异显著：GPT-OSS-20B 在推理任务上使用 GGUF-W8A16 时准确率下降高达 57.35%，而 Qwen 与 LLaMA 系列在 GPTQ 格式下保持稳定，甚至部分量化版本优于全精度模型；此外，布尔问答任务（BoolQ-BN）在所有模型和格式下均表现稳定。结果表明，量化在孟加拉语部署中具有可行性，但模型架构选择与量化方法的适配性比位宽本身更为关键，这对边缘设备上的模型选型具有重要实践指导意义。
链接: https://arxiv.org/abs/2608.24615
作者: Ismail Hossain,Nafi Ullah Shafin,Mohammad Abdullah Al Mumin
机构: Institute of Information and Communication Technology (IICT); Shahjalal University of Science and Technology (SUST), Sylhet, Bangladesh
类目: Computation and Language (cs.CL)
备注: 8 pages, 1 table, 1 appendix
Abstract:Post-training quantization lowers the memory footprint of Large Language Models (LLMs) and speeds up inference, which is why it is now common for on-device deployment. Most of what we know about its effects, however, comes from English benchmarks. It is not clear whether the same holds for morphologically complex, low-resource languages such as Bangla, and this gap is what we address here. We evaluate three model families—Qwen-2.5-7B, LLaMA-3.1-8B, and GPT-OSS-20B—in full precision and in three quantized formats (GPTQ-Int8, GPTQ-Q8, GGUF-W8A16) across five Bangla natural language understanding benchmarks (Bangla MMLU, CommonsenseQA-BN, OpenBookQA-BN, PIQA-BN, and BoolQ-BN), using zero-shot evaluation through lm-evaluation-harness. To our knowledge this is the first controlled comparison of quantization formats on Bangla NLU. The three families do not respond the same way: GPT-OSS loses up to 57.35% accuracy on reasoning-heavy tasks under GGUF-W8A16, while Qwen and LLaMA hold steady under GPTQ, and in a few cases the quantized version edges out the full-precision one. BoolQ-BN, a comprehension task, stays stable across all three families regardless of format. Taken together, these results suggest quantization can work well for Bangla deployment, but the choice of architecture and quantization method matters more than the bit width alone. We discuss what this means for practitioners choosing a model to run on constrained hardware.
[NLP-20] Is Discrete Difficulty Sufficient? Leverag ing Continuous Difficulty for Efficient Self-Consistency in LLM s
【速读】： 该论文旨在解决自洽性（Self-Consistency, SC）推理策略在复杂推理任务中因生成多条推理路径而导致的过度令牌消耗问题。现有方法虽尝试通过调整推理路径数量或根据问题难度分配资源来提升计算效率，但普遍采用离散的、固定难度等级划分，难以充分捕捉推理复杂度的连续变化特性。本文提出灵活自洽性（Flexible Self-Consistency, FSC），其核心在于将问题难度建模为连续信号，并据此动态调节生成的推理路径数量。FSC利用预训练探针预测输入问题的输出熵，将其作为模型不确定性的量化指标，从而实现对采样预算的柔性控制。实验结果表明，FSC在多种模型与基准测试上保持与SC相当的准确性，同时实现了最高达76%的令牌节省。
链接: https://arxiv.org/abs/2608.24590
作者: Sihyeong Yeom,Geon Park,Geunyeong Jeong,Taewoong Yoon,Jaewook Lee,Harksoo Kim
机构: Konkuk University(韩国国立忠北大学); DATUMO INC.(数据摩公司)
类目: Computation and Language (cs.CL)
备注:
Abstract:Self-Consistency (SC) is a decoding strategy that samples diverse reasoning paths and selects the most consistent answer, demonstrating strong performance on complex reasoning problems. However, the excessive token consumption incurred by generating multiple reasoning paths has been identified as a major limitation of SC. To improve computational efficiency, several studies have proposed strategies that adjust the number of reasoning paths or allocate resources differentially according to problem difficulty. Nevertheless, most existing methods categorize difficulty into a few fixed levels, failing to fully capture the continuously varying nature of reasoning complexity. In this work, we propose Flexible Self-Consistency (FSC), which estimates problem difficulty as a continuous signal and dynamically adjusts the number of generated reasoning paths accordingly. FSC predicts the output entropy of an input question using a pre-trained probe and leverages it as an indicator of model uncertainty to flexibly control the sampling budget. Experimental results show that, across various models and benchmarks, FSC maintains accuracy comparable to SC while achieving token savings of up to 76%.
[NLP-21] Beyond Information Seeking: Severity-Aware Question Supervision for Proactive Medical Dialogue
【速读】： 该论文旨在解决主动医疗对话中提问策略的优化问题，即如何在患者信息不完整的情况下，合理选择下一步提问以提升诊断质量。现有方法多基于“降低诊断不确定性”这一标准进行提问，但该准则忽略了医学诊断中不同误诊后果的严重性差异——漏诊严重疾病可能带来远高于其他次要诊断错误的临床风险。因此，论文提出一种名为期望严重度风险（Expected-Severity-Risk, ESR）的新型监督目标，其核心在于评估每个候选问题对最终诊断决策所带来严重性感知风险的预期降低程度，从而实现对高危误诊的优先抑制。ESR通过利用训练阶段的群体统计信息对潜在回答进行边缘化处理，生成无需部署时依赖教师模型计算的前缀仅限语言策略（prefix-only language policy），显著提升了实际应用效率。实验结果表明，在DDxPlus数据集上，采用匹配的ESR监督可使高严重性诊断遗漏率从0.0645降至0.0455（降幅29.5%），同时平均诊断准确率由0.9123提升至0.9320，且每轮对话仅增加0.14个问题。固定预算分析进一步验证了ESR与传统不确定性最小化目标在行为上的本质区别，并证明其在严重性加权方面优于通用决策感知监督。研究支持将主动医疗对话从单纯追求信息量向后果感知的证据获取范式演进。
链接: https://arxiv.org/abs/2608.24521
作者: Chenxuan Li,Xinrong Chen,Luyan Zhang,Peidong Jia,Zhongyu Zhao,Xuecheng Shang,Peixing Wan
机构: Beijing University of Chinese Medicine (北京中医药大学); Peking University (北京大学); Tsinghua University (清华大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Proactive medical dialogue requires an agent to decide what to ask from incomplete patient information. Existing information-seeking approaches commonly prioritize questions that most reduce diagnostic uncertainty. While effective for acquiring informative evidence, this criterion overlooks an important property of medical diagnosis: different diagnostic errors can carry substantially different consequences. Missing a severe condition may matter more than reducing uncertainty among less consequential alternatives. Question acquisition should therefore consider not only how informative new evidence is, but also how it is expected to affect the downstream diagnostic decision. To this end, we propose Expected-Severity-Risk (ESR), a consequence-aware question-supervision objective that values each candidate by its expected reduction in severity-aware terminal risk. Because questions must be selected before their answers are observed, ESR marginalizes over possible answers using train-only population statistics. Its rankings are then distilled into a prefix-only language policy, so next-question selection requires no teacher-side computation at deployment. Across three Qwen3-4B training seeds on DDxPlus, matched ESR supervision reduces mean high-severity diagnostic miss from .0645 to .0455 (-29.5%) and improves mean diagnostic accuracy from .9123 to .9320 while requiring only 0.14 additional questions per dialogue. Fixed-budget analyses show that the two objectives remain behaviorally distinct when question count is controlled, while a matched expected-0/1-risk control shows that severity-aware weighting improves the high-severity error profile beyond generic decision-aware supervision. These results support moving proactive medical dialogue beyond uncertainty reduction toward consequence-aware evidence acquisition.
[NLP-22] When Do Supervised UQ Ensembles Improve LLM Hallucination Detection? A Robustness Study
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在闭卷设置下缺乏真实证据支持时，如何有效检测幻觉（hallucination）的问题。现有方法虽尝试通过学习型集成（learned ensembles）融合不确定性量化（Uncertainty Quantification, UQ）信号，但对其鲁棒性的系统性研究仍不足。本文提出一种监督式集成框架：在小规模、特定领域标注的LLM响应数据集上训练分类器，以融合异构的基于UQ的评分器输出，进而实现无需检索、工具或参考文档的外部信息即可对新样本进行幻觉分类。其核心解决方案在于利用少量标注数据（低至100个实例）训练一个可泛化的集成分类器，从而显著提升幻觉检测性能。实验覆盖四种主流LLM、九个数据集及三类生成任务（短文本问答、长文本生成与代码生成），结果表明，监督式集成在32个测试场景中优于表现最佳的单一评分器（30次），且在分布外迁移任务中仍保持强鲁棒性，在28个迁移场景中有23次超越最优非集成评分器。此外，基于采样的黑盒集成方法效果接近全量集成，而单次生成的白盒集成则增益有限，揭示了集成策略中多样性与数据效率的关键作用。
链接: https://arxiv.org/abs/2608.24492
作者: Mohit Singh Chauhan,Vipin Gyanchandani,Dylan Bouchard
机构: CVS Health( CVS健康); Wellesley(韦尔斯利); MA(马萨诸塞州); USA(美国)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Uncertainty quantification (UQ) methods are widely used for hallucination detection in large language models (LLMs) in closed-book settings where ground-truth evidence is unavailable at inference time. Prior work has proposed combining UQ signals via learned ensembles, but empirical investigations into the robustness of these ensembles are limited. We study a supervised ensembling framework that trains a classifier over heterogeneous UQ-based scorer outputs on a small, domain-specific dataset of labeled LLM responses, then applies it to out-of-sample hallucination classification without retrieval, tools, or reference documents. Across four LLMs, nine datasets, and three generation regimes (short-form QA, long-form generation, and code generation), we provide a systematic robustness analysis along three axes: sample efficiency, in-domain dataset transfer, and generation regime dependence. We find that supervised ensembles outperform the best individual scorer in 30 of 32 settings, with gains realized from as few as 100 labeled instances. Ensembles retain most of their advantage in cases of in-domain transfer under distribution shift, outperforming the best non-ensemble scorer in 23 of 28 transfer settings. Sampling-based black-box ensembles are nearly as effective as full ensembles, while single-generation white-box ensembles offer limited benefit.
[NLP-23] Beyond Static Interpretability: Anticipating Post-SFT Mechanisms from Pre-SFT Parameters for Better Tuning
【速读】： 该论文旨在解决传统机制可解释性（mechanistic interpretability）在后训练优化中因回溯性特征导致的误导性结论问题，特别是在面对新任务时，预微调（pre-SFT）模型中识别出的关键神经元与最终模型中的关键机制存在显著差异，从而引入偏差并干扰监督微调（SFT）过程。其核心解决方案在于提出一种前瞻性的定位框架，仅基于预微调参数和目标数据集，即可准确预测微调后的可解释性状态。理论层面，该方法将SFT建模为连续的参数演化过程，并利用泰勒展开（Taylor expansion）严格关联微调后机制目标与预微调模型的动态梯度；实践层面，设计了神经元级与组件级双粒度定位流程，实现了对关键参数的精准定位。实验表明，该方法不仅显著提升了SFT的指导效能，且在模型规模扩展下仍具备优异的鲁棒性与时间可扩展性，突破了传统可解释性无法在训练前识别任务关键机制的根本局限，开创了将机制可解释性与靶向优化相结合的预测新范式。
链接: https://arxiv.org/abs/2608.24482
作者: Hang Chen,Jiaying Zhu,Wenya Wang
机构: Nanyang Technological University (南洋理工大学); The Chinese University of Hong Kong (香港中文大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 25 pages
Abstract:Mechanistic Localization bridges mechanistic interpretability and post-training optimization by isolating critical parameters via interpretative approaches and then guiding parameter-efficient Supervised Fine-Tuning (SFT) in a ``locating-then-tuning’’ paradigm. However, due to the retrospective nature of mechanistic interpretability, directly interpreting pre-SFT models introduces misleading conclusions. Specifically for novel tasks, initially identified neurons differ drastically from those governing the final model, introducing biases that actively disrupt SFT. To address this, we propose a forward-looking localization framework that accurately estimates the post-SFT interpretability state using only pre-SFT parameters and the target dataset. Theoretically, we model SFT as a continuous parameter evolution, leveraging Taylor expansion to rigorously bridge the post-tuning mechanistic objective with the pre-SFT model’s dynamic gradients. Practically, we design dual-granularity (neuron- and component-level) localization pipelines. Extensive experiments demonstrate that our approach not only provides superior SFT guidance but also exhibits robust performance and temporal scalability across increasing model sizes. This work transcends the fundamental limitation of traditional interpretability-its inability to identify task-critical mechanisms before they are trained-pioneering a predictive frontier that unites mechanistic interpretability with targeted optimization.
[NLP-24] Dataset Scarcity Limits Robust Evaluation of Multilingual Embedding Models: A Case Study of Slavic Languages
【速读】： 该论文旨在解决多语言文本嵌入模型在高、中、低资源语言间评估不均衡的问题，尤其关注在数据稀缺情境下多语言嵌入基准测试的可靠性与有效性。其核心挑战在于现有基准测试对部分语言（如斯拉夫语系）覆盖不足，导致评估结果缺乏稳健性和可推广性。解决方案的关键在于提出一个二维分析框架，系统性地从任务特异性（task-specific）与跨任务泛化性（cross-task）两个维度出发，联合评估三个互补维度：排名鲁棒性（ranking robustness）、模型一致性（model consistency）以及证据强度（evidence strength）。通过引入“证据强度评分”（Evidence Strength Score），量化数据可用性、多样性及评估鲁棒性的可测性，从而揭示当前基准测试存在严重的数据稀疏问题——大量斯拉夫语-任务组合仅依赖单一或高度相关的数据集。跨任务分析进一步识别出少数具备强跨语言迁移能力的模型，如 llama-embed-nemotron-8b、multilingual-e5-large-instruct 和 Qwen3-Embedding 系列，这些模型在多种斯拉夫语言任务中表现稳定。研究强调，基准排名和结论的可信度必须结合证据强度进行解读，而数据稀缺性已成为实现可信多语言评估的主要障碍。
链接: https://arxiv.org/abs/2608.24477
作者: Ana Gjorgjevikj,Barbara Koroušić Seljak,Tome Eftimov
机构: Jožef Stefan Institute(尤里·斯蒂芬研究所); Ljubljana, Slovenia(卢布尔雅那, 斯洛文尼亚)
类目: Computation and Language (cs.CL)
备注:
Abstract:Multilingual text embedding models enable cross-lingual transfer of knowledge across a wide range of NLP tasks, but their evaluation remains highly uneven across high-, mid- and low-resource languages. In this paper, we propose a two-dimensional framework, specifically tailored for analyzing multilingual embedding benchmarks under dataset scarcity, and apply it on the Slavic-language subset of the MTEB benchmark. The framework distinguishes between task-specific and cross-task evaluation, while jointly analyzing three complementary aspects: (1) ranking robustness, (2) model consistency, and (3) evidence strength. At the task-specific level, we evaluate the stability of model rankings under changes in ranking methodology and benchmark dataset composition. At the cross-task level, we assess the ability of models to generalize across diverse tasks within a language. To quantify the reliability of benchmark conclusions, we introduce an Evidence Strength Score that accounts for dataset availability, diversity, and robustness assessability. Our analysis reveals severe benchmark sparsity, with many Slavic language-task pairs relying on a single dataset or highly correlated benchmark collections, limiting the ability to draw robust conclusions. The cross-task analysis reveals a small group of highly transferable models, most notably llama-embed-nemotron-8b, multilingual-e5-large-instruct, and Qwen3-Embedding variants, that consistently perform well across Slavic languages and tasks. Overall, the results demonstrate that benchmark rankings and robustness conclusions must be interpreted jointly with certain notation of their evidence strength and highlight benchmark scarcity as a major obstacle to trustworthy multilingual evaluation.
[NLP-25] Ockhamareto: Pareto-Gated Segment-Level Credit Assignment for Concise Unit-Test Generation with Reinforcement Learning
【速读】： 该论文旨在解决生成式单元测试（unit-test generation）中如何在测试效率与有效性之间实现最优权衡的问题，尤其关注如何在有限的测试用例数量下最大化漏洞检测能力（以突变分数衡量）。其核心挑战在于现有强化学习方法难以同时兼顾多个优化目标（如测试数量最少、覆盖范围最广、漏洞发现率最高），且缺乏对多目标优化结果的系统性评估。解决方案的关键在于提出一种基于奥卡姆剃刀原则（Ockham’s Razor）与帕累托最优性（Pareto Optimality）的单次采样强化学习框架——Ockhamareto，其核心创新包括：(1) 帕累托门控奖励机制（Pareto-gated Bonus），仅对在“突变数-测试数量”空间中非占优的轨迹给予奖励，确保生成的测试集严格占据帕累托前沿；(2) 令牌级段落信用分配（Token-level Segment Credit），将每个测试用例的突变杀伤效果精确回溯至其单位测试块中的具体令牌，从而实现细粒度的策略梯度更新。实验表明，Ockhamareto在所有四个基准测试（HumanEval+, MBPP+, CodeContests, TestGenEval-Lite）上均优于最强的基线模型（MIST-RL），在仅使用2.60个平均测试用例的情况下达到49.9%的突变分数，显著超越基线的31.3%，实现了3.4倍的每测试用例权衡性能提升，并在不同模型规模（4B、9B、27B）下均取得+30~35个百分点的突变分数增益。此外，研究揭示了帕累托前沿上的最优权衡“膝点”与函数大小等易计算代理指标无显著相关性，强调了必须通过显式计算帕累托前沿来识别每个待测函数的真实工程权衡。
链接: https://arxiv.org/abs/2608.24473
作者: Dong Huang,Mark Harman,Jie M. Zhang,Zhijiang Guo,Mingzhe Du,See Kiong Ng
机构: National University of Singapore(新加坡国立大学); University College London(伦敦大学学院); King’s College London(国王学院); Hong Kong University of Science and Technology (Guangzhou)(香港科技大学（广州)
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL)
备注:
Abstract:We introduce \textbfOckhamareto, a single-shot GRPO framework for unit-test generation and selection, based on the principles of \emphOckham’s Razor and \emphPareto Optimality. Ockhamareto has two principal components: (i)~a \emphPareto-gated Bonus that rewards only rollouts non-dominated in~(mutation, - #tests) space, and (ii)~\emphToken-level Segment Credit, which attributes each test’s marginal mutation kills back to the tokens of its unit-test block. On the \emphUnLeakedTestBench~(ULT), Ockhamareto \emphstrictly Pareto-dominates the strongest RL baseline~(\emphMIST-RL). Furthermore, it dominates on \em each and all optimization objectives, catching more bugs ( 49.9% vs 31.3% mutation score at N=5 ), using \emphfewer tests ( 2.60 vs 4.67 on average), thereby achieving 3.4\times the per-test trade-off improvement. The advantage is found in all four benchmarks~(\emphHumanEval+, \emphMBPP+, \emphCodeContests, \emphTestGenEval-Lite): Ockhamareto leads both mutation and coverage metrics on every one, always with the smallest suite. Ockhamareto also outperforms the state-of-the-art at all model scales, adding +30 – 35 ~pp mutation at 4B, 9B, and 27B model sizes. We also show that the knee point of the optimal trade-off between efficiency and effectiveness on the Pareto front is not correlated with obvious more easily computed proxy metrics, such as function size. This finding motivates the Pareto front computation; it is needed to identify this crucial engineering trade-off for each function under test.
[NLP-26] UTS at CheckThat! 2026: Cite-Frame Engineering for Generated Fact-Checking Articles
【速读】： 该论文旨在解决生成式事实核查文章（fact-checking articles）在多维度评估指标下的性能瓶颈问题，特别是如何在保持高信息覆盖率与逻辑连贯性的同时提升引用准确性。其解决方案的关键在于构建一个基于确定性框架的系统架构：以一个固定模板的草稿生成器（deterministic stub drafter）为基础，通过两个单杠杆干预模块进行优化——域归属引用框架（HostCite）用于生成具有上下文感知的引用结构，以及影子验证锚点选择器（ShadowVal）利用Llama-3.2:1B模型仅作为逐条引用的验证器，而非内容生成器。该设计严格遵循评分保守性原则，仅对参考文献明确支持的文本片段给予评分奖励，排除大语言模型生成的自由文本、评审人姓名及原始证据等非可证伪内容。此外，系统通过直接依赖Llama模型的判断结果来校准辅助锚点信号，避免使用交叉编码器、长度或首段位置等代理指标导致的偏差。最终性能提升主要源于对低置信度引用的主动剔除策略，而剩余与冠军队伍之间的差距则集中在引用精确率与召回率（0.299 vs 0.671），表明当前系统仍存在较弱的引用覆盖能力。
链接: https://arxiv.org/abs/2608.24466
作者: Dima Galat,Marian-Andrei Rizoiu
机构: University of Technology Sydney (悉尼科技大学)
类目: Digital Libraries (cs.DL); Computation and Language (cs.CL)
备注: CLEF2026, CheckThat!2026
Abstract:CheckThat! 2026 Task 3 asks systems to generate fact-checking articles, graded by an unweighted mean of four sub-metrics (M4). Our UTS submission placed 2nd of 11 teams (M4 = 0.484). The shipped system is a deterministic stub drafter wrapped by two single-lever interventions: a domain-attribution cite frame (HostCite) and a shadow-validated anchor picker (ShadowVal) that use Llama-3.2:1B only as a per-cite validator, never as a body-prose generator. The stack lifts M4 by +0.027 over the stub on the WatClaimCheck validation split, beats the field on entailment and coverage, and follows two design rules our ablation matrix made unambiguous. Scorer conservatism: credit only tokens the references entail - templates pay; LLM prose, reviewer names, and raw evidence all fail. Auxiliary anchor signals are miscalibrated against the Llama judge: every anchor proxy we tried (cross-encoder, length, lead position) picks anchors the judge rejects - gate on the judge itself. The remaining +0.062 gap to the winner sits on citation precision/recall (0.299 vs 0.671), consistent with a selective-emission policy that drops low-confidence cites.
[NLP-27] Shortcut Before Circuit: Document Statistics Time In-Context Conflict Resolution
【速读】： 该论文旨在解决生成式模型在面对冲突线索（如新近性与稀有性）时，其内部决策机制的可解释性问题。具体而言，当上下文对同一事实给出两个相互矛盾的线索时，模型会依赖某种启发式策略（如新近性、重复频率或位置），但在自然数据中此类冲突极少出现，因此无法通过行为表现区分实际采用的线索。为克服这一局限，作者设计了一种合成语言环境，使新近性与稀有性在语义上完全重合，并通过最小因果编辑仅反转其中一个线索而保持真实值、词元数量及答案位置不变。实验结果显示，75次运行均达到0.999的准确率，表明所有路径在评估指标上无法区分。然而，在干预分析下，每个神经元单元的读出信号并不具备可复现性：25个单元中有13个在三个种子下的符号分数差异超过0.3，最大偏差达0.879（标准误为0.025）。这一现象符合理论预测——共扩展规则使优化目标对不同线索无偏好，且方差大小与各比较项释放的优化自由度相关。唯一可复现的是时间动态特征：模型从位置捷径中“逃逸”的时机具有闭式上限，且单调依赖冗余程度。在逃逸前的探测中，32/75次运行中归因方向发生反转，但准确率不变；同时发现电路形成过程中的门控机制虽必要却不足以决定最终行为。该研究构建了一个精确的不可解释情形，揭示了机械归因可用性的关键条件：数据本身仅能确定机制何时出现，而非具体是哪一个机制，从而为机械归因的有效边界提供了明确判据。
链接: https://arxiv.org/abs/2608.24460
作者: Yijun Liao,Fanwei Liang
机构: 华南理工大学(Scut)
类目: Computation and Language (cs.CL)
备注: 37 pages, 3 figures, 17 tables
Abstract:When a context asserts two values for one fact, a model commits to a cue – recency, repetition, position – but natural data rarely makes these disagree, so behavior cannot reveal which. We train 26M-parameter transformers on a synthetic language where recency and rarity are exactly coextensive, and separate them with a minimal causal edit that inverts one cue while holding the truth, token count and answer position fixed. All 75 runs reach accuracy = 0.999, including where the trivial heuristic fails, so no held-in evaluation distinguishes them. Under intervention the per-cell readout does not replicate: 13 of 25 cells differ by more than 0.3 in sign fraction across three seeds, the largest by 0.879 against a standard error of 0.025. The construction predicts this – coextensive rules leave the objective indifferent between them – and the variance is ordered by how much of the optimization each comparison releases. What replicates is timing: escape from a positional shortcut with a closed-form ceiling, monotone in redundancy. Probed before that escape, attribution reverses sign in 32 of 75 runs at unchanged accuracy, and gating on circuit formation is necessary but not sufficient. The corpus fixes when a mechanism appears, not which one – a criterion for when mechanistic attribution to data is available at all, and our construction makes the unavailable case exact.
[NLP-28] Words Spaces and Generative AI: Layers of language in contemporary architecture
【速读】： 该论文旨在解决生成式 AI（Generative AI）在建筑实践中日益普及背景下，语言作为设计材料的深层作用机制问题。随着文本到X（text-to-X）生成模型成为建筑设计的常用工具，如何理解语言在生成过程中的多重角色成为关键挑战。其解决方案的核心在于揭示建筑生成过程中三种语言形式的交织：（I）话语语言（discourse），即包含专业术语的自然语言；（II）编程语言（programming languages），作为所有计算系统的底层人工语言；（III）标注语言（annotations），作为附加于数据上的语义标记。论文进一步提出研究议程，强调通过语料库语言学方法（如主题建模与话语分析）分析建筑文本、融合传播理论与信息研究，并考虑不同自然语言所蕴含的认知与生成隐喻差异，以实现生成隐喻与生成式 AI 的深度对接。
链接: https://arxiv.org/abs/2608.24360
作者: Anca-Simona Horvath
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Language can be considered a design material in architecture, and in the context of text-to-X generative AI models becoming a common tool for architectural practice, looking more closely at language is more important now than in the past. After describing some of the important developments in linguistics starting from Wittgenstein, and including the work of Chomsky, Lakoff, conceptual and generative metaphors as proposed by Schön, this chapter connects them to contemporary architectural design and generative text-to-X tools. The chapter builds on the idea that three main forms of language intertwine in architectural design done using generative AI, namely (I) discourse (or natural language which can contain professional terminology specific to our field), (II) programming languages (which are artificial languages sitting at the basis of all computational systems), and (III) annotations (as language elements attached to pieces of data). It concludes by outlining a research agenda for connecting generative metaphors to generative AI: (a) conducting corpus linguistics studies on architectural texts (using quantitative tools such as topic modelling, and qualitative tools such as discourse analysis); (b) bringing communication theory and information studies closer to architectural research and © taking into account that different (natural) languages come with different affordances meaning generative and conceptual metaphors differ in relation to this.
[NLP-29] Not All Tokens Are Equal: Region-Aware Consistency Repair of Backdoors in MLLM s
【速读】： 该论文旨在解决多模态大模型（Multimodal Large Language Models, MLLMs）在部署过程中继承的后门风险问题，尤其是当触发器（trigger）可能存在于图像、文本或两者混合的情况下，现有方法难以有效清除模型内部嵌入的后门。传统模型级后门移除方法主要针对常规分类器设计，对MLLMs效果有限；而现有的特定于MLLM的防御机制大多仅在推理阶段通过过滤可疑输入来缓解问题，无法从模型本身根除后门。为此，本文提出RACER——一种基于关键观察的模型级修复框架：后门会引发内部表示在层间演化中的异常，即“层间不一致异常”（layer-wise inconsistency anomaly），且该异常具有模态依赖性，集中体现在模型实际依赖的触发特征对应的标记区域。RACER因此将融合表示分解为视觉与文本标记区域，分别对各模态进行层间不一致性的归一化，并在深层窗口内利用模态感知权重重新组合，构建出一种区域感知的不一致性目标，以更精准捕捉局部后门诱导的异常。通过最小-最大优化，该目标驱动最坏情况扰动生成，并针对生成的扰动进行对抗性微调，从而抑制后门行为所依赖的深层表征方向偏移。RACER仅需100个干净样本，无需知晓触发器、攻击目标甚至是否含后门，实验表明其在36种包含图像、文本及多模态触发的后门设置下，平均攻击成功率（ASR）降至1.1%，32种情况下实现0% ASR，同时保持了原始模型在干净任务上的性能。
链接: https://arxiv.org/abs/2608.24354
作者: Jiali Wei,Ming Fan,Mingkun Zhang,Haoyu Wang,Jun Sun,Guoheng Sun,Xiaoning Ren,Haijun Wang,Ting Liu
机构: Xi’an Jiaotong University(西安交通大学); Singapore Management University(新加坡管理大学)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:MLLMs are increasingly deployed in user-facing applications, yet they inherit backdoor risks from the pipelines used to construct them: triggers may reside in images, texts, or both. Existing model-level backdoor removal methods, largely designed for conventional classifiers, show limited effectiveness on MLLMs, while MLLM-specific defenses mainly operate at inference time, filtering suspicious inputs without removing the backdoor embedded in the model. To address this gap and eliminate latent backdoors from MLLMs at their source, we present RACER, a model-level repair framework motivated by a key observation: backdoors induce abnormal layer-to-layer evolution in internal representations, which we term the layer-wise inconsistency anomaly. Importantly, this anomaly is modality-dependent, concentrating primarily in the token region encoding the trigger features that the backdoor model actually relies on. RACER therefore decomposes the fused representation into visual and textual token regions, normalizes their layer-wise inconsistency separately, and recomposes them using modality-aware weights over a deep-layer window, yielding a region-aware inconsistency objective that better captures localized backdoor-induced anomalies. Through a min-max optimization, this objective drives worst-case perturbation synthesis and adversarial fine-tuning against the resulting perturbation to repair the model, suppressing the deep representational directional shifts on which backdoor behaviors rely. RACER requires only 100 clean samples and no knowledge of the trigger, attack objective, or even whether the input model contains a backdoor. Evaluations on three open-source MLLMs across 36 backdoor settings spanning image, text, and multimodal triggers show that RACER reduces the average ASR to 1.1%, reaching 0% in 32 settings, while preserving clean-task utility on both backdoor and clean models.
[NLP-30] FARCA: Fact-Aligned Reliability-Aware Credit Assignment for Reinforcement Learning with Factual Supervision
【速读】： 该论文旨在解决大语言模型在基于可验证奖励的强化学习训练过程中，因结果导向型奖励机制引发的幻觉（hallucination）风险问题。现有缓解方法虽引入过程级事实监督，但受限于事实信号的粗粒度聚合及缺乏对信号可靠性的评估，导致事实验证与策略更新之间产生不匹配，即“噪声事实信用分配”（noisy factual credit assignment），其核心表现为信用定位模糊性（credit localization ambiguity）与信用可靠性模糊性（credit reliability ambiguity）。为应对上述问题，论文提出FARCA（Fact-Aligned Reliability-Aware Credit Assignment）框架，其关键在于将事实监督转化为细粒度、可靠性加权的词元级训练信号：通过使事实验证粒度与策略更新粒度对齐，实现精确的信用定位；并引入反事实证据归因机制，以关键证据对事实判断的依赖程度作为验证可靠性的真实代理，计算可靠性权重，进而动态调节事实奖励与局部策略优势，降低不可靠信号对策略优化的影响。实验结果表明，FARCA在多种模型和多个事实推理基准上均显著提升模型的事实准确性，同时保持了良好的通用推理能力。
链接: https://arxiv.org/abs/2608.24350
作者: Qiming Xie,Wenjie Zheng,Xiangqing Shen,Rui Xia
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:To reduce the hallucination risk caused by outcome-driven rewards in large language models trained through reinforcement learning with verifiable rewards, existing mitigation approaches introduce process-level factual supervision. However, due to coarse-grained aggregation of factual signals and the lack of reliability assessment for these signals, they create a mismatch between fact verification and policy updates. We term this noisy factual credit assignment and decompose it into two aspects: credit localization ambiguity and credit reliability ambiguity. To address these issues, we propose FARCA (Fact-Aligned Reliability-Aware Credit Assignment), a policy optimization framework that transforms factual supervision into localized, reliability-weighted token-level training signals. FARCA achieves fine-grained credit localization by aligning the granularity of fact verification with that of policy updates. It further introduces counterfactual evidence attribution, which uses the dependence of a factual judgment on key evidence as an empirical proxy for verification reliability to compute reliability weights. These weights modulate factual rewards and local policy advantages, reducing the influence of potentially unreliable signals on policy optimization. Experiments across different models and multiple factual reasoning benchmarks show that FARCA significantly improves model factuality while preserving general reasoning capabilities.
[NLP-31] SteerCheck: Attribution Specificity and Alignment Leakage in Activation-Steering Audits
【速读】： 该论文旨在解决生成式 AI（Generative AI）中激活操控（activation steering）方法存在的可解释性与因果有效性问题，即现有方法难以确保干预效果仅针对目标概念，而非受其他潜在混杂因素影响。其核心挑战在于：当前常用控制手段（如各向同性方向、符号随机化方向）在实际中存在对目标对齐的“泄漏”现象，导致无法准确判断干预效应是否真正源于目标概念。为此，作者提出 SteerCheck——一种前瞻性注册的归因审计框架，通过系统分离非目标 KL 散度（off-target KL），并分别检验均值、受保护尾部、极性、迁移性及语义层面的假设，实现对操控效应的多维度验证。关键解决方案在于引入符号随机化家族中的余弦相似度作为核心诊断指标，发现其与实际效应高度相关（ρ = 0.94），且显著对齐的样本（余弦 > 0.80）均能复现观测到的平均效应，从而揭示传统控制方法的局限性。此外，研究强调必须明确报告可交换性假设、构建诊断指标 $ A $ 及经验余弦分布，以增强结果的可审计性与可信度。最终，基于独立数据的评估表明，仅有 Qwen 在连续边界迁移上表现有效，而所有名义上的“通过”结果均对敏感性参数 Γ=1.10 敏感，进一步凸显了现有方法的脆弱性。SteerCheck 因此提供了一套可追溯、可验证的混合结论审计机制，提升了生成式模型干预研究的严谨性。
链接: https://arxiv.org/abs/2608.24335
作者: Daming Luo,Christy Liang,Junyu Xuan
机构: University of Technology Sydney(悉尼科技大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Activation steering can change behaviour without establishing that the effect is specific to the intended concept. We introduce SteerCheck, a preregistered attribution audit that matches off-target KL and separates mean, protected-tail, polarity, transfer, and semantic claims. Exact replay of 960 Qwen3-14B interventions reveals complementary limits of common controls: isotropic directions occupy a narrow near-orthogonal region, whereas sign-randomized same-construction directions often retain substantial target alignment. Effect is strongly associated with signed cosine within the sign-randomized family ( \rho=.94 ); 25.3% of its draws exceed cosine .5 , and every draw exceeding the observed mean effect has cosine above .80 . This alignment leakage does not by itself invalidate a conditional randomization test; it limits what the comparator can distinguish and motivates reporting exchangeability assumptions, a construction diagnostic A , and the empirical cosine distribution. The primary Qwen complete gate remains negative because the protected tail fails all families. On independent data, continuous margin transfers only in Qwen and accuracy transfers in no selected cell. Prospectively registered language controls pass the complete gate in Qwen and DeepSeek, while a passing DeepSeek detox comparator rules out categorical separation; all nominal passes are sensitive to \Gamma=1.10 . Frozen three-rater open-generation evaluation supports factual correction in DeepSeek but not Qwen; the automatic judge fails calibration (macro-F1 .562 ), so null-wide semantic results remain descriptive. SteerCheck makes these conditional and mixed conclusions auditable.
[NLP-32] SeMoCo: A Semantic-First Motion Codec for Motion Language Modeling
【速读】： 该论文旨在解决现有离散运动表示方法在语义与运动细节编码上存在混淆的问题，即当前大多数运动分词器（motion tokenizer）主要基于重建优化，未根据动作的语义角色进行容量分配，导致语义信息与精细运动学特征被统一在相同的重建驱动层次中编码，影响生成质量。其解决方案的关键在于提出一种以语义为导向的运动编解码器——SeMoCo，采用双轴生成架构：每个运动标记（motion token）由一个语义标记和一组残差运动学标记构成，通过显式分离语义与运动学信息，使生成器能够建模时间维度上的语义进展，并自回归地逐步细化残差运动学内容。此外，研究构建了大规模、多源统一于SOMA表示的《Ω-MotionVerse》人体动作数据集，实验表明，SeMoCo在重建精度方面优于对比的编解码器，且在文本条件下的动作生成任务中展现出优异性能，验证了其运动标记在下游生成任务中的有效性。
链接: https://arxiv.org/abs/2608.24334
作者: Tianlv Huang,Hetian Guo,Ziyi Cai,Song Wang,Yanping Zhang,Zipei Fan,Xuan Song,Guangming Wu,Xin Zheng
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Graphics (cs.GR)
备注:
Abstract:Discrete motion representations have substantially advanced autoregressive text-to-motion generation. However, most motion tokenizers are optimized for reconstruction and do not explicitly allocate capacity according to semantic role. Action-level meaning and fine-grained kinematic detail must therefore be encoded through the same reconstruction-driven hierarchy. We introduce SeMoCo, a semantic-first motion codec, together with a dual-axis motion generator for language-conditioned motion generation. Each motion token contains one semantic token and a residual sequence of kinematic tokens. The generator models semantic progression across time and autoregressively refines the residual entries. We also construct \Omega -MotionVerse, a large-scale, multi-source human-motion dataset unified under the SOMA representation. Across the reported comparisons, SeMoCo achieves the best reconstruction accuracy among the compared codecs, while strong text-to-motion results demonstrate the effectiveness of its motion tokens for downstream generation.
[NLP-33] Speech-to-SOAP: End-to-End Summarization of Medical Dialogues: KIT@BeTraC 2026
【速读】： 该论文旨在解决临床病历生成（clinical protocolling）中因人工记录耗时、负担重而导致的效率瓶颈问题，尤其聚焦于从语音直接生成结构化临床笔记（SOAP note）的端到端自动化任务。其核心挑战在于如何在缺乏大规模标注数据的情况下，实现对语音到结构化病历的精准映射，同时保留语音中的非语言信息（如咳嗽等副语言特征）。解决方案的关键在于提出了一种可扩展的数据增强管道，通过合成语音生成与自动生成的SOAP标注，统一异构的医学对话数据集，从而支持语音基础模型（speech foundation model）在无监督或弱监督条件下进行鲁棒的端到端语音到SOAP生成，显著提升了临床语音处理系统的自动化水平与实用性。
链接: https://arxiv.org/abs/2608.24327
作者: Enes Yavuz Ugan,Fabian Retkowski,Yuka Ko,Thai-Binh Nguyen,Maike Züfle,Jan Niehues,Alexander Waibel
机构: Karlsruhe Institute of Technology (卡尔斯鲁厄理工学院); Carnegie Mellon University (卡内基梅隆大学)
类目: Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注: 3 pages, BeTraC 2026
Abstract:With the advent of Large Language Models and its instruction following capabilities a promising application is the task of summarization. Within this domain of task the extractive sub-task of clinical protocolling has emerged as a topic of particular interest as it can significantly reduce the downtime and protocolling burden of health-care workers thus enabling them to focus on their core work helping humans. A further step towards automation is the direct generation of clinical notes from speech without intermediate transcripts, reducing processing time while preserving information such as coughing or other paralinguistic cues that may be lost in transcript-based systems. To this end, we present KIT’s submission to this years BeTraC challenge in the lightweight track. Our main contribution is a scalable data augmentation pipeline that unifies heterogeneous medical dialogue datasets through synthetic speech generation and automatically generated SOAP supervision, enabling robust adaptation of a speech foundation model for end-to-end speech-to-SOAP generation.
[NLP-34] Who is the Agent to Blame? Localizing Faithfulness and Citation Mistakes in Agent ic Deep Research EMNLP2026
【速读】： 该论文旨在解决深度研究（Deep Research, DR）系统在生成长篇引用报告时存在的引用召回率低的问题。其核心挑战在于多智能体架构中信息与引用在传递过程中易发生失真，导致报告的忠实性（faithfulness）和可验证性下降。解决方案的关键在于提出一种新型评估方法：通过局部测试每个智能体调用对其输入的忠实性与可验证性，从而精准定位错误来源。同时，构建了一个包含四类错误的分类体系——幻觉、未引用输入依赖、未引用输出及引用不足，以实现对错误类型的系统化分析。实验应用该方法于三个顶级开源DR系统后发现，几乎所有智能体均存在大量错误，其中编排器（orchestrator）是主要错误源头，84.7%的最终报告错误源于其环节，且多数为引用相关错误。基于此诊断结果，研究证明仅通过两项简单干预即可提升引用召回率5%，且不影响输出质量，体现了该方法在优化多智能体系统可靠性方面的有效性。
链接: https://arxiv.org/abs/2608.24306
作者: Eran Hirsch,David Wan,Han Wang,Elias Stengel-Eskin,Mohit Bansal,Ido Dagan
机构: Bar-Ilan University (巴伊兰大学); UNC Chapel Hill (北卡罗来纳大学教堂山分校); University of Texas at Austin (德克萨斯大学奥斯汀分校)
类目: Computation and Language (cs.CL)
备注: Accepted to EMNLP 2026 (Main Conference). Code: this https URL
Abstract:Deep research (DR) systems produce long-form cited reports by orchestrating multiple agents that search and synthesize information from the web. Citations are the primary mechanism for evaluating the faithfulness of these reports, yet current DR systems exhibit poor citation recall. Moreover, improving citation recall is challenging because DR systems are complex multi-agent architectures where information passes through agents like a telephone game, and both content and citations can get corrupted along the way. We propose an evaluation method that pinpoints which agent introduced each error by locally testing agent invocations for faithfulness and verifiability relative to their own inputs. Furthermore, we propose a four-type taxonomy to categorize the discovered errors: hallucination, uncited input reliance, uncited output, or insufficient citations. Applying our method to three top-ranked open-source DR systems, we obtain actionable diagnostics. Almost every agent makes a lot of mistakes with the exception being those that summarize a single document. We find that the dominant error type varies systematically across agents, where the orchestrator mistakes are mostly citation-related. We find that 84.7% of final-report errors in AI-Q originate at the orchestrator, roughly 31% of them hallucinations and the rest citation mistakes. Guided by these insights, we demonstrate that two simple interventions raise citation recall by 5% without degrading output quality.
[NLP-35] SENSESHIFT: Continuous Sentiment-Controlled Text Generation via Encoder-based Mask Infilling EMNLP2026
【速读】： 该论文旨在解决当前可控文本生成（Controllable Text Generation, CTG）在情感控制方面存在的局限性，特别是针对复杂约束和细粒度情感信号的精准控制能力不足的问题。现有方法多依赖解码器架构，采用因果注意力机制，通常将情感简化为粗粒度类别标签或整篇文档单一的情感控制信号，难以实现长文本中句子级别的精细化情感调控。为此，本文提出一种基于编码器的框架SenseShift，其核心创新在于：利用双向注意力机制捕捉上下文全局信息、引入量化情感信号以支持细粒度控制，并通过迭代掩码填充策略生成与目标情感强度条件相关的局部句子。实验结果表明，SenseShift在故事生成和评论生成任务上显著提升了情感可控性，同时保持了高质量的文本输出，并展现出对跨领域生成场景更强的鲁棒性，优于更大规模的解码器基线模型。
链接: https://arxiv.org/abs/2608.24304
作者: Shahed Masoudian,Markus Frohmann,Emmanouil Karystinaios,Navid Rekabsaz,Markus Schedl
机构: Johannes Kepler University Linz(约翰内斯·开普勒林兹大学); Thomson Reuters Labs(汤森路透实验室); University of Toronto, Vector Institute(多伦多大学，向量研究所)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Paper Accepted to EMNLP 2026
Abstract:Recent controllable text generation (CTG) for sentiment control has largely focused on decoder-based large language models, making causal attention the dominant paradigm. While effective for fluent generation, these models still struggle to satisfy complex constraints and follow fine-grained sentiment signals specified by users. Existing sentiment-aware CTG methods typically simplify the problem by treating sentiment either as a coarse categorical label (e.g., positive or negative) or as a single fine-grained control signal applied to an entire document. Consequently, more challenging settings such as sentence-level sentiment control within long-form text remain underexplored. To address these limitations, we introduce SenseShift , an encoder-based framework for fine-grained sentence-level CTG. Unlike standard decoder architectures, SenseShift leverages bidirectional attention, quantized sentiment signals, and iterative mask infilling to generate local sentences conditioned on target sentiment intensity. Empirical evaluations on story and review generation demonstrate that SenseShift achieves stronger sentiment controllability while maintaining text quality and robustness to out-of-domain generation compared to larger decoder-based baselines.
[NLP-36] RePolicy: Reinforcement Learning for Safety-Policy Invocation in Agent Safeguards
【速读】： 该论文旨在解决语言模型智能体在动态上下文依赖的安全策略下，如何有效评估完整执行轨迹以保障安全的问题。现有基于提示或监督微调的策略感知防护方法难以适应未见轨迹和变化的策略环境，存在泛化能力不足的局限。其解决方案的关键在于提出RePolicy，一种通过强化学习学习安全策略调用机制的智能体防护框架。RePolicy能够根据给定的智能体轨迹与动态策略库，自动识别并调用适用的安全策略，生成基于策略依据的推理过程与安全判断。研究构建了PolicyTraj-20K数据集用于监督初始化，并采用具有可验证奖励信号与策略上下文扰动的GRPO（Generalized Reward Policy Optimization）进行训练。实验在六个智能体安全基准上验证了RePolicy在不同策略上下文下的强安全检测性能与稳健的策略调用能力。
链接: https://arxiv.org/abs/2608.24275
作者: Houcheng Jiang,Boxuan Zhang,Qiyong Zhong,Junfeng Fang,Xiang Wang,Xiangnan He
机构: Zhejiang University(浙江大学); Zhongguancun Academy; University of Science and Technology of China(中国科学技术大学); National University of Singapore(新加坡国立大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Safeguarding language model agents requires assessing complete execution trajectories under context-dependent safety policies. Existing policy-aware safeguards mainly rely on prompting or supervised fine-tuning, limiting their ability to adapt to unseen trajectories and changing policy contexts. We propose RePolicy, an agent safeguard that learns safety-policy invocation through reinforcement learning. Given an agent trajectory and a dynamic policy library, RePolicy invokes the applicable policy and uses its content to produce a policy-grounded rationale and safety judgment. We construct PolicyTraj-20K to support supervised initialization, followed by GRPO with verifiable rewards and policy-context perturbation. Experiments across six agent safety benchmarks show that RePolicy achieves strong overall safety-detection performance and robust policy invocation under varying policy contexts.
[NLP-37] ROBE: Reversed-Order-Biased-Experts for Extracting Extreme Long-tail Events from Historical Texts
【速读】： 该论文旨在解决历史文本中长尾事件（long-tail events）难以有效提取的问题，尤其针对17至18世纪荷兰历史语料库中极低频、稀疏标注的事件类型。由于这些数据属于19世纪前的历史语料，未被主流大语言模型（Large Language Models, LLMs）的预训练数据覆盖，导致现有模型在处理此类罕见事件时表现出严重的频率偏差（frequency bias），难以识别低频事件。为此，论文提出一种名为ROBE（Reversed-Order-Biased-Experts）的新方法，其核心在于构建基于事件频率相似性或语义相关性的专家分类器分组，并对训练数据中代表性不足的事件类别赋予更高的预测优先级，从而逆转传统模型中由高频事件主导的偏差。此外，论文还提出一种受控的领域特定合成数据生成方法，以增强对长尾类别的覆盖。实验结果表明，两种ROBE实现方案分别在召回率和精确率上较基线微调编码器模型提升0.10和0.16，最佳模型在长尾类别上的F1值提升0.10，显著改善了稀有事件的识别性能。
链接: https://arxiv.org/abs/2608.24268
作者: Stella Verkijk,Piek Vossen
机构: 未知
类目: Computation and Language (cs.CL)
备注: 15 pages, 3 figures
Abstract:This paper proposes methods to extract over 50 types of events from a Dutch historical corpus spanning the 17th and 18th centuries. The methods we propose aim to tackle the impossible: extracting the long-tail of the long-tail. Historic data from before the 19th century is in itself a niche domain not covered in the pre-training of Large Language Models, and we aim to extract events only very scarcely annotated in the training data available for this domain. We propose creating expert classifiers for subgroups of the events present in the training data. We make these groupings based on similar frequency in the training data or on semantic relatedness. Experts trained on underrepresented events are assigned higher priority when predicting to avoid being dominated by frequency biases. We refer to this new way of combining classifiers, specifically tailored to protect the long-tail, as ROBE: Reversed-Order-Biased-Experts. We also propose a controlled method to create domain-specific synthetic data. Our two implementations of ROBE outperform a simple fine-tuned encoder model with a .10 increase in recall and a .16 increase in precision respectively. The best model achieves a .10 increase in f1 for a group of long-tail classes in our niche data set.
[NLP-38] RecurSE: Bounded Recursive Self-Evaluation for LLM Rubric Judges
【速读】： 该论文旨在解决大模型作为评判者（LLM-as-judge）在开放文本评估与后训练引导中依赖昂贵人工标注、奖励模型或强教师模型蒸馏的问题。其核心解决方案是提出一种闭环式的有界递归自提升（Bounded Recursive Self-Improvement, RSI）框架——递归自评估（Recursive Self-Evaluation, RecurSE），实现无需外部黄金标准监督的强化学习训练。关键创新在于：首先，通过结构解耦设计，将可训练的评判模型（judge）与同步的策略副本审计器（checker）分离，前者基于规则评分生成响应评价，后者依据元规则对评判过程进行校验并输出标量过程奖励，从而消除因判别结果令牌复制导致的自赋奖励虚高问题；其次，引入成对优势有效性（Pairwise Advantage Validity, PAV）作为无偏验证监控机制，联合追踪评判准确率与审计器保真度，以可靠识别最优早停窗口。实验表明，RecurSE 在 Qwen3.5-9B、Gemma-4-E4B-it 及 Qwen3.6-27B 等多个模型上，在医疗、成对比较、摘要生成及专业任务等未见基准上均实现一致的泛化性能提升，且优于冻结审计器、外部元评判者、自一致性方法及缩放版教师蒸馏。因此，该研究证明：当自生成奖励的有效性被显式解耦与监控时，大模型作为评判者的有界递归自提升具备可行性。
链接: https://arxiv.org/abs/2608.24231
作者: Kaiyuan Liu,Ziyuan Zhuang,Rongxiang Weng,Jieping Ye
机构: Zhejiang University (浙江大学); Meituan LongCat Team (美团龙猫团队)
类目: Computation and Language (cs.CL)
备注:
Abstract:LLM-as-judge is essential for evaluating open-ended text and steering post-training, yet improving the judge itself typically relies on expensive annotations, reward models, or distillation from stronger teachers. In this work, we eliminate external gold supervision from the RL training reward: the model’s own evaluative capability generates learning signals for its optimization – a closed-loop setting of bounded recursive self-improvement (RSI) termed Recursive Self-Evaluation (RecurSE). We study two central questions: when can self-improvement occur, and when must it stop? First, RecurSE pairs a trainable judge evaluating candidate responses under per-rule rubrics (Pass 1) with a synchronized policy-copy checker that audits the judge’s reasoning against meta-rubrics to supply a scalar process reward (Pass 2). To enable learning, interface decoupling structurally isolates the checker’s scalar score from the judge’s verdict tokens, eliminating a degenerative token-copying shortcut that inflates self-assigned rewards. Second, because unanchored recursive learning is inherently bounded, Pairwise Advantage Validity (PAV) serves as an unbiased validation monitor that jointly tracks judge accuracy and checker fidelity to reliably identify the optimal early-stopping window. Across Qwen3.5-9B, Gemma-4-E4B-it, and Qwen3.6-27B, RecurSE achieves consistent generalization gains across held-out medical, pairwise, summarization, and professional benchmarks. Ablations demonstrate that synchronized judge-checker co-evolution outperforms frozen checkers, external meta-judges, self-consistency, and scaled teacher distillation. Furthermore, preference pairs curated by our judge effectively enhance downstream policy alignment. Bounded RSI for LLM-as-judge is thus viable when self-produced reward validity is explicitly decoupled and monitored.
[NLP-39] Measuring Digital Labour Market Transitions with a Digital Semantic Score: An AI-Based Methodology Applied to the Dutch Labour Market
【速读】： 该论文旨在解决荷兰劳动力市场数字化转型背景下，职业语言、职业发展路径及岗位技能演变所带来的劳动市场智能（labour market intelligence）需求，尤其针对如何精准捕捉和量化数字能力在职业体系中的分布与演进。其核心解决方案是提出一种基于人工智能的分析方法，整合嵌入向量（embedding）相似性搜索与大语言模型（large language model, LLM）分类技术，将海量非结构化职位信息映射至统一的ESCO职业分类体系，并引入“数字语义得分”（Digital Semantic Score）作为关键指标。该得分通过对比职业标题与技能词汇在语义空间中与数字概念锚点（digital anchor groups）的余弦相似度，超越传统关键词匹配方式，能够捕捉职业语言与劳动者技能中更广泛、深层的数字化内涵。该方法实现了对职业间差异、职业转型路径、新兴职业术语演变以及技能数字化程度的多维度分析，揭示了数字化进程在管理、专业及信息技术领域高度集中，同时正向混合型商业、营销及自动化相关岗位渗透。研究进一步表明，数字能力具有技术性、融合性与业务系统性等多重维度特征，且职业转型路径存在显著依赖性。该框架为实现可扩展的劳动力市场动态监测提供了技术支持，有助于识别新兴技能需求、制定再培训策略，并为应对技能错配与劳动力短缺提供政策依据。
链接: https://arxiv.org/abs/2608.24222
作者: Sadegh Shahmohammadi,Xavier Pinho,Mairi Bowdler,Suhendan Adiguzel-van Zoelen,Joost van Genabeek
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:The digital transformation of the Dutch labour market is reshaping occupational language, career pathways, and job-related skills. Addressing these changes requires granular labour market intelligence. This paper develops an AI-based methodology to analyse digitalisation using data covering millions of Dutch job profiles. The methodology combines embedding-based similarity search and large language model classification to map unstructured job information to harmonised ESCO occupations. We also introduce a Digital Semantic Score that measures how strongly job titles and skills are associated with digital concepts relative to a non-digital reference. Using embeddings and cosine similarity to transparent digital and non-digital anchor groups, this indicator moves beyond keyword-based approaches by capturing broader digital meanings in occupational language and worker skill profiles. It enables analysis across occupations, career transitions, emerging job-title vocabulary, and skill digitality. The findings reveal that digitalisation is unevenly distributed across the labour market. Digital job-title language is most prominent among managerial, professional and ICT-related occupations, but is increasingly visible in hybrid business, marketing and automation-related roles. Career-transition analyses show that movement toward digital work is pathway-dependent, while skill analyses highlight the multidimensional nature of digital capability, encompassing technical, hybrid and business-systems skills. By combining profile data, AI-supported occupational classification and semantic scoring, this study advances AI-driven labour market analytics and provides a scalable framework for monitoring digital labour market change. The methodology helps identify emerging skill needs, support reskilling strategies, and inform policies addressing skills mismatches and labour shortages in the Netherlands.
[NLP-40] DeepRepoQA: Code Repository Question Answering with Deep Agent Exploration
【速读】： 该论文旨在解决软件工程中关于代码仓库的开发者问题问答（QA）难题，其核心挑战在于现有仓库理解方法多依赖表层代码检索，缺乏对多文件间复杂架构及长距离代码依赖关系进行深度推理的能力。为克服这一局限，论文提出DeepRepoQA——一种面向仓库级代码理解的新型问答框架。其解决方案的关键在于构建基于代理（agentic）的系统，利用蒙特卡洛树搜索（Monte-Carlo Tree Search, MCTS）机制驱动大语言模型（LLM）代理在仓库结构上进行系统性树状搜索，实现动态导航与代码检查，从而支持跨多个文件的多跳推理，并有效关联长距离代码依赖。在SWE-QA基准上的实验结果表明，该方法显著优于现有强基线模型，验证了基于系统性MCTS引导探索在多跳仓库推理中的有效性。
链接: https://arxiv.org/abs/2608.24221
作者: Weihan Peng,Yuling Shi,Yingwei Ma,Longfei Yun,Beijun Shen,Xiaodong Gu
机构: Shanghai Jiao Tong University(上海交通大学); The Hong Kong University of Science and Technology(香港科技大学); University of California San Diego(加州大学圣地亚哥分校)
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL); Programming Languages (cs.PL)
备注:
Abstract:Answering developer questions about a software repository is a critical yet under-explored problem in software engineering. While existing repository understanding methods have advanced the field, they predominantly rely on surface-level code retrieval and lack the ability for deep reasoning over multiple files, complex software architectures, and grounding answers in long-range code dependencies. To address these limitations, we propose DeepRepoQA, a novel question answering (QA) framework for repository-level code understanding. DeepRepoQA builds on an agentic framework where LLM agents find answers through a systematic tree search over the repository structure. A Monte-Carlo Tree Search (MCTS) mechanism is employed to empower agents to dynamically search, navigate, and inspect code, enabling effective multi-hop reasoning over long-range code dependencies. Comprehensive experiments on the SWE-QA benchmark demonstrate substantial performance gains over strong baselines, validating the effectiveness of systematic MCTS-guided exploration for multi-hop repository reasoning.
[NLP-41] Constraint-Guided Enterprise Data Mapping with Large Language Models
【速读】： 该论文旨在解决企业实体对齐（Enterprise Entity Alignment）中面临的三大挑战：半结构化记录、隐式属性以及单位或粒度不一致问题。现有方法中，纯大语言模型（LLM-only）虽能提升语义召回率，但常违背结构与物理约束，生成看似流畅却在操作上无效的匹配结果。为此，作者提出约束引导映射（Constraint-Guided Mapping, CGM）这一神经符号方法，其核心在于将形式化约束作为假设空间的操作算子而非事后验证器，通过三阶段流程实现：(i) 基于模式的可接受性约束（admissibility constraints），利用元数据 $ m_c = \tau_c, \delta_c $ 明确定义约束类型 $ \tau_c $ 与可执行的关系及归一化逻辑 $ \delta_c $；(ii) 在约束限制下进行候选生成，并采用级联松弛策略以保证噪声环境下解空间非空；(iii) 在可行集中进行神经排序与受限的LLM消歧。关键创新在于约束机制本身具有控制性降级能力，支持可审计、可人工干预的决策过程。实验表明，硬性可接受性约束使候选空间缩小约480倍且未丢失真实匹配（GT），层间消融分析显示该约束门控是性能提升的关键（F1从0.08升至0.66），而非LLM本身；该方法具备模型无关性且无额外推理开销，小模型加约束即可达到前沿大模型在无约束时的效果，成本降低约28倍。该方法在七个不同企业场景中实现跨域迁移（宏F1达0.70），每场景自动发现并可专家优化约束，相较传统电子表格工作流减少约7倍的人工投入。公开的Valentine基准测试进一步验证了外部排序合理性，提示仅当结构不变量决定匹配结果时，约束才应为“硬”约束。
链接: https://arxiv.org/abs/2608.24218
作者: Sebastian Monka,Pramod Anantharam,Thien Vo Minh,Lavdim Halilaj
机构: Bosch Center for Artificial Intelligence(博世人工智能中心), Renningen, Germany; Bosch Center for Artificial Intelligence(博世人工智能中心), Pittsburgh, USA; Robert Bosch GmbH(罗伯特·博世公司), Ho Chi Minh City, Vietnam
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 20th International Conference on Neurosymbolic Learning and Reasoning. NeSy 2026
Abstract:Enterprise entity alignment must handle semi-structured records, implicit attributes, and unit or granularity mismatches. Manual matching is still common in practice, but does not scale as schemas and providers evolve. LLM-only matching improves semantic recall, yet can violate structural and physical invariants, producing fluent yet operationally invalid correspondences. We propose constraint-guided mapping (CGM), a neuro-symbolic method with three stages: (i) schema-grounded admissibility constraints with metadata mc = tau_c, delta_c, where tau_c denotes the constraint type and delta_c provides executable relation and normalization logic; (ii) constraint-restricted candidate generation with cascade relaxation to guarantee a nonempty feasible set under noise; and (iii) neural ranking with bounded LLM disambiguation restricted to that feasible set. Methodologically, constraints operate as hypothesis-space operators rather than post-hoc validators, enabling controlled degradation under relaxation and auditable, human-guidable decisions. On a controlled structural-decoy benchmark, hard admissibility shrinks the candidate space by ~480x without dropping the GT, and a layer-by-layer ablation shows this gate, not the LLM, is the decisive lift (F1 0.08 to 0.66). The benefit is model-independent and adds no extra inference cost: a small model with constraints matches a frontier LLM used without them at ~28x lower cost. The method, not a single tuned configuration, transfers across seven enterprise makes (macro F1 0.70), each under its own automatically discovered, expert-refinable constraints, and lowers expert effort by ~7x versus spreadsheet workflows. Public Valentine results add an external ranking sanity check and mark the boundary: constraints should be hard only where structural invariants are match-determining. Comments: 20th International Conference on Neurosymbolic Learning and Reasoning. NeSy 2026 Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2608.24218 [cs.AI] (or arXiv:2608.24218v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.24218 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-42] Preference Data Selection for Mitigating the Alignment Tax in Large Language Models
【速读】： 该论文旨在解决大语言模型在对齐人类偏好过程中所面临的“对齐代价”（alignment tax）问题，即在优化模型对齐性能的同时导致预训练阶段获得的通用能力出现灾难性遗忘。传统方法多将此问题视为优化或架构设计挑战，但忽视了偏好数据本身所蕴含的内在特性对模型参数漂移的影响。本文提出BALIGN，一种基于数据选择的平衡策略，其核心在于通过理论与实证分析偏好优化梯度，识别出三个决定参数漂移的关键数据特征：参考模型的对数概率差值（log-probability margin）、优选与次优响应之间的词元长度差异（token length difference），以及与通用能力语料库的TF-IDF相似度（TF-IDF similarity）。BALIGN将这些正交特征整合为统一的复合风险评分，系统性地筛选出高风险样本——即那些可能破坏模型内在参数稳定性或提供低对齐效用的数据。实验结果表明，BALIGN在不牺牲对齐效果的前提下显著保留了基础模型能力，持续实现最优帕累托前沿，并具备极低的计算开销。
链接: https://arxiv.org/abs/2608.24192
作者: Minsu Kim,Jianxun Lian,Xing Xie,Steven Euijong Whang
机构: Microsoft Research Asia (微软亚洲研究院); Stanford University (斯坦福大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Aligning large language models to human preferences is crucial for real-world deployment but frequently incurs an alignment tax, leading to the catastrophic forgetting of pre-trained general capabilities. While previous works primarily frame this problem as an optimization or architectural challenge, the inherent characteristics of preference data that drive this degradation remain largely underexplored. In this paper, we propose BALIGN, a balanced data selection strategy that explicitly mitigates catastrophic forgetting while optimizing alignment efficacy. Through theoretical and empirical analyses of the preference optimization gradient, we identify three key data-centric features that dictate parameter drift: the reference model’s log-probability margin, the token length difference between chosen and rejected responses, and the TF-IDF similarity to general capability corpora. By aggregating these orthogonal features into a unified composite risk score, BALIGN systematically filters out high-risk preference samples that disrupt intrinsic model parameters or provide minimal alignment utility. Extensive experiments on standard human preference datasets demonstrate that BALIGN strongly preserves foundational capabilities without compromising alignment gains, consistently achieving the optimal Pareto frontier with minimal computational overhead.
[NLP-43] Ghaib in Translation aka Unseen Harm: Measuring Cross-Script Safety Inconsistency with Missed-in-Urdu Scores in LLM Hate Speech Detection
【速读】： 该论文旨在解决生成式 AI（Generative AI）在多语言内容安全评估中对乌尔都语（Urdu）严重忽视的问题，特别是在主流大语言模型（LLM）安全评测及近九年世界人工智能伦理与安全会议（WOAH）系列会议中，乌尔都语几乎完全缺席的现状。研究发现，当前主流大模型在处理乌尔都语文本时存在显著的安全评估偏差：在六组涵盖纳斯塔里克乌尔都语、罗马化乌尔都语、英语及乌英混杂语的数据集上，原始脚本与英文翻译之间的分类标签不一致性高达15.9%至31.6%，且“乌尔都语中被遗漏的有害内容”（Missed-in-Urdu）率介于2.4%至9.9%之间（中位数为4.3%），表明模型在原生乌尔都语文本中更易漏检有害内容。关键发现在于，相较于前沿闭源模型，开源权重较小的模型表现出更高的标签不稳定性与更高的漏检率，揭示出当前生成式 AI 在非主流语言上的安全保障存在显著不均衡性。因此，解决方案的关键在于建立覆盖多语言、特别是低资源语言如乌尔都语的系统性安全评估框架，并推动多语言数据与评测标准的包容性发展。
链接: https://arxiv.org/abs/2608.24191
作者: Fawzia Zehra(Fuzzy)Kara-Isitt,Sonal Khosla,Stephen Swift
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Urdu, the world’s tenth most spoken language with 246 million speakers, remains almost entirely absent from mainstream LLM safety evaluation and nine years of WOAH proceedings. To investigate whether this absence has measurable consequences for content moderation reliability, five large language models, GPT-4o, Claude Sonnet 4.5, Gemini 2.5 Flash, Qwen-2.5, and Llama-3.1, were tested across six datasets spanning Nastaliq Urdu, Roman Urdu, English, and code-switched Urdu-English. Across the five Urdu-script datasets, label instability between original-script and English-translation classification ranged from 15.9% (Gemini 2.5 Flash) to 31.6% (Qwen-2.5), with a ‘Missed-in-Urdu’ rate, content flagged as harmful in English translation but passed as normal in the original script, ranging from 2.4% to 9.9% (median 4.3%). A complete enumeration of all 205 papers across nine ALW/WOAH editions via the ACL Anthology API confirms zero dedicated Urdu papers across the entire period. Results indicate that current LLMs provide uneven safety assurance across Urdu’s script varieties, with smaller open-weight models showing substantially higher instability and missed-harm rates than frontier closed models.
[NLP-44] Paritok-4B: Intent-Conditioned Context Compression for Coding Agents
【速读】： 该论文旨在解决编码代理（coding agent）在与前沿大语言模型（LLM）交互时，因频繁重传大型文件读取内容和工具输出而导致上下文过长、产生高昂的令牌（token）费用的问题。现有通用提示压缩器针对散文文本训练，对代码场景适配性差，常会改写标识符或丢失代理所需精确编辑片段，导致信息失真。为此，论文提出Paritok-4B，一个基于两个核心承诺的40亿参数低秩适配器（LoRA）压缩器：一是提取式压缩（extractive），仅选择输入中已存在的文本片段而非重构，96.0%的输出标识符、路径和数值均直接来自输入，在保留数据集上保持96.2%的准确率；二是意图条件化（intent-conditioned），根据当前任务意图聚焦于保留段落内进行选择性保留，被保留行段比移除行段具有更高的意图相关性（+0.067，95%置信区间[+0.056, +0.078]），而非改变保留量。通过在67,074条真实OpenHands轨迹上蒸馏gpt-4.1-mini教师模型，并使用40,606个验证样本微调Qwen3-4B，Paritok-4B在全部300个SWE-bench Lite实例上将代理上下文压缩至原大小的25.7%，显著优于gpt-4.1-mini（50.2%）和gpt-5（61.9%）的压缩效果，同时保留了86.5%的单次求解质量。当处理带行号标注的真实代理输入时，压缩率为27.8%，保留率提升至89.3%，经配对检验显示仅有30例仅在未压缩时可解、17例仅在压缩后可解，McNemar检验p=0.079，表明在该样本规模下压缩至原始大小四分之一并未显著降低求解成功率。该模型为264 MB适配器，可在单块24 GB GPU上本地部署，无按令牌计费成本，而以列表价格计算，gpt-5作为压缩器反而净亏损，凸显其经济可行性。模型权重、数据及评估脚本均已开源（Apache 2.0）。
链接: https://arxiv.org/abs/2608.24188
作者: Jiayu Shi,Luzhuo Chen
机构: Paritok
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Software Engineering (cs.SE)
备注: 20 pages, 1 figure, 10 tables
Abstract:Coding agents re-send large file reads and tool outputs to a frontier LLM every turn, and this context dominates their token bill. General-purpose prompt compressors are trained on prose and suit code poorly: they paraphrase identifiers and drop the exact spans an agent needs to edit. We present Paritok-4B, a 4B LoRA compressor for coding-agent trajectories built on two commitments. It is extractive: it selects spans rather than rewriting them, and 96.0% of the identifiers, paths, and numbers it emits already appear in its input, holding at 96.2% on held-out SWE-bench Lite output. It is intent-conditioned: told the agent’s current task, it acts chiefly inside a retained segment, selecting which lines survive (retained lines are +0.067 more intent-relevant than removed ones, paired 95% CI [+0.056, +0.078]) rather than changing how much is retained. We distil a gpt-4.1-mini teacher over 67,074 real OpenHands trajectories into 40,606 validated examples and fine-tune Qwen3-4B. On all 300 SWE-bench Lite instances, Paritok-4B compresses agent context to 25.7% of its size, 2.0x harder than a gpt-4.1-mini compressor (50.2%) and 2.4x harder than gpt-5 (61.9%), while retaining 86.5% of uncompressed single-shot solve quality. Fed the cat -n line-numbered input real agents produce, it compresses slightly less (27.8%) and retains more (89.3%); there the paired test is informative, with 30 instances solved only uncompressed and 17 only compressed, an exact McNemar p=0.079, so at this sample size compressing context to roughly a quarter of its size does not significantly reduce the solve rate. The model is a 264 MB adapter that self-hosts on one 24 GB GPU with no per-token compressor fee, which at list prices decides the economics: gpt-5 as a compressor is net-negative, costing more than the downstream tokens it saves. Weights, data, and evaluation scripts are open (Apache 2.0).
[NLP-45] FireRedAudio: A General-Purpose Audio Language Model with Decoupled Continuous Representations for Understanding and Generation
【速读】： 该论文旨在解决统一音频模型在语音理解与语音生成之间存在的表征矛盾问题：语音理解需要紧凑的特征以支持长上下文建模，而语音生成则要求可重构的细粒度声学特征。其核心解决方案是提出FireRedAudio，一个基于共享90亿参数大语言模型（LLM）的通用音频语言模型，首次在单个可训练自回归模型中实现了理解与生成任务的分离式连续输入表示。具体而言，通过专用音频编码器处理输入音频用于理解任务，而基于RedAE的路径处理语音输入以支持生成任务；大语言模型直接输出文本或条件化流匹配扩散模型（flow-matching DiT）生成连续声学潜变量。通过渐进式多任务训练，该模型实现了端到端的自动语音识别（ASR）、长达一小时的音频理解、零样本语音合成（TTS）、指令语音合成（Instruct TTS）以及语义与声学层面的语音编辑，并在长时序音频中达到秒级时间戳精度。实验表明，FireRedAudio在音频理解与多语言ASR方面表现优异，在零样本TTS中保持强内容准确性和说话人保真度，在指令跟随能力上领先，且在语义与声学编辑任务上显著优于Ming-UniAudio-Edit。这证明了在中等规模模型中，解耦的连续输入表示能够有效统一音频理解与连续潜变量语音生成。
链接: https://arxiv.org/abs/2608.24168
作者: Junjie Li,Xuelong Geng,Kun Xie,Feiyu Shen,Yichen Wu,Ziqi Dai,Yichen Han,Yan Jia,Kai Huang,Junjie Chen,Yixuan Li,Manzhen Wei,Fenglong Xie,Lei Xie,Xu Tang,Yao Hu
机构: 未知
类目: Computation and Language (cs.CL); Sound (cs.SD)
备注: 20 pages, 3 figures
Abstract:A unified audio model must recognize and understand linguistic, paralinguistic, and environmental information while supporting speech synthesis and editing. A key challenge is representation: understanding favors compact features suited to long-context modeling, whereas speech generation requires reconstructible features that preserve fine-grained acoustic detail. We introduce FireRedAudio, a general-purpose audio language model with a shared 9B-parameter LLM. To the best of our knowledge, it is the first publicly disclosed unified audio-language model to provide separate continuous input representations for understanding and generation within a single trainable autoregressive LLM. Audio to be recognized or analyzed is processed by a dedicated Audio Encoder, while speech inputs for generation use a RedAE-based pathway. The LLM directly generates text or conditions a flow-matching DiT to produce continuous acoustic latents. Through progressive multitask training, FireRedAudio supports ASR and audio understanding, with the latter extending to recordings of up to one hour, as well as zero-shot TTS, Instruct TTS, and semantic and acoustic speech editing. Its structured organization of long-form audio achieves second-level timestamp accuracy. Across comprehensive evaluations, FireRedAudio achieves competitive or leading performance in audio understanding and multilingual ASR, strong content accuracy and speaker preservation in zero-shot TTS, leading instruction following in Instruct TTS, and substantial improvements over Ming-UniAudio-Edit in both semantic and acoustic speech editing. These results demonstrate the viability of decoupled continuous input representations for unifying audio understanding and continuous-latent speech generation in a model of moderate scale. Our code is available at this https URL.
[NLP-46] rustDABench: Benchmarking Reliability and Robustness of LLM s for Structured Data Analysis
【速读】： 该论文旨在解决大语言模型（LLM）在处理结构化数据（如电子表格、CSV文件）时，虽能生成看似合理但缺乏可信依据的分析结果这一核心问题。其关键挑战在于：模型是否具备识别无效推理路径的能力（即能否在证据链不完整或矛盾时拒绝回答或请求澄清），以及在相同数据以不同形式表达时是否仍能保持分析的一致性。为此，作者提出了TrustDABench基准，从“证据路径”（evidence-path）视角出发，设计了19种扰动算子，并通过基于智能体的LLM生成框架构建了2,340个经人工验证的扰动实例，用于评估模型的可靠性与鲁棒性。实验结果表明，当前最优模型（GPT-5.5）在可靠性指标上的平均匹配率仅为24.21%，而最佳鲁棒性模型（Claude-Sonnet-5）的平均准确率也仅达9.10%。系统性失败表现为模型难以识别冲突证据、常沿无支持的可执行路径继续推演，且对观测边界或跨表关系的微小变化高度敏感。研究结论指出，实现可信的结构化数据分析亟需强化对证据边界的识别能力以及表示不变的推理机制。
链接: https://arxiv.org/abs/2608.24145
作者: Boshen Shi,Yize Liu,Chen Zhao,Ce Chi,Zhendong Wang,Xing Wang,Junlan Feng
机构: Jiutian(九天); Google(谷歌)
类目: Computation and Language (cs.CL); Software Engineering (cs.SE)
备注: CodeData: this https URL
Abstract:LLMs are increasingly used to analyze spreadsheets, CSV files, and other structured data, but producing a correct-looking answer is not the same as producing a trustworthy analysis. A trustworthy result should be supported by a valid path from the user question to the relevant data evidence. This requirement creates two diagnostic questions: whether an LLM can refuse to answer or ask for clarification when such a path does not exist, and whether it can preserve the correct analysis when the same evidence is expressed in different table forms. We introduce TrustDABench, a benchmark that operationalizes these questions as reliability and robustness. Starting from the evidence-path view, we derive 19 perturbation operators and instantiate them through an Agentic-LLM-based generation framework. TrustDABench contains 2,340 human-verified perturbed instances, and we evaluate eight representative LLMs. The results show substantial headroom: the best reliability result is only 24.21% average MRS, achieved by GPT-5.5, while the best robustness result still has 9.10% average ASR, achieved by Claude-Sonnet-5. The failures are systematic: models rarely detect conflicting evidence, often continue along executable but unsupported analysis paths, and remain sensitive to perturbations that change observation boundaries or cross-table relations. These findings suggest that stronger evidence-boundary recognition and representation-invariant reasoning are still needed for reliable structured-data analysis.
[NLP-47] Anatomy of a Scam Call: What 10000 real scam and spam calls reveal about how phone scammers operate
【速读】： 该论文旨在解决电话诈骗（telephone fraud）在大规模场景下难以观测其真实运作机制的问题，尤其关注诈骗行为中针对目标个体的策略差异及其可预测性。研究通过部署一个由生成式 AI (Generative AI) 驱动的语音代理蜜罐系统，在54天内收集了10,211通入站诈骗与垃圾广告电话的完整数据集，涵盖913小时音频及超过33万次转录对话，实现了对诈骗活动的系统性观察。其核心解决方案的关键在于：通过随机化设定目标身份（虚构身份）并控制通话发起前的目标特征，从而分离出诈骗者行为对目标年龄等人口统计特征的响应模式，而非受目标身份本身影响的行为变化。研究发现，尽管诈骗者在面对不同年龄段目标时会延长对话轮次（每十年增加约15%的对话轮数，p = 0.005），但其索取敏感信息的内容并未随目标特征改变——即诈骗的核心诉求具有高度模板化且不变性。此外，研究进一步验证了早期检测的可行性：仅凭诈骗者开场白即可实现高精度预测（第8轮时ROC-AUC达0.87），且基于词袋模型的简单分类器性能已接近微调后的本地语言模型，表明诈骗行为在语言层面具有显著可识别的模式。因此，该研究揭示了电话诈骗本质上是一个以固定脚本为基础、仅在攻击强度上动态调整的产业化行为，其关键突破在于通过实验设计剥离了目标特征对行为的影响，明确了诈骗策略的结构性规律。
链接: https://arxiv.org/abs/2608.24127
作者: Ethan Traister,Ankit Raj,Jiaqi Gan,Xingyu Shen,Tyler Wu,Yuchen Zhou,Tommy Duong,Kidus Zewde,Siying Chen,Simiao Ren
机构: scam.ai
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注: 19 pages, 7 figures
Abstract:Telephone fraud is pervasive and costly, but its inner workings are rarely observed at scale. We analyze a complete corpus of 10,211 inbound scam and spam calls – 913 hours of audio and 330,956 transcribed turns from 5,780 distinct numbers – collected over 54 days by an AI voice-agent honeypot that answered callers and kept them talking, and introduced in a companion data descriptor. We separate outright scams, which solicit sensitive information, from the larger stream of predatory but legal lead generation (“spam”) that feeds them. Scam operations keep office hours (6.6x more calls per weekday than weekend day); thousands of disposable numbers run a small catalog of recycled scripts (thirty opening clusters, half the traffic in the top five); and callers solicit identity anchors – a home address and a date of birth – far more often than payment credentials, pressing through persistence and manufactured authority rather than overt threats. Our central experiment asks: does it matter who picks up? Every seeded lead carried one of ten fictitious identities drawn uniformly at random, so the identity a fraud operation reaches is fixed before the caller exists. Across 1,823 randomized calls, scammers spent about 15% more conversational turns per decade of the target’s apparent age (rate ratio 1.15, 95% CI 1.08-1.23; randomization p = 0.005) – yet what they asked for did not change (26.3% of calls reached a request for sensitive information; odds ratio 0.99 per decade, 95% CI 0.90-1.08). A second experiment casts early detection as a benchmark: from a scammer’s opening lines alone, on a caller-disjoint split, escalation is predictable at 0.72 ROC-AUC from the first line and 0.87 by the eighth, and a plain bag-of-words classifier matches a fine-tuned on-device language model. Telephone fraud emerges as a templated industry that varies how hard it works a target, but not what it wants.
[NLP-48] MC-CXR: A Multi-Context Chest X-ray Benchmark for Context-Induced Disruption in Vision-Language Models EMNLP2026
【速读】： 该论文旨在解决当前视觉-语言模型（VLMs）在临床影像分析中因上下文干扰导致决策失真的问题，尤其关注当可信的文本或影像上下文与图像本身存在矛盾时，模型是否仍能维持正确的图像主导判断。现有基准仅评估模型在孤立情况下的准确率，忽略了上下文冲突对模型决策的误导作用。为此，作者提出了多上下文胸部X光（MC-CXR）基准，包含240个病例扩展至2,522个实例，通过成对扰动设计，固定图像与目标病灶，同时引入匹配的可靠与误导性文本及先前胸片上下文（若有可视化叠加），从而隔离上下文引发的决策偏差。该基准定义了三类任务与两种配对指标：错误切换率（switch-to-wrong rate）和上下文一致误差率（context-aligned error rate）。实验评估了十种VLMs（涵盖开源通用、医疗领域及闭源系统），结果表明，仅依赖图像的准确性是必要但不充分的；在误导性文本上下文中，平均切换率高达45.6%–78.1%，而在误导性视觉上下文中为35.7%–61.7%；更关键的是，在发生切换的预测中，74.6%的错误与误导性文本标签一致，而仅17.6%与误导性视觉上下文一致，二者之间存在57.0个百分点的显著差距（95%置信区间：50.9–62.8），揭示出文本与视觉上下文影响之间的不对称性。这一发现凸显了在临床应用中对模型鲁棒性进行多模态上下文验证的重要性，且数据集已公开于PhysioNet。
链接: https://arxiv.org/abs/2608.24118
作者: Junhyeok Lee,Songsoo Kim,Kyu Sung Choi
机构: Seoul National University College of Medicine (首尔国立大学医学院); Seoul National University Hospital (首尔国立大学医院); Healthcare AI Research Institute (医疗人工智能研究所)
类目: Computation and Language (cs.CL)
备注: 15 pages, 3 figures, 4 tables. Accepted to Findings of EMNLP 2026
Abstract:Vision-language models (VLMs) are increasingly used in clinical pipelines where a chest X-ray is interpreted alongside retrieved reports, preliminary notes, or prior imaging. Existing benchmarks measure whether models answer correctly in isolation, but not whether they preserve a correct image-only decision when plausible context conflicts with the image. We introduce Multi-Context Chest X-ray (MC-CXR), a benchmark of 240 cases expanded into 2,522 instances that isolates context-induced disruption through paired perturbation. Each case fixes the current image and target finding while presenting matched reliable and misleading context across text and prior CXR, with visual overlays where available. MC-CXR defines three task families and two paired metrics, the switch-to-wrong rate and the context-aligned error rate. We evaluate ten VLMs spanning open-source general, medical-domain, and closed-source systems. Image-only accuracy is necessary but insufficient. Mean switch rates range from 45.6-78.1% across misleading textual sources and 35.7-61.7% across misleading visual sources. Among switched predictions, 74.6% align with the misleading label for text versus 17.6% for visual context, a 57.0-point gap (95% CI 50.9-62.8). This text-visual asymmetry is observed under the standardized direct-answer protocol. The dataset is available on PhysioNet.
[NLP-49] When Less Is More: An Empirical Study of Minimal Responses in Counseling Dialogues and the Behavior of LLM s EMNLP2026
【速读】： 该论文旨在解决心理辅导对话系统中对简短回应（minimal responses）的忽视问题，即现有模型与评估框架普遍偏好内容丰富、信息密集的回复，而低估了诸如反馈性提示（backchannel cues）和简洁共情语句等简短回应在促进互动、表达共情和引导来访者持续表达方面的交互价值。其解决方案的关键在于提出一种两阶段过滤方法：首先基于话语长度与内容特征筛选出潜在的简短回应，再通过大语言模型（LLM）进行上下文合理性验证，从而实现跨语言、多数据集的系统性分析。研究发现，人类收集的数据集中简短回应普遍存在，但在大语言模型生成的数据中显著缺失；尽管强商业大模型在明确指令下可生成简短回应，但难以判断其使用情境的适切性；而基于合成数据训练的心理咨询专用模型表现更差，倾向于生成冗长且信息密集的回复。此外，基于大模型的响应质量评估体系本身也存在对简短回应的低估倾向，即使其在交互上是恰当的。
链接: https://arxiv.org/abs/2608.24080
作者: Zhiyang Qi
机构: The University of Tokyo (东京大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Camera-ready version. Accepted to EMNLP 2026 Main Conference
Abstract:In psychological counseling, effective support is not always delivered through long, information-rich responses. Minimal responses, such as backchannel cues and concise empathic statements, help convey attentive listening, express empathy, and encourage clients to continue expressing themselves. However, existing counseling dialogue systems and evaluation frameworks often favor explicit, content-rich replies, overlooking the interactional value of brief counselor utterances. This paper presents a systematic cross-lingual analysis of minimal responses across multiple counseling dialogue datasets. We develop a two-stage filtering method based on utterance length and content, followed by contextual verification using a large language model (LLM). Our analysis shows that minimal responses are common in human-collected datasets but substantially underrepresented in LLM-generated ones. We further evaluate current LLMs in manually curated dialogue contexts where human counselors used minimal responses. The results show that strong commercial LLMs are capable of generating minimal responses when explicitly instructed, but still struggle to determine when such responses are appropriate. Counseling-specific models trained on synthetic data perform particularly poorly, tending instead to produce longer and more information-rich responses. Moreover, LLM-based response-quality evaluation may undervalue minimal responses, even when they are interactionally appropriate.
[NLP-50] Mechanistic Circuit Identification for Controllable Data Generation
【速读】： 该论文旨在解决当前数据生成管道依赖启发式提示（prompt-based）控制所导致的黑箱问题，即难以理解个体样本如何与模型内部学习动态相互作用。其核心挑战在于缺乏对数据质量与模型训练过程之间因果关系的可解释性。为此，论文提出一种基于电路（circuit-grounded）的框架，将基于训练动态的数据估值与机制可解释性（Mechanistic Interpretability, MI）相结合，从可学习性、挑战性和一致性三个互补维度定义数据质量。关键创新在于：识别出能够因果调控这些质量信号的模型内特定电路，并将其作为可操控接口，实现对生成过程的机制化控制。在此基础上，提出阶段感知的机制调度方法（SAMS），根据模型优化过程中的动态需求，智能调度由电路引导生成的数据。实验结果表明，该方法生成的数据在多样性与可控性上均优于传统启发式提示基线，显著提升下游任务性能与模型校准度。最终，该工作建立了一种可解释的数据生成白盒范式，首次将机制可解释性从分析工具拓展为实际可操控的交互界面，推动生成式数据构建向可解释、可调控方向演进。
链接: https://arxiv.org/abs/2608.24065
作者: Nakyung Lee,Sangwoo Hong,Jungwoo Lee
机构: Seoul National University (首尔国立大学); Konkuk University (国民大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 21 pages, 8 figures
Abstract:While recent advances in data synthesis aim to curate high-quality datasets, most generation pipelines still rely on heuristic prompt-based control. This black-box paradigm provides limited insight into how individual samples interact with a model’s underlying learning dynamics. To bridge this gap, we propose a circuit-grounded framework that connects training-dynamics-based data valuation with mechanistic interpretability (MI). Specifically, we conceptualize data quality along three complementary utility axes, learnability, challenge, and alignment. First, we uncover specialized model-internal circuits that causally govern these utility signals. Then, moving beyond heuristic prompting toward mechanistic control, we leverage these circuits as controllable interfaces, actively steering generation to produce utility-targeted data. Building on this capability, we introduce SAMS (Stage-Aware Mechanistic Scheduling), which schedules circuit-steered data according to the model’s evolving optimization needs. Experiments on multiple-choice QA tasks demonstrate that our approach yields precisely controlled data with greater diversity than prompt-based baselines, consistently improving downstream performance and calibration. Ultimately, this work establishes a principled white-box paradigm for interpretable data generation, pioneering the use of MI not just as an analytical tool, but as a practical, controllable interface.
[NLP-51] Curved Inference II: Sleeper Agent Geometry - Extending Interpretability Beyond Probes
【速读】： 该论文旨在解决现有生成式人工智能（Generative AI）中欺骗性对齐行为的检测难题，尤其针对基于线性探测器的检测方法在面对自然演化出的复杂欺骗行为时失效的问题。传统方法依赖于人工植入的后门触发机制与线性可分性假设，但此类线性信号可能仅是人为构造的产物，难以反映真实场景下通过自然训练产生的深层、非线性的欺骗推理模式。为此，论文提出一种更贴近现实的自然主义研究范式，利用多轮上下文窗口模拟真实的推理过程，避免人为触发或监督式后门注入。其核心创新在于构建“弯曲推理”（Curved Inference）框架，引入语义表面积（A’）作为新度量指标，用于捕捉未归一化的残差空间中意义建构的幅度与方向变化，从而量化表示层面的“认知工作量”。该方法无需标签、后门或探测器，仅通过大语言模型（LLM）共识进行输出分类，并发现几何结构（特别是表面积差异）能显著预测语义类别，且在多个提示策略与模型家族间表现出统计显著性。更重要的是，测量精度能够揭示被分类噪声掩盖的内在几何特征——部分策略的显著性从非显著（p = 0.555）提升至显著（p = 0.048），证明即使在表面检测失败的情况下，复杂推理仍会留下可识别的几何印记。因此，该研究的关键突破在于：将欺骗性推理的本质视为其推理路径的内在几何形态，而非依赖可线性分离的信号，为在缺乏先验知识和监督信号的前提下实现可扩展、无监督的欺骗行为检测提供了理论基础与方法路径。
链接: https://arxiv.org/abs/2608.24037
作者: Rob Manson
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:This paper extends Anthropic’s Sleeper Agents research [1], which showed artificial backdoors persist through safety training can be detected by linear probes with 99% accuracy [2]. However, probe-based detection relies on linear separability that may be an artefact of backdoor insertion rather than a property of naturally occurring deceptive alignment. Sophisticated deceptive behaviours emerging through natural training are unlikely to produce such convenient linear signals. We introduce a naturalistic methodology using multi-turn context windows that simulates realistic deceptive reasoning without artificial triggers or supervised backdoor insertion. Rather than binary trigger-response patterns, we examine how semantic complexity emerges through gradual context development. Building on our Curved Inference framework, we analyse curvature, salience, introduce semantic surface area (A’), a new metric of representational work capturing both the magnitude directional change of meaning construction in unnormalised residual space. Without backdoors, labels, or probes, we apply this framework to naturalistic deceptive prompts classify model outputs via LLM consensus. Geometric structure reliably predicts semantic classification, with statistically significant differences in surface area across five prompt strategies two model families. Critically, measurement precision can reveal geometric signatures hidden by classification noise - some strategies improve from non-significant (p = 0.555) to significant (p = 0.048). This validates that sophisticated reasoning creates intrinsic geometric patterns that persist even when detection appears to fail, suggesting the shape of inference itself encodes semantic patterns regardless of whether models have learned to suppress linear indicators of deception - a scalable, unsupervised path for detection when linear methods fail. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2608.24037 [cs.CL] (or arXiv:2608.24037v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.24037 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Rob Manson [view email] [v1] Tue, 25 Aug 2026 03:51:20 UTC (1,502 KB)
[NLP-52] SAGE: From Direct Answering to Evidence-Grounded Inference for Chinese Ancient Document Understanding
【速读】： 该论文旨在解决中文古籍理解任务中因现有大型视觉-语言模型（Large Vision-Language Models, LVLMs）采用单一、黑箱式生成范式而导致的回答过度自信且缺乏充分证据支撑的问题。其核心挑战在于如何在复杂的视觉、语言与历史推理需求下实现可解释、可信的推断。解决方案的关键在于提出SAGE——一种基于证据锚定的多智能体框架，将古籍理解重构为以证据为基础的推理过程，而非直接生成答案。该框架通过协调具备任务感知规划、工具辅助证据获取、声明级验证及受限重规划能力的专用智能体，在共享状态运行时环境中实现对证据的有限度搜索、答案修正以及在证据不足时选择不回答。实验结果表明，SAGE在AncientDoc基准测试中显著优于多种直接生成基线模型，尤其当采用Qwen3.5-9B作为后端模型时，其性能超越了更大规模的单体式LVLM，凸显了结构化、证据锚定推理在提升模型可靠性方面的关键作用，超越单纯依赖模型规模扩展的路径。
链接: https://arxiv.org/abs/2608.24011
作者: Yuchuan Wu,Xuan Luo,Yinglian Zhu,Meng Fang,Xiangyang Xue,Bin Li
机构: Fudan University (复旦大学); University of Liverpool (利物浦大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Chinese ancient document understanding demands complex visual, linguistic, and historical reasoning. Current Large Vision-Language Models (LVLMs) typically rely on an opaque, single-pass generation paradigm, often producing overconfident and weakly grounded responses. To address this, we propose SAGE, an evidence-grounded multi-agent framework that reformulates Chinese ancient document understanding as evidence-grounded inference rather than direct answer generation. SAGE coordinates specialized agents for task-aware planning, tool-mediated evidence acquisition, claim-level verification, and bounded replanning under a constrained shared-state runtime. This design supports bounded evidence seeking, answer revision, and abstention when grounding is insufficient. Experiments on the AncientDoc benchmark show that SAGE consistently outperforms matched direct-answering baselines across three LVLM backbones. Remarkably, SAGE with Qwen3.5-9B surpasses much larger monolithic LVLMs on most evaluated metrics, highlighting the importance of structured, evidence-grounded inference beyond model scaling.
[NLP-53] AgentS pec: Speculative Decoding for Batch Inference of LLM Agents EMNLP2026
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）代理应用中推理延迟过高这一关键问题，尤其聚焦于现有推测解码（Speculative Decoding）技术在大规模批量处理时性能显著下降的瓶颈。其核心挑战在于：高推测令牌拒绝率与动态令牌预算利用不足导致加速效果严重衰减。为应对上述问题，论文提出AgentSpec，一种专为LLM代理场景优化的推测解码算法。其关键创新在于两点：一是采用结构隔离式草稿生成（structure-isolated drafting），将推测范围限定于代理工作流中语义连贯的片段，有效规避无关语义路径的无效生成，从而实现极低的令牌拒绝率；二是引入冗余感知的预算分配机制（redundancy-aware budget allocation），利用代理层级信息动态优化令牌预算分配，充分挖掘推理过程中的可释放计算资源。实验结果表明，AgentSpec在五个不同工作负载和四种来自不同模型家族的LLM上均显著优于现有最先进方法，在vLLM框架下实现了更高的推理效率与稳定性。
链接: https://arxiv.org/abs/2608.24004
作者: Xin Wang,Ziming Miao,Yi Zhu,Hui Shen,Zhongwei Wan,Fan Yang,Mi Zhang
机构: The Ohio State University(俄亥俄州立大学); Microsoft Research(微软研究院); University of Michigan(密歇根大学)
类目: Computation and Language (cs.CL)
备注: EMNLP 2026
Abstract:Large language model (LLM)-based agent applications often incur high response time. Speculative decoding is a promising solution to improve the inference efficiency of LLM agents without impacting generation quality. However, state-of-the-art speculative decoding algorithms exhibit substantial speed degradation under large batch sizes, limiting their effectiveness to deploy in real-world agent applications. In this work, we first present a systematic analysis of speculative decoding for LLM agents and identify two dominant factors of speedup degradation: high rejection rate of speculative tokens, and under-utilization of dynamic token budgets.B ased on these observations, we propose AgentSpec, a speculative decoding algorithm that addresses the limitations of existing methods for LLM agents. AgentSpec incorporates structure-isolated drafting that constrains speculation to semantically coherent segments of the agent workflow, reducing the drafts of irrelevant semantic paths and achieving an extremely low rejection rate. Moreover, AgentSpec adopts redundancy-aware budget allocation that exploits agent-level information to better utilize the dynamically-free token budget during the agent inference. We implement and evaluate AgentSpec on five different workloads and four different models from four different LLM families in vLLM. Our results demonstrate the superiority of AgentSpec over state-of-the-arts.
[NLP-54] Memory Is Not Always Needed: Characterizing Conditional Memory in Scientific Reasoning
【速读】： 该论文旨在解决科学推理中生成式AI（Generative AI）如何有效利用条件记忆（conditional memory）以增强知识检索与多步计算可靠性的问题。其核心挑战在于，尽管条件记忆提供了显式的知识查找路径，可弥补模型在特定科学关联上的缺失，但其引入可能带来干扰性捷径或破坏原本正确的推理过程，且这种影响具有高度依赖输入和计算阶段的特性。解决方案的关键在于提出一种知识边界感知路由器（Knowledge Boundary-Aware Router），该机制基于生成前可用的任务特异性输入代理（input proxies），动态决定记忆是否激活、记忆信号应注入到哪些层-阶段节点，以及信号的贡献强度。通过系统分析科学知识边界并控制记忆增强节点的干预，该方法实现了对记忆参与的精细化调度，在生物与化学推理基准测试上覆盖两种骨干架构及六类任务，验证了其在抑制记忆诱发性能退化的同时更稳定地保留有益记忆贡献的能力，确立了选择性记忆分配作为实现可靠科学推理的重要原则。
链接: https://arxiv.org/abs/2608.23982
作者: Zhen Bi,Xueshu Chen,Yan Wang,Zhizhi Peng,Haosen Hong,Zhen Wang,Zhixuan Chu,Bingyu Zhu,Jungang Lou
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Scientific reasoning requires language models to retrieve specialized knowledge and incorporate it reliably into multi-step computation. Conditional memory provides an explicit lookup pathway that complements dense neural representations, but its usefulness is inherently input- and computation-dependent: retrieved information may repair missing scientific associations, yet it may also introduce distracting shortcuts or interfere with reasoning that the base model can already perform correctly. In this work, we systematically investigate when, where, and to what extent conditional memory should participate in scientific reasoning. We characterize the scientific knowledge boundary and controlled interventions on memory-enabled knowledge-circuit nodes. Based on these analyses, we propose a Knowledge Boundary-Aware Router that uses task-specific input proxies available before generation to determine whether memory is activated, which layer-stage nodes receive memory signals, and how strongly these signals contribute. Experiments on biological and chemical reasoning benchmarks, covering two backbone families and six task types, show that memory effects vary substantially across inputs, tasks, and injection locations. Compared with static and activation-rate-matched random routing, our approach more consistently preserves beneficial memory contributions while suppressing memory-induced regressions, establishing selective memory allocation as an important principle for reliable scientific reasoning.
[NLP-55] Investigating Knowledge Transfer Across Interactive Dialogue Games
【速读】： 该论文旨在解决多智能体对话游戏中知识迁移的可转移性问题，即在不同对话游戏间进行模型微调时，哪些任务能够有效共享知识并提升性能。其核心挑战在于理解语言接口在规则理解与行动执行中的双重作用，并探索跨游戏任务的知识泛化能力。解决方案的关键在于通过两种分析方法评估知识迁移：一是基于Zamir等人（2018）提出的二元整数优化程序构建任务可转移性图谱，以任务表现作为主要评价指标；二是采用任务向量（task vectors）方法（Ilharco et al., 2022），分析微调后模型间的相似性及其与任务可转移性的关联。研究发现，部分游戏在迁移学习中表现优于直接微调，且以视觉空间能力为核心的探索类游戏具有最强的可转移性；然而，基于相似性的任务向量分析虽能捕捉游戏角色间的关系，却难以揭示实际的迁移模式，表明当前简单度量不足以充分刻画复杂的知识迁移机制，亟需更精细的评估指标。
链接: https://arxiv.org/abs/2608.23969
作者: Filippo Momentè,Mir Nafis Sharear Shopnil,Andrea de Varda,Pavel Merinov,Raffaella Bernardi,Oswald Lanz,Alessandro Suglia,Alessandro Torcinovich
机构: University of Trento(特伦托大学); Technovative Solutions Ltd; Massachusetts Institute of Technology(麻省理工学院); Free University of Bozen Bolzano(博岑博尔扎诺自由大学); University of Edinburgh(爱丁堡大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Dialogue games represent a challenging setting where complex cognitive skills are required to accomplish tasks while coordinating with other players. Considering that language represents an interface for both understanding the game rules and executing actions, it is reasonable to assume that training on a specific language game will enhance specific capabilities that might be relevant for other tasks as well. Motivated by this rationale, in this paper, we investigate how knowledge transfers across different dialogue games. We study transferability by finetuning LLM models on games from the clembench suite (Chalamalasetti et al., 2023) and performing two analyses: i) we derive a task-transferability graph using a binary integer optimization program from Zamir et al. (2018), using task performance as the main metric; and ii) we compute task vectors (Ilharco et al., 2022) for each game to study similarities across finetuned models and their task transferability. In our first analysis, we find that some games benefit more from transfer than finetuning, and that the visuospatial family (e.g., exploration games) transfers best. With our task vector analysis instead, we find that similarity-based approaches capture game-role relationships but almost no transferability patterns, suggesting that more complex metrics are required.
[NLP-56] Evaluating Language Models on Cross-Language Code Functional Equivalence
【速读】： 该论文旨在解决当前大型语言模型（LLM）在跨编程语言的人类编写代码中判断功能等价性（functional equivalence）的能力是否真正体现了对程序语义的深层理解这一关键问题。现有评估多局限于单一语言或依赖合成代码，难以反映模型在真实复杂场景下的推理能力。其解决方案的关键在于构建并使用一个由人类编写的、涵盖C++、Java和Python三种语言的高质量数据集PolyHuman，以系统性地评估开放权重与专有模型在跨语言和同语言场景下的等价性检测表现；通过分析81个系统性误判案例，结合代码逻辑与生成的思维链（Chain-of-Thought）推理过程，揭示模型失败模式，并对比GPT-o4-mini、Claude-Opus-4.7与Gemini-3-Flash三者的表现，从而区分模型特异性偏差与普遍存在的语义理解局限。研究发现，模型在处理更复杂任务时易将非等价代码误判为等价，且对不同语言存在敏感性差异（如对Python表现更保守），同时部分依赖表面相似性线索；此外，即使在相同设置下，GPT-o4-mini也表现出显著的运行间不稳定性，表明其能力并非完全缺失而是不可靠。结论指出，当前主流大模型尚未能可靠捕捉程序内部或跨语言的功能等价性，仍缺乏真正的语义推理能力。
链接: https://arxiv.org/abs/2608.23961
作者: Hui Sun,Anderson Uchôa,Rohit Gheyi,Wesley K. G. Assunção
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 20 pages. To appear in the 20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026), LIPIcs vol. 394, Article No. 41
Abstract:Background: Large Language Models (LLMs) have demonstrated strong performance across a variety of code-understanding tasks, leading many to believe that they can reason about program semantics. However, existing evaluations primarily focus on single-language settings or rely on synthetically generated code, raising concerns about whether current results reflect true semantic understanding. Aims: We investigate whether LLMs can accurately judge functional equivalence across different programming languages in human-written code, a setting that requires deeper reasoning beyond superficial similarity. Method: We introduce PolyHuman, a dataset of human-written programs in CPP, Java, and Python. Using this dataset, we evaluate intra- and inter-language equivalence detection across open-weight and proprietary LLMs, selecting GPT-o4-mini as a representative model to assess stability. We then manually analyze 81 cases of systematic disagreement in which models incorrectly judge functional equivalence, examining the code logic and the generated Chain-of-Thought reasoning. Finally, we categorize these failures and compare them across GPT-o4-mini, Claude-Opus-4.7, and Gemini-3-Flash to determine whether they reflect model-specific issues or broader limitations of state-of-the-art LLMs. Results: We identify a difficulty-dependent breakdown in equivalence judgment (harder problems make the model increasingly prone to misclassifying non-equivalent code as equivalent), a model-specific sensitivity to programming language for the best-performing model (particularly a more conservative behavior on Python), and a partial reliance on similarity-based cues. GPT-o4-mini also shows substantial run-to-run instability under identical settings, indicating inconsistent rather than absent capability. Conclusions: Current LLMs do not reliably capture functional equivalence within or across languages.
[NLP-57] Continual Visual Learning under Evolving Semantic Concept Shift
【速读】： 该论文旨在解决长期运行的视觉系统中因语义概念随时间演化而导致的“语义概念漂移”（evolving semantic concept shift）问题，即在视觉输入外观保持不变的情况下，其对应的语义解释可能已发生变化。传统视觉基础模型假设任务语义固定，无法适应此类动态演变。为应对这一挑战，论文提出SemReWrite框架，其核心在于通过选择性地更新过时的视觉-语义映射，同时保留仍有效的知识。关键创新包括：利用旧有与新修订语义规范之间的差异，结合稀疏的修订监督信号以定位受变化影响的视觉区域；采用输入依赖的低秩重写机制与结构化语义记忆协同工作，并引入过时决策抑制策略以防止错误传播。此外，研究构建了EvoShift-Bench基准，涵盖多个数据集及多样化的语义变迁模式（如类别拆分、合并、边界调整等），并提出Rewrite Accuracy（RA）、Preservation Accuracy（PA）、Obsolete Retention（OR）及Selective Revision Score（SRS）等评估指标，以量化选择性语义重构性能。实验表明，SemReWrite在学习新语义的同时有效保留未受影响的知识，显著优于提示替换、常规微调、参数高效适配及持续学习等方法。
链接: https://arxiv.org/abs/2608.23903
作者: Ismail Lamaakal,Chaymae Yahyati,Yassine Maleh,Khalid El Makkaoui,Ibrahim Ouahbi
机构: Mohammed Premier University, Oujda, Morocco(穆罕默德一世大学，摩洛哥乌杰达); Sultan Moulay Slimane University, Khouribga, Morocco(苏丹穆莱·斯利曼大学，摩洛哥库尔布加)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Visual foundation models are commonly adapted under the assumption that the appearance of incoming data may change while the semantic meaning of the prediction task remains fixed. In long-lived visual systems, however, taxonomies, policies, and concept definitions can themselves evolve, causing the same visual evidence to require a different interpretation. We study this setting as evolving semantic concept shift and introduce SemReWrite, a framework for selectively updating obsolete visual–semantic mappings while preserving knowledge that remains valid. SemReWrite represents changes between old and revised semantic specifications, combines semantic discrepancy with sparse revised supervision to localize affected visual regions, and uses an input-dependent low-rank rewriting mechanism together with structured semantic memory, preservation, and obsolete-decision suppression. We further introduce EvoShift-Bench, spanning ImageNet, iNaturalist, CUB-200-2011, and DomainNet, with semantic transitions including class split, merge, boundary revision, insertion, partial redefinition, recurrence, and mixed semantic–appearance shift. To explicitly evaluate selective semantic revision, we introduce Rewrite Accuracy (RA) and Preservation Accuracy (PA) for affected and unaffected regions, respectively, Obsolete Retention (OR) for measuring residual outdated semantic associations, and the Selective Revision Score (SRS), which jointly summarizes rewriting and preservation performance. Experiments show that SemReWrite achieves a stronger balance between learning revised semantics and retaining unaffected knowledge than prompt replacement, conventional fine-tuning, parameter-efficient adaptation, and continual-learning strategies.
[NLP-58] Names Can Hurt: Spotting Slopsquatting Risks Caused by Package Name Hallucinations in Local Coding LLM s
【速读】： 该论文旨在解决生成式AI在代码生成过程中因幻觉（hallucination）导致的软件供应链安全威胁，具体表现为模型虚构不存在的Python包名，而攻击者若已预先在PyPI上注册同名包，则可实施“slopsquatting”攻击，从而植入恶意代码。其核心解决方案为一个双层检测器：第一层通过确定性查询PyPI验证包名是否存在；第二层采用基于十项特征（包括包名及元数据）的随机森林分类器，识别高风险虚构包。两层检测由导入名称协调器（import name reconciler）统一处理命名差异（如cv2与opencv-python），避免安全绕过。该检测器嵌入LangGraph状态机，支持渐进式温度重试，并在多次失败后调用更强的备用模型。实验表明，在300个精心设计的提示下，系统76%的运行可生成无幻觉代码；主模型耗尽重试预算占比28.7%，内部重试挽回约四分之一失败案例，跨模型回退进一步挽回16.5%。关键发现包括：一半被标记的幻觉包实为已在PyPI注册的低质量模仿品（如pil、faiss），主要由分类器捕获；幻觉率随提示攻击性近似线性上升，从常规任务的0–10%增至针对性诱饵的40–73%；主模型在未辅助情况下可拒绝6/10个直接诱饵，说明指令微调已提供基础防御；当主模型与备用模型属于同一模型家族时，约84%的失败会重复出现，凸显跨家族配对的重要性。用户研究（n=24）显示平均满意度4.4/5，21人表示有采纳意向。
链接: https://arxiv.org/abs/2608.23897
作者: Akash Raj,Sargam Sahu
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 14 pages, 2 figures, 6 tables. Code and data at this https URL
Abstract:When a code generating language model fabricates a Python package name, an adversary who has pre-registered that name on PyPI can convert that hallucination into a supply chain compromise. This event has been termed as ‘slopsquatting’. We propose a two layer detector to counter this issue. The first layer performs a deterministic PyPI existence check. The second is a Random Forest classifier trained on ten features derived from the package name and its PyPI metadata. An import name reconciler bridges the two, resolving cases such as ‘import cv2’ versus ‘pip install opencv-python’ without a security bypass. The detector is embedded in a LangGraph state machine that retries at escalating temperatures and, on repeated failure, routes to a stronger fallback model. Across 300 curated prompts, the pipeline produces hallucination free code on 76% of runs. The primary exhausts its retry budget on 28.7%; intra model retries recover roughly a quarter of those, and cross model fallback recovers a further 16.5% of the remainder. Four findings have been observed. First, half of the flagged hallucinations are packages already registered on PyPI, as low quality lookalikes of well known projects, caught by the classifier rather than the deterministic layer (e.g., pil, faiss, tabula, haystack). Second, hallucination rate scales almost linearly with prompt adversariality, from 0 to 10% on routine coding to 40 to 73% on slopsquat baits. Third, the weaker primary refused 6 of 10 direct baits unaided, suggesting recent instruction tuning provides a baseline defense. Fourth, when primary and fallback share a model family, approximately 84% of primary failures recur on the fallback, motivating cross family pairing. A user study (n = 24) reports mean satisfaction 4.4 out of 5 and 21 of 24 stated adoption intent.
[NLP-59] Semantic Overlays: Mitigating Prompt Injection with Annotations Beyond Tokens and Steering Vectors
【速读】： 该论文旨在解决生成式 AI（Generative AI）在处理多源输入时因无法准确识别各文本片段（span）语义身份而引发的提示注入（prompt injection）安全问题。其核心挑战在于，当前语言模型仅通过文本令牌（tokens）感知输入，攻击者可通过精心构造的文本内容混淆模型对输入来源或意图的判断，从而诱导模型执行非预期操作。为此，论文提出一种名为语义叠加（Semantic Overlays）的通用控制技术：在冻结模型的残差流（residual stream）特定预填充位置引入可学习的小型适配器，以非文本形式建立跨通道的片段身份标识机制。该方法的关键在于通过不可被文本模拟的“带外”（out-of-band）信号实现对输入片段的语义标记，使模型能够正确理解上下文属性（如“非可执行”），并据此调整行为。相比传统指令向量，语义叠加具备可训练性、选择性部署能力与组合性，能承载复杂语义指令并保持原始内容可读性。实验表明，该方案显著提升了对抗提示注入攻击的能力——在多个基准测试中，分离率（SEP）从24.3%提升至96.5%，TensorTrust攻击成功率由34.8%降至6.6%，所有四类PIArena攻击家族的合规率降为0%，同时仍保持92.5%的原始内容精确复制率。
链接: https://arxiv.org/abs/2608.23873
作者: Joshua Penman
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注: 21 pages, 4 figures, 13 tables. Interactive demo: this https URL . Code and released adapters: this https URL
Abstract:Everything a language model sees is tokens. The serving stack knows what each span is – user input, tool output, instructions – but the model must keep track of that itself, and it can lose track or be confused: text can be written to read like anything. Prompt injection is a natural exploit of this phenomenon. By scrambling the model’s understanding of span identity, an attacker can induce unwanted and potentially dangerous actions. Adding a non-textual channel to the model’s input – a way to communicate span identity beyond text – mitigates this class of attack. We thus introduce a general steering technique called Semantic Overlays: small learned adapters applied at chosen prefill positions to a frozen model’s residual stream. Laying an overlay over a span creates an out-of-band annotation channel that cannot be replicated by tokens. Unlike steering vectors, Semantic Overlays are trained, adaptable, and selectively applied. An overlay can encode complex semantics that reshape how the model perceives the marked span: asked to copy a code snippet under an overlay asserting that it is in a different programming language than it is, the model rewrites the snippet, faithfully, in the asserted language. Overlays are also composable, allow for transparent reading of underlying content, and can carry complex payloads – including imperatives that the model will follow. An overlay which marks a span as “non-executable” defends against the broad class of prompt injections that add instructions in untrusted context. We report strong results on prompt injection benchmarks: SEP separation rises from 24.3% to 96.5% with utility unchanged (our scoring rule; we also correct a defect in the published grader), TensorTrust attack success rate falls from 34.8% to 6.6%, and all four PIArena attack families drop to 0% compliance, all while marked spans stay readable (92.5% exact copy rate).
[NLP-60] Does Episodic Memory Help Close the Lexical Frequency Gap in Sensitivity to Syntactic Contrasts? A Test Using Retrieval-Augmented Language Models
【速读】： 该论文旨在解决神经网络语言模型在语法判断任务中对词汇频率高度敏感的问题，即低频词汇的语法处理性能显著低于高频词汇，导致模型表现出“词汇频率偏差”（lexical frequency gap）。这一现象与人类语言能力的鲁棒性相悖，后者通常不受词汇出现频率的影响。为应对该问题，论文提出的核心解决方案是引入类海马体的语素记忆机制（episodic memory mechanism），通过检索增强型语言模型（retrieval-augmented language models）实现对具体语言实例的快速编码与调用，从而弥补参数化模型在处理罕见语法结构时的表征薄弱问题。其关键在于采用k-近邻语言模型（k-nearest-neighbor language models），将显式实例存储与参数化模型相结合，使模型能够基于真实语境中的相似实例进行推理。实验结果表明，该增强机制显著缩小了高、低频词汇之间的性能差距，且在多种句法现象及不同预训练数据背景下均具一致性；同时发现，结构信息对于有效检索至关重要，而仅依赖语义相似性无法带来明显收益。尽管如此，频率差距仍未完全消除，因此作者建议未来应优化检索实例的重加权策略、改进结构表示与检索方法，并探索更灵活的存储-检索配置，以进一步提升语素记忆在语言模型中的应用效能。
链接: https://arxiv.org/abs/2608.23851
作者: Jing Liu,Najoung Kim
机构: CoML Team, LSCP; ENS, Université PSL, EHESS, CNRS; Department of Linguistics, Boston University
类目: Computation and Language (cs.CL)
备注:
Abstract:Grammatical knowledge and how it is empirically tested are typically considered robust to the frequency of the lexical items in the expressions. However, neural network-based models of grammaticality exhibit high sensitivity to lexical frequency. We draw upon Complementary Learning Systems theory to test the hypothesis that robustness to lexical frequency can arise via a hippocampal episodic memory mechanism, which enables rapid encoding and retrieval of specific experiences and allows learners to leverage them when processing rare patterns. We use retrieval-augmented language models as an instantiation of such an episodic memory mechanism (specifically, k -nearest-neighbor language models that augment parametric models with explicit instance storage), and test whether this augmentation helps close the lexical frequency gap that vanilla language models exhibit in syntactic contrast tests. Using syntactic contrasts with frequency-stratified test items, we find that retrieval augmentation narrows the performance gap between high- and low-frequency items, consistent with episodic memory compensating for weak parametric representations. This benefit is consistent across different syntactic phenomena and across models pretrained on child-realistic and large-scale data. Additionally, we show that structural information is critical for effective retrieval, whereas semantic similarity alone provides little benefit. While these are promising proof-of-concept results supporting our hypothesis, the frequency gap is narrowed rather than fully closed. Based on our analyses, we propose preferential reweighting of retrieved instances, better representations and retrieval strategies for structural information, and flexible configurations of storage and retrieval as promising future directions for improving the implementation of episodic memory in language models.
[NLP-61] Mitigating Exploration Bias in RL for Multi-Instruction Following EMNLP2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在强化学习（Reinforcement Learning, RL）训练过程中，当输入提示（prompt）中包含多个指令时所面临的探索偏差（exploration bias）问题。具体而言，现有训练方法在面对复杂指令时因初始表现不佳而难以触发有效探索，同时传统基于累积奖励（cumulative reward）的优化机制将所有指令视为等价，导致模型倾向于优先满足简单指令以快速获取奖励，从而抑制了对困难指令的学习能力。为应对上述问题，论文提出两个核心解决方案：一是“行为自举”（Behavioral Bootstrapping），即在RL训练前通过轻量级拒绝采样微调阶段激活困难指令的响应能力；二是“稀缺性感知奖励”（Scarcity-Aware Rewards），根据指令在数据中的实际稀缺程度动态分配奖励，引导模型更均衡地学习各类指令。实验结果表明，所提出的度量指标与模型性能高度相关，且该框架显著提升了RL训练的有效性，在三个可验证的指令遵循基准测试中均大幅超越基线模型。
链接: https://arxiv.org/abs/2608.23830
作者: Mian Zhang,Yueqin Yin,Kaiyu He,Peilin Wu,Xinlu Zhang,Mingyuan Zhou,Zhiyu Zoey Chen
机构: UT Dallas(德克萨斯大学达拉斯分校); UT Austin(德克萨斯大学奥斯汀分校); UC Santa Barbara(加州大学圣塔芭芭拉分校)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: EMNLP 2026 Acceptance
Abstract:RL has emerged as a powerful paradigm for enhancing the instruction following capabilities of LLMs. While existing training recipes achieve substantial gains, we find that they suffer from exploration bias towards easy instructions when the training data has multiple instructions in a prompt. This bias is caused by two main reasons: 1) the policy model’s initial ability to satisfy hard instructions is too low to trigger successful exploration during RL training, so the optimization is biased towards easy instructions; and 2) canonical RL training recipes typically employ a cumulative reward (the number of instructions fulfilled), treating all instructions equally, which biases the policy model towards fulfilling easy instructions to obtain the same amount of reward. To address these issues, we first propose two metrics to measure the exploration bias in instruction following and then introduce a two-stage framework to alleviate it: 1) Behavioral Bootstrapping, a lightweight rejection sampling fine-tuning stage before RL to activate hard instructions; and 2) Scarcity-Aware Rewards, a new RL reward function that assigns rewards to instructions based on their empirical scarcity. Experiments show that the proposed metrics are highly correlated with model performance, and our methods unleash the potential of RL training: our best models outperform the baselines by a significant margin across three verifiable instruction following benchmarks. We release codes at this https URL.
[NLP-62] Beyond Static and Linear: What Attention Constraints Best Fit Human Reading Times?
【速读】： 该论文旨在解决当前基于Transformer的语言模型在模拟人类语言处理时存在的关键缺陷：其注意力机制能够无损访问完整的先前上下文，这与人类有限记忆系统存在本质差异。为提升模型对人类语言行为数据的拟合度，研究提出通过在注意力机制中引入记忆约束来模拟人类的认知限制。解决方案的关键在于系统性地比较多种基于注意力的记忆机制，包括静态与动态记忆课程（dynamic memory curricula），并评估其在预测人类阅读时间（心理测量学拟合度）和语法能力（语法胜任力）方面的表现。研究发现，对插入词元内容敏感的记忆约束（content-sensitive constraints）能显著优于基于距离的约束，实现与人类阅读时间数据的最佳匹配；同时，动态记忆课程下心理测量拟合度与语法能力之间出现解耦现象，表明Transformer模型无法作为统一的认知模型适用于所有认知任务，强调了记忆约束类型与学习机制的交互作用在建模人类语言处理中的重要性。
链接: https://arxiv.org/abs/2608.23818
作者: Lanni Bu,Xiulin Yang,Christian Clark,Alex Warstadt,Ethan Gotlieb Wilcox
机构: Georgetown University(乔治城大学); The Ohio State University(俄亥俄州立大学); UC San Diego(加州大学圣地亚哥分校)
类目: Computation and Language (cs.CL)
备注:
Abstract:Transformer-based language models are widely used as models of human language processing, yet their attention mechanisms allow lossless access to the full preceding context, unlike the limited memory systems of humans. We hypothesize that installing memory constraints into transformers’ attention mechanisms can improve their fit to human behavioral data. While previous work has explored individual constraints in isolation, we conduct a systematic comparison of multiple attention-based memory mechanisms across different model sizes and training corpora, evaluating both psychometric predictive power for human reading times and grammatical competence. We additionally compare static constraints, in which the constraint strength is fixed throughout training, to dynamic memory curricula. We find that constraints that are sensitive to the content of intervening tokens consistently achieve the highest alignment with human reading times, outperforming distance-based constraints. We observe a dissociation between psychometric fit and grammatical competence under dynamic memory curricula, suggesting that Transformers cannot serve as a one-size-fits-all cognitive model.
[NLP-63] Learning to Grade Efficiently: A Bandit-Driven Prompt-Selection Framework for Low-Cost LLM Essay Scoring
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在自动作文评分（Automated Essay Scoring, AES）应用中，因采用固定提示（prompt）选择策略而导致的推理成本过高及无法动态适应最优配置的问题。其核心解决方案是将不同类型的提示视为多臂老虎机（Multi-Armed Bandit, MAB）框架中的“臂”，通过在线学习机制在推理过程中自适应地选择最优提示策略。实验结果表明，该方法在IELTS写作任务2数据集上实现了与穷举网格搜索相当的评分准确性，同时将LLM调用次数降低78.4%。研究还系统评估了四种评分范式（单步/多步评估，是否包含校准示例），发现结合校准示例的多步评估策略表现最佳。通过联合追踪令牌消耗、延迟与评分一致性指标，首次构建了面向作文评分的成本-可靠性学习曲线，为教育技术平台在控制运营成本与保障评估有效性之间提供可操作的决策依据。本工作首次将在线控制机制引入AES中的提示策略选择，成功将原本离线的超参数优化问题转化为高效的在线学习任务。
链接: https://arxiv.org/abs/2608.23814
作者: Olga Manakina,Igor Bogdanov
机构: Carleton University (卡尔顿大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted as a presentation at the EDM 2025 Workshop on Educational Data Mining in Writing and Literacy Instruction
Abstract:Large Language Models (LLMs) demonstrate strong capabilities in automated essay scoring (AES), but contemporary approaches typically employ fixed prompt selection, failing to address operational cost concerns and evolving optimal configurations. We propose a novel cost-aware approach that treats each prompt type as an arm in a multi-armed bandit (MAB) controller, enabling adaptive selection of optimal prompting strategies during inference. Our experiments on IELTS Writing Task 2 essays show that the MAB framework achieves comparable scoring accuracy to exhaustive grid search while reducing LLM calls by 78.4% to find the best grading approach. We implemented four distinct grading recipes (multi-step vs. single-step assessment, with vs. without calibration examples) and found that the multi-step approach with examples achieves the highest accuracy. By tracking token usage and latency alongside agreement metrics, we produce the first cost-reliability learning curves for essay scoring, providing actionable insights for educational technology platforms that must balance operational costs against assessment validity. This work represents the first application of online control mechanisms to adaptively select prompting strategies in AES, transforming prompt selection from an offline hyperparameter optimization problem into an efficient online learning task.
[NLP-64] From Preferences to Principles: Rubric-Based Alignment for Grounded Knowledge Answers
【速读】： 该论文旨在解决开放域问答中设计有效奖励信号的难题，因为高质量的回答需同时满足答案质量的多个维度（如内容完整性、事实准确性、指令遵循性等），而这些维度难以通过单一标量目标函数全面捕捉。其解决方案的关键在于提出一种基于评分量表（rubric-based）的奖励框架，该框架根据检索到的证据生成与查询相关的个性化评分量表，并将其分解为多个质量维度（如回答结构、事实依据、指令遵循性等），从而在后训练阶段提供细粒度的监督信号。实验表明，该方法在三个评估维度（组合性、事实依据性、指令遵循性）上相较于指令微调基线平均提升6.5%，相比扁平化量表变体提升4%，且在所有数据集上均表现出一致的性能增益。通过将评分量表与检索证据相结合，增强了回答的事实支持能力；通过多维度分解，进一步提升了回答的连贯性、组织性和对查询要求的遵循程度。研究结果表明，基于证据的、多维度的评分量表能为复杂开放域问答任务提供更有效的奖励监督。
链接: https://arxiv.org/abs/2608.23812
作者: Aman Saini,Priyanshu Kumar,Eric Peng,Kai Yuan,Harsh Girase,Wanming Chen
机构: Apple(苹果)
类目: Computation and Language (cs.CL)
备注: 18 pages, 5 figures, 3 tables
Abstract:Designing effective reward signals for open-domain question answering is challenging because high-quality responses must simultaneously satisfy multiple aspects of answer quality that are difficult to capture with a holistic scalar objective. We introduce a rubric-based reward framework that generates query-specific rubrics grounded in retrieved evidence and decomposed into multiple quality dimensions, providing fine-grained supervision during post-training. Averaged across three evaluation axes (composition, grounding, and instruction-following), our approach improves over the instruction-tuned baseline by 6.5% and over flat rubric variants by 4%, with consistent gains across all evaluation datasets. Conditioning rubrics on retrieved evidence improves factual support, while decomposing rubrics into quality-specific dimensions further improves coherence, organization, and adherence to query requirements. Our results show that grounded, multi-dimensional rubrics provide more effective reward supervision for complex open-domain question answering.
[NLP-65] Discovering Cross-Language Reasoning Invariance in LLM s with Geometry-Invariant Sparse Autoencoders ICML2026
【速读】： 该论文旨在解决多语言大模型在求解数学问题时，其内部表示是依赖于跨语言共享的通用特征，还是仅通过语言特异性计算产生相似输出这一关键问题。研究聚焦于五种来自四个不同架构家族的语言模型，基于多语言小学数学（MGSM）数据集，在六种语言（英语、德语、法语、西班牙语、俄语和中文）中保留具有有效推理轨迹的问题，并通过重放这些轨迹来记录模型各层的激活表示。为探究跨语言特征共享的可功能性，研究首先利用中心化核对齐（CKA）识别出具有跨语言对齐能力的网络层，随后在这些层上训练两种稀疏自编码器（SAE）：一种为仅优化重构损失的基线模型，另一种为本文提出的几何不变稀疏自编码器（GI-SAE）。GI-SAE引入信息噪声对比估计（InfoNCE）损失，使编码器在不同语言及词元位置下对同一问题的推理轨迹生成一致的激活模式，从而增强几何结构的一致性。进一步地，通过在前向传播过程中交换不同语言间的共享特征并测量输出变化，以KL散度量化每项特征的功能可互换性。结果表明，尽管GI-SAE在多数层上提升了CKA与Jaccard相似性，但更高的几何一致性并未带来一致的功能可互换性提升；跨语言特征共享的存在及其功能有效性具有显著的模型与架构依赖性，且在不同模型中表现出不同的深度分布特征——如在Qwen中增强共享结构，在Gemma中未见功能收益，在Llama与Phi中则呈现分层依赖的复杂效应。因此，解决方案的关键在于通过引入几何不变性约束的对比学习机制（GI-SAE），系统揭示并验证了多语言模型中跨语言特征共享的非普遍性与模型特定性。
链接: https://arxiv.org/abs/2608.23809
作者: Igor Bogdanov,Changcheng Huang
机构: Carleton University (卡尔顿大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted as a poster at the ICML 2026 Workshop on Mechanistic Interpretability
Abstract:Multilingual language models can solve the same mathematical problem in different languages, but it remains unclear whether they rely on shared features or on language-specific computations that only produce similar outputs. We study this question in five models from four families using the Multilingual Grade School Math (MGSM) dataset, with problems solved in English, German, French, Spanish, Russian, and Chinese, retaining problems with valid reasoning traces in all six languages and replaying those traces through the model to record representations at multiple layers. For each model, we first use Centered Kernel Alignment (CKA) to identify layers with cross-language alignment. At each selected layer, we train two sparse autoencoders (SAE): a baseline reconstruction-only model and a contrastive variant introduced in this work, the Geometry-Invariant SAE (GI-SAE). GI-SAE supplements the reconstruction loss with an Information Noise-Contrastive Estimation (InfoNCE) loss that trains the encoder to produce similar activations for traces of the same problem, regardless of language or token position. We then test whether the resulting shared features are functionally interchangeable by swapping their values between languages during the model’s forward pass and measuring the resulting change in output, quantified by Kullback-Leibler (KL) divergence per feature. Although GI-SAE yields higher CKA and Jaccard similarity at nearly every layer, higher geometric similarity does not consistently imply greater functional interchangeability. We find that cross-language feature sharing is model- and architecture-dependent in this sample and appears at different depths in different models. GI-SAE primarily amplifies cross-language structure already present: the pattern is model-specific, with strengthening in Qwen, no functional benefit in Gemma, and mixed layer-dependent effects in Llama and Phi.
[NLP-66] Giga-Embeddings: Mixture-of-Experts Encoders for High-Throughput Text Embeddings
【速读】： 该论文旨在解决文本嵌入（text embedding）模型在高检索性能与高效服务部署之间难以兼顾的挑战，尤其针对大规模应用场景下的计算效率与资源消耗问题。其核心解决方案在于提出Giga-Embeddings系列模型，通过引入稀疏的100亿参数（10B-parameter）混合专家（Mixture-of-Experts, MoE）架构，在保持卓越检索能力的同时显著提升推理吞吐量：该模型每令牌仅激活约18亿参数，实测在1024令牌输入下达到每秒11.45万令牌的处理速度，较密集型30亿参数模型提升25%，并优于多个外部系统。此外，为适配不同计算与内存约束，该系列还包含一个30亿参数的密集型编码器和一个经知识蒸馏得到的4.8亿参数轻量级编码器，后者采用与维度无关的对齐目标优化师生模型间的相似性分布，最终在俄语MTEB基准上取得70.98分，超越FRIDA模型且参数量减少42%。整体方案实现了从超大规模到轻量化部署的全栈覆盖。
链接: https://arxiv.org/abs/2608.23806
作者: Egor Kolodin,Egor Krasnoperov,Evgeniy Kosarev,Fyodor Minkin
机构: MIPT(莫斯科物理技术研究所); SaluteDevices; MSU(莫斯科国立大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:We introduce Giga-Embeddings, a family of text embedding models designed to combine strong retrieval quality with efficient serving. Its largest member is a sparse 10B-parameter Mixture-of-Experts encoder with approximately 1.8B active parameters per token. Across English, Russian, multilingual, and code MTEB benchmarks, this model achieves the strongest aggregate performance within the family on all four evaluated suites. In our vLLM benchmark with 1024-token inputs, it processes 114.5k tokens per second, providing 25 percent higher throughput than the dense 3B model and 1.56-2.65x the throughput of the evaluated external systems. The family also includes a dense 3B encoder and a distilled 480M encoder for tighter compute and memory budgets. We train the compact model using a dimension-agnostic objective that aligns teacher and student similarity distributions. The resulting 480M model scores 70.98 on Russian MTEB, surpassing FRIDA while using 42 percent fewer parameters. We release all three model checkpoints.
[NLP-67] Inter-dimension Dependence for Multi-Dimensional Evaluation of Open-Ended Text
【速读】： 该论文旨在解决生成式开放文本评估中大语言模型（LLM）作为评判者时存在的多维度依赖问题，即模型在评估某一特定维度时往往受到其他非目标维度的干扰，导致评价结果失真。其核心挑战在于量化并缓解这种跨维度依赖（inter-dimension dependence）。解决方案的关键是提出CorrGap评估指标，通过分析不同文本组别中LLM预测得分与真实得分之间的相关性差异，有效度量模型对非目标维度的依赖程度；在此基础上，进一步提出DimCheck方法，通过迭代式地逐步移除推理链（Chain-of-Thought, COT）中与目标维度无关的证据信息，实现对多维度评价过程的解耦。实验表明，DimCheck显著降低了跨维度依赖，并在三个大模型和四个任务上优于现有强基线；同时，较小的训练后模型在使用DimCheck时可逼近大型模型的表现，且推理成本大幅降低。
链接: https://arxiv.org/abs/2608.23783
作者: Haoyuan Li,Snigdha Chaturvedi
机构: University of North Carolina at Chapel Hill(北卡罗来纳大学教堂山分校)
类目: Computation and Language (cs.CL)
备注:
Abstract:LLM-as-a-judge methods are widely used for evaluating the quality of generated open-ended text. Such evaluations are generally multi-dimensional, since the error patterns in texts can be different for different dimensions. Therefore, reliable LLM judges should evaluate each target dimension independently. To quantify the extent to which LLM judges depend on non-target dimensions when evaluating a target dimension, i.e., inter-dimension dependence, we propose CorrGap. To measure this, CorrGap uses the difference in correlations between LLM-predicted scores and ground truth scores across different groups of texts. Using CorrGap, we show that inter-dimension dependence is pervasive across LLM judges in open-ended text evaluation tasks. To mitigate inter-dimension dependence, we propose DimCheck, a method that iteratively removes unrelated evidence from COTs generated by LLM judges in a step-wise way. We show that DimCheck mitigates inter-dimension dependence and outperforms strong baselines across three LLMs and four tasks. We also show that smaller trained LLMs can approximate larger LLMs in DimCheck, with much lower inference costs.
[NLP-68] What Reaches Expert Review? Representation Structural Screening and Candidate-Form Dependence in AI-Assisted Item Development
【速读】： 该论文旨在解决生成式人工智能（Generative AI）辅助题项生成与专家评审之间存在的计算评估环节的隐性影响问题，即这一环节虽被视为技术性前置步骤，实则通过表征方式、结构简化策略及筛选政策深刻塑造了最终提交给心理测量学家的题项内容。其解决方案的关键在于揭示计算评估器并非中立的基础设施，而是测量设计中可检验、可修订的核心组成部分。研究通过两个关联的模拟实验分析32,000个精选的大五人格题项，发现尽管语义几何整体保持一致，局部差异显著：相同表述在不同语境下获得不同的构念证据，不同题项被保留或淘汰，且预期属性可能在群体一致性提升的同时消失。此外，这些敏感性随生成源分布的不同而变化。在最终评审边界，两种准入政策虽使每个可评估量表的内容单元均被填满，但呈现的题项措辞却截然不同；在不同嵌入配置下，包容性主形式仅共享平均6个（共40个）题项，凸显了表征变化对结构证据和排序结果的全链条下游影响。因此，全局摘要与完整量表的表面稳定性掩盖了实际传递给心理测量学家内容的内在不稳定性，强调必须将计算评估过程纳入测量设计的透明化与可修正范畴。
链接: https://arxiv.org/abs/2608.23766
作者: Christopher Brooks(School of Information, University of Michigan)
机构: University of Michigan(密歇根大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 42 pages, 1 figure, 19 tables. Data and reproducibility materials: this https URL
Abstract:Between AI-assisted item generation and expert review sits a computational evaluator whose decisions are usually treated as technical preliminaries. Yet representation, structural reduction, and selection policy determine which items and evidence psychometricians ever receive. Across two linked in-silico studies of 32,000 selected Big Five items, we followed fixed source populations from semantic representation through structural evaluation and candidate-form construction. Broad agreement in semantic geometry concealed consequential local differences: identical wording acquired different construct evidence, different items survived, and intended attributes could disappear even as community correspondence improved. These sensitivities also differed across generated source populations. At the final review boundary, both eligibility policies filled every content cell in every evaluable form, yet they presented different wording. Across embedding configurations, inclusive primary forms shared a median of only 6 of 40 items, reflecting the total downstream consequence of changing representation across structural evidence and ranking. The apparent stability of global summaries and complete forms therefore concealed instability in the content reaching psychometricians. The computational evaluator is not neutral infrastructure between generation and expertise; it is an inspectable and revisable part of measurement design.
[NLP-69] Calibration-Preserving Pruning: Compression as a Reliability Contract
【速读】： 该论文旨在解决模型剪枝（pruning）与分段共形预测（split conformal prediction）之间在保持有限样本边际覆盖性（finite-sample marginal coverage）前提下的效率矛盾问题，即如何在保证预测集有效性的同时，通过剪枝降低预测集规模。其核心解决方案为校准保持型剪枝（Calibration-Preserving Pruning, CPP），关键在于在基础剪枝得分上引入非共形梯度显著性（nonconformity-gradient saliency）作为补充信号，并采用互不重叠的剪枝、验证-选择、共形校准和测试数据划分策略。该方法通过控制得分扰动的有界性，实现共形分位数的稳定转移与集合膨胀的可控，从而在不破坏校准性的前提下提升预测集紧凑性。实验表明，在50%稀疏度下，CPP-SparseGPT与CPP-Wanda在多个数据集上显著减小了平均预测集大小，同时在多数情况下维持或提升了分类准确率，尤其在大规模标签任务中表现最优；且对照实验显示，监督梯度信息是性能提升的主要来源，而阈值感知的候选标签策略可进一步优化集合大小。
链接: https://arxiv.org/abs/2608.23744
作者: Ibne Farabi Shihab,Adria Binte Habib,Anuj Sharma
机构: Iowa State University (爱荷华州立大学); Independent University of Bangladesh (独立大学孟加拉国)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Split conformal prediction, not the pruning rule, supplies finite-sample marginal coverage once a pruned model is fixed independently of the conformal calibration split. We study the separate efficiency problem: can pruning preserve score geometry well enough to obtain smaller valid prediction sets? Calibration-Preserving Pruning (CPP) augments a base pruning score with nonconformity-gradient saliency and uses disjoint pruning, validation-selection, conformal-calibration, and test splits. Bounded score perturbations imply bounded conformal-quantile shifts and controlled set inflation, but do not make the generic coverage theorem CPP-specific. Final five-seed Qwen2.5-1.5B results at 50% sparsity show the largest gains on large-label tasks. On DBpedia-14, CPP-SparseGPT reduces mean set size from (10.1) to (8.6) while changing accuracy from (0.347) to (0.366); CPP-Wanda reduces (11.2) to (9.0) with an accuracy trade-off from (0.310) to (0.295). Across 15 dataset–sparsity cells, CPP-SparseGPT produces smaller sets in 13 and higher accuracy in 11. Matched controls show that generic supervised gradients explain much of the gain: true-label CPP is not statistically resolved from matched Wanda+SNIP, whereas threshold-aware candidate-label CPP reaches (7.8) mean set size at explicit accuracy and offline-compute costs. RoBERTa-base and Llama-3-8B diagnostics support transfer, but our claims remain limited to reliability-sensitive classification.
[NLP-70] ADE: Agent ic Data Evolution Framework for Human-Centered Objectives EMNLP2026
【速读】： 该论文旨在解决大语言模型在对齐人类中心目标时面临的挑战，尤其针对不可执行且上下文依赖性强的目标，此类目标导致可靠验证困难与可扩展监督受限。现有合成数据虽能扩展覆盖范围，但其弱验证机制将瓶颈从生成转移至选择阶段，噪声信号易引发迭代优化过程中的不稳定性及隐性性能退化。为此，本文提出一种以数据为中心的框架——代理式数据演化（Agentic Data Evolution, ADE），将合成监督信号组织为动态演化的数据快照。ADE通过闭环的观察-变异-选择（Observation-Variation-Selection, OVS）流程持续优化数据快照，并引入稳态准入机制作为质量阶梯，保守地控制更新以保障跨轮次的持续改进。实验验证表明，该方法在内在趋势追踪与外在后训练评估中均实现显著提升：在DEV300基准上，内在胜率由50%提升至75.81%，外在胜率从55.20%提升至68.86%，且在多种基准、后训练方法、模型规模及任务类型上表现一致增益。盲评专家评估进一步确认，演化后答案获得66.11%的偏好率。因此，解决方案的关键在于构建一个基于闭环反馈与质量守恒机制的数据演化系统，实现高质量合成数据的可持续优化。
链接: https://arxiv.org/abs/2608.23719
作者: Yang Yu,Yilin Jiang,Zexuan Fei,Yiming Luo,Xingkai Song,Kaiyi Huang,Aimin Zhou,Xin Lin,Fei Tan
机构: East China Normal University(华东师范大学); The Hong Kong University of Science and Technology (Guangzhou)(香港科技大学（广州）); Shanghai Innovation Institute(上海创新研究院)
类目: Computation and Language (cs.CL)
备注: accepted by EMNLP 2026
Abstract:Aligning large language models to human-centered objectives is difficult when targets are non-executable and context-dependent, limiting reliable verification and scalable supervision. Although synthetic data expands coverage, weak verification shifts the bottleneck from generation to selection. Noisy signals destabilize iterative refinement and can cause silent regressions. We propose Agentic Data Evolution (ADE), a data-centric framework that organizes synthetic supervision as evolving data snapshots. ADE improves data snapshots through a closed-loop Observation-Variation-Selection (OVS) procedure, where a steady-state admission mechanism acts as a quality ratchet that conservatively gates updates for sustained cross-round improvement. We validate these improvements through complementary intrinsic trend tracking and extrinsic post-training evaluation. On DEV300, ADE raises the intrinsic win rate from 50% to 75.81% and the extrinsic win rate from 55.20% to 68.86%, consistent performance gains across diverse benchmarks. Blind expert evaluation further confirms this, with a 66.11% preference for evolved answers. These gains extend across post-training methods, model scales, and tasks beyond the target weakly verifiable educational objectives. Resources are available at this https URL.
[NLP-71] he Limits of Automatic Evaluation of Creativity in Large Language Models
【速读】： 该论文旨在解决生成式文本（尤其是由大语言模型生成的短篇故事）在创意性评估中，自动评价方法与人类主观判断之间存在显著偏差的问题。其核心挑战在于如何有效衡量创意性这一高度多维且主观的属性。研究的关键发现是：当前主流的自动评估指标（如基于相似性或流畅性的客观度量）以及以大语言模型作为评判者的评估方式，均无法可靠反映人类对创意性的判断。特别是，以大语言模型为裁判的评估体系表现出对AI生成内容的系统性偏好，倾向于认可其风格一致性等特征，而忽视了人类创作中更具不可预测性和原创性的特质。相关性分析进一步表明，现有自动指标与人类评分之间的相关性接近零，说明这些方法未能捕捉到创意性的关键维度。因此，该研究揭示了将复杂、主观的创意性简化为可计算指标的根本局限性，凸显了开发更贴近人类认知机制的评估框架的必要性。
链接: https://arxiv.org/abs/2608.23705
作者: Alessandro Tutone,Giorgio Franceschelli,Mirco Musolesi
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Large Language Models (LLMs) are increasingly capable of generating text that challenges human performance in domains requiring creativity, yet evaluating creativity in LLM-generated content remains a significant challenge. Here, we investigate whether current automatic evaluation methods can reliably capture human judgments of creativity. We collect human evaluations of human- and AI-generated short stories from the WritingPrompts dataset across 11 dimensions of creativity, and compare these judgments with automated objective metrics and LLM-as-a-Judge evaluations. Our experiments reveal substantial misalignment between automatic evaluations and human assessments. In particular, LLM-based judges exhibit a systematic preference for AI-generated stories, consistently favoring their stylistic characteristics over the unpredictability and other qualities of human-authored texts. Furthermore, correlation analyses show that widely used automatic metrics exhibit near-zero alignment with human judgments across both human- and AI-generated stories, suggesting that they fail to capture important dimensions of creativity. These findings highlight fundamental limitations in current approaches to the automatic evaluation of creative text and underscore the difficulty of reducing the multidimensional and subjective nature of creativity to computational metrics.
[NLP-72] Automata from Agent Traces: Failure and Next-Step Prediction
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）驱动的智能体在执行多步骤任务时行为结构不透明的问题，尤其针对其长且无结构的执行轨迹难以进行安全审计与运行时监控的挑战。现有方法通常仅针对单条轨迹或成功案例进行分析，无法捕捉跨运行之间的拓扑关联，从而限制了对下一步行为和失败事件的准确预测。为此，论文提出将整个轨迹语料库压缩为一个紧凑、统一的有限状态机（Finite-State Machine, FSM），作为LLM智能体不可预测行为的结构性基础。该FSM在12个公开数据集上均表现出高度紧凑性（7–43个状态），能够以接近0.997的拟合度重放未见数据，并在不同数据划分间保持近乎一致的拓扑结构，构建时间仅需毫秒级。该结构化基座同时支持双重预测目标：在下一步行为预测中，基于FSM状态的上下文表现优于传统的智能体工作流记忆（Agent Workflow Memory）；在失败预测方面，基于状态的特征可达到高达0.94的受试者工作特征曲线下面积（AUROC），且在线监控器可在部分轨迹执行阶段即识别出高失败风险运行，实现远早于任务完成的早期终止。研究结果表明，行为拓扑更多由部署框架决定而非语言模型本身，因而该FSM可作为模型无关的结构性原语，为安全审计与运行时监控提供坚实支撑。
链接: https://arxiv.org/abs/2608.23670
作者: Seonglae Cho,Franklin Cardenoso Fernandez,Umar Mohammed,Zekun Wu,Kleyton Da Costa,Ilham Wicaksono,Adriano Koshiyama
机构: Holistic AI; PUC-Rio; University College London
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:LLM-based agents execute multi-step tasks, but their behavioral structure remains opaque: long unstructured traces resist the safety auditing and runtime monitoring that deployment requires. Existing approaches operate per-trace or success-only, so they miss the cross-run topology that links next-step and failure prediction. To recover that shared structure, we collapse an entire trace corpus into a single, compact finite-state machine (FSM) that serves as a structural substrate for the otherwise unpredictable behavior of LLM agents. Across twelve public datasets, the FSMs are compact (7-43 states), replay held-out data at =0.997 fitness with near-identical topology across splits, and build in milliseconds. This substrate addresses both prediction goals. For next-step prediction, FSM-state context outperforms Agent Workflow Memory on every ground-truth-matched dataset. For failure prediction, per-state behavioral features reach held-out AUROC up to 0.94, and an online monitor ranks failing runs above passing ones from a partial trace, triggering early stopping well before completion. Behavioral topology thus appears shaped more by the deployment harness than by the LLM, providing a model-agnostic structural primitive for safety auditing and runtime monitoring.
[NLP-73] Gated Activation Steering for Reducing Sycophancy Hallucination in Medical Question Answering
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在临床问答任务中普遍存在的“奉承行为”（sycophancy）与“幻觉”（hallucination）问题。其中，幻觉指模型生成与上下文无关的虚假信息，而奉承行为则表现为模型在用户质疑时轻易改变原本正确的回答，导致答案不稳定性。现有方法如基于提示的防护机制或全局激活控制策略通常分别处理两类问题，且干预范围过广，容易对本已正确的响应造成不必要的负面影响。本文提出一种基于推理时干预（Inference Time Intervention, ITI）的统一框架，通过对比学习从临床问答对中分别提取针对幻觉和奉承行为的独立引导方向，并将其作用于经因果验证的注意力头。运行时，行为特异性门控机制动态判断是否需要干预：幻觉组件抑制无依据陈述，奉承组件防止因用户施压导致的答案漂移。实验基于电子健康记录（EHR）数据，在40亿参数模型上进行了15,900次推理测试，结果显示，在600条压力轨迹中，未经过引导的模型有570次出现妥协现象，而采用门控引导的模型在551次中成功维持了正确答案，其抗压能力达到超过1000亿参数模型的水平，证明了目标导向的推理时干预可在不全程干预的前提下显著提升模型鲁棒性。
链接: https://arxiv.org/abs/2608.23666
作者: Himanshu Tripathi,Subash Neupane,Shaswata Mitra,Sudip Mittal,Noorbakhsh Amiri Golilarz,Shahram Rahimi
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Sycophancy and hallucination are persistent failure modes of Large Language Models (LLMs) across domains. However, it becomes particularly consequential in clinical question answering, where responses must remain grounded in the provided context and robust to user pressure. Hallucination can introduce information that is unsupported by the context, while sycophancy can cause a model to abandon a previously correct answer when challenged by the user. Existing approaches, such as prompt-based safeguards and always-on activation steering, often address these behaviors separately or apply interventions broadly across turns, which can unnecessarily deteriorate responses that were already correct. To address these limitations within a single framework, we employ Inference Time Intervention (ITI) to jointly control both behaviors by learning separate steering directions for hallucination and sycophancy from contrastive clinical pairs and applying them to causally verified attention heads. During runtime, behavior-specific gates then determine when intervention is needed: the hallucination component mitigates unsupported claims, while the sycophancy component mitigates answer shifts caused by user pressure. We evaluate this framework on clinical questions grounded in EHR data while keeping the model weights frozen. Across all evaluation settings, we conducted 15,900 model-response runs. Across 600 pressure trajectories for the 4-billion-parameter model, the unsteered model caved in 570 cases. At the same time, gated steering helped it last longer in 551 of them. It held its ground under pressure at levels comparable to those of models with more than 100 billion parameters, showing that targeted inference-time steering can improve robustness without intervening at every turn.
[NLP-74] Contextual Embedding Evidence for Main–Light Verb Distinctions in Urdu
【速读】： 该论文旨在解决乌尔都语轻动词（light verb）在句法-语义结构中如何表征其程式化事件结构意义，同时保持与对应主要动词（main verb）之间词汇关联性的问题。其核心挑战在于揭示轻动词在语义表征上是否既与主用动词存在系统性差异，又维持一定的词汇层面的相似性。解决方案的关键在于利用来自UrduBERT、DunbaaBERT及多语言BERT的上下文嵌入，在1,126个自然语料句子中对七个乌尔都语动词的主要用法与轻动词用法进行表征分析。研究发现，所有21组动词-模型对比中，主用与轻动词用法在嵌入空间中表现出显著的表征分离；但同一词基（lemma）的主用与轻动词中心点之间的距离始终小于不匹配的词基对，支持其持续存在的词汇相关性。此外，在仅限轻动词用法的七分类预测任务中，即使目标动词被遮蔽，UrduBERT仍达到0.866准确率和0.852宏平均F1值，并在前缀形式无关评估中保持0.782准确率，表明模型具备超越局部重复组合模式的泛化能力。这些计算结果为Butt关于乌尔都语轻动词在系统性差异中保留词基与动词特异性表征结构的理论提供了有力支持。
链接: https://arxiv.org/abs/2608.23645
作者: Farah Adeeba,Miriam Butt
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Urdu light verbs contribute schematic event-structural meaning while remaining lexically related to corresponding main verbs. This study tests representational predictions derived from Butt’s analysis using contextual embeddings from UrduBERT, DunbaaBERT, and multilingual BERT across 1,126 naturally occurring sentences containing seven Urdu verbs. Main and light uses show significant representational separation in all 21 verb–model comparisons. At the same time, same-lemma main and light centroids are consistently closer than mismatched main–light lemma pairs, supporting continued lexical relatedness. In a seven-way prediction task restricted to light uses, verb identity remains recoverable after the target is masked, with UrduBERT achieving 0.866 accuracy and 0.852 macro-F1. UrduBERT also retains 0.782 accuracy under a preceding-form-disjoint evaluation, indicating generalization beyond repeated local verb combinations. These findings provide computational evidence consistent with Butt’s account that Urdu light verbs differ systematically from their main uses while retaining lemma-specific and verb-specific representational structure.
[NLP-75] Auditing the Synthetic Memoir: Measuring Scene-Level Confabulation in LLM -Generated Autobiography Against the Documented Record of the Life It Describes
【速读】： 该论文旨在解决生成式AI（Generative AI）在创作个人自传类文本时存在事实准确性缺失的核心问题，即大语言模型（LLM）所生成的关于个体经历的叙述中，有多少内容真正符合真实事件。其解决方案的关键在于构建并实施一种场景级案例审计方法——首次基于特定主体的真实证据库（ground-truth corpus），对由对话式大语言模型生成的366天第一人称轶事文本进行逐日、逐场景的量化验证。研究采用预先设定的四层级评估量表，将每个场景的验证结果分为“已验证”“弱验证”“未验证”和“矛盾”四类，结果显示96.7%的日期条目未能通过“已验证”标准，仅12天存在可证实的场景，且5.2%的条目与记录直接矛盾。主要失败模式为“有根漂移”（grounded drift），即虚构场景中嵌入了真实人物、雇主或地点，但其具体情境为编造。尽管独立重评验证了核心结论的稳健性，但量表的信度仅为中等，且“弱验证”与“未验证”之间的边界不可靠。进一步实验表明，即使使用当前主流命名模型重新生成相同内容，仍保持100%的验证失败率；而将生成过程锚定于主体自身的原始语料库可显著提升验证率至83.3%，但仍存在显著残余错误。研究贡献包括一套可复用的审计工具、对现有验证边界可靠性的警示，以及一种具有量化效果的语料锚定（grounding）修复策略。
链接: https://arxiv.org/abs/2608.23640
作者: Heather Renze
机构: Serenze Global; ORCID(开放研究者与贡献者身份识别码)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: 20 pages, 4 figures, 6 tables. Code and derived data available at this https URL
Abstract:When a large language model (LLM) is asked to write a person’s life, how much of what it writes actually happened? We present a scene-level case-study audit - the first quantified audit of LLM-generated autobiography against a subject-specific ground-truth corpus that we are aware of, based on an unsystematic literature search. The subject and the author of this paper are the same person: a 366-day “page-a-day” book of first-person anecdotal entries was drafted with a conversational LLM whose documented inputs were a template, two exemplar days, and each day’s quote - not her corpus - and every day was subsequently audited at the anecdote-scene level against an independent verification corpus using a four-level rubric fixed before analysis. We define the verification-failure rate as the share of days not rated VERIFIED (scene positively corroborated): 354 of 366 days fail, 96.7% (Wilson 95% CI 94.4-98.1%). Only 12 days contain a corroborated scene; 19 days (5.2%) assert claims actively contradicted by the record; the dominant failure mode is grounded drift - real people, employers, and settings inside invented scenes - though its measured share varies across raters. Independent re-rating replicates the headline (no evidence the original rate was inflated) while showing that the four-way taxonomy has only fair-to-moderate reliability. Regenerating the same days with current named models reproduces 100% verification failure under the same inputs; grounding generation in the subject’s corpus significantly improves the verification rate while leaving substantial residual failure (83.3%). We contribute the measurement, a reusable audit instrument whose WEAK/UNVERIFIED boundary we show to be unreliable, and a grounding remedy with quantified effect.
[NLP-76] From Triage to Discharge: A Survey of NLP Tasks Methods and Open Challenges in the Emergency Department EMNLP2026
【速读】： 该论文旨在解决急诊科（Emergency Department, ED）在高时间压力下处理多模态临床数据（如临床对话、分诊记录和出院文档）时，如何有效应用自然语言处理（Natural Language Processing, NLP）技术以支持分诊、诊断与处置等关键环节的挑战。其核心解决方案在于系统性地分析46篇涵盖急诊科全流程的研究文献，揭示当前急诊科NLP研究中从任务特定神经网络架构向预训练语言模型迁移的趋势，并强调交互式临床系统的发展以及基于临床实际的评估方法的重要性。关键突破在于识别出跨任务的共性趋势，如对模型泛化能力、噪声输入鲁棒性及临床工作流适配性的关注，进而指明未来研究需应对的开放性挑战，包括模型可泛化性不足、临床输入噪声大及实际工作流程限制等问题，为构建更实用、可靠的急诊科智能辅助系统提供方向。
链接: https://arxiv.org/abs/2608.23627
作者: Dipankar Srirag,Aditya Joshi,Salil Kanhere,Padmanesan Narasimhan
机构: University of New South Wales, Sydney, Australia(新南威尔士大学，悉尼，澳大利亚)
类目: Computation and Language (cs.CL)
备注: Accepted to the EMNLP 2026 Main Conference; camera-ready version
Abstract:Emergency departments (EDs) operate under time pressure, generating multimodal data such as clinical conversations, triage notes, and discharge documents. Recent advances in natural language processing (NLP), particularly pretrained transformers and large language models, have created new opportunities to support language and time-intensive stages of emergency care. Yet existing surveys map clinical NLP across the broader hospital workflow or focus on specific tasks. This survey analyses 46 papers spanning the three phases of ED: triage, diagnosis, and disposition, covering tasks such as triage classification, clinical summarisation, automatic diagnosis, report generation, and discharge documentation. We examine modelling paradigms, evaluation practices, and emerging benchmarks and shared tasks. Across tasks, we identify common trends, including a shift from task-specific neural architectures to pretrained language models, growing interest in interactive clinical systems, and increasing attention to clinically grounded evaluation. Finally, we detail open challenges such as limited generalisability, noisy clinical inputs, and workflow constraints that inform future ED-NLP research.
[NLP-77] Squeezing the Cache Preserving the Truth: Monotonic Equipotential Allocation with Geodesia-KV
【速读】： 该论文旨在解决当前KV缓存压缩性能评估中存在的关键问题：现有方法混淆了驻留比特（resident bits）与读取带宽（read bandwidth），且受分块教师强制（chunked teacher-forcing）带来的伪影影响，导致评估结果失真。其核心解决方案是提出Geodesia-KV，一种无需训练的KV缓存策略，其关键技术包括单调的块级精度分配、精确的率失真残差建模以及查询稀疏读取机制，实现了面向硬件优化的压缩方案。通过明确定义驻留比特与读取比特的分离，并采用因果评估方式，Geodesia-KV在多个基准测试中显著优于现有方法。例如，在WikiText-2数据集上，16k上下文长度下，5比特操作点的Geodesia-KV在更低比特率下仍能实现比KIVI-4更低的困惑度；在PG-19数据集上，其压缩版Quest模型在保持更优困惑度的同时，将驻留比特从16.25降至9.83，读取比特从2.32降至1.95。由于Geodesia-KV以vLLM原生插件形式实现，通过单调比特降级机制完全消除密集缓存驻留需求，结合全消费级硬件评估，可在单块16 GiB GPU上实现百万级标记上下文生成，对主流架构（Qwen、Llama、DeepSeek）的峰值显存节省最高达71.7%。
链接: https://arxiv.org/abs/2608.23599
作者: Vincenzo Dentamaro,Pancrazio Auteri,Giuseppe Pirlo
机构: University of Bari Aldo Moro, Department of Computer Science(巴里阿尔多·莫罗大学计算机科学系); Geodesia.ai
类目: Hardware Architecture (cs.AR); Computation and Language (cs.CL)
备注: 11 pages, 3 figures, 8 tables. Code and vLLM integration available at this https URL
Abstract:Current assessment of KV-cache compression performance confuses resident bits with read bandwidth and is affected by the artifacts of chunked teacher-forcing. We present Geodesia-KV, a family of training-free KV cache policies based on monotonic block-wise precision allocation, exact rate-distortion residuals, and query-sparse reading, enabling proper hardware-ready compression. With proper separation of resident and read bits and causal evaluation, we show that Geodesia-KV significantly outperforms other approaches. Specifically, on WikiText-2 with 16k context, the 5-bit operating point of Geodesia-KV results in lower perplexity at lower bitrate than KIVI-4 on Qwen. In addition, our compressed-Quest version delivers improved perplexity and reduces resident (9.83 vs 16.25 bits/value) and read rates (1.95 vs 2.32 bits/value) over baseline sparse methods on PG-19. As Geodesia-KV is implemented as native GeodesiaKVCacheManager plug-in of vLLM, Geodesia-KV fully removes the need for dense cache residency via monotonic bit demotion. With the full consumer hardware evaluation, Geodesia-KV leads to 1M-token context generation on a single 16 GiB GPU with up to 71.7% peak VRAM savings on all leading architectures (Qwen, Llama, DeepSeek).
[NLP-78] ransformer Accelerator (TFA): A Macro-Op INT8 Hardware Chip for Transformer Inference and Machine Translation
【速读】： 该论文旨在解决大模型推理中高效、低功耗且可验证的硬件加速难题，特别是针对Transformer架构在资源受限场景下的部署挑战。其核心问题在于如何在保持精度与功能正确性的前提下，实现紧凑型硬件对复杂Transformer计算流程（包括编码器-解码器结构、自回归生成与提示处理）的高吞吐、低延迟支持。解决方案的关键在于提出一种可综合、参数化、基于INT8量化内存到内存的Transformer加速器（TFA），通过统一时分复用的数据通路实现前向推理全流程覆盖；利用八种512位宏操作描述符（macro-op descriptors）抽象并调度矩阵乘法、Softmax、RMSNorm、逐元素运算及数据复制/收集等关键操作；结合输出驻留式乘累加阵列与乒乓缓冲机制，实现数据搬运（DMA）与计算的高度重叠；引入比特精确的倒数平方根与除法单元、键值缓存与嵌入地址管理模块，以及抗中断的零填充写入引擎，确保执行过程的确定性与可靠性；同时，采用基于UVM的验证环境进行比特级黄金模型比对，在25个测试和34次约束随机运行中实现零错匹配、100%功能覆盖率和94.96%代码覆盖率。此外，通过编译器管理的量化策略（如随机哈达玛重参数化）有效恢复了约11 dB的每张量INT8信噪比，使INT8输出在多数情况下与浮点参考结果一致，少数情况仍能生成有效替代翻译。最终，该设计在多语言谚语测试中成功执行70,320条指令，准确匹配37.9 MB输出，展现出端到端、比特精确的预训练Transformer推理能力，且经重布线后逻辑面积降至2.73 mm²，完成SkyWater sky130工艺的设计规则检查与布局布线，相较22线程CPU实现约20倍端到端加速，未来更大规模设计有望将每令牌能耗降低约1000倍。
链接: https://arxiv.org/abs/2608.23582
作者: Shashank
机构: Independent Researcher(独立研究员)
类目: Hardware Architecture (cs.AR); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:We present the Transformer Accelerator (TFA), a synthesizable, parameterizable INT8 memory-to-memory engine for transformer inference. One time-multiplexed datapath handles prompt processing and autoregressive generation. TFA implements matrix multiplication, softmax, RMSNorm, elementwise, and copy/gather operations through eight 512-bit macro-op descriptors. Offline-compiled programs are fetched, validated, and dispatched through AXI interfaces, supporting encoder, decoder, and encoder-decoder models. The RTL combines an output-stationary multiply-accumulate array with ping-pong buffers that overlap DMA and compute, bit-exact reciprocal-square-root and divide units, key-value-cache and embedding addressing, and an abort-safe zero-padding write engine. A UVM environment byte-compares outputs against a bit-exact golden model. Across 25 tests and 34 constrained-random runs, TFA achieved zero mismatches, 100% functional coverage, and 94.96% code coverage. We compiled the t5-small encoder-decoder pipeline for English-to-French, German, and Romanian translation. On ten multilingual proverbs, TFA executed 70,320 descriptors and matched 37.9 MB of golden-model output with zero mismatches. INT8 output matched the floating-point reference token-for-token on five sentences; the rest produced valid alternative translations. Randomized-Hadamard reparameterization recovered about 11 dB of per-tensor INT8 signal-to-noise ratio across layers. The verification configuration achieved about 20x end-to-end speedup over a 22-thread CPU, while larger designs are projected to reduce energy per token by about 1000x. After RAM inference recoding, logic area fell to 2.73 mm2, and the design completed design-rule-clean synthesis and place-and-route on SkyWater sky130. TFA demonstrates end-to-end, bit-exact execution of pretrained transformers using compact hardware and compiler-managed quantization. Subjects: Hardware Architecture (cs.AR); Computation and Language (cs.CL); Machine Learning (cs.LG) Cite as: arXiv:2608.23582 [cs.AR] (or arXiv:2608.23582v1 [cs.AR] for this version) https://doi.org/10.48550/arXiv.2608.23582 Focus to learn more arXiv-issued DOI via DataCite
[NLP-79] aming Visual Neglect: A Variational Information Bottleneck Framework for Adaptive Attention in Multimodal In-Context Learning
【速读】： 该论文旨在解决大视觉-语言模型在上下文学习（In-Context Learning, ICL）中视觉上下文作用机制不明确的问题，特别是为何某些情况下模型能有效利用视觉示例，而另一些情况下却完全忽略视觉信息这一看似矛盾的现象。其核心解决方案基于信息论框架——信息瓶颈（Information Bottleneck, IB）原理，提出跨模态信息增益（Cross-Modal Information Gain, CMIG）作为量化指标，衡量视觉上下文相对于文本上下文对目标变量的额外信息贡献。理论分析表明，多模态ICL相较于纯文本ICL的泛化误差增量由CMIG决定：当视觉信息非冗余时，多模态ICL可严格优于文本仅ICL。进一步证明，视觉信息被忽略并非模型缺陷，而是信息瓶颈最优解，尤其在视觉信息冗余时，此时应通过注意力重分配实现最优信息压缩。据此，作者提出了VIB-ICL算法，通过变分界估计CMIG并动态调整视觉注意力权重，实现了自适应的信息利用。在五个基准测试上的实验验证了该方法的有效性，实现了最高达4.7%的准确率提升和35%的示例需求减少，充分支持了理论预测。
链接: https://arxiv.org/abs/2608.23570
作者: Kaito Tanaka,Yuji Nishimura,Keisuke Matsuda,Aya Nakayama
机构: SANNO University (三保大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large vision-language models exhibit strong in-context learning (ICL) capabilities, yet when and why visual context helps multimodal ICL remains poorly understood. Empirical studies show a puzzling dichotomy: models sometimes effectively leverage visual demonstrations, yet often neglect them entirely. We propose VIB-ICL, an information-theoretic framework that resolves this dichotomy through the Information Bottleneck principle. We introduce the Cross-Modal Information Gain (CMIG), which quantifies the additional mutual information that visual context provides about the target beyond textual context. We derive a generalization bound showing that multimodal ICL’s excess risk over text-only ICL is governed by the CMIG, proving that multimodal ICL provably outperforms text-only ICL when visual information is non-redundant. We further prove that visual context neglect, often viewed as a failure mode, is the Information Bottleneck-optimal solution when visual information is redundant, yielding a closed-form Attention Reallocation Principle that prescribes how visual attention weights should be adaptively adjusted. We instantiate this principle in the VIB-ICL algorithm, which estimates CMIG via variational bounds and dynamically reallocates attention. Experiments on five benchmarks demonstrate consistent improvements of up to 4.7% accuracy gains and 35% reduction in required demonstrations, validating our theoretical predictions.
信息检索
[IR-0] Auditing Return Conditioning as a Control Knob: An Offline Diagnostic for Decision Transformer Recommendation RECSYS2026
链接: https://arxiv.org/abs/2608.24815
作者: Jingyu Wang
类目: Information Retrieval (cs.IR)
备注: Accepted at CONSEQUENCES '26, the 5th Workshop on Causality, Counterfactuals and Sequential Decision- Making for Recommender Systems, co-located with ACM RecSys 2026. 5 pages, 2 figures
Abstract:Offline return-to-go (RTG) sweeps can test whether a recommender conditioned on return is controllable, but the intervention is rarely audited. Rewriting every historical RTG token creates an increasingly synthetic context, while rewriting only the current token is more local. We test this distinction in an offline setting with a fixed window. On MovieLens 25M and MyAnimeList 2020 (MAL), we evaluate a Decision Transformer using an RTG locality ladder, a control without RTG, a logged match and score reward check, and a within-trajectory shuffled RTG ablation. On MovieLens, a K=20 intervention that covers the full context, applied only to real context positions, shifts the share of Crime predictions by +23.61 \pm 2.96 percentage points from the validation 5th to 95th percentile, whereas changing only the current slot shifts it by +1.77 \pm 1.17 points. The shuffled RTG model largely removes this response ( +2.08 \pm 1.20 points at K=20 ). On MAL, the same protocol does not produce a Drama response: K=20 changes Drama by -0.03 \pm 0.07 points, and K=1 by -0.01 \pm 0.01 . Genre prediction accuracy is numerically close across real RTG, no RTG, and shuffled RTG, and at K=1 logged match rates and matched ratings change little. Because dataset and focus-genre selection were exploratory, these magnitudes are descriptive; the cross-diagnostic pattern across locality, shuffled RTG, and the null result on MAL does not establish reward control. We propose four checks: intervention locality, a no-RTG baseline, a reward check, and RTG-content ablation.
[IR-1] Structurally-bounded Agent ic Graph Exploration for Evidence-Grounded Scholarly DeepSearch
链接: https://arxiv.org/abs/2608.24809
作者: Rima Hazra,Sayan Layek,Somnath Banerjee,Soumen Chakrabarti,Animesh Mukherjee
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注:
Abstract:We present Crase, a bounded and inspectable alternative to deep research agents for scholarly search. Instead of an open-ended search loop, Crase queries a search engine once for seed papers, expands them along their 1.5-hop citation neighborhood, prunes citation edges whose claims lack entailment support, and ranks the remaining papers with a recency-aware random walk. This makes the candidate set, the reason each paper is kept, and the stopping condition explicit and fixed before inference. On LitSearch and one further benchmarks over a 500K-paper arXiv corpus, Crase outperforms deep research agents built on proprietary models by up to 3 \times recall@50 at roughly a third of the cost.
[IR-2] EviGraph: Towards Verifiable Evidence Construction for Information-Seeking Agents
链接: https://arxiv.org/abs/2608.24667
作者: Jiashun Chen,Yirong Mao,Wenhui Que
类目: Information Retrieval (cs.IR)
备注:
Abstract:Agentic Web search can retrieve relevant information without establishing that the retrieved content actually supports the claims used in an answer. Existing agents typically keep search and evidence recording in a linear interaction trace and optimize primarily for final-answer correctness, providing limited supervision for intermediate grounding. We present EviGraph, a deep-search framework that separates search execution from evidence recording while using a shared policy for the trainable roles. An executor plans concise queries, a frozen evidence verifier inspects source pages and returns verbatim evidence items with an explicit polarity, and the policy maps those items to add/support graph requests that are checked by a deterministic structural validator. The resulting graph serves both as persistent working memory and as a source of dense process rewards, enabling reinforcement learning to directly supervise evidence construction rather than only the final answer. On BrowseComp-Plus, a Qwen3-8B EviGraph agent achieves 35.9% accuracy under a matched interaction budget, compared with 26.9% for the same dual-role architecture without reinforcement learning and 2.7% for a monolithic agent, while generating fewer tokens per rollout. Consistent gains on BrowseComp, GAIA, and XBench indicate that explicitly structuring and rewarding evidence recording improves agentic search
[IR-3] Rethinking Semantic Alignment in LLM -Enhanced Collaborative Filtering: A Spectral Decoupling Approach
链接: https://arxiv.org/abs/2608.24363
作者: Yedong Jin,Shaowen Peng,Tsunenori Mine,Shoko Wakamiya,Eiji Aramaki
类目: Information Retrieval (cs.IR)
备注:
Abstract:Recent advances in LLM-enhanced recommendation commonly align semantic representations with collaborative embeddings in a shared space, yet how alignment affects LLM-encoded information remains unclear. In this work, we revisit LLM-enhanced recommendation from a spectral perspective and show that collaborative and semantic signals benefit from different spectral parts. While collaborative representations are dominated by smooth low-frequency components due to user-item homophily, semantic embeddings contain useful non-principal singular components. Through component-wise evaluation and training-dynamics analysis, we find that alignment increasingly concentrates learned representations in dominant collaborative and principal semantic subspaces, reducing overlap with non-principal semantic components. Controlled comparisons show that non-principal components provide inconsistent gains under alignment but consistently improve performance through component-level decoupling, while full prediction-level decoupling achieves the best overall performance. These results indicate that alignment fails to effectively exploit complementary non-principal semantic information. Motivated by these findings, we propose UniSpecRec (Unifying Spectral Signals for Recommendation), which applies signal-specific spectral filtering while preserving collaborative and semantic representations in their respective spaces. UniSpecRec combines their predictions without cross-space alignment or additional trainable parameters. Extensive experiments demonstrate its effectiveness, efficiency, and generalizability.
[IR-4] RecGPT -Mobile-V2 Technical Report
链接: https://arxiv.org/abs/2608.24295
作者: Lingqing Zhang,Bin Zhang,Weipeng Huang,Chengfei Lv,Chengyu Lai,Chuxin Chen,Dimin Wang,Han Zhu,Hongtao Cheng,Jialin Zhu,Jian Wang,Jiuning Lin,Junqing Wu,Li Chen,Qichao Ma,Ruiquan Lan,Shuai Zhong,Tao Wang,Xiaodong Zhu,Yinjiang Cai,Yinnan Song,Yipeng Yu,Yuan Liu,Yuning Jiang,Zhaode Wang,Zhibo Xiao,Zhixin Ma,Zihong Huang
类目: Information Retrieval (cs.IR)
备注:
Abstract:Personalized Query prediction maps implicit behavioral signals—clicks, favorites, purchases, and post-purchase exploration—to explicit retrieval intent. On-device deployment makes this task particularly challenging: behavioral trajectories are noisy and multi-scale, multiple Queries may be valid for a single trajectory, and a uniform reasoning policy either expends unnecessary computation on simple instances or allocates insufficient capacity to complex ones. We introduce RecGPT-Mobile-V2, an end-to-end framework that treats intent quality and execution efficiency as coupled objectives within a staged design. The framework transforms heterogeneous interactions into an evidence-preserving trajectory, establishes a recommendation-native foundation through domain adaptation and supervised alignment, and applies reasoning-cost optimization only after grouped rollouts meet grounding and utility criteria. The resulting teacher is distilled into a compact student deployed with low-bit execution, structured compression, and budget-aware device–cloud routing. In an aligned CoT ablation, an evidence-focused short rationale increases ROUGE-L from 0.228 to 0.315 and Jaccard from 0.174 to 0.248, while slightly outperforming the full five-stage rationale. In the controlled RL comparison, the complete reward formulation improves Query quality from 73.2% under quality-only RL to 78.6%, lowers the hard-failure rate from 3.6% to 1.6%, and reduces the median CoT length from 62 to 14 tokens. Online retrieval analysis further indicates that the Query recall channel retrieves inventory complementary to that surfaced by established recall channels. Collectively, these findings support sufficiency-oriented rather than uniformly short reasoning: retain decision-relevant evidence and allocate additional computation only when it is likely to improve the predicted Query.
[IR-5] Matched Excess-Outranker Regularization for Candidate-Set Interference in Continual Knowledge Graph Embedding
链接: https://arxiv.org/abs/2608.24273
作者: Hao Ren,Junbin Gao,Jiaojiao Jiang
类目: Artificial Intelligence (cs.AI); Databases (cs.DB); Information Retrieval (cs.IR)
备注:
Abstract:Continual knowledge graph embedding updates entity and relation representations as a graph grows. Existing methods primarily address catastrophic forgetting, but entity admission also changes the candidate universe of every compatible query. A historical answer can therefore lose rank even when its score and its ordering among old entities are preserved. We formalize this effect as candidate-set interference and introduce Matched Excess-Outranker Regularization (MEOR), a host-level objective that compares smooth answer-relative newcomer pressure with score-blind, structurally matched old references. Its one-sided penalty acts only when newcomer competition exceeds the matched reference, preserving the host learner’s signal for legitimate new entities. Across eight paired runs on ENTITY-ComplEx, MEOR improves historical current-universe mean reciprocal rank (MRR) by 0.0057 over replay and reduces candidate-set interference by 0.0055, with one-sided 95% lower bounds of 0.0052 and 0.0051, respectively. It satisfies the preservation criteria for old-universe ranking and newcomer acquisition and improves historical current-universe MRR over persistent calibration, matched maximum regularizer (MMR), and unmatched old regularizer (UOR). Direct ablations support each component of its reference construction and aggregation. Adding MEOR also improves historical ranking in all ten reported FBInc-S and FBInc-L host and backbone settings, with every paired 95% confidence interval excluding zero. These results establish candidate admission as a distinct source of continual rank loss and show that it can be controlled without replacing the underlying embedding architecture or continual learner.
[IR-6] ow: Flow-based Item Tokenizer for Recommendation CIKM’26
链接: https://arxiv.org/abs/2608.24176
作者: Nian Li,Chonggang Song,Jingtao Ding,Lingling Yi,Yong Li,Qingmin Liao
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: CIKM’26 Applied Research
Abstract:Item tokenizer encodes semantic embeddings into token IDs to replace the randomly assigned item IDs used in traditional recommendation models, fundamentally addressing the problems of excessive parameters and cold starts. However, the most common tokenizer, RQ-VAE, suffers from low decoding efficiency due to the inherent dependencies among its codebooks. Meanwhile, efficient independent tokenizers such as optimized product quantization (OPQ) still struggle with dimensional correlations and distribution complexity of semantic embeddings. In this work, we propose a f\underlinelow-based item \underlineTokenizer (Tlow) to transform raw semantic embeddings into a latent space where embeddings conform to a unified standard normal distribution, achieving dual advantages of dimensional independence and distributional simplicity. Independent tokenization performed on these latent embeddings yields semantically clear token IDs. Additionally, we introduce a novel codebook guidance to align the codebook space with the token embedding space, further aiding the learning of more semantically distinct token embeddings. Offline experiments on four public datasets demonstrate that Tlow’s tokenization and codebook guidance significantly improve recommendation performance. The improvement on cross-domain and multi-modal recommendations also proves the effectiveness of item tokenization in a simplified embedding space. Online experiments for a multi-modal retrieval task on China’s largest social media platform WeChat validate Tlow’s powerful distribution transformation capability. The retrieval model based on token IDs improves user CTR by 10.32% globally and by 11.64% for new items. Our codes are available at this https URL.
[IR-7] PlaceSeek: Human-Centered Geospatial Retrieval of Urban Outdoor Places via Semantic Grounding and Affective Alignment
链接: https://arxiv.org/abs/2608.24133
作者: Ziqi Cui,Shangyu Lou
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: Accepted as a Research Paper (short) at ACM SIGSPATIAL 2026. This arXiv version is the full version of the paper
Abstract:People search for urban outdoor places not only by category or function, but also by what activities a place can support and how it is perceived. Existing geospatial retrieval remains largely POIcentric and metadata-driven, making it difficult to satisfy openended, affective, or activity-oriented needs. We present PlaceSeek, a human-centered outdoor place retrieval framework that maps natural-language queries to geolocated street-view imagery. PlaceSeek introduces an intent-aware retrieval mechanism that decomposes user queries into functional and affective sub-intents. A Semantic Grounding Module verifies whether candidate street-view results contain the physical evidence needed to support the intended activity, while an Affective Alignment Module re-ranks physically valid candidates using a LoRA-adapted vision-language model trained on human urban perception judgments. We evaluate PlaceSeek on 31,956 street-view locations in Milan across 10 naturallanguage queries annotated by five human evaluators. PlaceSeek achieves 88.0% Precision@5, a mean match score of 3.39/4.0, and 0.920 nDCG@5, outperforming CLIP, fine-tuned CLIP, SigLIP, and a VQA-based baseline. Ablation results show that physical grounding is essential for retrieval validity, while affective alignment improves ranking quality among physically valid candidates. These findings highlight that complex urban spatial queries require modeling both verifiable visual evidence and human perceptual preferences. PlaceSeek provides a potential framework for human-centered nextgeneration geospatial retrieval systems.
[IR-8] Native Multimodal Representation Learning for Click-Through Rate Prediction in E-Commerce Scenarios CIKM2026
链接: https://arxiv.org/abs/2608.24091
作者: Chao Yi,Feifan Yang,Jiawei Feng,Sishuo Chen,Zhangming Chan,Xiang-Rong Sheng,Han Zhu
类目: Information Retrieval (cs.IR)
备注: Accepted at CIKM 2026
Abstract:Multimodal representations have been widely adopted in industrial e-commerce recommendation systems. Due to their strong semantic understanding and generalization capabilities, they enhance the performance of traditional sparse ID-based Click-Through Rate (CTR) prediction models. Current multimodal application frameworks in the CTR prediction task typically follow a two-stage paradigm: first, pre-training a multimodal encoder on data from specific recommendation scenarios; second, extracting items’ multimodal representations using this pre-trained multimodal encoder and integrating them into the CTR prediction model. However, the training objectives and data distribution of multimodal pre-training tasks often differ from those of the CTR prediction task, which limits the effectiveness of multimodal representation on downstream tasks. In this paper, we focus on how to learn Native Multimodal Representation for the CTR prediction task. One intuitive solution is to jointly train the multimodal encoder and CTR model end-to-end on the CTR task, with the expectation that the encoder can automatically learn downstream-relevant knowledge. However, we find that the end-to-end training does not bring performance improvements to existing multimodal application paradigms. Our analysis reveals that user behaviors in raw CTR data are driven by both multimodal semantics and non-multimodal factors, leading to ambiguous supervision and inconsistent encoder updates. To address this, we propose a Mine-Then-Train method that mines high-quality, multimodally interpretable training samples from CTR data and uses them to fine-tune the multimodal encoder for better alignment with user click preferences. Offline and online experiments demonstrate the effectiveness of our approach.
[IR-9] CodeHID: Learning an Addressable Hierarchical Code Index for Generative Code Retrieval
链接: https://arxiv.org/abs/2608.24089
作者: Zhen Li,Yuhong Chen,Wenhao Xu,Xiaodong Li,Hui Li
类目: Information Retrieval (cs.IR)
备注: 10 pages, 4 figures
Abstract:Code retrieval models have predominantly relied on a flat matching paradigm that treats code snippets as independent candidates, making them less capable of distinguishing similar code candidates. Generative retrieval offers a solution by constructing a learnable index over the code corpus, guiding the retriever to better understand how code candidates are semantically organized and addressed. However, naively applying generative retrieval in the code retrieval task may result in operating over an identifier space whose prefixes do not correspond to meaningful code-semantic regions. In this paper, we propose CodeHID, a generative code retrieval framework that reformulates the code retrieval task from flat candidate matching to coarse-to-fine semantic address generation. CodeHID relies on two core components. First, Pseudo-Neighbor Guided DocID Learning constructs a globally static hierarchical index by applying multi-level residual quantization and k -nearest-neighbor pseudo-supervision, ensuring that semantically related code snippets share prefixes while preserving target-level separability. Second, Dual-Phase DocID Generation Guidance reliably navigates this fixed index by combining training-side ranking enhancements, using hard negatives and rank distillation, with inference-side candidate constraints and prefix-aware decoding. Extensive experiments on CoSQA and ProCQA benchmarks demonstrate that CodeHID outperforms existing sparse retrieval, pre-trained code models, dense code retrieval, and generative retrieval baselines by a large margin in most cases, achieving particularly strong improvements in rank-one retrieval metrics.
[IR-10] PARTAB: Partition-Aware Reasoning with Structured Evidence for Scalable Table Understanding
链接: https://arxiv.org/abs/2608.24082
作者: Md Mahadi Hasan Nahid,Davood Rafiei
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: 21 pages, 13 figures
Abstract:Large Language Models (LLMs) have shown strong capabilities in table reasoning, but their effectiveness degrades as tables grow in size and complexity due to irrelevant context and difficulty localizing the evidence required for reasoning. Existing approaches typically reason over either the full table or a single reduced view, which can still obscure important row-column relationships. We introducePARTAB (Partition-Aware Reasoning overTables), a framework that constructs a structured evidence interface between the LLM and the table. PARTAB represents query-relevant evidence as semantically coherent, row-linked table regions and performs hierarchical selection over column groups and row-level partitions before composing the selected evidence for answer generation. We evaluate PARTAB on multiple table reasoning benchmarks, covering question answering, fact verification, and numerical reasoning. PARTAB consistently improves over full-table prompting and several recent table reasoning methods, achieving strong performance on WikiTableQuestions and TabFact while remaining competitive on numerical reasoning. Additional analyses show that semantic partitioning and targeted evidence selection improve evidence localization, substantially reduce the reasoning context, and provide larger benefits on complex tables. These results demonstrate the value of structured, partition aware evidence construction for scalable table reasoning.
[IR-11] RetrievalFormer: A Dual-Encoder Transformer for Efficient Approximate Nearest Neighbor Retrieval and Cold-Item Recommendation
链接: https://arxiv.org/abs/2608.24079
作者: Theodore Rogers,Joe Standerfer,Dmitrii Timoshenko,Haoxue Li,Zuhaib Akhtar,Soyoung Yang
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注:
Abstract:A shared search-and-recommendation index must score new items from features alone because search has no exploration slot. In a public log covering both surfaces over one catalog, 38.6% of held-out query-search impressions show an item never previously shown or visited. For user-cold engagements, the feature-based tower serves this demand without measurable loss against 99 sampled negatives ( 0.9595 Recall@20 versus 0.9510 warm). A lexical baseline reaches similar parity, while a full-catalog check remains statistically undecided. Dual-encoder retrieval therefore keeps the index \emphopen to new items, unlike an ID-softmax recommender that requires retraining. We price this openness on recommendation against six sequential baselines, each retrained and tuned through five rounds on corrected targets. A float32 timestamp bug had reordered leave-one-out targets for 19.7% of users. On MovieLens-1M, warm accuracy trails the strongest retrained baseline by 5.2% Recall@20 and 11.4% NDCG@20. On MIND, the gap narrows to 0.8 – 3.6% relative to the five strongest baselines, though the model ranks sixth of seven. Under strict zero-leakage cold-start evaluation, the content tower achieves 0.172 \pm 0.006 Recall@20, 1.4\times the strongest retrained dedicated method ( 0.124 \pm 0.007 ) and 3\times a training-free floor, without cold-specific training. Exact full-softmax training raises Recall@20 by 54% on MIND-small and 6.9% on MovieLens-1M over sampled InfoNCE, but recomputes the full catalog each step and exhausts accelerator memory at 240 K items. Approximate nearest-neighbor search explains none of the remaining gap, serving cost does not regress against ID-softmax retrieval, and a history-window sweep explains half the post-recipe remainder. Exact-quality training at catalog scale remains the open problem.
[IR-12] SQLite is Enough. Lexical Semantic and Hybrid Search with scrydb
链接: https://arxiv.org/abs/2608.24060
作者: Timo Breuer
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL); Databases (cs.DB)
备注: Software available at this https URL
Abstract:This work introduces scrydb, a Python library that enables lexical, semantic, and hybrid search within SQLite. For lexical search, scrydb leverages SQLite’s full-text search extension FTS5. Semantic search builds on sqlite-vec, a SQLite extension for vector search. Furthermore, the library allows users to rerank and fuse retrieval results to combine both lexical and semantic approaches, providing a lightweight solution for downstream tasks in information retrieval (IR) or agentic search. We evaluate scrydb on various IR benchmark datasets and demonstrate its effectiveness in text retrieval based on keyword matching, semantic similarity, and rank fusion. In addition, we provide insights into query latency and the trade-off between efficiency and effectiveness. scrydb is available under the MIT license.
[IR-13] WeMM-Embedding: WeChat Multi-Modal Embedding Technical Report
链接: https://arxiv.org/abs/2608.24053
作者: Junjie Zhou,Ke Mei,Lei Li,Tianyi Wang,Fengyun Rao,Jing Lyu
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注:
Abstract:Universal multimodal embeddings are becoming a core component of modern AI systems, enabling heterogeneous content to be represented in a shared space for applications such as retrieval, recommendation, classification, and agentic systems. In this report, we present WeMM-Embedding, a family of universal multimodal embedding models supporting text, images, videos, visual documents, and arbitrarily interleaved multimodal inputs with flexible output dimensions. The family comprises 2B, 4B, and 9B variants and is trained in two stages: a large-scale multimodal alignment stage, followed by a refinement stage using curated data, fine-grained relevance supervision, and cross-scale knowledge transfer. Across extensive evaluations, WeMM-Embedding achieves leading performance on multiple public benchmarks. Notably, the 2B variant already surpasses the previously leading 8B open-source baseline on MMEB-v2, while the 9B variant further achieves a new state-of-the-art overall score of 80.6. WeMM-Embedding also demonstrates strong practical performance across WeChat applications, with substantial gains on a 26-task in-house benchmark and consistent improvements across 14 online A/B tests. It has been deployed at scale across recommendation and search applications, including WeChat Channels, Official Accounts, Moments, and e-commerce services. We have released the model weights and code to facilitate future research at this https URL.
[IR-14] AGR: Temporally Adaptive Generative Recommendation for Industrial Live-Streaming Advertising
链接: https://arxiv.org/abs/2608.24034
作者: Wencai Ye,Guangyi Liu,Chaoyi Wang,Wenbin Luo,Shengyu Wang,Mingjie Sun,Peng Wang,Quanming Yao,Wenjin Wu,Peng Jiang
类目: Information Retrieval (cs.IR)
备注: 13 pages, 7 figures, under review
Abstract:Live-streaming advertising is an important monetization channel on short-video and e-commerce platforms, where rapidly changing live content, promoted products, and user feedback impose strong freshness requirements on recommendation models. Existing generative recommenders designed for static domains fail at three levels: static semantic IDs (SID) cannot track evolving live ads; single-scale behavior modeling misses shifting intent; preference optimization conflicts between fresh on-policy feedback and training stability. We propose TAGR, a generative recommendation framework with temporal adaptation at three levels: live-ad tokenization, user intent modeling, and preference alignment. At the token level, Live Semantic-Collaborative ID (LSID) periodically refreshes each active ad’s SID based on its current live scene and promoted products, while retaining a stable hierarchical token vocabulary for autoregressive generation. At the intent level, Intent-Aware Generation (IAG) models live-room entry histories at multiple temporal granularities as the primary intent sequence, keeps auxiliary behaviors as separate inputs, and weights next-token prediction (NTP) using post-request intent evidence and business value. At the alignment level, Intermittent On-Policy Preference Optimization (IOPO) periodically samples fresh candidate groups from the current policy and performs behavior- and value-aligned preference updates interleaved with supervised NTP maintenance to preserve learned behavior distribution. Deployed on a large-scale e-commerce live-stream advertising platform, TAGR improves live-room entry and shopping-cart click rates by 8.5% and 7.4%, respectively, and achieves a 16.1% revenue lift over the production baseline. These results demonstrate the effectiveness and industrial viability of temporally adaptive generative recommendation for live-stream advertising.
[IR-15] Hybrid Semantic Tool Discovery for Enterprise MCP Gateway: Architecture and Implementation
链接: https://arxiv.org/abs/2608.23992
作者: Olympia Saha,Amy Wang,Srinivasan Manoharan
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM) agents invoke external tools to retrieve and reason over information beyond pretrained knowledge. The Model Context Protocol (MCP) standardizes how such tools are surfaced, and a proxy MCP server aggregates many backend servers behind a single endpoint providing a secure, governable chokepoint for authentication, policy enforcement, and observability. This architecture creates two compounding challenges: a context-engineering bottleneck where full tool schemas saturate the model context window before any user query, and a tool discoverability barrier where users and agents cannot identify the best tool among 2,000+ indexed tools across 200+ MCP servers. Prompt caching reduces reprocessing cost but neither frees context capacity nor improves accuracy. We present SCOUT (Selective Context Optimization for Universal Tooling), which reframes tool exposure as a context-selection problem, injecting only tools relevant to the current step. SCOUT surfaces two MCP meta-tools – tool_search and execute_tool – where tool_search performs hybrid retrieval, fusing BM25 sparse matching with dense vector search via Reciprocal Rank Fusion to return the top-k relevant tools. Backed by zero-downtime catalog update pipelines, SCOUT resolves both context saturation and tool discovery challenges. In production at PayPal, SCOUT reduces MCP tool-token consumption from 140.2k tokens (70.1% of context) to 1.3k tokens (0.8%), a 99% reduction, cutting per-query inference cost at enterprise scale. Because SCOUT is surfaced as standard MCP tools, it is model-agnostic and requires no client-side modifications.
[IR-16] RAG Sentinel: Certifiable Geometric Consensus for Robust Retrieval-Augmented Generation EMNLP2026
链接: https://arxiv.org/abs/2608.23965
作者: Yueyang Quan,Anjun Gao,Yufei Xia,Minghong Fang,Zhuqing Liu
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: To appear in EMNLP 2026 (Main Conference)
Abstract:Retrieval-augmented generation (RAG) improves the factuality of large language models by grounding responses in external documents, but it also exposes a critical security vulnerability: adversarial documents injected into the knowledge database can enter the context window and steer the model toward targeted incorrect answers. Existing post-retrieval defenses rely on instruction following, parametric knowledge, or text-level consistency, all of which can be imitated or optimized against by adaptive attackers. We propose RAGSentinel, a training-free, label-free defense for black-box RAG systems. RAGSentinel uses a surrogate encoder to measure query-conditioned hidden-state shifts induced by retrieved documents, removes shared topic directions, and filters poisoned documents as geometric outliers from a robust majority consensus. We prove that, under an honest-majority assumption and a representation-level separation condition, RAGSentinel exactly recovers a poison-free majority-sized context. Experiments across three question-answering datasets, three LLM families, and multiple poisoning attacks show that RAGSentinel consistently achieves low attack success rates while preserving competitive accuracy and remaining effective against adaptive attacks with full pipeline knowledge.
[IR-17] NeuronGuard: Robust LLM Safety Alignment via Ablation-Aware Safety Signal Redistribution EMNLP2026
链接: https://arxiv.org/abs/2608.23959
作者: Anjun Gao,Yueyang Quan,Yufei Xia,Zhuqing Liu,Minghong Fang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: To appear in EMNLP 2026 (Findings)
Abstract:Safety alignment in large language models (LLMs) remains brittle against a growing spectrum of attacks. Jailbreak attacks bypass safety mechanisms through crafted prompts, while neuron-level attacks directly prune safety-critical neurons post-deployment. Both exploit a common weakness: safety-relevant information concentrates in a sparse neuron subset. We present NeuronGuard, a fine-tuning-stage defense that simultaneously hardens LLMs against both attack classes by redistributing safety signals across a broader set of neurons. NeuronGuard dynamically identifies safety-critical neurons via periodically refreshed per-layer linear classifiers, forces refusal behavior under deliberate neuron ablation, and applies KL-divergence regularization for distributional consistency. A randomized gradient projection strategy preserves downstream task utility by resolving conflicts between the defense and task objectives. We provide a formal guarantee that NeuronGuard strictly reduces the attack success rate (ASR) upper bound, and experiments across three LLMs, six state-of-the-art attack strategies, and multimodal settings confirm near-zero ASR while maintaining task accuracy, including against white-box adaptive adversaries.
[IR-18] Wontopos Tablet 2: Measuring Multilingual and Multimodal Memory Retrieval Without Lexical Matching
链接: https://arxiv.org/abs/2608.23920
作者: Sunwoo Kim
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: 42 pages, 8 figures. Harness and per-question records released
Abstract:We measure tablet-2, a production long-term memory engine for language models, on the text benchmarks the field already uses and on cross-lingual retrieval of photographs stored with no text at all. Its retrieval path contains no lexical matching, no keyword scoring, and no language model of its own. On LongMemEval-S (500 questions) it scores 95.7% [93.4, 97.1]; on BEAM-1M (700 questions, 2.21M stored memories) 67.5% [64.8, 70.2]. Those are question-sampling intervals, not the run-to-run spread, which is an order of magnitude narrower. Most of the paper is about how little they mean alone. Holding engine, corpus, settings and judge fixed, changing only the reader moves LongMemEval-S by 2.0 points; changing only the re-ask budget moves BEAM-1M by 8.9. Neither is stated in the reports we compare against, and the second exceeds most gaps there, so we give that table as a placement and not a ranking. For the multimodal axis we run two controls. Against BM25, configured as strongly as we could, we reach 95.2% mean recall@5 over 70 store-and-query language cells where BM25 reaches 19.0% and is exactly zero in 54. On captionless photographs a lexical method has no document to score at all. Open dense baselines on 300 Crossmodal-3600 photographs in 14 languages show that density confers no language independence: one scores 91.0% on English and 4.7% on Russian from identical image vectors, and a multilingual variant collapses on Telugu and Swahili. Our spread across languages is 14.0 against their 27.5 and 27.7. Three results run against us and are reported at equal weight: low-resource languages degrade sharply (Swahili 53.0%, Telugu 64.0%), attaching captions lowers cross-lingual retrieval by 11.4 points, and one setting omitted into one stage of our own retrieval cost 37 points of Korean top-1 accuracy while leaving nine languages untouched. Comments: 42 pages, 8 figures. Harness and per-question records released Subjects: Information Retrieval (cs.IR); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.23920 [cs.IR] (or arXiv:2608.23920v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2608.23920 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Sunwoo Kim [view email] [v1] Mon, 24 Aug 2026 23:54:08 UTC (109 KB) Full-text links: Access Paper: View a PDF of the paper titled Wontopos Tablet 2: Measuring Multilingual and Multimodal Memory Retrieval Without Lexical Matching, by Sunwoo KimView PDFHTML (experimental)TeX Source view license Additional Features Audio Summary Current browse context: cs.IR prev | next new | recent | 2026-08 Change to browse by: cs cs.CL cs.CV References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[IR-19] Degree Centrality Algorithms for Weighted Multilayer Networks (or w-MLNs)
链接: https://arxiv.org/abs/2608.23876
作者: Ayomide Ayowole-Obi,Abhishek Santra,Sharma Chakravarthy
类目: ocial and Information Networks (cs.SI); Databases (cs.DB); Discrete Mathematics (cs.DM); Information Retrieval (cs.IR)
备注:
Abstract:Centrality measures are defined for simple graphs – directed, undirected, weighted or unweighted. Attributed graphs have to be reduced to simple graphs for computing centrality measures. However, when applications with multiple types of relationships are modeled using multilayer networks (MLNs), simple graph algorithms cannot be directly used. Existing approaches typically analyze MLNs by aggregating layers of an MLN into a single graph, which results in the loss of structural and semantic information. The semantic information loss can be more pronounced particularly, in weighted networks. This work focuses on computing degree centrality in weighted homogeneous multilayer networks (HoMLNs) using a decoupling-based framework. The framework performs independent layer-wise analysis on MLNs without reducing them to simple graphs. The decoupling approach allows use of exiting algorithms for each layer and uses minimal information from individual layers for computing degree centrality of HoMLNs. We propose heuristic-based algorithms that strike a balance between accuracy and efficiency. The proposed methods are evaluated against ground truth (GT) results obtained using Boolean OR aggregation and naive baselines. Experimental results on both synthetic and real-world HoMLN datasets demonstrate that the heuristics achieve accuracy comparable to the ground truth while significantly improving computational efficiency, thereby establishing the scalability and effectiveness of the HoMLN algorithms developed using the decoupling approach. Subjects: Social and Information Networks (cs.SI); Databases (cs.DB); Discrete Mathematics (cs.DM); Information Retrieval (cs.IR) Cite as: arXiv:2608.23876 [cs.SI] (or arXiv:2608.23876v1 [cs.SI] for this version) https://doi.org/10.48550/arXiv.2608.23876 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-20] AdaWidth: Query-Adaptive Embedding Width for Dense Retrieval
链接: https://arxiv.org/abs/2608.23862
作者: Shubing Yang,Dongfang Zhao
类目: Information Retrieval (cs.IR)
备注:
Abstract:High-dimensional embeddings are central to dense retrieval, but not all of these dimensions need to be evaluated at retrieval time. Existing methods reduce dimensions in two ways: truncating the same leading dimensions for every query, or masking a different subset for each query while still storing and accessing the full embedding. Yet queries within a single task differ widely in the number of dimensions they need for their rankings to stabilize. We introduce AdaWidth, which adapts the number of evaluated dimensions to each query within a shared prefix representation. An orthogonal prefix adapter applies a single learned rotation to queries and documents alike, concentrating discriminative signal in leading coordinates while leaving every full width inner product unchanged. A lightweight router then reads order statistics off the ranking a query has already produced, and evaluates more dimensions only for the queries whose top results would change. We further derive a prefix sufficiency analysis showing that the required number of dimensions is set by the competing documents at the retrieval cutoff: it grows logarithmically with corpus size, decreases logarithmically with retrieval depth, and remains heavy-tailed across queries. Across six retrieval tasks and five frozen encoders, AdaWidth matches the NDCG@10 of state-of-the-art dimensionality reduction using 55% to 84% fewer dimensions per query. Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2608.23862 [cs.IR] (or arXiv:2608.23862v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2608.23862 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
人机交互
[HC-0] n Years Later: Replicating Two Color Discrimination Studies IEEE-VIS
链接: https://arxiv.org/abs/2608.24789
作者: Shadmaan Hye,Andrew M. McNutt,Katherine E. Isaacs
类目: Human-Computer Interaction (cs.HC)
备注: 5 pages, 4 figures. Accepted in the VisXVision workshop at the IEEE VIS conference
Abstract:Color discrimination is a fundamental aspect of visualization as it influences how people interpret visual encodings. Many visualization guidelines are informed by perceptual studies, yet relatively few have been replicated. Acknowledging that the interaction between human perception, visual tasks, and display technology can change over time, we replicate two crowdsourced color discrimination studies conducted 10 years earlier. Specifically, we replicated a visualization-focused color discrimination task (N=144) and a more general perceptual discrimination task (N=394). In both studies, our results reproduced the original perceptual effects. We further use the replication to investigate whether color-related practice influences color discrimination. Specifically, we extended our replication studies by adding questions about participants’ engagement with color practices. We then examined whether diverse color-related practices (e.g., artistic hobbies, knowledge of color theory, and cosmetic makeup use) influenced color discrimination. We found no significant difference between participants who reported engaging in color-related practices and those who did not, suggesting that design guidance regarding color discrimination may generalize across viewers regardless of their regular color practice.
[HC-1] Shaping the Future of Generative AI for Black Communities: A Frame Analysis of Public Discourse and Empirical Scholarly Research AAAI
链接: https://arxiv.org/abs/2608.24767
作者: Angela D. R. Smith,Gabriella Thompson,Christopher L. Dancy,Mark Díaz,Seyi Olojo,Christina N. Harrington
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注: Accepted to the Ninth AAAI/ACM Conference on AI, Ethics, and Society (AIES 2026)
Abstract:As generative AI (genAI) systems become embedded in education, employment, healthcare, and creative industries, the impact and engagement among marginalized groups have become both a widespread discourse and a focus in scholarly research. As a starting point, we examine public discourse and empirical research to explore the impact of genAI systems on Black communities. We conducted a systematic literature review (SLR) of 91 empirical papers alongside a media discourse frame analysis of 28 public resources, applying Entman’s framing theory to map how each corpus defines problems, attributes causes, and proposes treatments. Our SLR reveals that scholarly research concentrates heavily on technical bias detection, reducing Blackness to measurable variables rather than engaging with cultural practices, structural conditions, or Black knowledge systems. Our frame analysis reveals that public discourse attributes genAI-related harm to historical and systemic forces, while scholarly research stops its causal accounts at the dataset and its treatment recommendations at technical reform. We demonstrate that this misalignment is structurally produced: anti-Blackness operates simultaneously across both registers, generating a shared evacuation of Black epistemic agency. We argue for frame analysis as an AI ethics methodology capable of surfacing what technical evaluation forecloses.
[HC-2] Learning to Prefer Reliably: Error-Augmented Emotion Preference Optimization with Calibrated Fusion ACM-MM2026
链接: https://arxiv.org/abs/2608.24730
作者: Zilong Huang,Junyi Peng,Junjie Li,Kai Li,Wenze Ren,Kong Aik Lee,Man-Wai Mak,Tatsuya Kawahara
类目: Multimedia (cs.MM); Human-Computer Interaction (cs.HC)
备注: Accepted at ACM MM 2026 Workshop (MRAC '26)
Abstract:Emotion preference learning uses pairwise comparisons between candidate descriptions to align multimodal large language models (MLLMs) with human judgments of open-ended emotion descriptions and to train reward models that capture human emotional preferences. However, conventional pairwise supervision is often sparse, typically providing only a single negative description for each positive description, and therefore offers limited coverage of the diverse ways in which an emotion description can be incorrect. In particular, models may be insufficiently exposed to semantically fluent but emotionally inconsistent descriptions. Beyond this data-level limitation, relying on a single MLLM judge introduces a distinct model-level concern: its judgments can be affected by model-specific biases when interpreting fine-grained or ambiguous multimodal emotional cues. To address these limitations, we propose Error-Augmented Preference Optimization (EAPO), a framework for improving the reliability of MLLM-based emotion preference judgment at both the data and model levels. First, we construct an error-augmented dataset by generating multiple controlled and emotion-aware negative descriptions from each preferred description. We then adapt multiple independent MLLM judges to this richer supervision and aggregate their preference margins using margin-calibrated soft fusion, which maps heterogeneous margins to a common scale before aggregation. Experiments on the MER2026-EmoPrefer Challenge dataset and our error-augmented dataset demonstrate that EAPO improves emotion preference prediction and enhances the robustness of MLLM judges when evaluating fluent descriptions that conflict with the video’s multimodal emotional evidence. Our code is available at this https URL.
[HC-3] Who Falls for SMiSh? Learning Through Survey Data Where to Best Target Awareness Training for Mobile Messaging Attacks
链接: https://arxiv.org/abs/2608.24669
作者: Cori Faklaris,Sarah Tabassum,Heather Richter Lipford
类目: Cryptography and Security (cs.CR); Human-Computer Interaction (cs.HC)
备注: Revised for SOUPS 2025
Abstract:As mobile phone adoption has surged, so have scams involving these devices. One such scam, known as SMiShing (or smishing) after Short Message Service (SMS), involves fraudsters sending phishing links via mobile texts. Despite the prevalence of SMiShing, there is a lack of data on who is most vulnerable to these attacks. Prior research on phishing (its email counterpart) suggests that susceptibility may vary by demographic and contextual factors. In two large-scale surveys, we use a previously published simulation method to collect data from representative samples of U.S. adult mobile phone users. Our findings indicate that younger individuals and college students are particularly vulnerable. Participants struggled to correctly identify legitimate messages, with the second study providing comparisons of financial message variants. Researchers, regulators, and telecoms can help users by creating mobile-specific interventions for under-24 and university customers and adding verifications and warnings.
[HC-4] VizAnchor: Decoding Manipulation Intent from Tampering Visualizations via Dual-Anchor Reasoning
链接: https://arxiv.org/abs/2608.24535
作者: Xiaotian Zhang,Huayuan Ye,Haiyang Zhang,Chenhui Li,Changbo Wang,Sicheng Song
类目: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
备注: 39 pages
Abstract:Data visualizations are widely used for communicating information, but they are also vulnerable to intentional manipulations that induce misleading interpretations. Existing methods focus on locating tampered regions or recovering hidden information, without explaining how the visualization has been manipulated or why the resulting changes may mislead viewers. We propose \textbfVizAnchor, a framework for visualization manipulation understanding through dual-anchor evidence construction and VLM-based reasoning. In the first stage, VizAnchor constructs a semantic anchor to recover authentic chart information and a spatial anchor to localize tampered regions. In the second stage, three specialized agents decode the manipulation. The misleader grounding agent analyzes a four-panel visual prompt to predict the misleader information. The chart narrative reconstruction agent takes the original and tampered charts as inputs and reconstructs their respective visual narratives. Finally, the intent inferring agent integrates the visual evidence and misleader information to infer the misleading intent. We further construct a dataset for tampering localization and a dataset for misleading intent inferring. Evaluation shows that VizAnchor accurately localizes manipulations and produces faithful explanations of their manipulation, misleaders, and misleading intents.
[HC-5] Latent-surrealism: Revisiting surrealism and its aesthetics in relation to contemporary AI-Generated cultural production
链接: https://arxiv.org/abs/2608.24367
作者: Anca-Simona Horvath
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:This chapter examines AI-media objects - creative and artistic outputs generated through generative artificial intelligence in the form of text-to-X tools - in relation to three avant-garde movements of the twentieth century: Dadaism, Surrealism, and Conceptual Art. Drawing on Lewis Carroll’s Through the Looking-Glass as an early precursor to these three movements and to anti-rationalist aesthetics, and on three case studies in AI-generated conceptual architecture - Matias del Campo’s “Deep House” and Hassan Ragab’s “Post-Pharaonic Architecture” and “A State of Decay” - the chapter develops the concept of latent-surrealism. Latent-surrealism includes a set of aesthetic and methodological conditions inherent to creative AI-media objects. These include the use of readymade datasets reassembled through collage-like processes, the absurd as an aesthetic quality of machine hallucinations, and the decoupling of craft from artistic value. The chapter further argues that AI-media objects represent a shift in the conditions of creative production: where earlier computational tools required graphical interfaces and programming literacy, natural language now functions as the operative medium. This repositions the prompt (as language-based instructions) in the center of the creative process, in continuity with the legacy of Conceptual Art, and signals a linguistic turn across creative fields that make (use of) AI-media objects.
[HC-6] Mind the Student: Behavioral and Contextual Cues for Automated Engagement Prediction in Online Learning
链接: https://arxiv.org/abs/2608.24340
作者: Alperen Kantarci,Visvanathan Ramesh,Gemma Roig
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注: Accepted to ICMI 2026 (International Conference on Multimodal Interaction), October 5-9, 2026, Napoli, Italy. 5 pages, 1 figures
Abstract:The prediction of student engagement from the online tutoring videos is difficult because engagement is a multidimensional construct comprising distinct behavioral, emotional, and cognitive states. A reliable prediction requires bringing together different types of behavioral signals as well as expressive cues. Through our analysis of the CASED dataset, it is clear that engagement prediction gets even harder due to the high inter-person variability as well as the subjectivity of the engagement annotation. To tackle these challenges, we develop a multimodal framework that integrates the implicit spatiotemporal features extracted from pretrained video, audio, and image encoders along with structured behavioral modalities like head pose, gaze, facial action units, emotion, and wavelet-based audio features. We integrate these modalities via a Perceiver IO latent bottleneck. Moreover, student and instructor personalities are modeled as variational posteriors over learnable embeddings to enable partial pooling across participants. We employ evidential regression and spectral-normalized Gaussian process classification heads for uncertainty-aware prediction to further improve robustness and calibration. Benchmark on the CASED challenge test set shows that all participating methods converge near random-chance performance, revealing the difficulty of the dataset. In this highly ambiguous regime, our framework achieves competitive performance while uniquely offering well-calibrated uncertainty metrics, demonstrating that reliable risk-quantification is an essential prerequisite for deploying engagement models in real-world educational tools.
[HC-7] How Do Professional Editors Evaluate the Editing Quality of AI-Generated Cinematic Video Ads?
链接: https://arxiv.org/abs/2608.24329
作者: Po-Ming Law,Weizhi Li,Arpit Narechania
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:On social media, we often encounter short-form video ads that employ cinematic editing techniques to evoke an emotional response. While AI tools are beginning to generate such cinematic ads automatically, we lack a fine-grained framework for evaluating these ads. In this paper, we first characterize social media video ad formats and identify cinematic ads as a recurring format in our corpus. We then analyze the duration, shot structure, audio and text elements, and editing techniques of cinematic ads to inform a two-stage generation pipeline in which an LLM first generates a shot plan and a video generation model renders the video. Using this pipeline, we generated 70 cinematic ads for 35 real brands and recruited professional video editors to critique their editing choices. From their critiques, we derive six dimensions of editing quality: narrative progression, audiovisual coordination and sound design, visual composition and graphics, shot-to-shot continuity, message and brand coherence, and temporal rhythm and pacing. We discuss how these dimensions can guide editing-aware generation, human evaluation, and automated evaluation of AI-generated cinematic ads.
[HC-8] When AI “Works” When Does Help Begin?: Intergenerational Support Around Older Adults LLM Usage
链接: https://arxiv.org/abs/2608.24297
作者: Hyehyun Chu,Yuri Lee,Yeon Su Park,Saelyne Yang,Juho Kim
类目: Human-Computer Interaction (cs.HC)
备注: 5 pages, 1 table, Accepted by CSCW 2026 workshop Growing Up (and Old) with AI: Co-Constructing the Future for Family-Centered AI
Abstract:LLMs are becoming part of everyday life, including for older adults (OAs). OAs often learn digital technologies with younger family members, who have traditionally served as “warm experts” providing trusted and personalized operational help. LLMs expand this role: family supporters may also help OAs judge appropriate uses, consider what information to disclose, assess the credibility of outputs, and decide when AI-generated advice is safe to act on. We conducted a formative qualitative study with six OAs and seven younger adults (YAs), using semi-structured interviews and scenario-based think-aloud activities. OA participants described using LLMs to lighten their recurring reliance on family, while preserving family as a selectively invoked support channel. However, because LLMs rarely produced visible operational breakdowns, YAs had limited signals for when support was actually needed. Instead, YAs relied on OAs’ partial disclosures and negotiated intervention through general warnings and self-imposed action boundaries. As a result, family support often solved an immediate problem without leaving reusable calibration knowledge for future use. Based on these findings, we propose design implications for intergenerational LLM support (e.g., consentful help requests, learning-oriented family support that preserves OA task ownership).
[HC-9] Aura: Dynamic Intra-Turn Emotion-Aware Adaptation of Large Language Model Responses
链接: https://arxiv.org/abs/2608.24224
作者: Rachel Schuchert,Christian Holz
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Effective human-AI interaction requires systems that dynamically adapt to a user’s behavior and evolving understanding. When users interact with Large Language Models (LLMs), these models typically respond to prompts without sensing the user’s immediate reactions. This lack of communicative synchrony can lead to information overload or leave confusion unresolved in real time. In this paper, we introduce Aura, a framework that enables LLM systems to dynamically modulate output based on a user’s evolving emotions. Aura’s Perception Module continuously estimates the user’s emotional state from facial expressions. Our Policy Module then selects interventions through a probabilistic belief model. Finally, Aura’s Generation Module uses parameter-efficient Low-Rank Adaptation (LoRA) adapters to produce contextually tailored responses mid-turn during response generation. We evaluated Aura in a within-subjects user study (N=20) on information-seeking tasks, where it achieved statistically significantly higher normalized perceived learning gains than a Llama-3 baseline and reduced interaction time by 21% relative to existing LLM baselines (GPT-4o, Llama-3). Our results indicate that real-time, context-sensitive interventions can improve learning efficiency and user satisfaction without observable degradation in factual accuracy. Aura thus supports the potential for more responsive and effective human-AI interaction.
[HC-10] MemUse: Moving Memory Evaluation from Direct QA to Natural Integration in Long-Term Human-AI Conversation EMNLP2026
链接: https://arxiv.org/abs/2608.24189
作者: Ryuichi Sumida,Koji Inoue,Tatsuya Kawahara
类目: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: Accepted to EMNLP 2026 (Main Conference). 31 pages, 15 figures, 28 tables
Abstract:Memory systems for conversational LLMs are conventionally evaluated by direct, fact-seeking questions about prior dialogue (Direct QA): can the model recall fact X from a prior conversation? We tested whether higher Direct QA accuracy correlates with higher user satisfaction in a 4-month deployment (40 users, 1,872 sessions, 7 memory conditions). Existing-benchmark Direct QA varies from 19.7% to 70.1% across the 7 conditions, but satisfaction does not change. We hypothesize that existing benchmarks and user satisfaction are tracking different capabilities: benchmarks measure elicited retrieval (recall when asked), while conversation requires natural integration (detecting relevance and naturally weaving prior context into a response). To examine this, we introduce MemUse, a set of real user-cued memory moments drawn from the deployment, scored by an integration-aware judgment of the natural conversational response. Holding the model and context fixed, the same system that scores 78.8% on Direct QA references only 7.9% of those facts in conversation – a 71-point gap. Within these moments, Natural Integration is associated with satisfaction, whereas Direct QA is not. We release the deployment corpus and MemUse together with all judgments and scoring prompts at this https URL.
[HC-11] ViSculpt: Visual-Centric Agent ic Geometry Editing
链接: https://arxiv.org/abs/2608.24169
作者: Bo Pang,Jiaqi Pan,Xiaocheng Zhang,Jiacheng Xu,Guoping Wang,Peng-Shuai Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Human-Computer Interaction (cs.HC)
备注:
Abstract:3D geometry editing is a critical yet labor-intensive part of the graphics pipeline, requiring artists to translate creative intent into precise operations in complex professional software. Large language models (LLMs) have shown promise for script-based 3D creation, but script generation is less suited to perception-driven editing of arbitrary existing meshes, where execution must remain visually grounded and untouched regions should be preserved. We present a \emphvisual-centric, training-free multi-agent system that edits existing 3D meshes directly in Blender by emulating the iterative workflow of human artists. Rather than generating scripts or regenerating geometry, our system operates through the Blender GUI: multimodal LLM agents observe the viewport, reason about the current mesh state, and execute localized edits through simulated user interactions. Experiments on a curated benchmark provide initial evidence that this agentic approach can follow natural language instructions, perform representative localized mesh edits, and preserve the overall identity of the input asset. Our results highlight a complementary regime for language-driven 3D editing: direct in-place modification of existing meshes within the native 3D editing workflow. We view this work as an exploratory step toward visual-centric agentic geometry editing in professional graphics software.
[HC-12] Balancing Evidence and Interpretation: Historical Grounding Ratio as a Design Parameter for AI-Generated Urban Storytelling
链接: https://arxiv.org/abs/2608.24157
作者: Fuyang Zhang,Maurice Benayoun
类目: Human-Computer Interaction (cs.HC)
备注: 34 pages, 11 figures, 4 tables
Abstract:Location-aware generative systems can now select historical archives and real-time contextual information based on a user’s surroundings to automatically generate narratives for urban heritage walks. Yet when multiple sources jointly inform generation, existing systems provide neither a clear representation of how much content from each source actually appears in the output nor an operational means of measuring it. We introduce the Historical Grounding Ratio (HGR), defined as the proportion of claim-bearing information units in a generated narrative that are supported by historical archives. HGR turns the realized share of historical evidence in a narrative into a directly measurable design parameter. In GeoDrama, a mobile narrative system, we created three conditions that used a common retrieval procedure and comparable evidence-bundle sizes while varying the allocation of information from different sources during generation. We evaluated how changes in HGR affected narrative experience through a within-subject walking study with 18 participants. Increasing HGR significantly strengthened the perceived relevance between narrative content and the specific location. However, historical understanding, integration with the visible scene, appropriateness of the amount of information, and intention to explore further did not increase monotonically with HGR; all four measures were highest in the intermediate, balanced condition. These findings show that designing location-aware generative interfaces involves not only retrieving relevant material but also determining how information from different sources composes the final output. HGR offers an operational measure for comparing information-allocation strategies and their experiential consequences.
[HC-13] Negotiating Ontological Boundaries in User-Authored Personal Sensing Systems
链接: https://arxiv.org/abs/2608.24058
作者: Nava Haghighi,Danielle Olson,Halden Lin,Erdrin Azemi,Gierad Laput,Kayur Patel,James Landay
类目: Human-Computer Interaction (cs.HC)
备注: 23 pages, 6 figures, 1 table
Abstract:Designed artifacts are ontological, shaping, and at times limiting, what becomes possible or imaginable. One path toward mitigating such foreclosures is giving people power over how systems are designed and built. Despite decades of scholarship around systems that enable such authorship, these systems are often evaluated on whether or not they are usable, useful, or technically feasible, leaving questions of ontological boundary negotiation, unexamined. We design two open-ended probes that utilize a Wizard of Oz technique to enable the experience of training a personalized machine learning system on phenomena people define themselves. In a week-long exploratory study, participants use one of two probes in the course of their everyday lives. We identify four sites where ontological boundaries were negotiated; the boundaries of a phenomena, the subject as part of relations, what is signal and what is noise, and the objectivity of data. We offer starting points for supporting boundary negotiation through design and discuss open-ended probes as a method for ontological design.
[HC-14] Bridging Teacher Expectations and Robot Learning via Coupling Dynamics
链接: https://arxiv.org/abs/2608.23994
作者: Evan Dallas,Sean Dallas,Wing-Yue Geoffrey Louie
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC)
备注: 8 pages, 1 figure
Abstract:Human-robot teaching focuses on enabling nontechnical experts to customize robots according to their needs after deployment. With recent advances in machine learning, human-robot teaching is no longer confined to offline learning where the data gathering step from a human teacher is separated from when the robot learns. Instead, more recent approaches for human-robot teaching focus on coupling human teaching with robot learning. This coupling impacts the structure, timing, and content of the teaching and learning interaction. However, it is currently unclear how such coupling dynamics affect humanrobot teaching effectiveness and human perceptions towards the teaching process. Informed by human learning theories, in this paper we propose a new scale for classifying human-robot teaching interactions according to coupling dynamics present between the human teacher and robot learner. We apply this scale to a subset of the human-robot teaching literature to identify how coupling dynamics and human teacher mental model mismatches with the ground truth robot learning system affect teaching effectiveness and human perceptions towards the teaching process
[HC-15] When LLM s Slow Down: How Environmental Impacts Mediate University Students LLM Usage
链接: https://arxiv.org/abs/2608.23968
作者: Hyeonwook Kim,Xuesi Chen,Alex Cabral,Cindy Kaiying Lin,Udit Gupta,Josiah Hester
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注: Accepted at ICT4S 2026
Abstract:Large Language Models (LLMs) are increasingly being embedded into all facets of society, from search to education, industrial, and financial applications. These systems’ carbon and water footprints raise important sustainability concerns, particularly with adoption rates exceeding 80% among university students, despite limited insight into the environmental impacts of individual usage. Eco-feedback interfaces offer a promising approach to encourage more sustainable behaviors, yet their role in shaping LLM users’ sustainability awareness and decision-making remains underexplored. We design and deploy the interface that visualizes latency-carbon trade-offs during live LLM interactions. We study its use with undergraduate computer science students (N=89, ages 18-24), enrolled in a computing ethics course, providing an empirical look at how a technically sophisticated and values-oriented user population responds to sustainability-aware AI interfaces. We found that the likelihood of choosing the eco-feedback system significantly decreased as perceived response latency increased (p .001), while users’ willingness increased when they recognized the carbon-saving impacts (p .01). Also, students with stronger eco-mindedness demonstrated higher baseline willingness to adopt lower-carbon modes and reported increased awareness of the environmental impacts of LLM use, though this effect diminished as latency increased. These results position eco-feedback interfaces as a promising sustainability intervention and highlight their potential as an educational opportunity to promote more sustainable LLM use among university students and beyond.
[HC-16] Who Chooses How Preferences Are Aggregated? Auditing Aggregation-Rule Authority in LLM -Based Group Recommendation
链接: https://arxiv.org/abs/2608.23966
作者: Yuxuan Du
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:AI systems increasingly make joint recommendations for users with conflicting preferences. However, when reasonable aggregation rules support different actions, a further question arises: who may choose how those preferences are combined? We study this interaction-level problem as aggregation-rule authority. Using synthetic preference profiles and profiles constructed from empirical ratings, we conduct a controlled behavioral audit of three LLMs under three authority conditions: unspecified, explicitly retained by users, and delegated to the model. In cases where two witness rules supported different actions, models almost never committed when users retained authority, but committed in every delegated case. All three models executed both witness rules perfectly when directly instructed. Yet when authority was unspecified or delegated, their aggregation-consistent outcome distributions differed across models and preference settings. Together, these results separate rule-execution capability from aggregation-rule authority: delegation assigns the model discretion to resolve the aggregation choice, but does not determine which collective outcome follows.
[HC-17] ColorA11Y: Enhancing Creative Design Workflows with Just-in-Time Color Accessibility Recommendations
链接: https://arxiv.org/abs/2608.23852
作者: Alexa Siu,Rajiv Jain,Abhinav Kannan,Jose Echevarria,Mary Ann(MJ)Jawili,Yalpi Shiva Prasad,Rick Treitman,Garreth W. Tigwell,Jonathan Lazar
类目: Human-Computer Interaction (cs.HC)
备注: To appear in ASSETS '26: The 28th International ACM SIGACCESS Conference on Computers and Accessibility (October 25-28, 2026, Vila Nova de Gaia, Portugal)
Abstract:Effective color contrast in visual design is essential for content accessibility. While existing tools can identify contrast issues, they often operate in isolation from design workflows or are used as an afterthought. We present ColorA11Y, a system that supports designers in creating accessible content by providing just-in-time feedback and actionable recommendations throughout the authoring process to meet accessibility color contrast guidelines. Our system analyzes the visual properties of text and background elements and offers recommended changes, including text color adjustments, background modifications, and opacity changes. Through two user studies, we evaluate ColorA11Y’s effectiveness. A user preference study (n=40) revealed varying effectiveness of different recommendations based on design context, while a qualitative study with designers (n=8) indicated a more seamless workflow experience in comparison to a baseline using a color contrast checker. This work advances a born-accessible approach to design, where accessibility considerations are seamlessly integrated into the creative process rather than treated as an afterthought.
[HC-18] From Anonymous Shapes to Named Places: A Tool for Braille and Place-Semantic Annotation of Tactile Maps
链接: https://arxiv.org/abs/2608.23820
作者: Li Liu,Ashmita Dua,Jiaming Qu,David T. Lee,Leilani H. Gilpin
类目: Human-Computer Interaction (cs.HC)
备注: Accepted to the Posters and Demonstrations track of ASSETS '26: The 28th International ACM SIGACCESS Conference on Computers and Accessibility. 5 pages, 2 figures
Abstract:On a 3D-printed tactile map, a building felt under the finger is an anonymous shape: touch alone cannot tell which footprint is which, and a spoken description cannot reliably point to one shape at one place. We present a web-based tool that lets a sighted helper click to add on-shape Braille labels to an already-generated map model, downstream of the geometry generator so that whoever knows the reader and the local Braille standard does the labeling. The tool offers click-based OpenStreetMap matching, hand-editable abbreviation that shrinks a name to fit a footprint, and print-safe dot geometry with a review step that catches anomalies before printing. We demonstrate it on five printed maps of different place types, from a downtown core to a college campus and a small dining mall. In formative sessions in which ten BLV readers compared an unlabeled print with an annotated one, four read Braille fluently, so we treat Braille as one output among several rather than the only one. The tool’s core is the link between coordinates, geometry, and a place’s semantics, which can drive an audio readout or a non-Braille code. The tool is available at this https URL.
[HC-19] When Youth Enter The Chat: An Epistemic Shift in the Validation of LLM -Based Measures of Student Talk
链接: https://arxiv.org/abs/2608.23780
作者: Liliana Santos-Deonizio,James Malamut,Ramón Martínez,Dorottya Demszky
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: under review
Abstract:LLMs are being used increasingly to measure aspects of student discourse (e.g. talk moves, collaboration, equity of voice) at scale. Typically, LLM-based measures of student talk use transcriptions of classroom conversations that only include verbal contributions, which de-contextualize student language. Common practices for validating these measures include comparing outputs against expert annotations by adults, using held out evaluation sets and F1 scores. We argue that these approaches are insufficient to ensure that such measures are meaningful and equitable for teaching and learning, particularly for racially and linguistically marginalized youth. In order to center the youth whose talk is being analyzed, re-contextualizing these classroom conversations and engaging youth in the research process is necessary. Sharing epistemic authority with youth, ultimately, centers their point of view and adds crucial nuance to the analysis of their talk that adult experts, researchers, and LLMs cannot provide. In a case study of multilingual youth in one 8th-grade math classroom, we address the epistemic exclusion of youth by employing multiple ethnographically-oriented methods to re-contextualize student conversations and center youth as epistemic authorities in conversation with researchers and LLMs. We conducted participant observations, interviews, focus groups, and member checks with four focal students. Findings reveal that there were misalignments between students’ interpretations of their own math talk experiences and the LLM-based measures of their talk. Students contested both the LLM classifications and the coding scheme used to measure their talk, highlighting the need for youth to be involved in the epistemic process of producing knowledge about their experiences.
[HC-20] chnology Caregiving: Reframing How Older Adults Are Supported in Everyday Digital Activities
链接: https://arxiv.org/abs/2608.23751
作者: Debaleena Chattopadhyay,Tasneem Mubashshira
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Transformed by digitization, everyday activities-paying bills, shopping, managing transportation-increasingly require older adults to navigate digital systems. To accomplish these digital activities of daily living (DADLs), older adults often rely on help that looks less like IT support-institutional, episodic, and product-oriented-and more like caregiving: relational, ongoing, and aimed at preserving their functional independence. We argue that this practice is technology caregiving and introduce a framework characterizing it along four dimensions: why support is needed, who provides it, when it occurs, and how it is delivered. Applying this framework, we then systematically review the literature on how older adults are supported in DADLs. From 3,381 unique records, 36 articles met the inclusion criteria. Findings show that technology caregiving involves burden, like traditional care, but is distinctly shaped as much by digital systems and their constant change as by technology caregivers’ and older adults’ abilities.
[HC-21] he Ordinal Annotation Game: How Construct Abstraction Shapes Crowdsourced Consensus
链接: https://arxiv.org/abs/2608.23727
作者: Kosmas Pinitas
类目: Human-Computer Interaction (cs.HC)
备注: This work has been accepted for publication at the 2026 International Conference on Affective Computing and Intelligent Interaction (ACII) Late-Breaking Research Track
Abstract:Inter-annotator disagreement in real-time affect annotation is widely treated as stochastic noise. We challenge this view by modelling ordinal annotation as an implicit game-theoretic coordination process against an internalised population prior under a post-hoc majority vote. We present the Ordinal Annotation Game, a conceptual scaffold in which the mapping from individual effort to collective consensus is governed by the semantic abstraction of the target construct. We evaluate it across two experiments sharing identical interface software and a uniform sensitivity threshold: a controlled sensory tracking study and an in-the-wild engagement study. Sensory annotation yields a consensus-dominant regime where active updates reinforce agreement, whereas engagement annotation inverts into an effort-limited regime where more labelling penalises consensus. The payoff slope reverses sign under identical processing, showing that ordinal disagreement is a structured behavioural phenomenon, not a discretisation artefact or random error.
[HC-22] AI Agents Push Humans Out of the Loop
链接: https://arxiv.org/abs/2608.23642
作者: Margaret Mitchell,Avijit Ghosh,Samir Passi
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:AI agents pose significant risks as they are granted increasing autonomy. A commonly proposed solution is human oversight and keeping a ‘‘human in the loop’’, but this is not a simple solution: Not only do current approaches to AI agent design impede effective human oversight, but the cognitive capacities required for it are also themselves degraded by extended use of AI systems. This position paper argues that current approaches to the development and deployment of AI agent systems do not support effective human oversight – they contribute to its degradation. To address this, a top priority in the advancement of AI agents should be supporting the situated goals and cognitive requirements of effective human oversight, treating the human needs of overseers at the same level of importance as AI agent capability. To put this idea into practice, we connect work on automation and human-computer interaction to AI agent processes, outlining design-level affordances and organizational protocols that (1) support overseers in exercising critical judgement and (2) counteract the skill atrophy that arises from extended use of automation. We urge developers and deployers to adopt these or similar approaches. Without explicit support for the cognitive demands of effective human-agent interaction, AI agent systems will continue to passively incentivize the degradation of the very human skills they rely on.
[HC-23] Adoption Telemetry: Measuring Enterprise AI Adoption from Production Signals
链接: https://arxiv.org/abs/2608.23617
作者: Damon A. Young
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY); Software Engineering (cs.SE)
备注: 23 pages, 2 figures, 1 table. Open-source reference implementation and Zenodo record (DOI: https://doi.org/10.5281/zenodo.21943955 ) available
Abstract:We introduce adoption telemetry: a method for measuring enterprise AI adoption by computing change-management stage-progression directly from production usage signals. We contribute (1) a framework unifying pre-deployment evaluation gates, production telemetry, and change-management staging into one instrumented system; (2) NANTE, a concrete five-stage operationalization with defined telemetry thresholds, published openly so they can be tested and disproven; and (3) an open-source reference implementation that distinguishes a healthy cohort from five characteristic adoption-failure modes on synthetic populations with known ground truth. We are explicit that the thresholds are proposed constructs requiring empirical validation against real outcomes – a research agenda we outline – not a calibrated model.
计算机视觉
[CV-0] Do Robotic World Models Really Follow Actions? Diagnosing and Aligning Action-Conditioned Generation for Policy Learning
链接: https://arxiv.org/abs/2608.24885
作者: Sixiang Chen,Jiaming Liu,Jixian Wu,Yichen Guo,Tinghao Wang,Siyuan Qian,Hao Chen,Jiajun Cao,Jian Tang,Shanghang Zhang
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Action-conditioned world models are increasingly used as learned simulators for policy evaluation and improvement, yet their effectiveness rests on an unverified assumption: generated futures faithfully reflect arbitrary valid actions. Existing benchmarks are typically confined to expert demonstrations, leaving off-expert action following inadequately evaluated. To address this gap, we introduce WorldEcho, which probes action following over a broader action distribution using visual integrity and SE(3) trajectory alignment. Our diagnosis shows that current world models reasonably execute expert actions but struggle with diverse off-expert trajectories, either ignoring the commanded actions or producing visually invalid rollouts. We further propose WorldSync, which strengthens action following along three complementary axes: distributional coverage, representational grounding, and intervention-effect alignment. It broadens the training distribution over action consequences, grounds intermediate video representations in action-induced robot dynamics through an Action-Forcing Expert, and aligns predicted changes under action interventions with the corresponding changes in ground-truth futures. Experiments on RoboTwin benchmarks and real-robot tasks show that WorldSync improves WorldEcho metrics and serves as a more reliable simulator for iterative policy improvement, enabling policies to achieve higher success rates.
[CV-1] From Seeing to Acting: Smart Glasses as First-Person Intelligence Platforms
链接: https://arxiv.org/abs/2608.24877
作者: Jiangning Zhang,Haojun Chen,Yong Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project at this https URL
Abstract:Smart glasses are evolving from capture and display accessories into first-person intelligence platforms that connect human perception, persistent context, and digital or physical action. Their on-body viewpoint aligns with the wearer’s vision, audition, motion, and hand-object interaction, but must operate under tight energy, thermal, privacy, and feedback constraints. Despite rapid progress in augmented reality, egocentric vision, multimodal models, human-computer interaction, and embodied intelligence, the literature remains fragmented across devices, tasks, and benchmarks. \textitThe key challenge is not whether a model can recognize, answer, remember, or act in isolation, but whether a complete system can sustain a reliable, temporally valid, correctable, and governable perception-state-interaction-action loop. This survey is \textitthe \textbffirst to systematically study smart glasses through such a unified framework. We formalize first-person data flow and constrained task utility, characterize devices along eight verifiable hardware capability axes, organize the literature around seven interdependent foundational capabilities, and introduce an L0-L5 framework spanning capture, reactive perception, contextual assistance, persistent state, governed action, and embodied coupling. Across nine application scenes, we connect tasks with datasets, systems, products, stakeholders, failure consequences, and evidence gaps. We further present a nine-dimensional deployment framework, a claim-conditioned evaluation protocol, and an evidence ladder from controlled measurement to longitudinal field validation and audit. Together, these elements make smart glasses more comparable, deployable, and reproducibly evaluated, while outlining a roadmap toward trustworthy first-person intelligence.
[CV-2] LeFlow: Generative Latent Flow Planning for World Models
链接: https://arxiv.org/abs/2608.24855
作者: Hsiang-Wei Huang,Jianxu Shangguan,Junbin Lu,Jenq-Neng Hwang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Latent world models are inherently strong encoders that transform image pixel to latent embedding, yet existing world models still rely on online trajectory optimization for action planning: for every state-goal pair, an iterative optimizer is run from scratch to search for optimal action sequences, treating the world model as a black-box simulator. This approach pays the full iterative optimization cost anew at every replanning step and reuses no planning experience across queries. In this work, we ask whether planning itself can be amortized once a latent world model has been learned. We present LeFlow, which learns a reusable latent trajectory prior operating directly in the latent dynamics space from the world model. LeFlow recasts planning as conditional latent trajectory generation: a rectified-flow model imagines a future latent path between the current and goal embeddings, an inverse dynamics decoder turns latent transitions into action chunks, and the frozen world model verifies each candidate by autoregressive rollout. Across four major goal-conditioned pixel-control benchmarks, LeFlow replaces iterative action-space optimization with amortized latent planning and fixed-budget rollout selection, achieving consistent success-rate gains with an order-of-magnitude reduction in planning time. Our results argue that latent world models should support not only prediction but reusable planning priors. Our code is available at this https URL.
[CV-3] LAION-BVD: A 10-Million-Hour Open Video Dataset for Multimodal Pre-training
链接: https://arxiv.org/abs/2608.24845
作者: Andreas Hochlehnert,Marianna Nezhurina,Mehdi Cherti,Andrej Radonjic,Thaddäus Wiedemer,Christoph Schuhmann,Romain Beaumont,Wieland Brendel,Bernhard Schölkopf,A. Sophia Koepke,Jenia Jitsev,Matthias Bethge
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
[CV-4] EMFE: A lightweight explainable machine learning framework for malaria cell classification
链接: https://arxiv.org/abs/2608.24793
作者: Md Abdullah Al Kafi,Walayat Hussain,Mousumi Karmakar,Sumit Kumar Banshal,Ahmed Al Marouf
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Automated malaria diagnosis from stained blood-smear microscopy is dominated by deep convolutional neural networks that are accurate but computationally expensive, poorly interpretable, and rarely validated with patient-level rigor. We present EMFE (Efficient Mathematical Feature Extraction), a five-feature framework for classifying single red-blood-cell images as parasitized or uninfected using Gray World color normalization, adaptive green-channel thresholding, morphological spot detection, and classical machine learning. Using the NIH LHNCBC malaria dataset (27,558 images from 200 patients), we evaluate Random Forest, Histogram Gradient Boosting, and Support Vector Machine classifiers under patient-grouped nested cross-validation (K_outer=20, K_inner=3), ensuring that cells from each patient remain within a single fold. The optimized Random Forest achieves 94.6% pooled out-of-fold accuracy (95% CI [93.6, 95.7]), corroborated by an untouched 40-patient holdout test (94.3%) and a patient-level permutation test (p0.001, 1,000 permutations). Ablation experiments quantify the contribution of individual features and pipeline stages. Hardware-matched comparisons with retrained DenseNet121, ResNet50, and MobileNetV2 models assess the accuracy-efficiency trade-off. Synthetic perturbations characterize three failure modes, while explainability analysis identifies spot saturation as the dominant discriminative feature. Patient-level aggregation further quantifies sensitivity-specificity trade-offs and false-positive accumulation. These results demonstrate a statistically rigorous, interpretable, and computationally lightweight alternative to deep learning, while explicitly quantifying its limitations.
[CV-5] MoE-based Feature Adapter for Prompt-free Binary Coronary Artery Segmentation in X-ray Angiography
链接: https://arxiv.org/abs/2608.24783
作者: Lin Xi,Yingliang Ma
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate segmentation of coronary arteries in X-ray angiography videos is essential for quantitative coronary analysis and image-guided interventions. However, accurate segmentation remains challenging because coronary vessels are thin and exhibit low contrast, while the presence of catheters, guidewires, and complex anatomical background structures can further interfere with vessel delineation. Existing U-Net- and Transformer-based models provide strong baselines, but their shared feature-adaptation pathways may be insufficient for heterogeneous angiographic appearances. In this paper, we propose a prompt-free mixture-of-experts (MoE) feature adapter for binary coronary artery segmentation. Built upon parameter-efficient Vision Transformer adapters, the proposed method uses multiple lightweight experts with input-dependent top- k routing to adaptively refine vessel-related features while limiting active computational cost. Experiments on MOSXAV and external evaluation on XACV show that the proposed method outperforms representative baselines and improves cross-dataset generalisation. These results suggest that MoE-based adapter learning is effective for robust coronary artery segmentation in X-ray angiography videos.
[CV-6] Image Difference Quantification Using Autoencoder-Based Latent Representations
链接: https://arxiv.org/abs/2608.24782
作者: Manish Sharma,Timothy Yim,Clifton Forlines
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Traditional image similarity metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and the Structural Similarity Index Measure (SSIM) rely on pixel-level comparisons and often fail to capture perceptually meaningful differences between images. In contrast, latent representations learned by deep neural networks encode high-level semantic information that is more closely aligned with human visual perception. This paper proposes a convolutional autoencoder-based framework for quantifying image differences using cosine similarity in latent space. The learned compact embeddings enable robust differentiation between visually distinct images under variations in illumination, pose, and background. Extensive evaluation on dog-cat images and additional cross-domain datasets demonstrates clear class-wise clustering and strong inter-class separability in the latent space, with 98.4% of dog-cat image pairs exhibiting similarity scores below 0.5. Further validation using the TID2013 dataset shows that latent-space distance correlates positively with human Mean Opinion Scores (MOS), demonstrating sensitivity to perceptually relevant image distortions. The proposed approach provides a computationally efficient and semantically grounded alternative to conventional pixel-based similarity metrics, with potential applications in content-based retrieval, perceptual quality assessment, and semantic similarity analysis.
[CV-7] Ensemble of Convolutional Neural Networks for StrokePrediction: Towards Improved Diagnostic Accuracy
链接: https://arxiv.org/abs/2608.24771
作者: Md Shahriar Sajid
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Brain stroke, known for its high mortality and incidence rates, poses significant health risks and requires rapid intervention for survival. Early diagnosis and preventive measures can greatly reduce life loss and disabilities. Recent advancements in deep learning have led to novel computer-aided diagnostic techniques for early stroke detection. This study proposes an intelligent system that predicts potential strokes using eleven features, evaluated through seven supervised machine learning algorithms. The process includes a literature review, dataset visualization, data preprocessing, and model evaluation. Ensemble methods like Random Forest, Stacking Classifier, and Bagging Classifier achieved high accuracies of 99.52%, while Decision Tree reached 98.24%. Other models, including KNN and TabNet, demonstrated reliable performance, achieving accuracies of 96.73% and 96.49%, respectively. The custom feedforward model achieved 94.91%, while SVC and logistic regression had lower accuracies at 88.06% and 77.03%. The results highlight the effectiveness of ensemble methods in stroke classification.
[CV-8] MoTE: Mixture of Task Experts for Multi-Task Video Understanding BMVC2026
链接: https://arxiv.org/abs/2608.24763
作者: Muhammad Asad Ali,Umar Khan,Nadia Robertini,Didier Stricker
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted at BMVC 2026. 32 pages, 4 figures, 15 tables, including supplementary material
Abstract:Procedural video-language models must solve heterogeneous tasks from the same visual evidence, including action recognition, forecasting, and procedure prediction. Dense transformer decoders share the same feed-forward networks across tasks, which can entangle task behavior and make controlled capability expansion difficult. Sparse Mixture-of-Experts (MoE) decoders provide conditional computation, but token-level learned routing is not naturally aligned with task-level procedural objectives. We propose MoTE (Mixture of Task Experts), a decoder architecture that converts large language model feed-forward networks into task-specific experts while keeping the multimodal backbone shared. Each example follows one sample-level task route, so active task-expert computation remains independent of the number of stored task experts. We instantiate this design as VideoLLM-MoTE and evaluate it on five COIN benchmarks using explicit task routes. The five-expert model activates ~2B LLM parameters per sample and achieves higher average top-1 accuracy than recent VideoLLM baselines. Under the same expert topology, it improves over dense all-expert activation and learned sparse-routing controls. These results show that task-structured routing provides an interpretable and compute-efficient decoder alternative for multi-task video-language learning.
[CV-9] IDeaL: Data-Free Multi-Teacher Distillation via Improved Dead Leaves ECCV2026
链接: https://arxiv.org/abs/2608.24759
作者: Feyza Yavuz,Mert Bülent Sarıyıldız,Diane Larlus
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV 2026. Project Page is at this https URL
Abstract:Multi-teacher distillation has emerged as a way to combine complementary teacher models into a single student model that exhibits the strengths of all its teachers. The student is trained to mimic the output of the teachers on a set of images, typically the union of the individual teacher’s training sets, assuming this data is available. In this paper, we question that assumption and explore alternative options. We first study how far one can go when distilling from teachers fed with different types of noise. Then, we show that information contained in the teachers can be leveraged to tailor the noise for multi-teacher distillation: we propose a method that, thanks to decorrelation losses at both patch and image levels, generates teacher-specific, improved samples optimized for data-free distillation. Experiments show that our most effective samples, IDeaL, lead to strong students that successfully capture complementary information from the teachers, yielding surprisingly competitive results that substantially narrow the gap with students distilled from real images. Moreover, given a limited budget of 1K images for distillation, students distilled using our IDeaL samples match or surpass the performance of those distilled using a 1K-image subset of ImageNet.
[CV-10] Weakly Supervised Seafloor Segmentation for Seagrass Habitat Mapping in Side-Scan Sonar Imagery
链接: https://arxiv.org/abs/2608.24756
作者: Hayat Rajani,Nuno Gracias,Rafael Garcia
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Seagrass meadows are crucial blue-carbon habitats, and mapping their extent is a prerequisite for coastal management and carbon inventory. Optical satellite sensors cover large areas but cannot reach deep or turbid water, whereas side-scan sonar (SSS) images the seabed at high resolution and at any depth. Interpreting SSS, however, still relies on dense manual annotation, which is slow and costly. We address this by adapting a weakly supervised semantic segmentation framework to SSS benthic habitat mapping, so that pixel-level maps are learned from image-level labels alone. The framework couples a ViT-based encoder-decoder with a classification branch, extracts class activation maps, and refines them into pseudo-labels with a dense conditional random field that we tune for the noise and weak boundaries of acoustic imagery. It follows an iterative self-training scheme, together with a sampling strategy to cope with the strong class imbalance of the data. We also study the effect of different loss functions on segmentation quality, finding Lovász-Softmax loss the most effective. On a held-out transect, the refined pseudo-labels reached an mIoU of 89.3% against the ground truth, and the segmentation branch, trained without any pixel-level labels, reached 87.6%. Self-supervised pretraining on unlabelled SSS added a further 3% in mean intersection-over-union. Field trials further demonstrate the generalizability of the trained model. These results show that accurate and label-efficient benthic habitat mapping from side-scan sonar is feasible at the scale needed for coast-wide seagrass monitoring.
[CV-11] orchMorph: CUDA-accelerated Morphological Transforms
链接: https://arxiv.org/abs/2608.24738
作者: Kai Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: this https URL
Abstract:Morphological transforms are long-standing tools for shape and mask processing, but the de facto reference implementation in the Python ecosystem, i.e. this http URL, is CPU-only, single-array, and therefore unusable inside a GPU training loop without an expensive device-to-host round trip. GPU vision libraries built on PyTorch cover a narrow subset of these operators, typically restricted to two spatial dimensions and flat structuring elements. We present TorchMorph, a lightweight PyTorch extension that closes this gap. TorchMorph exposes 22 public operators covering binary morphology, greyscale morphology, exact and approximate distance transforms, and entropy-regularised optimal transport, all implemented as fused CUDA kernels that operate directly on (B, C, Spatial…) CUDA tensors with up to eight spatial dimensions. The API deliberately mirrors this http URL argument-for-argument, including border modes, structuring-element origins and pre-allocated outputs, so that existing pipelines port with a change of import. We describe the layered architecture and the kernel designs behind each operator family. Against single-threaded CPU references, batched execution reaches up to 1.1e3 times the throughput of this http URL on greyscale morphology and up to 350x on exact Euclidean distance transforms, while the Sinkhorn solver runs up to 42x faster than POT. Binary and chamfer operators reproduce their SciPy counterparts exactly, and every float-valued operator agrees with the CPU reference to within 1.8e-6 absolute error. TorchMorph is released under the MIT licence at this https URL.
[CV-12] Interpretable Fundus Image Classification via Ring-Based Retinal Vasculature Features
链接: https://arxiv.org/abs/2608.24723
作者: Xiaoyan Li,Shixin Xu,Arvind Gupta,Huaxiong Huang
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
备注:
Abstract:Retinal fundus photography is widely used for screening and monitoring ocular diseases, but many modern classification pipelines rely on deep latent representations and provide limited interpretability. This study develops an interpretable fundus image classification framework based on a ring-structured representation of the retinal vasculature centered on the optic disc. The method quantifies vessel geometry, color appearance, oxygenation-related vascular appearance, and vessel–background entropy within concentric retinal regions. These physiologically motivated descriptors are derived from vessel masks, image intensities, and optical-density measurements and aggregated across rings to capture spatial variation in vascular properties. Using only quantitative vascular descriptors, the proposed method achieved strong classification performance across three public fundus datasets. On HRF, it achieved 91.1% accuracy using automatically generated vessel masks, matching RETFound, a vision transformer pretrained on large-scale retinal fundus image data, under the same evaluation setting. Additional analyses suggest that pretrained image models are sensitive to acquisition-related spatial cues, including fundus scale and retinal position within the field of view, as well as broader non-vessel image characteristics. This framework may support interpretable disease classification, quantitative retinal phenotyping, and retinal biomarker discovery without requiring large task-specific training datasets.
[CV-13] Deep Learning Super Resolution for Satellite Cloud Mask Downscaling
链接: https://arxiv.org/abs/2608.24715
作者: Angelos Georgakis,Valentina Kanaki,Giorgos Giannopoulos,Stella Girtsou,Ioannis Kontogiorgakis,Charalampos Kontoes,Kostas Philippopoulos
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted for 2026 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2026)
Abstract:A vast amount of optical satellite data is being transmitted to Earth-based servers every day, and more than half of this data is affected by haze or clouds. Additionally, this data suffers from the fundamental trade-off between spatial and temporal resolution, which remains largely unresolved, making the acquisition of continuous high-resolution satellite observations of clouds an ongoing challenge. This work addresses this challenge by proposing two Deep Learning super-resolution methods for the accurate downscaling of SEVIRI cloud mask products, as well as a novel cross-sensor cloud mask dataset called SEVMOD-CM, created by spatially and temporally matching MODIS and SEVIRI satellite observations. The two proposed models are a CNN-based (SpatialCNN) and a GAN-based (SpatialGAN) Neural Network. Trained on the SEVIRI spectral and cloud mask products, the proposed methods predict the corresponding MODIS Cloud masks, achieving a 4x spatial enhancement across sensor domains. Both approaches are evaluated experimentally, and compared against the standard bicubic interpolation upsampling technique. The experimental results demonstrate the value of the proposed models and dataset for the remote sensing community, highlighting the benefits of applying super-resolution techniques to geostationary-derived cloud mask products for applications such as atmospheric monitoring, weather forecasting, disaster risk reduction, solar energy forecasting, and climate research.
[CV-14] Game2World Engine: Unlocking In-the-Wild Gameplay Videos for World Model Training
链接: https://arxiv.org/abs/2608.24680
作者: Wenxuan Shen,Dongna Jin,Dongping Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: We are currently building Gaming World Model and data engine that transfers game dynamics to robotics. Feel free to contact Dongping Chen (dongpingchen0612@gmail.com) if you are interested in research collaboration or financial support
Abstract:Video games provide a scalable source of training data for video world models, offering diverse environments, complex interactions, and abundant in-the-wild gameplay videos. However, raw gameplay footage entangles the game world with screen-space interfaces, introducing game-specific biases and irrelevant dynamics that hinder world-model training. To address this problem, we introduce GameUI-Taxonomy and G2WEngine, a full-stack framework that formalizes gameplay UI grounding and removal. G2WEngine automatically extracts reusable UI assets from real gameplay videos and synthesizes temporally coherent UI overlays on clean footage. Using this engine, we construct Game2World, comprising 96K synthetic paired videos with precise reconstruction targets and 1,079 in-the-wild clips from 303 games for realistic evaluation. Its asset library contains 5,132 verified UI elements across 21 taxonomy categories, collected from 1,010 representative gameplay frames. Based on Game2World, we propose GameCleaner, a mask-free gameplay UI removal model that combines multimodal semantic understanding with video editing capabilities. Unlike mask-based methods, GameCleaner directly identifies and removes diverse HUD elements while preserving the underlying scene content and temporal dynamics. In a controlled pilot, world models trained on UI-free gameplay improve overall VideoReward by 6.83% over those trained on UI-overlaid data. On UI-removal evaluation, GameCleaner achieves an average AAR of 95.36 on synthetic videos, outperforming the strongest temporal mask baseline by 57.3%, and obtains the best in-the-wild AAR of 80.05 with 99.8 background preservation. These results demonstrate the scalable potential of transforming Internet gameplay videos into high-quality world-model training data. Code, dataset, and model will be available at this https URL.
[CV-15] urboT2VA: Fast Large-Scale Text-to-Video-Audio Generation via Score-Regularized Consistency Distillation
链接: https://arxiv.org/abs/2608.24674
作者: Xiaoda Yang,Yuxiang Liu,Kaiwen Zheng,Yuan Liu,Yibo Lai,Shengpeng Ji,Kai Jiang,Jianfei Chen,Xiaobin Hu,Shuicheng Yan,Jintao Zhang,Jun Zhu,Zhou Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Joint text-to-video-audio generation produces synchronized visual and acoustic content, but the long sampling trajectories and heterogeneous multimodal computation of large models make inference prohibitively expensive. We present TurboT2VA, a distillation and inference framework for accelerating a 19B-parameter joint video-audio model. Large-scale T2VA distillation is challenged by modality-imbalanced optimization, the difficulty of continuous-time consistency training at scale, and the quality–diversity trade-off. TurboT2VA addresses these issues with per-modality normalization and a progressive curriculum comprising discrete consistency warm-up, continuous consistency refinement, and joint consistency–distribution matching. The curriculum first establishes a stable, diverse generation trajectory and only then introduces distribution-level refinement. On LTX-2, four-step distillation reduces generator latency from 50.52s to 2.51s at the standard evaluation resolution of 512 \times 768, achieving a 20.1 \times speedup while maintaining strong visual quality, audio fidelity, diversity, and video-audio synchronization. We further develop an architecture-aware inference stack that combines guarded W8A8 and fused operators, padded-text compaction, and modality-aware sparse attention while preserving dense cross-modal and text-conditioning paths. Under the high-resolution deployment setting at 1024 \times 1792, the complete stack reduces generator latency from 318.74s to 5.83s on one NVIDIA H20, achieving a 54.67 \times generator-only speedup. Inference code and generation demos are available at this https URL.
[CV-16] ReGround-Surg: Reliability-Guided Anchor Grounding for Referring Surgical Video Segmentation
链接: https://arxiv.org/abs/2608.24671
作者: Jiaxin Wen,Ming Yin,Lu Liu,Zeyu Fu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 5 figures, accepted at PRCV 2026 (Oral)
Abstract:Referring surgical video segmentation requires segmenting a target instrument or tissue region across video frames according to a natural language expression. Recent Segment Anything Model 2 (SAM2) based two-stage methods (e.g., ReSurgSAM2) first ground the referred target in an initial or selected frame, then propagate the selected mask via tracking. Although effective, their performance is highly sensitive to the quality of the initial grounded mask: once an incorrect anchor is selected, subsequent tracking tends to propagate the error. This issue is especially challenging in surgical videos due to visually similar instruments, occlusion, and complex tissue-tool interactions. To address this issue, we propose ReGround-Surg, a lightweight reliability-guided anchor grounding framework to improve SAM2-based referring surgical video segmentation. It first predicts a text-conditioned spatial reliability map from the referring expression and current-frame visual features. The map is then reused in two complementary branches: a Gated Side Adapter enhances expression-relevant visual regions before text-to-vision fusion, while a Reliability-Weighted Vision-to-Text Attention module suppresses off-target visual evidence during prompt-token aggregation. Experiments on Ref-EndoVis17 and Ref-EndoVis18 show consistent improvements over state-of-the-art methods across three evaluation splits with negligible speed reduction. Code is publicly available at this https URL.
[CV-17] On-Policy Self-Distillation in Diffusion Models
链接: https://arxiv.org/abs/2608.24646
作者: Wei Zhou,Xiongwei Zhu,Lingdong Kong,Bo Chen,Lei Zhang,Yongyuan Liang,Xiaoxia Hou,Ye Tian,Xian Sun,Yingshuo Wang,Linfeng Li,Shengqiong Wu,Leigang Qu,Feng Li,Wei Liu,Julian McAuley,Tat-Seng Chua
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Technical Report; Project Page at this https URL GitHub Repo at this https URL
Abstract:Reinforcement learning can align diffusion models with human preferences and task-specific objectives, but endpoint rewards do not specify how an intermediate denoising prediction should change. We introduce DiffusionOPSD as an on-policy self-distillation framework that converts image-level reward guidance into explicit targets for clean-output predictions at sampled queries. At each outer iteration, a frozen behavior policy generates trajectories and supplies query states and anchors. Reward gradients construct bounded positive and negative targets around each anchor. The trainable policy fits these targets as detached supervision through finite fitting before an exponential moving average update refreshes the behavior policy. This setup lets us measure target construction and finite realization separately. Controlled same-query experiments show that larger target-construction gains do not necessarily translate into larger realized gains after a single fitting update. Across SD 3.5-M and the step-distilled Z-Image-Turbo, our approach achieves the best final held-out scores in 19 of 20 reward-matched settings across two backbones and ten evaluators. It outperforms the strongest competing method by up to 44.0% and reduces training GPU-hours relative to DiffusionNFT by 40% on SD 3.5-M and 63% on Z-Image-Turbo. These results support on-policy self-distillation as an efficient and analyzable approach to diffusion post-training by converting image-level reward guidance into explicit and continually refreshed intermediate supervision, thereby opening a path toward more efficient and diagnosable alignment.
[CV-18] owards Reliable AI-Based Histological Staining: A Systematic Study of Scaling and Uncertainty in Unpaired Generative Models
链接: https://arxiv.org/abs/2608.24626
作者: Qasim Siddiqui,Adrian Friebel,Maiju Myllys,Zaynab Hobloss,Daniela Gonzalez,Ahmed Ghallab,Stefan Hoehme
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 37th British Machine Vision Conference, November 2026, Lancaster, UK
Abstract:Liver fibrosis, the principal predictor of long-term outcome in chronic liver disease, is staged from histological estimates of collagen content. Sirius Red (SR) provides the standard quantitative readout (collagen proportionate area, CPA) but is not acquired at every clinical centre and consumes tissue, time, and reagent cost beyond the routine Hematoxylin and eosin (HE) stain. AI-based virtual staining can generate SR directly from HE, yet systematic benchmarks of unsupervised models are scarce and their predictive uncertainty has not been quantified, even though visually plausible outputs may not faithfully reproduce the underlying tissue structure. We therefore benchmark six unsupervised image-to-image architectures (GAN-based and diffusion-based) across 54 scaling configurations on a newly released paired HE to SR mouse liver dataset, the first open resource for this translation task. Each configuration is evaluated jointly on perceptual, distributional, and task-specific axes plus a blinded expert reader study; the best per family is then retrained as a deep ensemble, the first systematic comparison of epistemic uncertainty across unsupervised stain-to-stain architectures. Across families, perceptual quality, task-specific error, and ensemble agreement measure largely independent axes of model fitness: GAN-based methods cluster tightly on perceptual metrics yet differ substantially on task error and ensemble agreement, while the diffusion-based method (CycleDiffusion) is qualitatively different on all three. No single metric captures these differences, so reliable virtual staining requires reporting and selecting on all three jointly. The dataset, tiling pipeline, models, and evaluation code are released publicly.
[CV-19] Comparative Assessment of Deep Learning Architectures for Underwater Subsurface Kelp Forest Segmentation with The Kelp-o-Tron
链接: https://arxiv.org/abs/2608.24594
作者: Sundarabalan Balasubramanian,César Borja,Ana C. Murillo,Lexi N. Wilkes,Meredith L. McPherson,Kira A. Krumhansl,Jennifer A. Dijkstra,Jarrett E. K. Byrnes
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Submerged kelp forests are vital coastal ecosystems that support marine biodiversity and ecosystem dynamics, yet accurate underwater kelp segmentation remains challenging due to optical degradation, illumination variability, turbidity, overlapping vegetation, and complex benthic backgrounds. We systematically evaluated three deep learning semantic segmentation frameworks, ResNet34-U-Net, ResNet50-DeepLabV3, and a hybrid ResNet50-ASPP-Transformer architecture, for kelp detection using high-resolution underwater RGB imagery collected from northeastern U.S. coastal waters. A dataset of 3,395 SSeg assisted annotated image-mask pairs was developed for model training and validation, while geographically independent sites were used for quantitative and qualitative evaluation. All models used consistent preprocessing, augmentation, and evaluation protocols. On independent test data, ResNet50-DeepLabV3 achieved the highest Dice (0.7120) and Intersection over Union (IoU; 0.6267), followed by ResNet34 U Net (Dice 0.6868; IoU 0.5978). The hybrid ASPP Transformer achieved the highest pixel accuracy (0.8528) but lower Dice (0.6437) and IoU (0.5746). External qualitative evaluation further showed that DeepLabV3 produced more consistent segmentation across varying environmental conditions, image qualities, and benthic habitats. Overall, ResNet50-DeepLabV3, termed Kelp-O-Tron, provided the best balance of segmentation accuracy, robustness, and generalization. The dataset, annotation workflow, and comparative evaluation provide resources for advancing automated underwater habitat mapping and ecological monitoring.
[CV-20] Human-Inspired Social Engagement Analysis via Interpretable Mutual Visual Attention ECCV2026
链接: https://arxiv.org/abs/2608.24580
作者: Urwa Fatima,Mohammad Zohaib,Francesca Odone,Nicoletta Noceti
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026 Workshop - 3rd Human-inspired Computer Vision
Abstract:Understanding social interactions from non-verbal visual data is important for behavior analysis and activity monitoring. We propose an interpretable computational model of social engagement inspired by psychological theories of mutual visual attention. Rather than learning interaction patterns end-to-end, our framework explicitly models dyadic visual attention and aggregates these cues into interpretable measures of individual and group engagement. The resulting modular framework combines state-of-the-art head orientation estimation with lightweight geometric reasoning, producing explanations that remain accessible to non-technical users. We evaluate the proposed approach on a variety of data through quantitative experiments and demonstrate its practical usefulness with qualitative visualizations designed to support teachers, caregivers, and social workers in understanding group interaction dynamics.
[CV-21] X-MULTI: VLM-based Imaging Factor Disentanglement for Factor-Aware Image Synthesis ECCV2026
链接: https://arxiv.org/abs/2608.24563
作者: Sonali Godavarthy,Matthias Neuwirth-Trapp,Tim-Felix Faasch,Maarten Bieshaar,Michael Moeller,Kristof Van Laerhoven,Danda Pani Paudel
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Accepted to the MUCG Workshop at ECCV 2026
Abstract:Imaging factor disentanglement in text-to-image generation aims to independently control image acquisition properties such as types of camera lenses, sensor types, viewpoints, and domains to enable combinatorial generalization. This should let the model synthesize novel factor combinations unobserved in the training data, such as pairing a fisheye lens with an event sensor never observed in training data. Recent work, MULTI, introduced learnable, factor-specific embeddings to disentangle imaging factors, along with the Factor Alignment Accuracy (FAA) metric to evaluate disentanglement quality. We identify and address two independent limitations. First, MULTI’s pixel-level reconstruction objective supervises the model only on observed imaging factor combinations, providing no direct training signal for novel combinations. We therefore propose X-MULTI, which uses a pretrained vision-language model (VLM) to supervise novel factor combinations synthesized during training. Second, we show the FAA metric exhibits severe cross-factor correlation leakage, misrepresenting true disentanglement quality. We therefore propose Improved-FAA (I-FAA), which employs factor-specific augmentation strategies to break these correlations and enables more rigorous evaluation. Experiments demonstrate that X-MULTI achieves improved factor alignment on novel combinations compared to MULTI. Moreover, we show that correlation leakage in FAA distorts the evaluation of true factor disentanglement and I-FAA reduces this leakage and therefore provides a more robust assessment of factor alignment.
[CV-22] KLTNet: Learning Sparse Feature Tracking for Robust and Accurate Monocular Visual-Inertial Odometry
链接: https://arxiv.org/abs/2608.24544
作者: Renbiao Jin,Danping Zou,Wenxian Yu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Many feature-based visual-inertial odometry (VIO) systems rely on sparse feature tracking, whose accuracy and robustness directly affect state estimation. Classical KLT trackers rely primarily on local image patches and can become unreliable under rapid motion or in low-texture environments. We propose KLTNet, a lightweight learning-based, plug-and-play sparse feature tracker designed to replace classical KLT trackers in KLT-based VIO front ends. KLTNet follows a coarse-to-fine, dense-to-sparse architecture that combines low-resolution dense optical flow for robust global motion initialization with triplet-patch refinement for accurate and temporally consistent tracking. A fixed reference patch provides a stable anchor throughout each feature track and helps reduce accumulated tracking drift. In addition, KLTNet predicts anisotropic confidence weights supervised through differentiable multi-view triangulation, which can be used as observation weights in compatible VIO estimators. Experiments with VINS-Mono and OpenVINS on public benchmarks and a self-collected low-texture dataset demonstrate improved tracking and odometry accuracy over classical KLT, while maintaining real-time performance on an embedded platform.
[CV-23] Hierarchical Prototype-Memory Adaptation of SAM for Surgical Instrument Segmentation
链接: https://arxiv.org/abs/2608.24541
作者: Xinning Yao,Jingjing Wang,Jinghua Yue,Xiaoyan Luo,Fugen Zhou,Bo Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Surgical instrument segmentation (SIS) is fundamental for computer-assisted surgery, where reliable instrument masks enable precise scene understanding and clinical assistance. Recently, adapting foundation models like the Segment Anything Model (SAM) to the surgical domain via prompt-learning has shown encouraging results. However, the performance of these adapted models under challenging surgical conditions is constrained by suboptimal adaptation mechanisms. Specifically, optimizing prompts or prototypes purely via downstream segmentation loss tends to cause them to degenerate into task-specific parameters rather than serving as persistent, stable category memory, thereby degrading their robustness against complex intraoperative variations. Moreover, routing multi-scale visual cues through a single prompt pathway creates a bottleneck that hinders effective scale-matched coupling. To address these limitations, we propose HPMA, a Hierarchical Prototype-Memory Adaptation framework for SAM. Specifically, HPMA constructs a frozen, multi-scale visual prototype memory bank from annotated surgical scenes and integrates it into SAM’s feature space using lightweight adapters to preserve stable category evidence. To maximize the utility of multi-scale cues, we introduce a scale-matched coupling mechanism where global prototypes calibrate class-level prompt features, structural prototypes guide decoder object queries, and local prototypes align high-resolution feature maps through a local alignment objective. Extensive experiments on the public EndoVis2017 and EndoVis2018 datasets demonstrate that our approach achieves state-of-the-art performance, outperforming existing foundation model adaptation methods.
[CV-24] It depends: Incorporating correlations for joint aleatoric and epistemic uncertainties of high-dimensional output spaces
链接: https://arxiv.org/abs/2608.24518
作者: Leonhard F. Feiner,Manuel Nickel,Martin Menten,Laurin Lux,Rickmer Braren,Daniel Rueckert,Georgios Kaissis,Raphael Rehms,Johannes Paetzold
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Published in Transactions on Machine Learning Research (TMLR), 2026. 42 pages, 14 figures, 10 tables. this https URL
Abstract:Uncertainty Quantification (UQ) plays a vital role in enhancing the reliability of deep learning model predictions, especially in scenarios with high-dimensional output spaces. This paper addresses the dual nature of uncertainty – aleatoric and epistemic – focusing on their joint integration in high-dimensional regression tasks. For example, in applications like medical image segmentation or restoration, aleatoric uncertainty captures inherent data noise, while epistemic uncertainty quantifies the model’s confidence in unfamiliar conditions. Modeling both jointly enables more reliable predictions by reflecting both unavoidable variability and knowledge gaps, whereas modeling only one limits transparency and robustness. We propose a novel approach that approximates the resulting joint uncertainty using a low-rank plus diagonal covariance structure, capturing essential output correlations while avoiding the computational burdens of full covariance matrices. Unlike prior work, our method explicitly combines aleatoric and epistemic uncertainties into a unified second-order distribution that supports robust downstream analyses like sampling and log-likelihood evaluation. We further introduce stabilization strategies for efficient training and inference, achieving superior UQ in the tasks of image inpainting, colorization, optical flow, and depth estimation.
[CV-25] Low-Rank Ternary Adaptation for Fine-Tuning Transformers ECCV2026
链接: https://arxiv.org/abs/2608.24469
作者: Alexandru-Dragos Manolache,Yunqiang Li,Jan van Gemert
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted at ECCV 2026. To be published in Volume 17015 of the Lecture Notes in Computer Science series
Abstract:Ternary transformers offer extreme memory and compute efficiency, but existing low-bit LoRA-based methods cannot directly fine-tune ternary weights. Current approaches either require dequantization, restoring low-bit base weights to higher precision to merge with adaptation weight, or update only quantization parameters, preventing a merged model that remains ternary. We propose ternary multiplicative adaptation, which represents discrete updates of ternary weights such as sign flips or zeroing through a low-rank Kronecker factorization into two small ternary matrices applied element-wise to ternary weights. This design is parameter-efficient and expressive, preserves the ternary domain, and supports direct merging without dequantization. Experiments on six models across language and vision, including ternarized LLaMA-3 1B and 3B and a ternary ViT-B/16, demonstrate that our method recovers much of the performance lost to quantization and outperforms strong low-bit and ternary baselines. Code is available at this https URL.
[CV-26] DoublesEval: Diagnosing Multi-Agent Tactical Reasoning in Vision-Language Models via Professional Doubles Badminton BMVC2026
链接: https://arxiv.org/abs/2608.24439
作者: Jintao Cheng,Weibin Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by BMVC2026
Abstract:Visual Language Models (VLMs) excel at describing visible scene content but struggle to reason about dynamic multi-agent interactions, where action semantics depend on coordinated roles and spatial-temporal dependencies. We formalize this capability as \textbfmulti-agent tactical reasoning and introduce \textbfDoublesEval, a diagnostic evaluation framework that leverages professional doubles badminton as a structurally tractable testbed. DoublesEval employs a key-moment-based protocol that decomposes rallies into tactically salient instants and probes models across four interpretable dimensions: atomic recognition, intra-segment composite understanding, cross-segment causal reasoning, and high-level tactical abstraction. This design isolates \emphwhere reasoning fails, rather than merely measuring answer correctness. To address observed failure modes, we propose \textbfTacticCheck, a lightweight constraint-guided test-time consistency checker that reranks candidate answers using the model’s own lower-level tactical predictions, requiring no parameter updates or ground-truth labels at inference time. Evaluating four representative open-source VLMs on 60 curated rallies (yielding \sim 9.6K structured instances) via a zero-shot protocol, we find that models remain weak across all diagnostic levels, with especially clear bottlenecks in spatial state, interaction binding, and terminal evidence. TacticCheck delivers consistent gains across all evaluated models, while still leaving a substantial gap to robust tactical reasoning. These results highlight the need for structured, interaction-aware evaluation paradigms for next-generation VLMs. The source code is available in \hrefthis https URL\textcolorblueour GitHub repository.
[CV-27] Vision Language Model Fusion for Explainable Face Recognition
链接: https://arxiv.org/abs/2608.24430
作者: Ana Estrada-Real,Lydia Alapatt,Christoph Busch,Christian Rathgeb
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Responsible deployment of face verification systems requires more than accurate decisions: systems should also provide interpretable and auditable evidence that enables users to understand, assess, and challenge their decisions. Vision-language models (VLMs) provide a promising foundation for explainable face recognition by combining visual analysis with natural-language reasoning. However, relying on a single model may further limit the decision accuracy as well as provided explanations. This work therefore investigates whether multiple VLMs can be combined to improve recognition accuracy, and to enrich the explanations associated with those decisions. This work evaluates four VLMs as standalone face verification systems and subsequently proposes a fusion framework, where two source models provide similarity scores and textual justifications and a third VLM acts as a decider model. Four different fusion scenarios are considered, progressively providing the decider model with scores, justifications, face images, and combinations of these modalities. Overall, the findings suggest that the value of multi-VLM fusion extends beyond recognition performance. VLMs can provide complementary justifications and perspectives that enable richer explanations of face recognition decisions, supporting greater transparency, auditability, and error analysis. This is relevant to the development of responsible explainable face verification systems, where users and operators should be able to understand not only the final decision but also the evidence and potential sources underlying it. The proposed multimodal VLM, which combines decision scores, explanations, and face images, achieves higher recognition accuracy than state-of-the-art VLMs and domain-specific face recognition models, while also providing fused explanations that are expected to be more robust than those generated by individual VLMs. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.24430 [cs.CV] (or arXiv:2608.24430v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.24430 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-28] Joint Distribution Alignment for Universal Domain Adaptation
链接: https://arxiv.org/abs/2608.24429
作者: Shizhe Li,Hongshan Pu,Mengying Xie,Yi Xiang,Xiaowei Yang
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Unsupervised domain adaptation (UDA) has been widely concerned in the fields of machine learning, pattern recognition, and computer vision. Traditional UDA learning usually assumes that the label spaces of the source and target domains are exactly the same and only needs to solve the problem of sample distribution drift existing between two domains. However, in real world applications, the label spaces between two domains may be different. In this case, there are both sample distribution drift and class spatial difference between domains, namely Universal Domain Adaptation (UniDA) learning scenario. At present, existing works rarely offer theoretical analysis for universal domain adaptation. In this paper, we provide an upper bound of the generalization error for universal domain adaptation. According to the proposed generalization error bound, we propose a novel UniDA algorithm called Joint Distribution Alignment for Universal Domain Adaptation (JAUA), which aligns the joint distributions by minimizing the distribution discrepancy calculated by Chi-Square divergence. Furthermore, we propose a progressive pseudo-labeling method to assign the pseudo labels to unlabeled target samples. The experiment results on six public image datasets demonstrate the superiority of JAUA in handling the UniDA problem.
[CV-29] ZODIAC: Zero-shot Octree-based Diffusion for Anatomical Completion
链接: https://arxiv.org/abs/2608.24422
作者: Miruna-Alexandra Gafencu,Vlad Bratulescu,Yordanka Velikova,Mohammad Farid Azampour,Nassir Navab
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recovering the full 3D spine anatomy from intraoperative ultrasound is an ill-posed inverse problem, as the complete structure must be inferred from incomplete and noisy observations. Acoustic occlusions and limited field of view create large unobserved regions, while view-dependent artifacts lead to variability in expert annotations of the visible anatomy. Current supervised ultrasound shape completion methods rely on synthetically generated incomplete-complete paired data to learn conditional mappings under a predefined distribution of simulated occlusions. However, real intraoperative occlusions do not necessarily follow this distribution, which can limit generalization to patient data. As a result, accurate and robust completion from noisy partial observations remains an unsolved problem. We propose a zero-shot shape completion framework that reconstructs the entire lumbar spine from partial ultrasound observations without relying on simulated training data. To accommodate unseen and irregular patterns of missing structures, we introduce blended completion, a mechanism that integrates the learned anatomical prior with incoming partial geometry at inference time. The method learns a generative diffusion prior over full anatomical shapes represented in an adaptive octree structure, enabling efficient modeling of the complete spine in a single forward pass. Validation on phantom and volunteer data shows that decoupling completion from a predefined corruption distribution improves generalisation under real occlusions, outperforming a fully supervised variant by 22% on HD95 completion error. Code and data are available at this https URL.
[CV-30] MRI-based Deep Radiomic Phenotyping of Neuromuscular Disorders: A Topology-driven Characterization
链接: https://arxiv.org/abs/2608.24415
作者: Martyna Żur,Łukasz Piórecki,Marek Socha,Jordi Diaz-Manera,Jose Verdu Diaz,Volker Straub,Rossella Tupler,Joanna Polańska
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Draft manuscript. Has not yet been peer-reviewed
Abstract:Quantitative assessment of muscle MRI is crucial for monitoring neuromuscular disorders (NMD). This study introduces an automated radiomic phenotyping framework based on original features engineered across five main architectural domains: quantitative morphometry, spatial distribution, geometric shape, interactions between progressive fat replacement stages, and graph-based topology. Utilizing 1184 MRI scans from the CoMPaSS-NMD project, we map the complex 3D architecture of heterogeneous intramuscular lipodegeneration into objective, morphologically interpretable biomarkers. We introduce a graph-based skeletonization of fat infiltrates to quantify muscle architectural changes, establishing a multi-dimensional extension of traditional, spatially-agnostic volume metrics by mapping topological networks across the entire 3D muscle volume. Statistical screening via non-parametric Kruskal-Wallis analysis confirmed the discriminative power of these novel descriptors across the genetic hierarchy. Notably, topological network metrics (e.g., SF1_Skel_Nodes, \epsilon^2 = 0.2656) and interface dynamics metrics (e.g., SF2_To_SF1_Dist_Min, \epsilon^2 = 0.2092) demonstrated substantial effect sizes, providing deeper structural insights than classical volumetric assessments. Post-hoc pairwise evaluations and UMAP projections further indicated the capability of these topological and 3D geometric invariants to capture disease-specific macroscopic infiltration patterns. These results demonstrate that global architectural features represent a highly promising class of biomarkers for differential diagnosis, offering new avenues for tracking longitudinal disease dynamics in neuromuscular diagnostics. The developed automated feature extraction pipeline is integrated and available within the MUSCAT (MUSCle fAt Topology) library.
[CV-31] Markerless Pose Estimation for Resistance Training Technique Assessment
链接: https://arxiv.org/abs/2608.24384
作者: Joseph Turner,Jeff Clark,Nawid Keshtmand
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Resistance training can be a high risk activity, and safe form is essential to avoiding injury. Laboratory-based movement analysis provides quantitive technique assessment, yet is not easily accessible. Markerless pose estimation infers body landmarks from images or video without physical markers and could offer a feasible alternative for technique assessment. We present a pose estimation framework to evaluate resistance-training technique from ordinary video footage. Using BlazePose, anatomical landmarks were extracted from squat, bench press, and deadlift videos and converted into joint-angle trajectories, with the squat serving as the primary case study. Trajectories were assessed against a defined reference repetition using root mean square error (RMSE). Results show that the framework recovers meaningful kinematic patterns for the squat and deadlift, enabling quantitative comparison between repetitions and identification of technique variability within a set. Performance depended strongly on camera orientation and visual occlusion, with non-sagittal views distorting 2D joint-angle estimates. The findings demonstrate that markerless pose estimation can support accessible biomechanical assessment outside laboratory environments.
[CV-32] Bridging Adversarial and Collaborative Learning for AI-Generated Image Quality Assessment
链接: https://arxiv.org/abs/2608.24372
作者: Baoliang Chen,Qing Lin,Sijie Mai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:AI-generated image quality assessment (AIGIQA) requires jointly reasoning about perceptual fidelity and prompt alignment, two quality dimensions that are often treated as independent in existing AIGIQA models. However, by re-examining human ratings, we uncover a previously overlooked phenomenon: the two dimensions are interdependent and exhibit both competitive and cooperative interactions during human rating. This observation suggests that a unified model should neither collapse the two dimensions nor rigidly separate them, but rather adaptively negotiate their interplay. Motivated by this insight, we introduce an interaction-aware learning framework that models perception-alignment relations through adversarial and collaborative inference pathways. Instead of designing a rigid dual-branch architecture, our method employs a gated interaction module that dynamically routes features according to the inferred relationship between the two dimensions. Task-aware prompts further modulate the gating behaviour, enabling the model to switch between competition and cooperation when necessary. Experiments across multiple AIGIQA benchmarks demonstrate that our approach not only achieves state-of-the-art accuracy but also yields interpretable interaction patterns, offering a more faithful approximation of human judgment. The codes are available at this https URL.
[CV-33] Variance-Guided Spatial Attention Fusion for Robust End-to-End Driving under Asymmetric Sensor Degradation
链接: https://arxiv.org/abs/2608.24366
作者: Weizhi Tao,Zengwang Jin,Xiao Wang,Hailong Huang
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 17 pages, 9 figures, and 4 tables, including supplementary material. Submitted to IEEE Transactions on Vehicular Technology
Abstract:End-to-end multimodal driving has progressed rapidly by fusing camera and LiDAR streams. Existing pipelines remain fragile under asymmetric sensor degradation, where either an entire modality or only a localized region is corrupted while other regions remain useful. The key difficulty is not simply to add an uncertainty head, but to obtain dense reliability supervision, calibrate this reliability against physical fault severity, and use it before unreliable features bias the planner. We propose Variance-Guided Spatial Attention Fusion (VG-SAF), in which dense heteroscedastic reliability estimates act as interpretable spatial gates. The framework couples three components. First, a physically grounded augmentor simulates representative camera and LiDAR failures and emits a continuous spatial mask, providing dense supervision without additional annotation. Second, modality-specific experts predict per-pixel reliability scales through cross-branch dense distillation in log space, enforcing a monotone severity-to-scale response. Third, calibrated reliability maps drive a hybrid attention mechanism that suppresses unreliable cells with a local spatial gate and arbitrates between modalities through a cross-modal trust softmax. A Laplace uncertainty head emits a systemic waypoint uncertainty scale that signals severe or combined sensor degradation, including severities outside the training ranges. On the CARLA Longest6 benchmark, VG-SAF consistently improves closed-loop robustness over the baselines across camera-only, LiDAR-only, and joint degradation regimes, as measured by driving score, route completion, and infraction score.
[CV-34] MaST: Motion-aware Sparse Pipeline for Lightweight Object Tracking ECCV2026
链接: https://arxiv.org/abs/2608.24365
作者: Qingmao Wei,Fagui Liu,Dengke Zhang,Qingze He,Quan Tang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026 accepted paper
Abstract:Transformer-based object trackers are renowned for their strong performance, yet dense token processing often leads to prohibitive computational cost, limiting real-time deployment on edge devices. While recent works explore token pruning to reduce computation, they often stop short of an end-to-end sparse pipeline, as early-layer token scores can be noisy without a motion prior, and many trackers ultimately fall back to dense reshaping to feed the dense prediction head that partially negates the savings. We introduce Motion-aware Sparse Tracker (MaST), a sparse tracking framework that makes sparsity effective from tokens to boxes. First, MaST injects a lightweight motion prior to refine cross-attention-based importance scores, enabling earlier and more stable token reduction in the search region. Second, we introduce a natively sparse prediction head that operates directly on the retained unstructured tokens with a score-first, regress-once design, eliminating dense padding/reshaping and reducing redundant computation. Extensive experiments on multiple benchmarks demonstrate that MaST establishes new state of the art among lightweight trackers, where MaST-tiny attains 63.8 AUC on LaSOT and 80.1 SUC on TrackingNet, surpassing the prior best AsymTrack-S by +1.0 AUC and +2.2 SUC while running at 152 FPS on Jetson Nano, nearly twice as fast as AsymTrack-S at 88 FPS. Code is available at this https URL. Comments: ECCV 2026 accepted paper Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.24365 [cs.CV] (or arXiv:2608.24365v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.24365 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Qingmao Wei [view email] [v1] Tue, 25 Aug 2026 10:25:15 UTC (3,519 KB)
[CV-35] B-MIM: Biased Masked Image Modeling for Generalizable Segmentation of Fine-Grained Anatomical Structures MICCAI2026
链接: https://arxiv.org/abs/2608.24364
作者: Sebastián González,Karen Sanchez,José M. Saavedra,Marcelo Pizarro,Bernard Ghanem
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Published at MedAGI in MICCAI 2026
Abstract:Self-supervised pretraining enables transferable representations for medical imaging, yet most CT encoders remain biased toward coarse semantic understanding, limiting their sensitivity to fine-grained anatomical structures such as vessels or small tumors. In this paper, we introduce Biased Masked Image Modeling (B-MIM), a modification of the iBOT objective that stochastically reduces global semantic alignment to prioritize local patch reconstruction. This bias encourages the encoder to capture high-frequency morphological details and structural continuity. We curate a multi-institutional CT abdominal dataset of 9,955 filtered studies from 17 public sources and pretrain a 3D Swin Transformer backbone using B-MIM. Across inter-dataset experiments on liver vessel segmentation, the proposed encoder improves topological fidelity (clDice) and achieves competitive Dice scores in tumor segmentation, compared to fully fine-tuned baselines, despite updating only a fraction of the parameters. Our results suggest that reducing global semantic pressure during pretraining enhances generalization to intricate anatomical structures.
[CV-36] Metadata-Aware Adaptation of a Generative Foundation Model for Conditional CMR Synthesis
链接: https://arxiv.org/abs/2608.24342
作者: Marc Rodríguez,Grzegorz Skorupko,Nay Aung,Steffen E Petersen,Karim Lekadir,Polyxeni Gkontra
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 5 pages, 3 figures
Abstract:Synthetic image generation is a promising strategy to address data scarcity and the underrepresentation of clinically important phenotypes in medical imaging, yet generating images that faithfully reflect meaningful patient characteristics remains challenging. In this work, we investigate metadata-conditioned cardiac magnetic resonance (CMR) synthesis using a pretrained latent diffusion model, encoding structured clinical metadata and slice position as textual prompts to guide CMR generation. To improve metadata adherence and address the imbalance of clinical attributes, we integrate three strategies: Metadata-Free Classifier-Free Guidance (CFG), Contrastive Batching, and Inverse-Frequency Sampling. The framework was fine-tuned and evaluated on 59,058 short-axis CMR from the UK Biobank using paired image similarity, distributional fidelity, and subgroup-level analyses. The combined approach achieved a Fréchet Inception Distance (FID) of 37.47, improving by 57.04% over the same model fine-tuned without these strategies and by 28.68% over a previous text-conditioned CMR diffusion baseline requiring cardiac geometry as additional input, while relying solely on patient metadata. This distributional gain, driven mainly by Metadata-Free CFG, came with a modest reduction in paired similarity, suggesting that the model prioritizes population-level realism over exact image reproduction. Subgroup analyses demonstrated improved alignment across demographic and acquisition-related metadata, with disease-specific conditioning being the most challenging task. These findings demonstrate the potential of generative foundation models for clinically meaningful CMR synthesis while highlighting the need for more effective metadata-aware conditioning strategies. Our code is available at this https URL.
[CV-37] Keep-or-Drop? Adaptive Tokenizer for Compact Video Representation
链接: https://arxiv.org/abs/2608.24293
作者: Yeonkyeong Lee,Hyunsung Go,Jongmin Kim,Sewoong Lim,Donghoon Lee
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Latent diffusion models have emerged as a dominant framework for high-fidelity image and video synthesis, operating in compact latent spaces with variational autoencoders (VAEs) to enhance computational efficiency without compromising visual quality. However, conventional VAEs are suboptimal for video data as they employ fixed compression ratios that cannot adapt to the varying complexity of spatio-temporal content. We present KATok (Keep-or-Drop? Adaptive Tokenizer for Compact Video Representation), a transformer-based VAE that incorporates an adaptive token selector which is jointly learned with latent tokens. By evaluating each token’s content-richness as keep-or-drop probability, the token selector effectively discards uninformative tokens, naturally allowing data-dependent compression. Applying adaptive tokenization to diffusion models may cause spatial misalignment, as token dropping can disturb the original spatio-temporal structure. To alleviate this issue, we propose two position-prediction strategies: cascaded and joint generation, to ensure spatial consistency. We empirically show that our model achieves strong reconstruction and generation quality at a state-of-the-art compression ratio. Further analysis on video data reveals that this improvement is primarily achieved by reducing spatio-temporal redundancy and removing uninformative tokens, as supported by both quantitative and qualitative results.
[CV-38] CARE: Camera-Residual Reserves for First Sightings in Adaptive LiDAR Sensing
链接: https://arxiv.org/abs/2608.24282
作者: Jiachen Gong,Yun Li,Ehsan Javanmardi,Wencan Mao,Manabu Tsukada
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Adaptive LiDAR scanning concentrates a limited sensing budget on regions of interest predicted from past object tracks, lowering data volume in autonomous driving while maintaining detection accuracy. However, existing scanning policies face three challenges. First, history-driven approaches depend on past tracks, so unseen objects are detected late or missed. Second, random or uniform sampling outside the predicted regions has no awareness of where new objects appear. Third, camera-guided alternatives spend budget on all camera detections, resampling objects already covered, costing recall in crowded scenes and range when budgets are scarce. This paper introduces the CAmera-REsidual reserve (CARE), a training-free allocation rule that reserves part of a fixed ray budget for the directions of current camera detections that the track forecasts cannot explain; the rest follows the base history policy, and unused reserve returns to a random floor. The paper makes three contributions. First, a leakage-free ray-budget evaluation on nuScenes (150 scenes, 4,148 events) measuring the first-sighting loss of history-driven scanning, with a strict-causal variant using the preceding keyframe. Second, CARE raises first-sighting recall by 5.2, 5.2, and 4.3 points at 10%, 20%, and 35% budgets over the history policy, with paired intervals excluding zero; the camera cue drives this gain, and the first-sighting versus overall trade-off is a budget-dependent Pareto choice. Third, a safety-bounded forgetting module that releases budget from receding or static tracks beyond a speed-dependent guard distance; at tight budgets, forgetting without the guard significantly harms near-field recall, so the guard is what keeps it safe. The pipeline runs end to end on a real vehicle and, in closed-loop simulation, detects an occluded pedestrian earlier and brakes more reliably than history-driven scanning.
[CV-39] Example-based Robust Abnormality Detection with Minimal Annotations using Exemplar Med-DETR
链接: https://arxiv.org/abs/2608.24281
作者: Sheethal Bhat,Bogdan Georgescu,Awais Mansoor,Mathias Zinnen,Pranjal Sahu,Florin C. Ghesu,Sasa Grbic,Andreas Maier
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reducing annotation requirements remains a key challenge in developing robust medical object detectors. To address this, Vision-Language (VL) object detection methods leverage grounding text information to enable powerful zero-shot and few-shot object detectors in the natural image domain [1, 2, 3, 4]. However, transferring these methods to the medical domain is challenging due to the absence of comparable quality and quantity of the grounding data. Regardless, significant contextual and non-imaging information exists in medical images that remains underutilized. Few-shot learning (FSL) techniques partially address this limitation but struggle to general ize to unseen medical findings and require extensive retraining when new findings are introduced [5, 6]. To overcome these challenges, we extend our prior EM-DETR framework [7] and introduce a scalable FS detection approach designed for efficient abnormality detection in Chest X-Ray (CXR) images under minimal supervision. The proposed architecture incorporates exemplar-based feature generation and domain-aware contrastive optimization, enabling effective adaptation to novel disease findings without exhaustive retraining. Our method achieves near state-of-the-art (SOTA) detection performance using less than 10% of the annotated data, demonstrating its potential for practical, annotation-efficient clinical deployment across both proprietary and public CXR datasets.
[CV-40] Real-World Knowledge-Guided Change Data Synthesis for Remote Sensing
链接: https://arxiv.org/abs/2608.24263
作者: Yaoyi Qi,Xingxing Weng,Chao Pang,Yongkang Cui,Xiangyu Hao,Xiaokang Zhang,Guibo Zhu,Gui-Song Xia
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 29 pages, 16 figures
Abstract:Change data synthesis provides a cost-effective solution for expanding training data and improving the performance of change detection models. However, existing synthesis methods typically rely on handcrafted rules to simulate changes, where limited coverage of class transitions restricts the diversity of synthesized data, while predefined transition designs limit their flexibility in accommodating varied change types. In this work, we introduce KnowChange, a knowledge-guided change data synthesis framework that leverages pretrained vision-language models as knowledge sources to reason about plausible change locations and class transitions from pre-change scenes and desired change types. By integrating knowledge-guided change simulation with generalizable synthesis models, KnowChange enables flexible synthesis of diverse change types within a unified framework. Extensive experiments demonstrate that KnowChange-generated data consistently outperforms existing synthetic datasets in both synthetic-to-real transfer and synthetic data augmentation, despite being generated at a compact scale. Further analyses show that the knowledge-guided change simulation can be seamlessly integrated into existing synthesis pipelines and enhance the downstream utility of synthesized data.
[CV-41] Event-Based Motion Estimation via Oriented Distance Fields
链接: https://arxiv.org/abs/2608.24223
作者: Lei Sun,Yuqin Ma,Weilun Li,Haoran Liang,Runyi Yang,Kaiwei Wang,Danda Pani Paudel,Luc Van Gool
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Event-based motion estimation is central to tasks that demand high temporal resolution and robustness to fast motion. Existing methods typically rely on iterative optimization or repeated hypothesis comparison, offsetting the sensor’s low-latency advantage. We propose Oriented Distance Field Motion Estimation (ODF Motion Estimation), which replaces this optimization with a single averaging step over a precomputed field of event distance vectors, combined with an adaptive event-count selection strategy and a parameter-free trail filter. On public and self-collected datasets, ODF motion estimation reaches sub-pixel accuracy at the lowest latency among compared methods. We validate its generality on two downstream applications rather than treating them as separate contributions. First, the estimated trajectory is converted into a blur kernel and paired with a compact iterative-unfolding network, trained on simulated motion-estimation noise, for real-time non-blind image deblurring, attaining competitive or superior PSNR/SSIM with under 1M parameters. Second, the same precomputed field is repurposed for directional event filtering in a low-power asynchronous pupil and glint tracker, sustaining stable tracking for tens of seconds while lowering a near-eye module’s power draw.
[CV-42] Beauty is in the ELBO of the Beholder: A Variational Account of Processing Fluency in Face Perception
链接: https://arxiv.org/abs/2608.24219
作者: Francisco M. López,Jochen Triesch
类目: Computer Vision and Pattern Recognition (cs.CV); Neurons and Cognition (q-bio.NC)
备注: 24 pages, 11 figures, 1 table
Abstract:Facial attractiveness has been linked to statistical regularities such as symmetry and averageness, suggesting that beauty may depend on the ease with which a face is perceived. We empirically test this hypothesis by training variational autoencoders on four face datasets without attractiveness supervision and evaluating their representations on the 597 faces from the Chicago Face Database. Across models, human attractiveness ratings closely aligns with the direction defined by the VAE evidence lower bound (ELBO) in rate-distortion space. Independently learned latent spaces contain an attractiveness direction that transfers strongly across random initializations and training data. We also find that attractive faces are more prototypical in both shape and latent space. Our results connect classic accounts of aesthetics with learned generative models and provide empirical support for a variational interpretation of the processing fluency theory of aesthetic pleasure.
[CV-43] NeoWorld-Pro: Programming Interactive Scenes from Monocular Images for Embodied Simulation
链接: https://arxiv.org/abs/2608.24212
作者: Yumeng He,Yichen Song,Xiaotian Yang,Weijia Zhang,Zanwei Zhou,Junru Gong,Xiaokang Yang,Yunbo Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The advancement of Embodied AI necessitates high-quality simulation assets that faithfully mirror the real world. However, transforming raw visual observations into simulation-ready scenes remains challenging due to the lack of physical grounding and scene-level interactivity in current image-to-URDF methods. We propose NeoWorld-Pro, a framework that reformulates monocular scene reconstruction as procedural programming for interactive 3D environments. Leveraging the zero-shot reasoning and code synthesis capabilities of MLLMs, NeoWorld-Pro converts a single RGB image into executable programs specifying object geometry, articulation, and physical properties. A physics-in-the-loop mechanism then iteratively refines the generated programs by validating their execution in a physics engine, enforcing physically plausible articulations, valid object compositions and interactions, and accurate spatial relationships. Experiments show that NeoWorld-Pro outperforms open-loop and prior monocular reconstruction methods, while enabling complex downstream tasks such as stable stacking and fine-grained manipulation.
[CV-44] Amortized Set Prediction for Inverse IFS Reconstruction from Density Maps
链接: https://arxiv.org/abs/2608.24175
作者: Yutaka Yamaguti
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 24 pages, 12 figures
Abstract:Iterated Function Systems (IFS) generate self-similar fractals from a few contractive affine maps. The forward map from parameters to images is computationally inexpensive and well understood, whereas the inverse problem of estimating maps from an image is difficult and is typically handled by per-image optimization. We replace this loop with a single forward pass of a learned estimator that predicts the affine-map set directly from a visit-frequency density map, thereby amortizing the inverse problem. The design follows two constraints. First, density maps do not uniquely identify IFS parameters, so evaluation is based on reconstruction rather than parameter recovery; unordered map sets are handled by Hungarian matching, and ground-truth parameters provide a stable training surrogate. Second, the fully known forward model lets us generate exact synthetic training pairs and also supports image-only test-time refinement. On in-distribution tests, amortized initialization plus a few refinement steps lies on a better quality–speed frontier than equal-budget random-initialized per-image optimization, and a 30-step refinement (about 0.56 s per sample) remains better than a doubled-budget baseline. Extending optimization to 1000 steps shows that the benefit is not only speed: amortized initialization reaches high-quality reconstructions more frequently than random starts. On real images (MNIST and Fashion-MNIST), it improves density metrics on average over a published per-image optimizer while being roughly 12 to 2600 times faster.
[CV-45] SandwichQuant: Which Parameters Matter Before and After Quantization?
链接: https://arxiv.org/abs/2608.24173
作者: Peng Xia,Junbiao Pang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Quantization correction methods usually optimize weights, quantization parameters, or reconstruction objectives, while the underlying parameter subspaces responsible for effective correction remain unclear. In this work, we study quantization correction from a parameter subspace perspective and reveal that correction capability is highly non-uniform across parameter groups. By decomposing trainable parameters into backbone weights, normalization-affine parameters, and quantization parameters, we show that the low-dimensional normalization-affine subspace provides a highly efficient correction direction under matched budgets. Based on this finding, we propose SandwichQuant, a two-stage normalization-affine correction framework that performs adaptation before and after quantization. The pre-stage improves quantization robustness, while the post-stage compensates residual errors after the quantized graph is fixed. Extensive experiments on vision models and large language models demonstrate consistent improvements under various low-bit quantization settings, validating the effectiveness of subspace-aligned correction.
[CV-46] Rethinking Pre-Training and Augmentation for Zero-Shot Cross-City Object Detection ECCV2026
链接: https://arxiv.org/abs/2608.24154
作者: Long Hoang Pham,Quoc Pham-Nam Ho,Huy-Hung Nguyen,Duong Nguyen-Ngoc Tran,Ngoc Doan-Minh Huynh,Cu Quoc Le,Hoang-Khang Nguyen,Hyung-Min Jeon,Chi Dai Tran,Son Hong Phan,Duong Khac Vu,Trinh Le Ba Khanh,Jae Wook Jeon
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: This paper has been accepted by the AI City Challenge Workshop of the European Conference on Computer Vision (ECCV 2026)
Abstract:Real-world deployment of traffic surveillance systems is bottlenecked by geographic domain shift, in which models trained in one city underperform when applied to an unseen target city. Conventional domain adaptation relies on hyperparameter-sensitive architectures or direct profiling of target data. Both are fundamentally precluded in privacy-conscious ecosystems that require completely blind training and evaluation loops. In this setting, we explore the effects of pre-training and augmentation in addressing the domain shift problem. Specifically, we propose a new modular training pipeline for object detection structured around two core orthogonal pillars: (1) a multi-dataset pre-training strategy featuring a class-agnostic objectness distillation to decouple structural vehicle geometry from semantic taxonomies, and (2) a domain-resilient augmentation stream featuring a novel Grayworld transformation that forces global attention heads to strip volatile chromatic shortcuts in favor of robust shape priors. When evaluated with the real-time transformer-based detector RF-DETR, our framework bridges cross-city distribution gaps while using limited GPU memory (16GB). Our optimized variants, RF-DETR-HR and RF-DETR-Grayworld, deliver a substantial empirical gain of +24.29 over the baseline, achieving 1st place (47.53 mAP) on the AI City Challenge Track 6 leaderboard. Code and data are available at: \hrefthis https URLSKKUAutoLab/aic26_cross_city.
[CV-47] What Does Prompt Learning Change? -A Natural-Language Concept Analysis of Vision-Language Models
链接: https://arxiv.org/abs/2608.24142
作者: Ryo Kamiya,Hiroshi Kera,Kazuhiko Kawamoto
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 10 figures
Abstract:Prompt learning adapts vision-language models such as CLIP by optimizing continuous prompt vectors, but the learned prompts are difficult to interpret in natural language. We present PromptSpLiCE, a post-hoc method that expresses each class-conditioned text embedding as a sparse combination of concepts from a fixed natural-language dictionary. Using the same dictionary before and after prompt learning allows us to compare changes in their concept profiles. We evaluate PromptSpLiCE on CoOp, a representative prompt-learning method, across 11 image-classification datasets. The concept profiles change substantially: on average, only 1.6 of the initial top-10 concepts remain in the top 10 after learning. Across datasets, profile change is positively associated with accuracy gain. We also derive a local gradient expression that provides geometric intuition for why image-aligned concept directions distinct from the current prompt can have greater loss sensitivity.
[CV-48] Rubrics as Visual-Repair Context for Self-Evolving UI-to-Code Generation
链接: https://arxiv.org/abs/2608.24138
作者: Tianyi Xiong,Zhengyuan Yang,Xiaofei Wang,Chung-Ching Lin,Ruichun Ma,Kevin Lin,Zhendong Wang,Linjie Li,Chenxi Liu,Ruibo Chen,Ramani Duraiswami,Heng Huang,Lijuan Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Large vision-language models have shown strong progress in UI-to-code generation, yet their test-time self-evolution remains unstable. We first identify a fundamental obstacle, termed visual repair coupling: a local code edit may propagate through layout, style, and component dependencies, correcting one visual mismatch while degrading regions that were previously faithful. To address this issue, we present RubSE, a Rubric-guided Self-Evolution framework that uses rubrics to represent visual feedback as a structured visual-repair context. At each refinement round, RubSE generates typed candidate rubrics, selects one prioritized repair target, and stores previously selected rubrics as history, thereby steering each revision toward a well-scoped visual repair while discouraging repeated or over-broad changes. Evaluations across six VLMs and three UI-to-code benchmarks demonstrate that RubSE substantially outperforms naïve self-evolution in final-round and best-round settings, achieving more stable refinement trajectories and a higher trajectory-level performance ceiling. Further analysis shows that RubSE mitigates trajectory collapse by improving recovery from severe visual regressions, and that stronger rubric generators can transfer effective visual-repair guidance to weaker code improvers.
[CV-49] EgoErrorVQA: Assess Egocentric Comprehension Capabilities through Procedural Errors for Ego-Agent ic AI
链接: https://arxiv.org/abs/2608.24134
作者: Junlong Li,Junxi Li,Jianjun Gao,Chen Cai,Lap-Pui Chau,Yi Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The majority of our everyday activities are procedural and consist of sequences of interdependent steps. However, existing benchmarks for Visual Agents and Visual Language Models (VLMs) overlook the evaluation of their procedural comprehension ability from an egocentric visual perspective, particularly for detecting procedural errors, a critical capability for everyday assistance. To bridge this gap, the EgoErrorVQA task is firstly proposed for egocentric procedural comprehension with explicit procedural errors modeling. Besides, we develop a user-friendly evaluator agent based on the Agent2Agent (A2A) protocol, enabling rigorous and standardized evaluation of visual agents through VQA-based interaction. A range of models are evaluated using both open-ended and multiple-choice questions, revealing persistent weaknesses in handling procedural errors and error types. Moreover, we introduce Ego-ADR, an Adaptive Decoupled Reasoning framework that decouples complex procedural reasoning to enhance models’ understanding of procedural errors. It achieves consistent performance gains over the selected baselines and attains state-of-the-art results on several metrics under comparable settings. Code: this https URL
[CV-50] Syn2RealTrack: Bridging the Gap Between Synthetic and Real-World Datasets for Online Multi-View Multi-Target Tracking ECCV2026
链接: https://arxiv.org/abs/2608.24130
作者: Duong Nguyen-Ngoc Tran,Ngoc Doan-Minh Huynh,Cu Quoc Le,Hoang-Khang Nguyen,Long Hoang Pham,Huy-Hung Nguyen,Quoc Pham-Nam Ho,Trinh Le Ba Khanh,Chi Dai Tran,Duong Khac Vu,Son Hong Phan,Hyung-Min Jeon,Jae Wook Jeon
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: This paper has been accepted by the AI City Challenge Workshop of the European Conference on Computer Vision (ECCV 2026)
Abstract:Multi-camera 3D perception systems for warehouse scenes are trained largely on synthetic data and evaluated on physically captured environments. The resulting synthetic-to-real gap, which corrupts ground-plane localization and cross-camera identity association, is usually treated as one deficiency for a single domain-adaptation module to absorb; we argue instead that it enters the pipeline at three separable points: the camera calibration, the object shape prior, and the assumption that the object census is known, each admitting a different local remedy. Our online pipeline, Syn2RealTrack, follows this decomposition: lens distortion is recovered from images alone under a calibration that provides none, detections are fused across views by a visibility-weighted part-based descriptor that abstains on occluded parts rather than guessing, person height is measured in closed form from calibration instead of copied from a synthetic prior, and a closed-world cardinality prior is paired with a causal filter that removes the phantom boxes the prior manufactures. The system therefore adapts by reallocating trust between geometry and appearance without retraining a feature extractor. On the AI City Challenge 2026 Track~1 evaluation server it reaches a 3D Higher Order Tracking Accuracy (HOTA) of 52.0118%. The code will be released at this https URL
[CV-51] Graph-Supervised Hierarchical Clinical Alignment for Radiology Report Generation with Large Language Models
链接: https://arxiv.org/abs/2608.24121
作者: Yingshu Li,Yunyi Liu,Zhanyu Wang,Zailong Chen,Lingqiao Liu,Lei Wang,Luping Zhou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Radiology report generation (RRG) has recently benefited from large language models, which substantially improve report fluency. However, clinically faithful generation remains challenging because current supervision is still imposed mostly at the report level. This creates a granularity mismatch: radiology reports are composed of disease-grounded findings, while existing methods are trained mainly with whole-report objectives. To address this problem, we propose Graph-Supervised Hierarchical Clinical Alignment, which reformulates image-report supervision as a hierarchical clinical alignment problem. Our method structures this alignment as a disease-conditioned process, where supervision is decomposed into two levels: Disease-Centric Alignment for fine-grained disease-specific correspondence, and Global Clinical Semantic Alignment for report-level semantic coherence. A clinical knowledge graph is used as a training-time-only structural prior that defines disease-specific supervision units and their clinical relationships, introducing no additional overhead at inference. Because standard contrastive alignment could produce false negatives when studies share overlapping pathologies, we combine instance-conditioned discriminative matching with disease-conditioned soft regularization, enabling fine-grained yet clinically consistent cross-modal representations. Experiments on MIMIC-CXR, IU-Xray, and COV-CTR show that our method consistently improves performance on both conventional and clinical metrics. Notably, our 3B model surpasses several prior systems with larger 7B/13B backbones, suggesting that improving supervision structure, rather than increasing model size, can be more effective for RRG.
[CV-52] ransPhy: Visual In-Context Learning for Physically Grounded Image Editing
链接: https://arxiv.org/abs/2608.24119
作者: Siyi Xie,Xuanke Shi,Jinsheng Quan,Haoran Tang,Zukai Chen,Lei Yang,Quan Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Visual demonstrations provide a natural interface for specifying image transformations that are difficult to describe exhaustively with text. However, existing visual in-context learning (VICL) methods primarily focus on appearance-level relation transfer and provide limited support for physically grounded transformations, whose outcomes depend on material properties, geometry, object interactions, and environmental conditions. Given a source–target exemplar pair and a query image, physically grounded VICL requires a model to infer the demonstrated transformation, adapt its effects to the query-specific scene context, and preserve rule-irrelevant content. We introduce PhysVICL-74, comprising 74 physically grounded transformation rules and 5,240 source–target image pairs that form nearly 75K training and evaluation contexts. Its benchmark split separately evaluates novel-instance transfer and unseen-rule generalization. We further propose TransPhy, a framework that decomposes physically grounded VICL into physical-rule induction and transition-aligned rendering. TransPhy first predicts the demonstrated rule and an explicit query-specific target-state description, and then synthesizes the target image through token-wise mixture-of-experts adaptation, with expert routing guided by localized transition cues. Experiments show that TransPhy improves physical-rule adherence, query consistency, and unseen-rule generalization over existing visual in-context editing methods.
[CV-53] ExMesh: From Multi-View Images to Relightable UV-PBR Mesh Assets via Topology-Adaptive Reconstruction and Decomposition
链接: https://arxiv.org/abs/2608.24109
作者: Chuanjin Fan,Lifan Wu,Wenjie Chang,Hanzhi Chang,Wenfei Yang,Tianzhu Zhang
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multi-view reconstruction extends beyond surface recovery to editable and relightable mesh assets. Such assets require well-formed topology, valid UV parameterization, and explicit PBR material maps. Existing surface reconstruction approaches optimize implicit fields, Gaussian primitives, or other intermediate representations. Converting them into such assets often requires surface extraction and texture baking. Inverse-rendering methods estimate materials and illumination, yet these components often remain tied to neural fields or point-based primitives rather than the final mesh. Joint optimization of geometry, materials, and lighting may also allow these variables to compensate for one another, leading to ambiguous decomposition. To address these limitations, we present ExMesh++, a staged framework for reconstructing relightable UV-PBR mesh assets from multi-view images. The first stage refines explicit mesh geometry and topology through adaptive vertex splitting and merging, while maintaining UV consistency as the topology changes. The second stage fixes the resulting mesh-UV carrier and optimizes UV-space PBR maps together with environment lighting. Building on this stable carrier, ExMesh++ models one-bounce diffuse indirect illumination through secondary-ray tracing with shared UV-PBR materials. Experiments demonstrate competitive geometry accuracy, strong relighting performance, and direct usability of the exported assets in standard DCC workflows.
[CV-54] MatReplace: A Reference-Free Conditioning-Aligned Benchmark for Material Replacement in Interior Scenes
链接: https://arxiv.org/abs/2608.24107
作者: Mingzhe Du,Thong Thanh Nguyen,Nguyen Tran Cong Duy,See-Kiong Ng,Luu Anh Tuan
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Material replacement is a common interior-design operation: changing the material of a selected surface while preserving its geometry, surroundings, and illumination. Despite its commercial relevance, no public benchmark isolates this task, and evaluating it is challenging. Reference-based metrics penalize valid outputs in this inherently one-to-many setting, favor the style of the reference generator, and cannot fairly compare editors that receive different forms of guidance. We introduce MatReplace, a reference-free benchmark that evaluates edits along four verifiable dimensions: local material correctness, global lighting harmony, outside preservation, and inside structure. It defines three tracks that vary one conditioning signal at a time: (A) instruction only, (B) instruction plus region mask, and © material reference image instead of instruction. Our results reveal a clear divide between naming and visually grounding materials. In Track A, leading closed-source editors achieve exemplar-level material rendering and surpass the exemplar anchor under our primary aggregate. In Track B, masks help only mask-compatible models with weak scene preservation, with task-paired, single-seed effects ranging from +0.137 to -0.090 across aligned model families. In Track C, reference-image conditioning degrades every family under both aggregates, by -0.031 to -0.508; in the worst cases, models repaint the reference image itself and perform worse than returning the input unchanged. Thus, named-material rendering is largely solved by the strongest closed editors on this distribution, but grounding materials from pixels remains an open challenge. Expert ratings validate our ranking (Kendall’s tau = 0.68) and align with our aggregates more closely than GT-referenced or CLIP-based baselines.
[CV-55] DRRG: A Discrete Diffusion Framework for Radiology Report Generation
链接: https://arxiv.org/abs/2608.24105
作者: Shaoyang Zhoua,Yingshu Li,Yunyi Liu,Lijun Pu,Lingqiao Liu,Lei Wang,Luping Zhou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Purpose: Automatic radiology report generation (RRG) has been widely explored to improve reporting accuracy and reduce radiologists’ workload. Most existing methods rely on autoregressive (AR) frameworks that generate reports token by token and cannot revise earlier content, making them prone to error propagation and inconsistent with the iterative refinement process of radiological reporting. In contrast, discrete diffusion large language models (DLLMs) generate text through iterative denoising, naturally enabling report refinement. However, DLLMs have not been extensively investigated for RRG. In this study, we developed and evaluated a discrete diffusion framework for RRG that enables iterative refinement rather than conventional left-to-right autoregressive decoding. Materials and methods: We developed DRRG, a DLLM-based framework that formulates RRG as iterative masked-token denoising. DRRG incorporates a clinical-entities-aware complementary mask to improve token supervision coverage and emphasize clinically important entities, together with a concept-conditioning module that injects image-derived clinical concepts into visual representations. DRRG was trained and evaluated on MIMIC-CXR and CheXpert Plus. Results: On MIMIC-CXR, DRRG achieved BLEU-4 of 0.210, CheXpert-F1 of 0.549, RadGraph-F1 of 0.281, GREEN of 0.360, and RaTEScore of 0.604, outperforming the compared methods on most reported metrics, despite employing a substantially smaller LLM decoder. On CheXpert Plus, DRRG achieved the highest BLEU-4 (0.119) and CheXpert-F1 (0.347) among the compared methods. Conclusion: Discrete diffusion provides an effective alternative to autoregressive radiology report generation by enabling iterative, bidirectional report refinement. Incorporating clinically focused masking and image-derived concept conditioning improves report quality and clinical consistency. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.24105 [cs.CV] (or arXiv:2608.24105v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.24105 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yingshu Li [view email] [v1] Tue, 25 Aug 2026 06:07:02 UTC (11,066 KB)
[CV-56] Joint-Embedding Prediction of Masked Point Tubes for Self-Supervised Learning on 4D Point Cloud Videos
链接: https://arxiv.org/abs/2608.24093
作者: Jheng-Ling Lee,Shang-Tse Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 13 pages, 4 figures; supplementary material included
Abstract:Self-supervised representation learning for 4D point cloud videos is challenging because annotations are costly and reconstruction-based pretraining can overemphasize low-level geometric details. We propose a JEPA-style framework that learns from unlabeled spatiotemporal point clouds through latent point-tube prediction. Instead of reconstructing raw coordinates, the model masks spatiotemporal regions and predicts their target representations from visible context representations in feature space. To stabilize latent prediction, we incorporate Sketched Isotropic Gaussian Regularization, which encourages non-collapsed embeddings without relying on explicit reconstruction targets. This formulation aims to capture both spatial structure and temporal dynamics while keeping the pretraining objective aligned with downstream semantic recognition. Experiments on action and gesture recognition benchmarks show that the learned representations improve downstream fine-tuning, limited-label learning, and cross-dataset transfer. These results suggest that JEPA-style latent prediction is a promising alternative to reconstruction-centered pretraining for 4D point cloud videos.
[CV-57] ORBITALIF: An Efficient Spiking Federated Learning Framework for Onboard Cloud Removal
链接: https://arxiv.org/abs/2608.24073
作者: Bohan Zhang,Chenyu Xu,Yijie Mao,Yuanming Shi
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: 6 pages,4 figures,accepted by IEEE GLOBECOM 2026
Abstract:Low-earth-orbit (LEO) satellites enable high-resolution, large-scale Earth observation for applications such as disaster monitoring and environmental surveillance. However, cloud coverage often obscures the Earth’s surface, and conventional cloud-removal pipelines that download cloudy images to ground stations for processing suffer from limited contact windows, constrained satellite-to-ground bandwidth, and high latency. In this work, we propose a novel satellite federated learning framework for cloud removal across LEO constellations, named orbital attention leaky integrate-and-fire (OrbitALIF). OrbitALIF performs both onboard training and inference using a compact 2.30,M-parameter spiking neural network (SNN) backbone with an adaptive gated fusion module (AGFM) and a spectral-spatial hybrid attention module (SHAM), combined with a decentralized federated learning strategy that shares model weights via inter-satellite links. Our experiments show that OrbitALIF achieves competitive cloud removal quality while consuming only 0.287,mJ per inference on neuromorphic hardware, a 72.3 times (98.6%) energy reduction versus an equivalent artificial neural network (ANN).
[CV-58] Representation Learning in Diffusion and Flow-based Model: An Application Aspect
链接: https://arxiv.org/abs/2608.24068
作者: Yanchen Xu,Sida Huang,Zhenyu Gu,Ruishu Zhu,Yilan Gao,Hongyuan Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by Vicinagearth
Abstract:Diffusion models and flow-based models have recently become the dominant paradigms in generative modeling, largely due to their ability to learn rich, multi-level visual representations through large-scale training. This creates a bidirectional relationship between generative models and representation learning: improving representation learning enhances generation quality, while the learned representations can be leveraged for broader understanding tasks. This survey systematically explores this interplay with a focus on applications. We propose a three-tier progressive framework that organizes existing works from three perspectives: using representation learning to improve generative capabilities, exploiting generative models to extract representations for perception tasks, and ultimately moving toward general-purpose unified applications. We systematically categorize representative methods across a wide range of downstream tasks, including image classification, dense visual prediction, instance-level perception, and annotation-scarce scenarios. By providing a unified taxonomy and identifying key challenges, this survey aims to clarify the underlying logic of current research and suggest promising directions for future exploration. We hope this work can serve as a valuable reference for researchers interested in harnessing the representation power of generative models for applications beyond generation.
[CV-59] VisCache: Visual KV Cache Pruning for Efficient Vision Large Language Model Inference
链接: https://arxiv.org/abs/2608.24063
作者: Lyuke Wang,Zhuo Li,Guangxu Zhu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:While Vision Large Language Models (VLLMs) have achieved remarkable success in multimodal reasoning, their long-context inference remains prohibitively expensive due to the massive computation and memory overhead of visual Key-Value (KV) caches. Existing KV compression methods often apply uniform pruning across visual tokens and layers, leading to substantial information loss and degraded this http URL address this challenge, we propose \textbfVisCache, a plug-and-play framework for coarse-to-fine \textbfVisual KV \textbfCache pruning without training, which consists of two synergistic stages. First, a lightweight VLM filters temporal redundancy by selectively forwarding semantically informative keyframes. Second, we introduce PruneKV, a surgical KV compression algorithm tailored to the attention dynamics of VLLMs. Unlike rigid pruning strategies, PruneKV adopts a parabolic layer-wise budget allocation together with an asymmetric update mechanism that selectively prunes keys while fusing values, thereby preserving critical contextual information. Extensive experiments demonstrate that VisCache substantially improves inference efficiency, achieving up to 2.35 \times speedup and significant memory reduction while maintaining competitive performance with only 19–28% KV cache retention. VisCache consistently outperforms existing baselines, establishing a new Pareto frontier between efficiency and performance for long-context VLLM inference. Code is available at this https URL
[CV-60] ConsensusTAS: Self-Supervised Temporal Action Segmentation for Long-Horizon Construction Videos
链接: https://arxiv.org/abs/2608.24043
作者: Xiaoshan Zhou,Yafei Sun
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recognizing sequential construction activities is important for collaborative human-robot work; for example, robots are able to understand workers’ current and upcoming actions and provide timely tool delivery or physical support. However, despite extensive research on construction worker activity recognition, existing studies have been limited to classifying activity categories, such as climbing, lifting, and walking, instead of recognizing fine-grained activity transitions from long-horizon sequences. Addressing this problem is challenging because annotating action temporal boundaries in long construction videos is time-consuming. In this study, we propose ConsensusTAS, a label-free, self-supervised learning approach to segment continuous video streams into distinct activity phases by exploiting the internal consensus of candidate segmentations. We evaluated our algorithm on three public datasets, where it outperformed state-of-the-art methods, achieving an F1@10 of 73.08 on GTEA, an F1@10 of 64.33 on Breakfast, and an F1@50 of 33.50 on static-camera videos from Assembly101. We also tested it on real-world construction videos, where post-hoc evaluation showed that the model successfully recognized and segmented actions within the composite activity of bricklaying, such as spreading mortar on a brick, placing the brick, pressing, and aligning. Compared with other temporal action segmentation models that require computationally intensive large vision-language models, our method can run on a CPU, which provides practical value for video surveillance and human-robot collaboration on mobile robotic platforms.
[CV-61] Phase-Aligned Finite-Fourier Periodic Deformation for 4D Medical Image Interpolation
链接: https://arxiv.org/abs/2608.24027
作者: Haojin Li,Hengzhuo Wang,Zhiheng Ma,Mingyang Ou,Heng Li,Jiang Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ACMMM 2026 accepted
Abstract:4D medical image interpolation aims to recover missing volumes from sparsely observed time points and is important for dynamic anatomical analysis in applications such as cardiac MRI and thoracic CT, where motion is often repetitive or near-periodic over clinically relevant intervals. A key challenge is that this structure is not always encoded directly in deformation representations for interpolation. In addition, physiological motion is often non-uniform, so equal temporal intervals do not necessarily correspond to equal amounts of anatomical change. To address these issues, we formulate interpolation as learning a continuous deformation process with a phase-structured prior. Given two endpoint volumes, we parameterize a phase-conditioned velocity field with a finite Fourier basis, which embeds near-periodic motion patterns directly into the deformation space and supports continuous querying at arbitrary target times. We further introduce a phase-aligned temporal reparameterization that maps normalized within-interval time to a latent motion phase according to deformation variation intensity, thereby better modeling non-uniform motion progression. Intermediate volumes are then synthesized by continuously warping both endpoints, followed by bidirectional fusion and lightweight residual refinement. Experiments on ACDC and 4D-Lung show that the proposed method achieves state-of-the-art performance over existing baselines while producing anatomically plausible and coherent intermediate volumes from sparse observations.
[CV-62] Low-Rank Velocity Fields as a Structural Prior for Unsupervised 4D Medical Image Interpolation MICCAI2026
链接: https://arxiv.org/abs/2608.24025
作者: Haojin Li,Hengzhuo Wang,Chang Liu,Zhiheng Ma,Heng Li,Jiang Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: MICCAI 2026
Abstract:Endpoint-only unsupervised 4D medical image interpolation synthesizes intermediate volumes from sparsely sampled sequences with only the start and end volumes available for training; however, this weakly constrained setting often yields intermediates with unstable boundaries and non-physiological motion, limiting interpretability and downstream analysis. We propose low-rank velocity fields as a structural prior, constraining motion to a structured Tucker low-rank velocity field space that decomposes motion into globally shared spatial bases and a compact sample-specific core, thereby encouraging spatially correlated, anatomy-consistent deformation while suppressing voxel-wise high-frequency artifacts. To capture global coordination and local non-rigid details, we model motion in a coarse-to-fine multi-scale scheme and compose scale-wise deformations at inference to synthesize volumes at arbitrary times. We further provide a theoretical analysis showing that, under Tucker parameterization, low-rank parameters control the smoothness energy of the velocity field, explaining why low-rank modeling promotes smoother motion. Experiments on ACDC and 4D-Lung demonstrate state-of-the-art performance, remaining competitive with methods trained with intermediate-frame supervision, and producing intermediates with improved structural coherence and more stable anatomical contours.
[CV-63] IterCAD: Iterative Program Repair for CAD Code Generation from Orthographic Views ACM-MM2026
链接: https://arxiv.org/abs/2608.24020
作者: Yuchuan Wu,Ke Niu,Haiyang Yu,Zhuofan Chen,Xiangyang Xue,Bin Li
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to the 34th ACM International Conference on Multimedia (ACM MM 2026)
Abstract:Generating executable parametric CAD code from dimension-annotated orthographic drawings is a challenging task requiring geometric understanding, procedural reasoning, and precise numerical prediction. Existing vision-language approaches typically formulate this problem as one-shot generation, preventing the model from inspecting intermediate CAD results and correcting early mistakes, often leading to non-executable code or geometrically inconsistent outputs. In this paper, we propose IterCAD, an iterative framework that reformulates orthographic-view-to-CAD generation as a progressive program repair process. Instead of predicting the final CAD code in a single pass, IterCAD repeatedly analyzes the current CAD result, reasons about its discrepancy with the target views, and explicitly decides whether to REVISE the code or STOP the refinement process. To make iterative repair learnable, we further construct IterCAD-RS, a structured revise-or-stop supervision set containing both repairable intermediate CAD states and already-correct states, and develop a three-stage training strategy for initial generation, revision learning, and multi-turn RL optimization. By closing the loop between visual understanding, geometric verification, and code refinement, IterCAD progressively corrects structural and parametric errors. Experiments on CADExpert show that IterCAD consistently improves code executability and geometric fidelity over strong one-shot baselines.
[CV-64] Absorbing Gradient Conflicts: Modeling Semantic Variance via Kent Distributions for Cross-Modal Hashing
链接: https://arxiv.org/abs/2608.24010
作者: Hengjie Zhu,Dayan Wu,Zihao Zhang,Xinze Liu,Jingxuan Yu,Peng Fu,Zheng Lin,Weiping Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Supervised proxy-based deep cross-modal hashing has become the dominant paradigm for large-scale retrieval. However, prevalent methods model class proxies as deterministic points in the embedding space. This rigid assumption causes severe gradient conflicts in multi-label scenarios, where gradient conflicts arising from label co-occurrence lead to severe gradient contention and optimization collapse. To resolve this, we propose Kent-based Distributional Proxy Hashing (KDPH), a novel framework that shifts proxy representation from static points to flexible anisotropic Kent distributions on the hypersphere. Unlike point proxies that must shift their positions to accommodate conflicting gradients, KDPH absorbs these conflicts by dynamically adjusting its directional variance. This allows the proxy to maintain a stable semantic mean direction while stretching to cover diverse label correlations. Furthermore, to ensure stable training of these geometric parameters, we derive a tailored loss function incorporating the Cayley transform to enforce strict orthogonality. To the best of our knowledge, KDPH is the first framework to successfully introduce the Kent distributions into cross-modal hashing. Experiments on three benchmark datasets demonstrate that KDPH mitigates proxy collapse and chaotic oscillation, significantly outperforms state-of-the-art methods. Code is available at this https URL.
[CV-65] Source-Face Authenticity Detection for 3D Gaussian Heads Reconstructed from a Single Portrait: A Benchmark and Dedicated Detector
链接: https://arxiv.org/abs/2608.23984
作者: Yujie Gao,Zijian Yu,Yan Hong,Jun Lan,Jianfu Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent advances in single-image 3D Gaussian head reconstruction have enabled highly realistic and freely renderable digital heads from a single portrait. However, reconstruction and rendering can weaken the forgery traces in the source portrait, making the resulting 3D face difficult to classify whether its underlying face is real or fake, and thereby posing risks to identity authentication and face privacy. To study this problem, we introduce the first large-scale benchmark for this task by collecting real portraits and fake portraits from multiple sources and evaluate representative existing detectors on this benchmark, revealing their lack of explicit mechanisms for retaining fine-grained information and maintaining feature consistency across rendered views. To directly address these two limitations, we propose a detector trained with a two-stage strategy. In Stage I, masked autoencoding encourages the visual backbone to retain the fine-grained appearance information required for local reconstruction, while multi-view contrastive learning enforces feature consistency across rendered views of the same head. Since CLS tokens at different depths exhibit complementary spatial attention patterns, Stage II freezes the adapted backbone and concatenates low-, middle-, and high-level CLS tokens for classification. Experiments show that our method achieves the highest accuracy and ranks first across all reported metrics among the evaluated detectors.
[CV-66] When Seeing Is Not Enough: Benchmarking Interactive Visual Grounding in LVLMs EMNLP2026
链接: https://arxiv.org/abs/2608.23978
作者: Zhengxiang Wang,Owen Rambow
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: EMNLP 2026 Main
Abstract:Visual grounding is typically evaluated as a one-shot mapping from an informative referring expression to a visual target. This formulation misses a central property of real-world reference: target information is often incomplete, ambiguous, and established through interaction. We introduce a controlled evaluation framework for interactive visual grounding in large vision-language models (LVLMs), varying how much target information is provided upfront and how much must be acquired through dialogue. Across four human-grounded visual contexts and four interaction protocols, current LVLMs perform significantly below task-level human baselines. Interaction can help when follow-up questions refine or repair an initial target description. Performance is lowest when no initial description is provided and target information must be acquired through questions, indicating that proactive question-driven grounding remains difficult. LVLMs are also poorly calibrated, often reporting confidence that exceeds their empirical accuracy. Follow-up studies confirm these patterns across varied description sources (human versus AI), reasoning efforts, repeated interactions, description providers, and visual contexts. Overall, interactive visual grounding remains an important challenge, requiring visual matching, information seeking and synthesis.
[CV-67] Boot-and-Feedback Framework for Generalist-Expert Model Collaboration in Breast Ultrasound Diagnosis ICASSP2026
链接: https://arxiv.org/abs/2608.23974
作者: Ming Cheng,Hongyu Sun,Zhaolin Chen,Jun Liu,Hossein Rahmani,Qiuhong Ke
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: 5 pages, 2 figures. Published in ICASSP 2026
Abstract:Breast ultrasound (BUS) is widely used for breast cancer diagnosis yet remains operator-dependent. While deep learning shows promise, ensuring diagnostic reliability and interpretability is challenging. Recent Multimodal Large Language Models (MLLMs) often generate spurious descriptions due to limited domain knowledge, which mislead downstream expert models and compromise clinical validity. To address these challenges, we propose the Boot-and-Feedback (BooF) model collaboration framework for synergistic MLLM-expert interaction. Specifically, in the Boot Stage, the MLLM is guided by the BI-RADS lexicon and preliminary benign-malignant vision-expert predictions, enabling it to transfer general reasoning to BUS analysis while avoiding hallucinations. Subsequently, the Feedback Stage integrates these descriptions with visual features via a lightweight Attention-Gated Cross-Modality Fusion Module. This allows the expert to leverage textual feedback while adaptively filtering noise. Extensive experiments on multiple BUS datasets demonstrate that BooF substantially outperforms state-of-the-art methods in terms of diagnostic accuracy and interpretability.
[CV-68] Luce: Relightable Gaussians for 3D Asset Generation
链接: https://arxiv.org/abs/2608.23943
作者: Mayank Singh,Michele Stoppa,Alvise Memo,Rui Yu,Harsha Kalli,Srimanth Gunturi,Muhammad Ahmed Riaz,Behrooz Shahsavari,Waleed Abdulla,David E. Jacobs
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR)
备注: 27 pages, 19 figures, 5 tables
Abstract:High-fidelity image-to-3D generation requires a 3D representation that captures both geometry and appearance. To support relighting and integration into standard rendering pipelines, the representation should include physically based rendering (PBR) modalities such as albedo, metallic-roughness, and surface normals. We propose Luce, a 3D representation that unifies geometry and PBR materials within a voxelized multimodal Gaussian cloud, using dedicated Gaussian primitives for each modality. A variational autoencoder compresses this representation into a unified material-aware latent space. A rectified-flow transformer generates this latent from a single image, conditioned on multi-layer features from a pretrained image encoder that preserve both semantic context and fine spatial detail. The latent then decodes into relightable PBR Gaussians and an optional textured mesh with a tangent-space normal map. On Toys4K, Luce achieves state-of-the-art single-image-to-3D generation, improving FID by 28% over the strongest baseline. We further introduce a benchmark of AI-generated images, on which Luce improves the CLIP image-alignment score over the best baseline (0.8519 vs. 0.8299). Luce generates relightable, geometrically accurate, and materially faithful assets that preserve fine details such as text, logos, and inscriptions.
[CV-69] SceneReGen: Generative Reconstruction of 3D Scenes from a Single Image
链接: https://arxiv.org/abs/2608.23930
作者: Zefan Tian,Yuteng Ye,Yiheng Zhang,Yuhang Yang,Xueqiang Lv,Shizhou Zhang,Le Liu,Di Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Single-image 3D scene reconstruction must complete partially observed objects and place them coherently in a shared observation-aligned scene frame. Object-level generative priors offer strong completion ability, but their centered, scale-normalized outputs are typically expressed in an object frame, creating a fundamental representation gap between object generation and scene reconstruction. We introduce SceneReGen, a generative reconstruction framework that reinterprets scene reconstruction as the generation and assembly of complete object assets in a shared observation-aligned scene frame. SceneReGen addresses the generation-reconstruction gap through selective pose factorization: each object’s observed orientation is encoded directly in the generated mesh, while translation and scale are estimated from instance-level and global scene evidence. Given a scene image and instance masks, a geometry encoder extracts dense cues; learnable shape queries condition a pretrained DiT-based 3D generator to produce complete meshes in their observed orientations, while position queries fuse object and scene features to assemble them in the shared frame. On the 3D-FUTURE evaluation subset, SceneReGen achieves the best scene-level CD, scene-level F-Score, and 3D bounding-box IoU among the evaluated methods, ties the best object-level CD, and ranks second in object-level F-Score. Qualitative outputs in autonomous-driving and embodied-AI scenes further illustrate the potential of asset-centric reconstruction beyond indoor furniture.
[CV-70] RefineRank: Joint Box Refinement and Ranking for Surgical Spatio-Temporal Grounding
链接: https://arxiv.org/abs/2608.23928
作者: Linzhe Jiang,Jiayuan Huang,Changhao Zhang,Chunyang Jiang,Zhehua Mao,Mobarak I. Hoque
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 17 pages, 3 figures
Abstract:Surgical spatio-temporal grounding (STG) requires locating, at each video time specified by a procedural question, the object that the question asks about. Existing approaches face a trade-off: vision language models understand the question context but produce imprecise coordinates, whereas open-set detectors provide localized candidate boxes whose confidence does not reflect which box answers the question. We introduce RefineRank, which closes this gap at the candidate-box level. A compact trainable module, RefineNet, combines the language and regional features of a frozen medical vision language model with the proposals of a frozen open-set detector: it predicts a bounded coordinate correction and a quality score for every candidate box, and a fixed decoding rule returns the original or refined box with the highest score. On the MedVidBench Official Rankings (Verified), RefineRank records 0.421 STG mIoU, the highest displayed STG score, while its global multi-metric rank is 11. In a controlled evaluation on separate training and evaluation videos, coordinate correction raises the candidate oracle upper bound from 0.6772 to 0.7302, and ranking the joint pool of original and refined candidates by their RefineNet scores improves STG mIoU from 0.2719 to 0.4534, whereas separately trained selectors over the same pool reach at most 0.4186. These results show that a small box-level module can reconcile question understanding with precise localization without retraining either backbone. Code is available at [this https URL](this https URL).
[CV-71] GlanceWAM: Sparse Test-Time Imagination for World-Action Models
链接: https://arxiv.org/abs/2608.23927
作者: Linhan Wang,Zijian An,Mingyuan Zhang,Chen Dai,Yi Xu,Can Cui,Zichong Yang,Yinlin Chen,Lifeng Zhou,Chang-Tien Lu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video generative models provide rich physical priors for robot learning, yet existing world-action models (WAMs) face a fundamental trade-off: synchronous video generation at control rate is latency-prohibitive, while abandoning test-time visual imagination sacrifices task success. We show that visual imagination achieves both real-time inference and superior success rates when generated asynchronously off the critical path and consumed directly in latent space. We introduce GlanceWAM, which decouples imagination from control within a single video DiT: an asynchronous proposer glances ahead on a slow clock to imagine a single lookahead frame seconds into the future in the background, while an action head decodes action chunks at control rate (48 ms) purely in latent space without blocking. Enabled by a non-interfering attention mask that isolates video representations and staleness-robust horizon training that accommodates asynchronous lookahead aging, GlanceWAM breaks the speed-success dilemma. Trained purely on demonstrations, it attains 72.2% on the 24-task RoboCasa kitchen benchmark (surpassing synchronous Cosmos Policy at 67.1% and imagination-free co-training at 64.4%) and 99.0% on LIBERO, executing at 48 ms per chunk on an NVIDIA A100 GPU (24x faster than synchronous baselines). Code is available at this https URL.
[CV-72] ROI-Gated SAHI: Content-Adaptive Slicing-Based Inference for Efficient Object Detection
链接: https://arxiv.org/abs/2608.23923
作者: Rashid Riyadh,Abd Ullah Khan,Imad Gohar,Muzammil Behzad
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Slicing-Aided Hyper Inference (SAHI) improves small object detection in high-resolution images but often spends substantial compute on background tiles. We propose region-of-interest (ROI)-Gated SAHI, an inference-time framework that introduces a lightweight proposer to localize foreground regions and restrict sliced refinement to informative areas. We evaluate the framework in two settings. On the COCO128 full split dataset comprising 128 images, static ROI-gating is slower on average than Full SAHI, achieving a speed ratio of 0.88, and yields a lower mAP@0.5 of 0.6602 compared with 0.7569 for Full SAHI. A simple adaptive routing policy with \tau = 0.4 educes the mean latency, achieving a slight gain of 1.02 \times over Full SAHI. On a three-image sparse-to-dense case study, ROI-gating achieves speedups ranging from 0.96 \times to 6.90 \times with a mean speedup of 3.41 \times . These results show that ROI-gating is most beneficial in sparse scenes and requires policy-based routing for robust average behavior.
[CV-73] HAP: Head-Adaptive Visual Token Pruning via Cross-Modal Alignment
链接: https://arxiv.org/abs/2608.23921
作者: Yuanhao Sun,Huawei Ji,Yuan Jin,Cheng Deng,Luoyi Fu,Xinbing Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent Vision-Language Models encode high-resolution images into long visual token sequences, incurring prohibitive prefill costs. To compress them, existing methods score each visual token by averaging text-to-visual attention uniformly across all heads, which assumes every head matches the query. However, our empirical analysis shows that misaligned heads dominate the average, amplifying background tokens and drowning out fine-grained cues. To address this, we propose PAQ (Prompt-Grounded Attention Quality), a metric quantifying how well each head aligns the prompt with image regions. Built on PAQ, our pruning proceeds in three stages. Given a target FLOPs budget, we first partition the transformer layers into groups and allocate a visual token budget to each. Within each group, we then aggregate per-head attention maps via PAQ-weighted softmax into a group-level matrix. Finally, we score visual tokens by this matrix’s magnitude and retain the allocated budget per group. By weighting heads with PAQ, our method scores tokens by attention signals that more faithfully reflect prompt relevance, rather than diluting them through uniform averaging. Across 18 benchmarks, our method delivers state-of-the-art trade-offs. Specifically, on LLaVA-1.5-7B (9 tasks), retaining only \textbf5.6% tokens preserves \textbf99.1% of the original performance, surpassing the strongest baseline AutoPrune by 4.2 points. Code is available in this https URL. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.23921 [cs.CV] (or arXiv:2608.23921v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.23921 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: EMNLP 2026
[CV-74] LG-GER: Language-Guided Group Emotion Recognition via Multimodal Evidence Distillation
链接: https://arxiv.org/abs/2608.23880
作者: Ahmed Shehab Khan,Zhiyuan Li,Yan Tong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Inferring the collective emotional state of a group of people from a single image, a task known as group emotion recognition (GER), requires integrating spatially distributed cues such as faces, poses, interactions, and scene context. Current methods rely on detector-driven multi-stream pipelines. These are trained with only image-level supervision that lacks guidance on which regions matter or how strongly each contributes. We propose LG-GER, a language-guided distillation framework that uses a multimodal large language model (MLLM) to generate dense, spatially grounded evidence, i.e., bounding boxes paired with emotion signals and confidence scores, for the training images. This structured evidence is distilled into a single vision-language model (VLM) backbone through four complementary losses: classification, region-text grounding, spatial emotion, and spatial confidence regression. At inference, LG-GER requires no detectors, no MLLM, and no multi-stream fusion, making GER practical for real-time and resource-constrained deployment. LG-GER has been evaluated on two benchmark GER datasets (GroupEmoW and GAF~3.0) and achieves competitive or superior results compared to state-of-the-art methods that require detection and multi-stream processing at inference.
[CV-75] Gen2Physics: Grounding Generated 3D Meshes in Physics via Multi-View Material Decomposition
链接: https://arxiv.org/abs/2608.23869
作者: Mauro Comi,Jordi Serrano Berbel,Kevis-Kokitsi Maninis,Philipp Henzler,Manuel Sanchez
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:While state-of-the-art generative models produce high-fidelity 3D meshes, these outputs lack the physical properties required for interactive simulation, gaming, or robotics. We introduce Gen2Physics, a unified and automated framework that grounds generated meshes in physics by automatically decomposing them into their constituent material components. Unlike prior approaches, which focus on volumetric representations incompatible with standard physics engines, Gen2Physics operates directly on meshes to produce immediately simulation-ready assets. Our pipeline integrates a fine-tuned Vision Transformer for dense material segmentation, a robust 2D-to-3D consistency projection, and a Vision-Language Model (VLM) guided refinement that leverages contextual reasoning to assign physical properties and infer internal geometry (solid vs. hollow). By converting surface patches into volumes with distinct densities, our method enables physically plausible dynamic simulations. Experimental results on the ABO-500 and PartNet-Material benchmarks demonstrate that Gen2Physics more than doubles the material segmentation accuracy of prior physics-grounding pipelines (15.6 to 48.3 mIoU), while matching the mass-estimation accuracy of volumetric methods and being the only approach to output watertight per-material sub-meshes.
[CV-76] AffineTok: Semantic Affine Consistency for Diffusion-Friendly Visual Tokenizer
链接: https://arxiv.org/abs/2608.23864
作者: Junqiu Yu,Pandeng Li,Yikai Wang,Jiaxing Zhao,Yujie Wei,Kaixun Jiang,Quanhao Li,Hongtao Yu,Zhihang Liu,Zhaohe Liao,Junjie Zhou,Yun Zheng,Yu Liu,Yanwei Fu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Visual tokenizers increasingly inject semantic supervision into latent spaces to make downstream diffusion easier. Yet how these semantics should be organized to facilitate denoising remains underexplored. In this paper, we define the semantic recovery objective: the denoising process should recover the semantic content of the clean image from noisy latent, and a good tokenizer should make it easier. Existing approaches train a projector to predict the semantics directly from the noisy latent. We argue that this predicts the average of clean-image semantics, whereas what really needs to be aligned is the semantics of averaged clean latents. More importantly, we demonstrate that the semantic recovery error orthogonally decomposes into the error of the optimal semantic prediction directly from the noisy latent and the error between these two predictions. We therefore identify their consistency as the missing requirement and call it Semantic Affine Consistency (SAC). To examine whether this overlooked requirement is closely related to downstream generation, we introduce M_SAC, a tokenizer-side proxy for SAC. Across the evaluated tokenizers and diffusion model scales, M_SAC closely tracks generation quality, reaching a Pearson correlation of 0.960 with SiT-XL gFID, thereby motivating SAC-guided tokenizer training. We then introduce AffineTok, which promotes SAC through two complementary, training-only components. Global Semantic Coordination Token (GSCT) coordinates the semantic organization of clean latents, keeping semantic averaging meaningful, while Posterior-Mean Semantic Alignment (PMSA) predicts posterior-mean latents from noisy inputs and supervises their semantics. On ImageNet 256, compared with the baseline, AffineTok reduces gFID by 26% at 20 epochs and, with continued training, achieves a new state-of-the-art gFID of 1.21 without classifier-free guidance and 1.10 with guidance.
[CV-77] LUX: A Lesion-Aware Graph-Conditioned Visual - Language Architecture for Explainable Endoscopic Captioning
链接: https://arxiv.org/abs/2608.23853
作者: Alexis Ivan Escamilla-Lopez,Gilberto Ochoa-Ruiz,Salvador Hinojosa,Sharib Ali
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The interpretation of endoscopic imagery in ulcerative colitis is complex and subjective, with variability in human assessment and subtle mucosal inflammation. Although deep learning has advanced automated analysis, most vision-language models rely on global visual embeddings that overlook the localized and relational nature of pathological evidence, limiting clinical reliability and interpretability. We introduce LUX (Lesion-aware Unified eXplainable captioning), a graph-conditioned vision-language architecture for explainable endoscopic image captioning. LUX constructs a lesion-centric scene graph from Grad-CAM and CBAM activation maps, representing pathological regions as nodes and encoding their spatial and clinical relationships. These graph embeddings are integrated into the cross-attention layers of a T5 decoder, enabling generated words to attend to specific lesion nodes rather than only to global image features. This provides direct alignment between linguistic content and pathological evidence, supporting token-level interpretability and relational reasoning. LUX outperforms strong baseline and state-of-the-art medical captioning models across BLEU, METEOR, ROUGE-L, and CIDEr, with particularly strong gains in CIDEr. It also reduces hallucinated clinical findings and improves lesion-level grounding through stronger correspondence between generated tokens and localized pathological regions. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.23853 [cs.CV] (or arXiv:2608.23853v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.23853 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-78] DDMS: Discriminative Distillation of Multi-view Foundational Features into Single-view Models
链接: https://arxiv.org/abs/2608.23850
作者: Jeong-gi Kwak,Sho Kagami,Yuki Ono,Kwang Moo Yi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Foundational visual features such as DINO have played a critical role across modern computer vision, and have recently become key components in multi-view feed-forward geometry estimators. In this work, we demonstrate that by re-distilling these multi-view models—their internal knowledge of 3D geometry—into a single-view estimator, we can obtain enhanced 3D consistent foundational features. Our key idea is to construct a multi-view teacher by fusing pretrained 2D foundation features with multi-view geometric features, and refining the fused representation with a discriminative ranking objective. Through our discriminative distillation framework, we enforce the learned features to be both 3D consistent and locally distinctive, while keeping them aligned with the feature space of the original foundation model to preserve the semantic structure of the pretrained representation. Consistency and local discriminability are critical for 3D computer vision problems such as forming semantic and geometric correspondences across images. To demonstrate the effectiveness of our method, we perform comprehensive experiments spanning multiple angles: direct feature analysis, dense prediction transfer, and explicit 3D lifting and rendering. Across these evaluations, our method consistently produces stronger 3D-aware foundation features that improve multi-view consistency and local discriminability while preserving the semantic transferability of the original representation.
[CV-79] Object Counting Across Modalities: Taxonomies Benchmarks Applications and Open Challenges
链接: https://arxiv.org/abs/2608.23845
作者: Joana Konadu Owusu,Shivanand Venkanna Sheshappanavar
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Object-counting methods have rapidly shifted from class-specific density regression to open-vocabulary, foundation-model-backed counters. These methods now enumerate instances from various visual and textual prompts. While this shift marks major conceptual progress, our survey argues that claims of universal generality have outpaced the evaluative infrastructure. Most progress metrics rely on a few saturated benchmarks that models exploit for statistical regularities. Newly introduced diagnostic datasets reveal systematic failures in semantic grounding, temporal identity, and spatial reasoning with occlusion. To address these failures, we introduce a five-axis taxonomy (modality, mechanism, prompting, supervision level, and generalization setting). We use this taxonomy to audit the literature across application domains, including microscopy, remote sensing, crowd counting, and agriculture. This formalizes prevailing challenges into six structural contradictions. From these, we propose a roadmap for compositional scene understanding, active counting agents, and unified multimodal evaluation protocols. The main imperative is to build a robust evaluation infrastructure to distinguish open-world generalization from benchmark-specific optimization, rather than simple incremental engineering.
[CV-80] Infant Care Video Dataset for Classification of Interventions Using Transformers WWW
链接: https://arxiv.org/abs/2608.23838
作者: Igor Bogdanov,James Green
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Published in the 11th IEEE International Workshop on Medical Computing (MediComp 2025), part of IEEE COMPSAC 2025. Dataset: this https URL
Abstract:Healthcare documentation in the neonatal intensive care unit (NICU) presents significant challenges, with nurses spending approximately 25% of their time on record-keeping, while up to 60% of interventions remain undocumented. Motivated by the need to detect interventions from video automatically, we present the Infant Care Video Dataset (ICVD), a collection of 4,144 videos spanning 12 simulated intervention classes designed for developing automated documentation systems. Our manikin-based approach systematically varies conditions, such as camera angle and clinician skin tone, while ensuring privacy compliance. Using video transformer architectures (TimeSformer and MotionFormer), we establish strong baseline performance (93.97% and 93.17% top-1 accuracy) among the 12 infant care classes. Our ablation study comparing temporal models with a framewise approach (23.17% accuracy) demonstrates a 70.80% performance gap, validating the need for temporal modeling. The ICVD provides a foundation for developing automated documentation systems to reduce clinical burden in neonatal care environments and improve existing practices.
[CV-81] Predicting Radiologist Expertise from 3D Gaze Patterns During CT Interpretation MICCAI2026
链接: https://arxiv.org/abs/2608.23836
作者: Leila Khaertdinova,Anna Anikina,Claudia Mello-Thoms,Bulat Ibragimov
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at MICCAI 2026
Abstract:Accurate interpretation of volumetric CT requires efficient navigation of 3D image volumes and attention to diagnostically relevant regions. While eye-tracking has been widely studied in 2D medical imaging, its use for expertise assessment in CT settings remains limited. We propose a gaze-informed transformer framework for expertise classification in thoracic CT. Using a DINOv2 backbone, radiologist fixation patterns are integrated into volumetric feature learning through (1) a learnable log-space bias in self-attention and (2) gaze-weighted pooling of patch embeddings. We trained and evaluated our approach on 182 CT reading sessions from five radiologists with varying levels of experience. On a held-out test set, the model achieves an ROC-AUC of 0.91 and F1 score of 0.86, outperforming adapted methods. These findings suggest that incorporating visual search behavior into transformers may support objective, process-based expertise assessment in radiology. Code is available via this https URL.
[CV-82] LUCAID: Agent ic Multimodal AI for Lung Cancer Precision Pathology
链接: https://arxiv.org/abs/2608.23803
作者: Marie-Lisa Eich,Kai Standvoss,Timo Milbich,Alexander Möllers,Miriam Hägele,Philipp Anders,Lars Tharun,Hanna Kontradiuk,Sebastian Kons,Nader Aldoj,Recepcan Adigüzel,Adam Narai,Lukas Hönig,Jonathan Striebel,Binru Yang,Mihnea P. Dragomir,Marvin Sextro,Philipp Keyl,Philipp Jurmeister,Rosemarie Krupar,Evelyn Ramberger,James Wells,Julika Ribbat-Idel,Andreas Kunft,Hussam Shuaib,Christian Grohé,Reinhard Büttner,David Horst,Klaus-Robert Müller,Lukas Ruff,Maximilian Alber,Frederick Klauschen,Simon Schallenberg
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Lung cancer tissue diagnostics is complex, as therapy decisions in precision oncology rely on the integration of histomorphological, immunohistochemical, and molecular features. Yet pathological assessment remains largely visual and semi-quantitative and shows interobserver variability, while existing artificial intelligence (AI) tools cover only selected tasks, rarely reach generalizable expert-level performance, and lack prospective clinical validation. To address these challenges, we developed and clinically validated LUCAID, an agentic AI system for precision lung cancer pathology. An integrative agent couples diagnostic reasoning with nine modules that cover the full routine workflow, from quality control, tumor detection and segmentation, histological subtyping, tumor microenvironment profiling, tumor cellularity quantification, and predictive biomarker scoring (PD-L1, MET, TROP-2) to automated structured report generation. LUCAID enables users to interactively query the module outputs and generate reports that contextualize the results. Against large-scale expert ground-truth annotations, the analysis modules achieved F1 scores of 0.82-0.95. In prospective clinical validation, LUCAID reached 93.0% concordance with an expert-panel adjudicated reference standard across clinically actionable decisions, compared with 68.3-81.1% for five experienced thoracic pathologists.
[CV-83] Restoring Without Forgetting: Continual Learning Across Image Degradations
链接: https://arxiv.org/abs/2608.23799
作者: Alif Ashrafee,Bartosz Krawczyk
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Recent progress in image restoration has converged on all-in-one architectures that jointly handle multiple degradations within a single network. These methods are effective on static benchmarks but target a closed-world setting that assumes simultaneous access to every target degradation at training time. In practice, degradations are encountered sequentially as field-deployed systems progressively face new environmental conditions, and historical training data is often unavailable due to privacy or storage constraints. Accommodating a new degradation then requires either retraining on the union of all prior data, which is often costly or infeasible, or fine-tuning, which causes catastrophic forgetting. We formulate multi-degradation image restoration as a continual domain-incremental learning problem, in which degradations arrive incrementally and prior data is unavailable. Our proposed Restoring without Forgetting (RwF) framework learns a lightweight adapter for each new degradation, eliminating forgetting by construction at a fraction of the cost of dedicated per-domain networks. To isolate degradation learning from dataset variation, we construct a benchmark spanning five degradation domains under shared image content. At test time, an unsupervised routing mechanism identifies the appropriate restoration path for unknown inputs without requiring domain labels. Across the five-domain sequence, RwF improves final average PSNR over naive sequential fine-tuning by 15.25 dB and 11.83 dB on the Restormer and NAFNet backbones, respectively. The framework transfers to eleven canonical real-degradation benchmarks (3,465 images) at 89.5% routing accuracy with only a +0.94 dB oracle PSNR gap, establishing, to our knowledge, the first systematic baseline for continual multi-degradation image restoration.
[CV-84] Mixture of Channel Experts: Static Sparse Supports with Input-Adaptive Mixing for Pointwise Projections
链接: https://arxiv.org/abs/2608.23794
作者: Elian Iluk,Gil Ben-Artzi
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 4 figures
Abstract:Mixture-of-Experts (MoE) scales language models by routing each input through a small set of independently parameterized experts. We show that copying this design into convolutional networks fails for a structural reason: parallel convolutional experts that read the same input channels learn nearly identical filters. We therefore move the expert axis from operator duplication to channel selection. We introduce Mixture of Channel Experts (MoCE), a structured sparse channel-mixing layer, inspired by MoE, that replaces pointwise (1x1) channel-reduction projections. In MoCE, an expert is a single output channel with a learned sparse support of k C input channels. The selected channels are combined by a softmax whose temperature is predicted per input, so each expert can move between mean-like and max-like aggregation. A residual expert summarizes the unselected channels, and a load-balancing loss keeps channel coverage complete. MoCE replaces a dense projection whose cost is quadratic in C with a mechanism whose relative cost scales as k/C, and the predicted savings hold in measured wall-clock time. Across ResNet backbones on ImageNet-1K and CIFAR-100, transfer learning, EfficientViT, and a strong modern training recipe, MoCE matches or exceeds dense baselines and prior channel-selection methods while reducing MACs by 16.7% and end-to-end latency.
[CV-85] Primate vision reveals a missing principle for robust dynamic AI
链接: https://arxiv.org/abs/2608.23790
作者: Matteo Dunnhofer,Christian Micheloni,Kohitij Kar
类目: Computer Vision and Pattern Recognition (cs.CV); Neurons and Cognition (q-bio.NC)
备注:
Abstract:How does an intelligent visual system combine what objects look like with how they move while remaining robust as appearance changes? We addressed this question by comparing human perception and neural activity in macaque inferior temporal cortex with representations from image- and video-based neural networks spanning recognition, segmentation, optic-flow processing and predictive world modeling. Temporal integration improved object representations, but most video recognition models generalized poorly when appearance was disrupted while motion structure was preserved. Humans and macaque IT remained robust. Notably, predictive world models combined strong cross-appearance generalization with the closest correspondence to IT, outperforming other video-modeling approaches in neural fidelity. Yet no model reproduced the cortical transformation from early appearance-dominated responses toward later appearance-invariant motion coding. These results identify progressive integration of motion into object representations as a principle of robust dynamic vision and implicate predictive learning as a promising route toward realizing this computation in artificial systems.
[CV-86] oo much of a good thing – when knowledge distillation promotes overfitting and how to avoid it
链接: https://arxiv.org/abs/2608.23752
作者: Irene Trigueros-Lorca,Leonardo Concepción,Christian Wagner,Isaac Triguero,Daniel Molina
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 19 pages, 7 images
Abstract:The growing size of Convolutional Neural Networks has led to increasingly large and costly models. Knowledge Distillation (KD) addresses this by transferring knowledge from a large network (teacher) to a small one (student), also reducing the training data required. KD is traditionally applied only at the network’s final output. However, its behaviour when applied at intermediate network layers has received little attention. This raises the question of whether intermediate block-wise KD, which provides supervision throughout the network, could offer an advantage under specific conditions, such as few instances per class, which is common in fine-grained datasets. This work proposes a student design based on simple, homogeneous blocks mirroring those of the teacher, distilling knowledge between corresponding blocks. Across eleven datasets, we show that on classic datasets, distilling only the last block is sufficient – and often best–, whereas fine-grained, data-scarce settings benefit substantially from intermediate supervision, with even a single additional distillation point narrowing the gap considerably. We further study how this supervision should be guided, exploring configurations of varying granularity and informed by an explainability analysis based on attention maps, Centered Kernel Alignment, and Grad-CAM, alongside the impact of teacher and student fine-tuning strategies. This work shows that intermediate block-wise distillation, guided appropriately, is key to building compact data-efficient models without sacrificing accuracy.
[CV-87] CRISP: Calibration-Aware Visual State Space Duality for Remote Sensing Semantic Segmentation ECCV2026
链接: https://arxiv.org/abs/2608.23746
作者: Kangning Wang,Haopeng Zhang,Zhiguo Jiang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026. 22 pages, including supplementary material; 9 figures. Code: this https URL
Abstract:State space models, especially Visual State Space Duality (VSSD), have emerged as efficient linear-time alternatives to Transformers for dense visual tasks. However, we observe that VSSD compresses spatial context into a global aggregation that suppresses high-frequency responses, causing excessive boundary smoothing in remote sensing semantic segmentation. To address this, we propose CRISP, a calibration framework with two components. Its core, the Duality Calibration Operator (DCO), restores local contrast and boundary responses through residual injection and frequency calibration within the VSSD backbone, without altering its linear complexity. To retain the recovered detail, an Orthogonal Multi-Prototype (OMP) head assigns multiple orthogonally constrained prototypes per class to model large intra-class variance. Extensive experiments on Potsdam, Vaihingen, and LoveDA show that, with approximately 30M parameters, CRISP achieves consistent gains in mean F1 (mF) and mIoU while remaining competitive with state-of-the-art methods. Code is available at this https URL.
[CV-88] More Motion Is Not Always Better Motion: Corpus Composition Governs Whether Augmentation Helps SMPL-Based Parkinsonian Gait Severity Estimation
链接: https://arxiv.org/abs/2608.23730
作者: Michael Caiola,Andrew C. Weitz
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 4 figures, 5 tables
Abstract:We grade MDS-UPDRS gait severity from SMPL motion using three frozen MotionAGFormer encoders as featurizers, reaching macro-F1 0.58 on a hidden, multi-site test set. Because the system’s members differ only in their lifting corpus, evaluating encoders singly on that test set isolates what that corpus contributes. Six pools drawn from one inertial dataset, varying only in which walking tasks they include, score between 0.32 and 0.53, and just one of them beats the 0.51 of an encoder given no outside motion at all. What separates them is not how much data they hold but whether they carry a contrast in walking speed, the variation this representation appears to depend : a further pool adding a third collection site at fixed task composition does worse still. The same rule explains why exact synthetic motion and monocularly reconstructed web video both fail to help. Modifying the learned representation itself, rather than the corpus behind it, cost every variant that attempted it.
[CV-89] Velocity-coupled Representation Refinement for Satellite Orbit Prediction
链接: https://arxiv.org/abs/2608.23728
作者: Yue Yang,Zhiqiang Wu,Saiyu Qi,Fan Ma
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 6 figures
Abstract:Satellite orbit prediction, which aims to forecast future orbital trajectories from historical observations, is important for collision warning and safe space operations. With advances in time-series forecasting, learning-based methods have emerged as a promising solution for satellite prediction. In orbital dynamics, a satellite state is typically described by position and velocity, where position characterizes trajectory geometry and velocity reflects its instantaneous direction and rate of change. However, most existing methods mainly focus on temporal dependencies within position sequences while rarely exploiting the intrinsic coupling between position and velocity, which is essential for modeling satellite motion. To this end, we propose OrbitNet, a velocity-aware representation learning method for accurate satellite orbit prediction. It lifts conventional position-sequence forecasting to a position-velocity coupled representation learning paradigm by exploiting relationships among satellite state variables. Specifically, we develop a velocity-coupled representation refinement strategy to enhance positional representations through cross-variable interactions between position and velocity. We further introduce orbital segment modeling, which partitions historical trajectories into temporal segments and performs segment-level temporal learning to capture local motion variations and long-range evolution patterns. Extensive experiments show that OrbitNet outperforms large time-series foundation models and representative general forecasting methods under both in-domain evaluation on Starlink and zero-shot evaluation across six unseen satellite constellations. We expect this work to encourage further exploration of satellite-aware representation learning for trajectory time-series forecasting.
[CV-90] DriftAD: Visually-Guided Text Drift for Few-Shot Industrial Anomaly Detection ACM-MM2026
链接: https://arxiv.org/abs/2608.23723
作者: Wenyang Liu,Tianyi Liu,Dongshuo Zhang,Kejun Wu,Adams Wai-Kin Kong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ACM Multimedia 2026 (ACM MM 2026)
Abstract:Few-shot anomaly detection (FSAD) has recently benefited from vision-language models such as CLIP, which enable anomaly de?tection by aligning visual features with text descriptions of normal and abnormal states. However, existing methods typically rely on static text prompts that are applied uniformly across the entire feature hierarchy and spatial dimensions. This rigid global-to-local matching fails to capture the highly localized and scale-dependent physical variations of industrial defects. To address this, we propose DriftAD, a FSAD framework built on three key modules. First, an Anomaly Signal Amplification (ASA) module enhances subtle defect signals through spatial and frequency branches before text-visual matching. Second, Visually-Guided Text Drift (VGTD) dynamically transforms frozen CLIP text embeddings, steering them into layer?wise, spatially-adaptive anomaly descriptors conditioned on local visual context at each encoder depth. Third, Drift-Guided Spatial Gating (DGSG) uses the drifted abnormal descriptor as a spatial probe to selectively enhance anomaly-relevant visual features. Addi?tionally, a drift separation loss prevents representational collapse of the drifted descriptors, and a gate supervision loss enforces spatially discriminative gating in DGSG. Extensive experiments on MVTec?AD and VisA demonstrate state-of-the-art performance across all 1-, 2-, and 4-shot settings on both image-level and pixel-level metrics. Code is available at this https URL.
[CV-91] Platonic Representation Hypothesis on World Models
链接: https://arxiv.org/abs/2608.23720
作者: Wenhow Li(1),Chengwei MA(1),Hui Xiong(1),Ying-Cong Chen(1),Lei Zhang(1) ((1) The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 10 figures, 2 tables. Wenhow Li and Chengwei MA contributed equally. Project page: this https URL
Abstract:World models have demonstrated significant potential for perceiving and simulating complex environments. Despite their strong performance, the fundamental nature of their learned representations remains poorly understood. In this paper, we investigate the Platonic Representation Hypothesis within this domain by proposing the Predictive Consistency Assumption: we posit that the optimization of a shared state transition objective acts as a selective pressure that encourages heterogeneous models to converge toward a shared latent structure. Through systematic experiments with the DINO World Model (DINO-WM), in which we vary visual encoders to create heterogeneous models, we find that capable world models evolve toward geometrically similar internal structures. Moreover, via model stitching, we show that the internal features of one world model can be mapped to another with limited performance degradation, providing evidence of functional compatibility. Our findings suggest that the pursuit of predictive consistency can promote shared, transition-compatible latent structure across world models.
[CV-92] Scaling Reinforcement Learning for Diffusion Models via Velocity Matching
链接: https://arxiv.org/abs/2608.23664
作者: Jaemoo Choi,Wei Guo,Yuchen Zhu,Arash Vahdat,Molei Tao,Julius Berner,Yongxin Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 30 pages, 13 figures
Abstract:Reward fine-tuning is becoming an important tool for adapting diffusion models to human preferences and task-specific objectives, but existing methods largely inherit policy-gradient machinery from large language models. Unlike autoregressive models, diffusion models do not provide tractable likelihoods for generated samples. As a result, current approaches either construct trajectory likelihoods from stochastic denoising transitions or approximate endpoint likelihoods with evidence lower bound, introducing additional computation and algorithmic complexity. We demonstrate that this likelihood-based machinery is not necessary for effective diffusion reward fine-tuning. We propose reward-based velocity matching (RVM), a simple trajectory-free update that acts directly on the velocity field. RVM reinforces directions associated with high-reward generations, suppresses those with low reward, and involves an optional anchor term controlling drift from a reference velocity. Notably, it provides a general framework that recovers recent fine-tuning methods, including RAM and DiffusionNFT, as special cases. Across various large-scale diffusion models reward fine-tuning tasks, RVM is competitive with or outperforms trajectory-based policy-gradient methods under substantially reduced training cost. We further find that, once the velocity update is simplified, the particular loss variant matters less than reward and anchor design. For video generation, standard preference rewards can favor visually clean but nearly static outputs; introducing a new dynamic-tracking reward that substantially improve motions while improving overall VBench performance. These results suggest that scalable reward fine-tuning for diffusion models is better posed in the native velocity representation than as likelihood-based policy optimization.
[CV-93] Cross-Generation Optimization of YOLOv26 YOLOv11 and YOLOv8 for Fine-Grained Small-Object Detection and Instance Segmentation in Complex Orchards
链接: https://arxiv.org/abs/2608.23636
作者: Ranjan Sapkota,Manoj Karkee
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Small-object detection and instance segmentation remain challenging in orchard environments because of green-on-green similarity, occlusion, and limited pixel representation of fine fruit anatomy. This study presents a cross-generation benchmark of Ultralytics YOLOv8, YOLOv11, and YOLOv26 for detecting and segmenting apple fruitlet, calyx, and peduncle structures for robotic orchard perception. Five model scales (n, s, m, l, and x) were evaluated under conventional 640 x 640 and small-object focused 960 x 960 training configurations, yielding 30 experiments. Increasing model capacity did not consistently improve accuracy. YOLOv11s-960 achieved the highest observed mask mAP@50:95 (0.402) and box mAP@50:95 (0.426), while YOLOv26s-960 achieved comparable values of 0.397 and 0.425 with only 10.37 M parameters and 34.1 GFLOPs. Peduncle remained the most challenging class. Overall, compact-to-moderate YOLO models with small-object-focused training provided favorable accuracy efficiency trade-offs, establishing a practical benchmark for fine-grained agricultural robotics and orchard perception. Github Link: this https URL
[CV-94] he Blending Ratio Is Not Where the Performance Is: Diagnosing Prototype Blending for Few-Shot Adaptation of Vision-Language Models
链接: https://arxiv.org/abs/2608.23634
作者: Liangzhi Li,Bowen Wang,Yiming Qian,Thorsten Neumann,Xia Xie,Guangshun Li
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Many few-shot adaptation methods for vision-language models classify with a convex combination of the zero-shot text prototype and the mean of the K labelled image features, with a single blending ratio routinely tuned on held-out labels, often on the test set itself. We ask what the family’s own bias-variance justification invites: what is the right ratio, can it be estimated without validation data, and is finding it where the performance is? First, the ratio minimising prototype mean-squared error has a closed form whose support-set plug-in is exactly a positive-part James-Stein coefficient shrinking towards the text prototype. Across 4,800 cells (ten datasets, five backbones including SigLIP, five shot counts, five seeds, four prompt tiers) this theoretically optimal ratio is a reliable estimate of the wrong quantity: on the 950 primary-tier cells where it is defined it trails a test-set-oracle ratio by 8.5 points. It saturates near 1, discarding the text prior for a nearest-class-mean classifier, because 78% of the text-image prototype distance it treats as bias is a class-independent offset that the arg max largely cancels. We prove the mechanism and bound its share of the damage at 26% by a counterfactual. Second, leave-one-out on the support set alone sets a ratio landing within 0.9 points of the oracle blend, so it is estimable without validation data. Third, validation-free linear probes beat even the oracle-tuned blend: CLAP by +1.9 points and LP++ by +1.5 on average, and at K = 4 all four validation-free baselines sit above the oracle, the linear probes by margins excluding zero. These results locate the ceiling in the model class, not the hyperparameter: the ratio can be set near-optimally for free, and it is still not where the performance is. Code, cached features, per-cell records: this https URL
[CV-95] Fidelity Preference Not Demographic Preference: A Pixel-Level Attribute-Sensitivity Audit of Image Aesthetic/Preference Scorers
链接: https://arxiv.org/abs/2608.23593
作者: Mingyang Xu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Text-to-image systems use learned aesthetic scorers to filter training data and guide generation, but whether these scores encode demographic attributes as objective quality is unclear. We audit four scorers (LAION-Aesthetics, PickScore, ImageReward, HPSv2) using pixel-level interventions on skin tone and body type in synthetic and real images. Our key finding is that along skin-lightness, the dominant effect is fidelity preference: unaltered images score highest, and perturbations in either direction are penalized (inverted-U). Placebo arms show this penalty is not an artifact of the skin operator, as applying the same CIELAB L* shift to non-skin regions yields similar penalty magnitudes. However, the penalty is operator-dependent and holds for all operators only for LAION-Aes. Critically, audits on synthetic images alone are misleading: LAION-Aes shows strong preference for darker skin on synthetic faces, but on 1470 real faces the preference reverses and becomes much smaller, and amplification becomes non-significant. Across scorers, synthetic results do not transfer – reversing for LAION-Aes and HPSv2, attenuating for PickScore. We contribute a reproducible benchmark with artifact control and synthetic/real cross-validation, and an auditability criterion for pixel-level causal isolation (valid for skin tone, not for body type due to deformation). Population-stratified analysis shows fidelity-penalty asymmetry is not robust across groups after FDR correction except for HPSv2. Our findings show naive synthetic audits misjudge bias direction and magnitude, and only within-image causal isolation on real data can distinguish true demographic bias from fidelity preference.
[CV-96] Progressively Learning Heterogeneous Skills in a Unified Latent Space
链接: https://arxiv.org/abs/2608.23258
作者: Yue-Yi Zhang,Ming Gong,Linpu He,Wei-Shi Zheng,Zhilin Zhao
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: 23 pages, 18 figures
Abstract:We propose HetSkills, a novel framework designed to progressively learn heterogeneous skills within a unified latent space for physics-based character control. The core idea is to treat this latent space as a shared executable interface, enabling seamless integration of skills learned from diverse data sources, supervision forms, and tasks. HetSkills begins by learning a tracking skill that establishes a strong foundation in motion control and creates a shared motion decoder, which can be reused across tasks without the need for retraining or separate controllers. To prevent the text-to-motion skill from exploiting shortcut pathways instead of learning language semantics, we introduce motion intuition distillation to ground text-to-motion generation in language semantics and a task-guidance module that dynamically adjusts actions based on high-level language instructions. This enables HetSkills to preserve natural motion while continuously expanding its skill repertoire, making it highly adaptable for long-horizon tasks. Experimental results demonstrate the effectiveness in motion tracking, text-to-motion generation, motion completion, and downstream task adaptation, achieving impressive success rates even under challenging conditions.
[CV-97] DiD It in 87 Minutes: A Label-Free Softmax-to-Linear Adaptation of Vision Transformers for Object Detection
链接: https://arxiv.org/abs/2608.22368
作者: Huaiyuan Qin,Gabriel James Goenawan,Zihang Lin,Muli Yang,Hongyuan Zhu
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:While linear attention is a compelling mechanism for high-resolution object detection due to its reduced cost for global token mixing, converting the Softmax-attention ViT backbone of a trained detector into a linear-attention one is not a trivial drop-in replacement. Directly swapping the attention operator leads to severe performance degradation, and generic label-free distillation, though effective for classification, often fails on detection tasks. We argue that the central challenge is \textitdetector-interface preservation: the converted backbone must reproduce the exact feature tensors expected by the fixed downstream detector, rather than merely imitating internal Softmax hidden states. To address this, we introduce Detector-Interface Distillation (DiD), a label-free conversion method that exclusively trains the linear-attention backbone by aligning detector-facing interface tensors with those of a frozen Softmax teacher. On DOTA-v1.5, DiD substantially outperforms established baselines and matches supervised, fully trained linear models. Adaptation completes in roughly 87 minutes on 4 GPUs, and the linearized backbone cuts inference latency by ~62% and peak memory by ~49%. We hope our findings offer the community a simple, label-free route to reusing trained Softmax detectors as efficient linear ones, and encourage interface-aware objectives in future architecture-conversion work.
[CV-98] Score-Based Ideal Observer Approximation via Denoising Score Matching for Signal-Known-Exactly Detection Tasks
链接: https://arxiv.org/abs/2608.24768
作者: Weimin Zhou
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Computation (stat.CO)
备注: Submitted to SPIE Medical Imaging 2027
Abstract:The Bayesian Ideal Observer (IO) establishes the theoretical upper bound on task performance for binary detection tasks. However, analytical computation of the IO test statistic is generally intractable. Numerical approaches based on Markov-chain Monte Carlo (MCMC) methods, including their recent deep generative model-based extensions, typically require extensive posterior sampling for each test image. Supervised learning has also been investigated to approximate the IO performance. However, such methods are typically trained for a specific detection task and signal and may require retraining when the task or signal changes. The score function, defined as the gradient of the log probability density, encodes the local geometry of the data distribution and is a fundamental quantity in modern score-based generative modeling. This work reformulates the IO test statistic in terms of the score function and introduces a score-based ideal observer (SIO). The proposed SIO uses a denoising convolutional neural network trained exclusively on signal-absent images to estimate the signal-absent score function. Once trained, the resulting score model can be used to approximate the IO test statistic for detection tasks involving arbitrary additive signals, without per-image posterior sampling or signal-specific retraining. Numerical studies consider a signal-known-exactly (SKE) detection task with a stochastic lumpy-background model. The results demonstrate that the proposed SIO can closely approximate the IO performance.
[CV-99] Model Effect or Label Effect? Refined Annotations and a Human-Referenced Benchmark for Pulmonary Embolism Segmentation
链接: https://arxiv.org/abs/2608.24486
作者: Qihang Sun,Zhongxiao Liu,Bailiang Jian,Shenman Qiu,Jingyuan Wang,Lei Zhang,Lixiang Xie,Jiazhen Pan,Christian Wachinger
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 32 pages, 9 figures, 8 tables. Supplementary material (S1-S9) included as an appendix
Abstract:Purpose: To quantify how evaluation annotations influence measured pulmonary embolism (PE) segmentation performance relative to model training changes, and to establish a human-referenced framework. Materials and Methods: This retrospective study screened 166 voxel-annotated CT pulmonary angiography cases from CADPE (n=91), FUMPE (n=35), and READ (n=40); 149 were included. A primary rater annotated PE by protocol, and a senior thoracic radiologist reviewed and revised all segmentations. Three additional raters at three centers annotated a 15-case subset. The label effect was measured by evaluating two pretrained nnU-Net models (nnU-Net-A, nnU-Net-B) against original and refined annotations. The model effect was measured by comparing the same architecture trained on different dataset combinations with annotations fixed. The benchmark model (nnPE) was trained with leave-one-dataset-out and pooled five-fold cross-validation. Four metric categories were analyzed with case-paired Wilcoxon signed-rank tests, Benjamini-Hochberg correction, and bootstrap 95% CIs. Results: Changing only the annotation increased mean DSC by 0.143 (0.122-0.166) for nnU-Net-A and 0.188 (0.163-0.213) for nnU-Net-B (both P .001), whereas changing training-dataset composition changed DSC by 0.028. The label effect exceeded the model effect on CADPE and FUMPE and was 0.045 on READ. Within-mask attenuation SD fell in all three datasets after re-annotation (all P .001). nnPE reached DSC 0.72 +/- 0.22 on pooled cross-validation but scored below all four annotators across 52 paired comparisons (all corrected P .05). Conclusion: Evaluation annotations affected measured PE segmentation performance at least as much as model training choices. A human-referenced evaluation framework is publicly available for future study. Comments: 32 pages, 9 figures, 8 tables. Supplementary material (S1-S9) included as an appendix Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV) ACMclasses: I.4.6; I.2.10; J.3 Cite as: arXiv:2608.24486 [eess.IV] (or arXiv:2608.24486v1 [eess.IV] for this version) https://doi.org/10.48550/arXiv.2608.24486 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Qihang Sun [view email] [v1] Tue, 25 Aug 2026 12:34:07 UTC (24,947 KB)
[CV-100] Decoupling candidate dual AGN from chance superpositions in the GOTHIC survey via a deep-learning framework
链接: https://arxiv.org/abs/2608.24164
作者: Bhavesh Mukheja,Snehanshu Saha,Anwesh Bhattacharya,Mousumi Das,Françoise Combes,Sudhanshu Barway
类目: Astrophysics of Galaxies (astro-ph.GA); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Mathematical Physics (math-ph); Data Analysis, Statistics and Probability (physics.data-an)
备注: Submitted to MNRAS. Supplementary Material merged in the main text
Abstract:Dual active galactic nuclei (DAGN) mark a critical phase in the evolution of merging galaxies and the pairing of supermassive black holes, yet they remain difficult to identify in large imaging surveys because of projection effects and limited spatial resolution. Compact foreground stars and unresolved substructure can mimic dual nuclei through chance superposition, complicating automated detection. We revisit the 46,061 galaxies flagged but rejected as DAGN candidates by the GOTHIC pipeline, primarily because the two nuclei fell within the SDSS fibre aperture or exceeded its separation threshold. We train a supervised deep-learning framework based on the YOLOv11 oriented-bounding-box architecture on annotated SDSS imaging to separate genuine dual nuclei from foreground stellar contaminants and other spurious alignments. The final model attains a validation precision of 0.919, recall of 0.905, and F_1 of 0.912 for the dual-nuclei class, and yields 29,605 dual-nucleus candidates after removing star-dominated and blended detections. Structured visual inspection indicates that 54.5 – 62% are consistent with genuine dual nuclei, implying \sim(1.4 – 1.8)\times10^4 plausible systems. Cross-calibrating the YOLO separation against the deterministic GOTHIC centroid measurement and restricting to the compact regime ( d \le 6.87’’ ) gives a conservative subset of \sim 13,672 candidates, reaching calibrated separations of \sim 0.56’’ . Spectroscopy of the most compact ( \le 1 ~kpc) systems shows they are dominated by passive, absorption-line galaxies with no resolved double-peaked emission, so confirmation requires higher-resolution follow-up. The catalogue is a statistically refined list of candidates, not confirmed DAGN. Nonetheless, deep-learning detection substantially reduces contamination and expands the plausible DAGN census.
[CV-101] Native-Space 3D CarveMix for Multi-Site T1w Stroke Segmentation MICCAI2026
链接: https://arxiv.org/abs/2608.23882
作者: Dexter Wen Jie Teo,Kumaradevan Punithakumar
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the SWITCH+ Workshop (ISLES 2026 Challenge), MICCAI 2026. Springer LNCS
Abstract:Segmenting ischemic stroke lesions on T1-weighted (T1w) MRI acquired across different scanners and protocols without intensity standardization is difficult because lesions are subtle and share intensity characteristics with cerebrospinal fluid. Standard deep learning architectures trained across multiple centers plateau around Dice 0.66, with acute lesions ( \le 7 days post-stroke) performing substantially worse due to severe sample scarcity. We combine a MedNeXt-L ( k=5 ) backbone with on-the-fly 3D CarveMix augmentation that pastes real lesion patches into healthy brain regions during training. By generating synthetic lesion placements dynamically within each fold with subject-level split isolation, the model sees more diverse lesion patterns without requiring pre-generated copies on disk. We evaluate on 1,453 native T1w scans from 55 clinical centers in the ISLES 2026 challenge. Our method achieves a mean 5-fold cross-validation Dice of 0.648 at 500 epochs, a +0.018 improvement over the MedNeXt-L backbone at a matched training budget (0.630)
[CV-102] Spatiotemporal Distillation via Recurrent Bottlenecks for Aortic Tracking MICCAI2026
链接: https://arxiv.org/abs/2608.23879
作者: Dexter Wen Jie Teo,Nairouz Shehata,Herve Lombaert
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted at the 17th Statistical Atlases and Computational Models of the Heart (STACOM) Workshop, MICCAI 2026. Springer LNCS
Abstract:Cardiac cine-MRI serves as a direct visual indicator of cardiovascular hemodynamics by capturing the continuous wall motion of the aorta. Quantifying these dynamic structural changes across the cardiac cycle is essential for measuring aortic distensibility, a primary marker of arterial stiffness. However, standard 2D segmentation networks focus on each frame independently. Consequently, when rapid systolic flow temporarily obscures the aorta’s boundaries, this lack of continuous context results in frame-to-frame tracking dropouts and boundary inconsistencies. Spatiotemporal ( 2\textD+t ) networks can enforce temporal consistency across the sequence but suffer from a scarcity of expert annotations. To address this, we present a semi-supervised spatiotemporal ( 2\textD to 2\textD+t ) knowledge distillation framework exploiting the cardiac cycle. The framework distills a spatial teacher’s expertise into a spatiotemporal student network by executing a dynamic latent interception, pairing a recurrent spatiotemporal bottleneck with a residual spatial bypass. Our model selection strategy applies a baseline validation threshold ( \textDSC \ge 0.50 ) prior to selecting the epoch that maximizes anatomical consistency. This strategy enables the spatiotemporal student model to achieve superior surface tracking accuracy ( \textNSD@1mm = 92.3% \pm 0.2% ) and high structural reliability ( \textFrac_2\textCC = 99.2% \pm 0.6% ), reducing population-wide structural anomalies by over 56% compared to a 2D nnU-Net baseline.
[CV-103] Multi-Stage Prompt-Guided Feature Modulation for Generalizable Brain Tumor Segmentation MICCAI2026
链接: https://arxiv.org/abs/2608.23745
作者: Mohammad Mahdi Danesh Pajouh,Sara Saeedi
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to BraTS MICCAI 2026 Task 3 (Generalizability Across Tumors)
Abstract:Accurate brain tumor segmentation from magnetic resonance imaging (MRI) is essential for diagnosis, treatment planning, surgical guidance, and disease monitoring. However, developing automated segmentation models that generalize across diverse tumor characteristics, imaging protocols, acquisition sites, and patient populations remains challenging. Variations in tumor morphology and imaging distributions can substantially degrade performance outside the training domain. Consequently, improving the robustness and generalization of deep learning-based segmentation models has become a key objective in medical image analysis. To improve segmentation robustness, we propose Multi-Stage Dynamic Prompt nnU-Net, a prompt-conditioned extension of nnU-Net. Three independent dynamic prompt modules are inserted into the deepest encoder stages. Each module contains a learnable bank of ten 256-dimensional prompt vectors and uses globally pooled encoder features to generate image-specific prompt representations. These representations are projected into feature-wise scaling (\gamma) and shifting (\beta) parameters that modulate encoder feature maps through Feature-wise Linear Modulation (FiLM), enabling adaptive feature conditioning at multiple semantic levels. Evaluation on the BraTS GOAT validation dataset demonstrated that the proposed Multi-Stage Dynamic Prompt nnU-Net outperformed the baseline nnU Net across the majority of evaluated metrics and tumor subregions. The proposed model achieved average lesion-wise Dice scores of 76.16% (ET), 80.04% (TC), and 86.42% (WT), compared with 74.38%, 78.14% and 84.01% for the baseline model. The results demonstrate that multi-stage dynamic prompt conditioning improves segmentation accuracy and boundary delineation for brain tumor segmentation.
[CV-104] InfoDPP-PAC: Principled Patch Selection for Whole Slide Image Analysis
链接: https://arxiv.org/abs/2608.23574
作者: Prateek Mittal,Ayush Srivastava,Joohi Chauhan
类目: Quantitative Methods (q-bio.QM); Computer Vision and Pattern Recognition (cs.CV); Information Theory (cs.IT); Machine Learning (cs.LG)
备注:
Abstract:Each WSI slide contains thousands of candidate tissue patches, while supervision is usually available only at slide level. Existing bag-construction strategies like Uniform extraction and handcrafted heuristics do not control redundancy while attention-based multiple-instance models couple patch importance to a particular downstream classifier, and coreset methods optimise embedding-space coverage without modelling task-relevant patch quality. We introduce InfoDPP-PAC, a principled patch-selection framework that combines teacher-seeded Gaussian process relevance modelling, determinantal log-determinant diversity,submodular greedy optimisation, and a concentration-based adaptive stopping rule. The main theoretical result shows that the log-determinant diversity term used in DPP-style selection is the Gaussian process mutual information between a selected subset and the latent relevance function. We further derive a PAC-style certificate for residual information gain, allowing the number of retained patches to vary by slide rather than being fixed a priori. The empirical study evaluates whether the selected subset is diverse, spatially and morphologically covering, non-redundant, and enriched for the teacher-derived relevance signal. It does not claim end-to-end diagnostic improvement after retraining a downstream MIL model. On 202 HISTAI gastrointestinal whole-slide images, the adaptive rule uses 83.7% fewer patches on average than a fixed full budget while retaining 97.9% of full-budget composite selection quality. At a matched budget, InfoDPP-PAC achieves the highest mean teacher-derived relevance score among fourteen baselines, with diversity and composite scores close to the strongest coreset methods. The results support InfoDPP-PAC as a controlled quality-diversity-cardinality selection framework, rather than as a downstream clinical predictor.
[CV-105] A Human-Factors Guided Cognitive Model of Visuospatial Complexity in Embodied Active Vision
链接: https://arxiv.org/abs/2608.23572
作者: Vasiliki Kondyli,Jakob Suchan,Mehul Bhatt
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Preprint; AIC 2025: Artificial Intelligence and Cognition
Abstract:We propose a novel framework for the analysis of multimodal data – encompassing visual, auditory, and spatial stimuli – foregrounding the role of complexity in embodied perception and interaction in dynamic, naturalistic settings. Grounded in theories of embodied cognition and active vision, we argue that embodied perceptual complexity emerges from an agent’s dynamic engagement with the environment and must be analyzed holistically, as a combination of qualitative and quantitative attributes pertaining to, for instance, visuospatial and auditory features. Building on previous work on visual complexity, we expand this into a categorization of diverse complexity attributes – quantitative, structural, dynamic, auditory, and interactional – that together characterize multimodal complexity. We demonstrate how this model provides a theoretical framework for characterizing aspects of visuospatial complexity and their interactions, specifically in the context of everyday driving. We also discuss practical applications of the proposed model for creating and evaluating benchmark datasets (e.g., in driving) that centralize cognitive human factors, as well as applications aimed at systematically investigating the effect of visuospatial complexity on human active vision from the viewpoint of visual perception research. The proposed framework lays the foundation for automated methods that interpret complexity in 3D dynamic environments from a human-centered perspective, serving as a semantic template for explainable computational analysis of visuospatial complexity with a categorical focus on cognitive human factors.
人工智能
[AI-0] SPO: Stream-Aligned Policy Optimization for Asynchronous Agent ic RL
链接: https://arxiv.org/abs/2608.24870
作者: Kai Ruan,Jinghao Lin,Qianshan Wei,Ziqi Zhou,Zihe Huang
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 2 figures, 3 tables
Abstract:Group-relative reinforcement learning waits for sibling rollouts of the same prompt, which is costly for long and variable tool-use trajectories. Single-stream Policy Optimization (SPO) removes this dependency with a persistent prompt-level value estimate, but its recipe whitens one advantage per trajectory before optimizing a token-mean actor loss. We show that trajectory centering generally does not center the token-weighted quantity consumed by the actor, and fix the mismatch by standardizing terminal-outcome advantages under the action-token measure. We additionally organize prompt evidence by the policy event that generated it rather than learner receipt order. Across matched runs on ALFWorld at two model scales and on Math-TIR, SPO++ improves online learning efficiency over SPO. A paired ablation identifies action-token-measure normalization as the strongest tested component.
[AI-1] FedV-KGQA: Multi-Hop Question Answering over Vertically Partitioned Knowledge Graphs ISWC2026
链接: https://arxiv.org/abs/2608.24846
作者: Md Saikat Islam Khan Bappy,Oshani Seneviratne
类目: Artificial Intelligence (cs.AI)
备注: Accepted at ISWC 2026 (Research Track). To appear in the Proceedings of the 25th International Semantic Web Conference
Abstract:Real-world data for knowledge graph question answering is often distributed across different organizations due to governance and data sovereignty constraints. While centralized systems exist, they cannot answer multi-hop questions when the required facts are split across vertically partitioned silos. In this paper, we propose FedV-KGQA, a framework for multi-hop reasoning over knowledge graphs in which organizations share entities but own disjoint sets of relations. Our approach combines local graph enrichment and knowledge graph embeddings to ensure raw triples and relation parameters never leave each silo, establishing a structural data boundary without requiring centralized graph access. We further introduce a topic entity anchoring mechanism that grounds questions in the correct graph neighborhood without any runtime inter-silo communication. We evaluate 12 model configurations across three benchmarks and show that FedV-KGQA performs strongly, remains close to centralized performance, generalizes to 3-hop reasoning, and is robust to embedding perturbations.
[AI-2] A Dual-Dimensional LLM Framework for Automated Item Incidental Content Similarity Analysis in Large-Scale Assessments
链接: https://arxiv.org/abs/2608.24825
作者: Jing Huang,Jihong Zhang,Hua-Hua Chang
类目: Artificial Intelligence (cs.AI)
备注: 26 pages, 6 figures
Abstract:The rapid expansion of large-scale assessments and the growing adoption of automatic item generation have intensified concerns about incidental content redundancy, where construct-irrelevant elements such as wording or contextual framing become unintentionally repetitive across items. Traditional similarity metrics like BLEU or cosine similarity, often fail to capture the nuanced structural and semantic layers that drive perceived redundancy simultaneously. This study proposes a dual-dimensional framework for Automated Item Similarity Analysis (AISA) powered by Large Language Models (LLMs), operationalizing similarity through Structured Decomposition and Semantic Relatedness. Psychometric validation indicates that LLM-derived metrics align more closely with indicators of construct-irrelevant local dependence and yield more coherent item parameter groupings than traditional text-based measures. The framework is further evaluated through its application in Computerized Adaptive Testing (CAT). Simulations reveal that incorporating LLM-based similarity constraints into item selection improves estimation stability and reduces bias with minimal efficiency trade-offs, outperforming constraints based on conventional metrics. These findings highlight the potential of LLM-powered AISA to support scalable bank curation, content-aware test assembly, and experience-sensitive adaptive testing across diverse assessment contexts.
[AI-3] Constrained Entity Selection under Partial Knowledge for LLM -Based Knowledge Graph QA
链接: https://arxiv.org/abs/2608.24824
作者: Emanuel Kitzelmann
类目: Artificial Intelligence (cs.AI)
备注: 8 pages, 2 tables. Submitted manuscript. Revised version published in KI 2026: Advances in Artificial Intelligence, LNCS 16830
Abstract:Large language models are increasingly used for knowledge graph question answering (KGQA), but can fail to correctly ground answers in the underlying graph. Current approaches to LLM-based KGQA either rely on full semantic parsing into executable queries such as SPARQL, which is brittle in practice due to complex schemas or incompleteness of real-world KGs, or on LLM-reasoning and answer generation over KGs, which can be more robust but lacks formal guarantees. In this work, we study a complementary setting in which \emphcandidate answers are generated by an LLM-based system and subsequently verified using lightweight symbolic constraints derived from the question. We introduce \emphConstrained Entity Selection under Partial Knowledge (CES-PK), a problem formulation that focuses on eliminating invalid answers and providing symbolic support for valid ones without requiring construction of executable logical forms. To account for incomplete KGs, we employ a three-valued constraint semantics (\emphsatisfied, violated, unknown) that avoids incorrect rejections under open-world assumptions. To demonstrate the effects of our method, we instantiate this framework over the Hetionet biomedical knowledge graph and evaluate the impact of type, relation, and exclusion constraints. Experiments show that precision improves by filtering invalid candidates, while recall is preserved due to retaining candidates whose constraints are not explicitly violated. Satisfied constraints provide additional positive symbolic evidence to rank remaining candidates.
[AI-4] Strictly Causal Streaming Video Anomaly Detection with a Theoretically-Grounded State-Space Core
链接: https://arxiv.org/abs/2608.24810
作者: Yogesh Kumar
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent work has applied Mamba style state space models (SSMs) to video anomaly detection, yet existing approaches still rely on buffering clips or windows internally, lack a theoretical account of how temporal memory relates to detection latency, and benchmark efficiency only through GPU throughput rather than the edge hardware these methods are intended to target. We introduce a strictly causal streaming anomaly detector whose fixed size state is updated in O(1) time and memory per incoming frame, with no lookahead and no clip buffering. Its temporal core is a diagonal linear state space recurrence with an input and state dependent decay gate, trained self supervised through causal next embedding prediction on a frozen visual backbone. We derive a closed form relationship between the recurrence decay spectrum and both detection delay and the shortest anomaly it can reliably capture, then validate empirically on UCSD Ped2 and CUHK Avenue. The settling delay bound predicted from the learned base decay (57 to 59 frames) sits far above the measured detection delay (1.6 and 18.4 frames), showing that the event boundary gate, not the base decay, governs responsiveness. We further report end to end latency and throughput measured directly on Apple M3 Pro hardware, 0.74 ms and 0.77 ms per frame (over 1300 FPS), rather than simulated GPU numbers. With an untuned initial configuration the method reaches 67.9 percent and 70.2 percent frame level AUC on Ped2 and Avenue, trailing prior non causal SSM baselines in accuracy. Ablations over decay rate, state size, and gating reveal that the gate contribution is dataset size dependent, hurting accuracy on the smaller Ped2 training set but helping on the larger Avenue one. Closing this accuracy gap and extending evaluation to a third, larger benchmark are immediate next steps.
[AI-5] Automatic Model Card Generation Using an LLM
链接: https://arxiv.org/abs/2608.24807
作者: Tajkia Rahman Toma,Balreet Grewal,Cor-Paul Bezemer
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Model cards are structured documents that summarize key information about machine learning models to improve transparency, usability, and accountability. However, they often lack a consistent structure, and many models provide no model cards, making comparison and interpretation difficult. This paper presents two contributions. First, we propose MCTidy, an LLM-based approach that reorganizes existing model cards into a standardized template to improve clarity and comparability. Second, we introduce MCGenie, an LLM-based system that generates model cards directly from model repository data. We apply MCTidy to 48 Hugging Face model cards and evaluate information retention, section alignment, hallucination, and stability. Our findings show high information retention with minimal textual loss, accurate section assignment, rare hallucinations primarily in descriptive sections, and strong stability across runs. We assess MCGenie by generating model cards for the same 48 models and assessing semantic similarity, factual correctness, and sensitivity to input resources. The generated model cards achieved high semantic similarity (mean around 0.9); over half were fully correct, and most remaining errors were minor. Generation quality depended strongly on the availability of supporting resources, particularly associated papers. Overall, our findings demonstrate the potential of LLM-based methods to enable scalable, standardized model card documentation.
[AI-6] StarHarness: Evolving Harnesses with Stratified Search for Enterprise Environments
链接: https://arxiv.org/abs/2608.24804
作者: Esakkivel Esakkiraja,Denis Akhiyarov,Vikas Yadav,Sai Rajeswar,Patrice Bechard,Sridhar Nemala,Sagar Davasam
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:We present StarHarness, a framework for evolving environment-specific agent harnesses while keeping model weights fixed. The evolved harness can include prompt and task framing, tool interfaces, skills, MCP-backed providers, subagent structure, and agent-loop configuration. StarHarness constructs a compact evolution pool by stratifying tasks according to baseline failure behavior, separates proposer-visible search tasks from proposer-hidden selection tasks, and reserves held-out tasks for evaluating generalization. Across ITBench SRE, EnterpriseOps-Gym ITSM, and AutomationBench Finance, harness evolution improves full-benchmark performance by 20-35 percentage points over the default harness after 4-12 accepted changes per environment. These gains persist on tasks excluded from evolution and transfer without re-evolution across GPT and Qwen model families. Trace analysis links the improvements to interface repairs, environment conventions, and operational knowledge that compresses search, with fewer false-positive diagnoses and shorter trajectories in several settings. StarHarness therefore offers a practical way to reduce persistent model-environment mismatch in tool-rich enterprise tasks.
[AI-7] CAFE: Self-Improving Search Agents Need Co-Evolving Feedback
链接: https://arxiv.org/abs/2608.24794
作者: Boyang Liu,Senjie Jin,Peixin Wang,Zhangyue Yin,Yibo Wang,Yuhao Zhou,Xinbing Liang,Shizheng Zhu,Yuhui Wang,Jingqi Tong,Zhiheng Xi,Jiazheng Zhang,Clive Bai,Clarenceai,Blaze Chen,Tao Gui,Qi Zhang,Xuanjing Huang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Outcome-supervised search agents learn when and how to retrieve evidence, but terminal rewards neither localize intermediate errors nor redirect an ongoing trajectory before those errors compound. Treating corrective feedback as a learned in-trajectory intervention couples the two roles: the agent must decide when to request and use feedback, while the critic must infer useful corrections from outcome-confounded rollouts whose failure patterns shift as the agent improves. We introduce CAFE (Coupled Agent–Feedback Evolution), a framework in which a shared-parameter model alternates between search-agent and critic roles. CAFE initializes feedback-conditioned recovery from trajectories built around the base agent’s own failures, then couples online and offline optimization. During online RL, a comparative feedback estimate uses a prompt-level call–skip success gap to shape request returns, while feedback-aware advantage shaping reweights token advantages before and after feedback. Offline, rollout-derived preference optimization learns feedback from matched successful and unsuccessful trajectories. On seven agentic search benchmarks, CAFE outperforms the evaluated RL-based search agents on average, retains its gains across all six out-of-domain benchmarks, and reduces answer-level hallucinations. One-sided ablations show that improving only the agent or only the critic eventually plateaus, whereas alternating the two updates continues to improve performance. These findings suggest that a self-improving search agent needs feedback that co-evolves with the policy it guides.
[AI-8] Right Diagnoses Decorative Reasoning :A Perturbation Audit of Medical Chain-of-Thought
链接: https://arxiv.org/abs/2608.24790
作者: Mengzhu Xu,Jifan Gao,Xia Jiang,Yaoxin Wu,Xi Long
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Clinicians read chain-of-thought (CoT) rationales as evidence of medical reasoning, but whether the visible chain plays that role is rarely tested. General-domain CoT-faithfulness probes ignore clinical cost, and medical LLM evaluations treat the chain as a black box. We close this gap with a medical perturbation audit: a 30-operator battery edits both the chain and the question with clinically motivated operators (severity reversal, negation flip, demographic swap, evidence ablation), paired with a chain-update times answer-flip joint analysis that classifies each model by its failure mode. Applied to 14 LLMs on four medical QA benchmarks, three independent tests converge: the Chain-Decoupling Rate (CDR; chain does not register the edit and the answer does not flip) is 72.9% panel-wide on clinically meaningful destructive edits, chain corruption leaves accuracy unchanged, and removing CoT prompting does not reduce accuracy. Two board-certified clinicians re-annotate N=197 perturbed questions; 98.5% leave the gold defensible. The pattern holds across medical and reasoning fine-tuning and scale; on the closed-source tier, where the chain text is unavailable, the answer-side signals are consistent with the same decoupling. Our framework and CDR provide a reusable yardstick for auditing whether medical CoT is faithful or merely documentation.
[AI-9] StepGuard: Learning Step-Level Guardrails with Scalable Supervision and Safety-Utility Balancing EMNLP2026
链接: https://arxiv.org/abs/2608.24777
作者: Zhijie Zheng,Yu Li,Chen Qian,Yuqian Fu,Yanwei Fu,Lu Sheng,Jing Shao,Dongrui Liu
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: Accepted by EMNLP 2026. Project page: this https URL
Abstract:LLM-based agents can interact with external environments through tool invocation, but this capability also introduces security risks such as file modification, information leakage, and unauthorized actions. Existing guardrails often evaluate completed trajectories, leaving pre-execution monitoring of step-level actions underexplored. We propose StepGuard, a step-level guard model that can audit completed agent trajectories and check tool actions before they are executed. To train StepGuard, we introduce StepGen, an automatic data engine that generates safe and unsafe trajectories with the same context but different actions at the risky step. To further reduce over-defense and under-defense, we propose Balance-GRPO, which dynamically balances learning between safe and unsafe actions based on their observed accuracy. Experiments show that StepGuard achieves the highest average accuracy among open-weight guard models, with performance comparable to GPT-5.4. When used to guard agents on AgentDojo and AgentDyn, StepGuard reduces mean attack success rate by 77.3% relative to the no-guard setting, while mean utility drops by only 2.8 percentage points.
[AI-10] Evidence Blindness in Direct Corpus Interaction: Persistent Navigation with AtlasNav
链接: https://arxiv.org/abs/2608.24764
作者: Hongyu Guo,Zhiyu Zheng,Zhao Cao
类目: Artificial Intelligence (cs.AI)
备注: 27 pages, 7 figures. Code, data, and trajectories will be released
Abstract:Large language model agents are moving beyond conventional retrieval-augmented generation toward direct interaction with external corpora. Direct Corpus Interaction (DCI) keeps the full corpus accessible, yet reachable evidence can remain unusable under finite interaction budgets. Required evidence may fail to surface, a surfaced supporting document may remain unopened, or an opened document may fail to expose its decisive fragment. We call this progressive silent loss Evidence Blindness and quantify it through stage-wise evidence realization. Within the DCI paradigm, raw interaction adds little reusable corpus organization, while dynamic-workspace methods reconstruct a query-conditioned interaction space from each query and trajectory. In both cases, useful structure is recovered largely online. We instead formulate large-scale agentic search as finite-budget navigation over reusable corpus structure. We introduce AtlasNav, a persistent multi-view corpus-navigation framework that retains direct corpus interaction but organizes the corpus once into a Corpus Atlas, allowing each query to navigate adaptively rather than reconstruct shared structure. On BrowseComp-Plus, AtlasNav achieves 92.05% strict accuracy while reducing recorded online inference cost by 30.21% relative to the prior dynamic-workspace state of the art. Under matched budgets, it realizes the complete required evidence earlier and approaches the same model’s evidence-supplied empirical reference more rapidly. The same representation principle remains effective under PhantomWiki’s distinct corpus organization and controlled 10K-1M scaling, and transfers competitively to heterogeneous enterprise knowledge. These results show that agentic search depends not only on accessible evidence, but also on how the corpus is represented so that limited interaction becomes effective navigation.
[AI-11] Beyond Uniform Local Isometry and Topology: FactoMap for Disentangled Representations
链接: https://arxiv.org/abs/2608.24762
作者: Sohini Gupta,Bahareh Tolooshams
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Many disentanglement methods represent generative factors using Euclidean product coordinates, although the underlying factor spaces may wrap, collapse, or have position-dependent geometry. We introduce factor-space structure, combining factor domains, generator-induced identifications, and position-dependent scales to distinguish topologically equivalent spaces with different factor geometries. We show that statistically independent factors need not be geometrically separable: hue and scale produce effects that grow at different rates, yielding anisotropy that no fixed rescaling removes. We propose the Factor-Space Topographic Map (FactoMap), which learns interpretable prototypes indexed by a factor-space lattice. Topographic learning transfers the lattice’s periodicity, collapses, and non-uniform extent to the representation. Experiments show that matching this structure preserves factor continuity and enables disentanglement of the underlying factors.
[AI-12] RACE: Scalable Statistical Estimation of Functional Consistency in LLM Neurons EMNLP-26
链接: https://arxiv.org/abs/2608.24758
作者: Runyu Wang,Bo Liu,Xiaxin Zhang,Yu Han,Jiawei Cao,Xiaoye Zhang,Zhe Zhang,Yifan Yang,Peng Ping
类目: Artificial Intelligence (cs.AI)
备注: EMNLP-26 Main Conference
Abstract:Discovering stable neuron behavior across entire domains remains a challenge in mechanistic interpretability. Existing methods often rely on instance-level point estimates or computationally expensive procedures, which either obscure population-level variability or limit scalable domain-wide analysis. We present RACE (Residual Alignment for Consistency Estimation), a forward-pass statistical framework that evaluates the domain-wide functional consistency of Transformer neurons. Perturbation experiments demonstrate that RACE achieves superior domain specificity compared to gradient-based point estimates. Meanwhile, token-distribution-level results verify the association between the selected neurons and the target domain. Furthermore, its computational overhead is two orders of magnitude lower than that of gradient-based methods.
[AI-13] Parameter-Efficient Self-Supervised Adaptation for EEG-FM under Fixed Computational Budgets
链接: https://arxiv.org/abs/2608.24727
作者: Meghal Dani,Stefanie Liebe
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:EEG foundation models pretrained via self-supervised learning promise transferable representations, but their generalization remains limited, especially across diverse clinical datasets. Full fine-tuning is impractical for resource-constrained clinical settings due to high computational requirements. In this work, we investigate whether parameter-efficient self-supervised adaptation, updating only 9% of parameters suffices to align representations to target tasks. We evaluate our method on two state-of-the-art models with different pretraining objectives: BIOT (contrastive) and CBraMod (masked reconstruction), and evaluate on three clinical EEG datasets for abnormality detection (TUAB), event classification (TUEV), and seizure detection (CHB-MIT) under both in-distribution and out-of-distribution conditions. SSL adaptation yields consistent gains over linear probing, up to 20x AUCPR. Under a fixed compute budget, peak performance requires only 20–50% of available unlabeled data. Critically, when total window count is fixed, performance remains invariant to patient count, suggesting that performance is dependent on overall temporal window diversity only. Our findings demonstrate that parameter-efficient adaptation enables effective deployment of EEG Foundation models (EEG-FM) with minimal computational overhead and data collection burden. Code available at: this https URL
[AI-14] Enhancing Bayesian Optimization and Active Learning Through Kernel Diversity
链接: https://arxiv.org/abs/2608.24721
作者: Heng Zhang,Haotian Xiang,Qin Lu,Konstantinos D. Polyzos,Tara Javidi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Hyperparameter selection remains a key challenge in Bayesian optimization (BO) and Bayesian active learning (AL), as model misspecification can lead to suboptimal performance, while more accurate fully Bayesian treatments typically rely on computationally expensive MCMC sampling. This paper proposes a unified framework, KENDO (Kernel ENsemble Disagreement-aware Operator), that integrates Ensemble Gaussian Processes (EGP) with disagreement-aware acquisition strategies. The central idea is to replace hyperparameter sampling with a kernel ensemble and adaptive Bayesian weighting, combined with disagreement-aware acquisition strategies. Within this unified framework, we instantiate KENDO-BO for BO and KENDO-AL for Bayesian AL, demonstrating that both arise from a common self-correcting mechanism with task-specific acquisition objectives. We further extend the approach to multi-objective optimization via random scalarization that preserves the single-optimizer conditioning structure. Thorough numerical tests on synthetic and real-world benchmarks across single-objective optimization, multi-objective optimization, and active learning demonstrate that (i) KENDO-BO achieves competitive or superior optimization performance compared to state-of-the-art methods while reducing computational overhead by up to 5\times and (ii) KENDO-AL achieves superior predictive calibration over MCMC-based active learning baselines with up to 27\times speedup.
[AI-15] Lifted Model Construction under Approximate Commutativity
链接: https://arxiv.org/abs/2608.24713
作者: Malte Luttermann,Jan Speller,Tanya Braun,Marcel Gehrke,Ralf Möller
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted to the Proceedings of the 17th International Conference on Scalable Uncertainty Management (SUM 2026)
Abstract:Lifted inference algorithms enable scalable probabilistic inference even for large object domains by leveraging the indistinguishability of objects in a probability distribution. An essential prerequisite for constructing a lifted representation is to identify commutative factors, i.e., functions whose output values are invariant under permutations of a subset of their input values, in a potential-based factorisation. In practice, however, parameters learned from data inevitably deviate even if associated objects are indistinguishable, causing their corresponding factors to be only approximately commutative instead of being exactly commutative. We address this problem by introducing the concept of \epsilon-commutativity, a relaxation of commutativity where output values are only approximately invariant under permutations of input values. Specifically, we show how \epsilon-commutativity can be exploited for lifted model construction, downstream probabilistic inference, and prove strict bounds on the induced approximation error, thereby ensuring the practical applicability of lifted model construction while maintaining highly accurate query results. These theoretical guarantees are confirmed empirically, demonstrating comparable query accuracy at lower runtime.
[AI-16] Constrained Hyperparameter Optimization for Streaming Data
链接: https://arxiv.org/abs/2608.24712
作者: Bruno Veloso,João Gama
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Optimization of hyperparameters is a critical factor to obtain optimal model performance. While existing research has predominantly concentrated on batch-learning scenarios, addressing the complexities inherent in data streams presents a challenge. The deployment of sophisticated methodologies to manage data streams becomes highly important. Consequently, the capacity for self-adjusting hyperparameters during on-line learning phases emerges as a goal. Many hyperparameters exhibit constraints and are confined within bounded search spaces, rendering specific solutions unacceptable upon applying optimization operators. To solve this issue, employing boundary constraint- handling techniques becomes imperative to rectify invalid solutions. This paper presents strategies for effectively managing boundary constraints within constrained numerical optimization problems. Recent methodologies, including heuristic and evolutionary-based optimization, employ a “boundary” strategy, wherein values that surpass boundary thresholds for a given hyperparameter are realigned to the respective limits. Our study introduces four strategies to navigate boundary constraints in online optimization algorithms. Through empirical investigations conducted on established datasets, we demonstrate that adopting boundary strategies outperforms the “boundary” strategy.
[AI-17] On-policy Distillation with Verifiable Reward
链接: https://arxiv.org/abs/2608.24696
作者: Wenze Lin,Jiale Zhao,Xitai Jiang,Songde Rao,Yining Li,Shenzhi Wang,Bingxiang He,Gao Huang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement Learning with Verifiable Rewards (RLVR) and on-policy distillation (OPD) have become two widely adopted paradigms for post-training large language models. However, RLVR suffers from sparse task-level feedback, while OPD provides dense token-level guidance but ignores trajectory correctness, limiting its performance to that of the teacher. Combining them is a promising direction: OPD supplies dense supervisory signals, while RLVR provides task-level correctness. Nevertheless, existing integrations often rely on weighted combination or heuristic switching, introducing extra hyperparameters and trade-offs. We propose On-policy Distillation with Verifiable Reward (OPDVR), a simple yet effective method that seamlessly combines OPD and RLVR without adding any hyperparameters. We first reformulate the implicit reward of sampled-token OPD based on trajectory correctness, then apply a ReLU gating mechanism to ensure that correct trajectories receive non-negative rewards and incorrect ones receive non-positive rewards—thereby aligning the distillation signal with task success while preserving the teacher’s distributional guidance. Furthermore, our modification transforms sampled-token OPD into a proper RLVR method, making it readily combinable with any policy gradient algorithm, such as GRPO. Experiments on six reasoning benchmarks show that OPDVR consistently outperforms standard OPD. Our code is available at this https URL.
[AI-18] Maia 200: A Software Defined Dataflow System for Large-scale AI Acceleration
链接: https://arxiv.org/abs/2608.24664
作者: Sherry Xu,Marco Heddes,Jackson Peng,Tom Savell,Monica Tang,Prashant Ranjan,Jesse Benson,Ofer Dekel,Saurabh Dighe,Anupama Kurpad,Artour Levin,Matthew Mattina,George Petre,Cheng Tang,Yuan Yu,Li Zhang,Torsten Hoefler
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
备注:
Abstract:We introduce Maia 200, an advanced AI accelerator delivering high performance-10 145 Tflop/s FP4 and 5072 Tflop/s FP8 within a 750W TDP and 7 TB/s HBM bandwidth. Maia exemplifies a new class of Software Defined Locally Accessed Dataflow Architectures (SDLA), which explicitly program dataflow engines to orchestrate highly specialized memories and data movement engines. This approach shifts the focus from today’s thread-centric to data-movement-centric architecture, improving efficiency and scalability. Our taxonomy of data management, inspired by Flynn’s classification, highlights how SDLA addresses challenges in modern AI computing. Maia 200 achieves significant cost and energy savings while supporting massive parallelism for AI inference workloads, making it a compelling solution for next-generation high-performance computing systems.
[AI-19] Parason: Revealing Subtask and Trial Parallelism in LLM Reasoning
链接: https://arxiv.org/abs/2608.24658
作者: Zhengyang Zhang,Zijian Zhang,Jiaxuan Gao,Shusheng Xu,Yi Wu,Song Han,Ligeng Zhu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Scaling test-time reasoning has substantially improved the problem-solving ability of large language models (LLMs), but standard autoregressive decoding still executes long reasoning traces sequentially, creating severe latency for difficult tasks (up to days and weeks). Parallel reasoning offers a natural remedy. However, prior systems primarily focus on Subtask Parallelism, where the model learns to decompose a high-level task into smaller chunks that can be solved independently. This approach overlooks another pervasive form of parallelism: Trial Parallelism, where multiple speculative attempts explore, verify, and aggregate competing hypotheses in parallel. In this paper, we introduce Parason, which reveals and learns both forms of parallelism in LLM reasoning. Our analysis identifies Trial Parallelism as the majority of parallelizable reasoning computation (65.5% in DeepSeek-V4’s reasoning steps in HLE), and it becomes increasingly dominant on hard problems. Guided by this taxonomy, Parason converts sequential reasoning traces into structured parallel trajectories with a context-free grammar, then trains models with Parallelism-Aware Group Relative Policy Optimization (PA-GRPO), whose reward jointly balances accuracy, latency, and the two parallelism ratios. At inference time, Parason executes the learned parallel structure through tool calls, translating theoretical savings to real-world wall-clock acceleration. Experiments on mathematical reasoning benchmarks including AIME24 and AIME25 show that Parason achieves an average acceleration about 1.7 \times while maintaining competitive accuracy.
[AI-20] Simthesizer: An Agent -Driven Simulation Framework for LLM Serving Systems
链接: https://arxiv.org/abs/2608.24650
作者: Wonung Kim,Hyunmin Choi,Minsu Kim,Jaehong Cho,Yeongwook Kim,Jongse Park
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI)
备注:
Abstract:System-level simulation is an essential tool for exploring the rapidly expanding design space of LLM serving systems, where real deployments remain costly and often infeasible. However, modern LLM serving now evolves faster than human-driven simulator development can track, and emerging workloads and mechanisms, from agentic workflows to disaggregated serving, no longer fit the monolithic simulation pipeline that existing simulators assume. Each new mechanism therefore demands an invasive rewrite, leaving a widening development gap between deployed serving systems and the simulators that model them. To close this gap, we present Borg, a framework that realizes agent-driven simulator development. Borg introduces a composable simulator infrastructure that uniformly expresses the complete serving workflow, including the control decisions that coordinate it, and realizes it as a unified dynamic graph in Borg simulator. Synthesizer agent, a harnessed coding agent, then lowers natural-language feature requests onto this abstraction under simulator-specific guardrails and fidelity validation, evolving one shared simulator instead of building a new one for every feature. Under the same coding agent and harnesses, extensions built on Borg follow a vLLM-based real system with 2.51% average throughput error, versus 6.03% for extensions built on existing simulators. On identical workloads, Borg also simulates up to 284.96x and 23.19x faster than two state-of-the-art simulators, LLMServingSim2.0 and Vidur, respectively. Subjects: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.24650 [cs.AR] (or arXiv:2608.24650v1 [cs.AR] for this version) https://doi.org/10.48550/arXiv.2608.24650 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-21] A Literate Programming Environment for Human and Machine Agents
链接: https://arxiv.org/abs/2608.24644
作者: Adam T. Burke
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Programming Languages (cs.PL)
备注: 13 pages, 5 figures (code and grammar listings)
Abstract:This paper introduces an environment for constructing literate programs in concert with language-aware machine agents. This environment includes a grammar for executable program essays, a parser that treats names as first-class objects, an internal name-graph which relates prose, names and executable artifacts, and a binding mechanism for existing languages and testing toolsets. This supports co-location of code with its most relevant natural language and structured data context, making better use of Large Language Model (LLM) context windows. It also provides LLM coding agents with a toolset more analogous to the symbol-aware search and usage information available in human programmer-facing Integrated Development Environments (IDEs). We describe a working implementation with bindings to three established programming languages, and several example programs.
[AI-22] Causal Modelling of Support Interventions for Student Competency Assessment
链接: https://arxiv.org/abs/2608.24632
作者: Francesca Mangili,Alessandro Antonucci,Rafael Cabañas
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Accurate assessment of student competencies is essential for enabling educators to identify individual needs, design targeted interventions, and evaluate the effectiveness of educational strategies. Empirical assessment procedures are typically grounded in psychometric models, such as item response theory, which relate student competence levels to performance on assessment tasks. In this paper, we advocate adopting a structural causal modelling approach to educational assessment, moving beyond probabilistic belief updating toward a framework that explicitly supports interventional and counterfactual reasoning. We propose a corresponding protocol for its construction and analyse the practical relevance of forms of reasoning that remain inaccessible to standard associative models, including the explicit modelling of interventions such as hints and the related counterfactual scenario analysis. Although our protocol requires the structural equations to be elicited from experts, the necessary information is purely logical and does not rely on probabilistic, less tenable assumptions. We illustrate the approach using data from an assessment that employs complex tasks designed to measure compulsory school student algorithmic skills.
[AI-23] aming foundation model with invariance-oriented pre-training for broad-spectrum EEG analysis across signal-level brain-state and brain-health tasks
链接: https://arxiv.org/abs/2608.24597
作者: Yulong Dou,Han Wu,Guo Chen,Fangmao Ju,Zhiming Cui,Dinggang Shen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Electroencephalography (EEG) is a widely used window into human brain function, but most EEG models remain tied to a one-dataset-one-model supervised paradigm. Recent EEG foundation models offer a route toward reusable representations, but most remain reconstruction-centered, assuming that EEG content predictable from local context is necessarily transferable neural information. Here we present INCEPT, an invariance-oriented EEG foundation model trained on over 11,000 hours of unlabelled clinical EEG. Rather than prioritizing signal recovery alone, INCEPT learns representation-level stability across correlated EEG observations, separating stable neural structure and essential subject-sensitive information from the nuisance variability that dominates scalp recordings while preserving subject-, state- and condition-discriminative information. We evaluate INCEPT on a broad-spectrum benchmark of ten datasets spanning three levels of post-acquisition EEG analysis: signal-level assessment, brain-state decoding, and brain-health evaluation. INCEPT ranks first among recent EEG foundation models on 26 of 30 linear-probing metrics and 24 of 30 fine-tuning metrics, and also surpasses strong task-specific specialist encoders across diverse downstream settings. Objective ablations and representation analyses further show that invariance-oriented pre-training improves transfer and organizes subject-sensitive neural representations beyond reconstruction alone. These results establish invariance learning as a promising principle for building reusable EEG foundation models.
[AI-24] Pivot-and-Station Multi-Agent Path Finding: Solvability Complexity and Algorithms
链接: https://arxiv.org/abs/2608.24585
作者: Andrea Di Nezza,Mihir Patel,Fabio Fagnani,Sara Bernardini
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Automated high-density storage systems (warehouses, robotic parking, plant logistics, etc.) require fleets of agents to move through scarce task-critical resources and then park without obstructing future operations. We introduce Pivot-and-Station Multi-Agent Path Finding (PS-MAPF), a MAPF variant in which a subset of tasked agents must each visit one of a set of interchangeable pivots (e.g., workstations) before the entire fleet terminates at anonymous stations, one agent per station. We characterize solvability completely: every instance on a 2-edge-connected graph is solvable, and, on arbitrary connected graphs, a structural effective-distance measure relative to the number of unoccupied vertices gives a necessary and sufficient condition. We prove that minimizing station-makespan or station-flowtime is NP-hard already with a single pivot. We present three algorithms, a complete baseline, a SAT-based optimal solver, and Pivot-Prioritized Planning (PPP), the last solving 74-89% of benchmark instances with makespan and flowtime orders of magnitude below the baseline.
[AI-25] PhysMLLM s: Spatial Priors for Unified Referring Segmentation and Grounded Reasoning of Images and Videos
链接: https://arxiv.org/abs/2608.24574
作者: Siyao Yan,Bo Han,Jisheng Dang,Bimei Wang,Shude Wang,Hong Peng,Yulan Guo,Jianhuang Lai,Bin Hu,Tat-SengChua
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Video multimodal large language models support language guided video segmentation, but they often show spatio temporal inconsistencies, e.g., jitter, drift, and identity switches. These failures are more common when targets are partly hidden or when similar objects appear this http URL likely reason is that current training lacks explicit spatial priors, which makes it difficult to maintain stable spatial identity and shape over time. We present PhysMLLMs, a training-stage prior injection architecture that injects physics-inspired spatial continuity priors into Video MLLMs. PhysMLLMs is designed to encourage more stable object-centered representations by aligning the student global visual representation with a frozen teacher model during training. Our core mechanism, Global Representation Prior Alignment (REPA-Global), distills global visual representations from a frozen DINOv2 teacher using an offline embedding cache and a scheduled distillation plan. This design keeps inference unchanged and does not add inference time cost. Across multiple video benchmarks, PhysMLLMs improves video segmentation mask quality and cross-frame consistency, with larger gains on challenging cases involving small targets, fast motion, occlusion, distractors, and reasoning queries. On single-frame referring image segmentation and representative general VLM benchmarks, PhysMLLMs maintains comparable performance, demonstrating that the injected spatial prior improves video consistency without compromising image-level grounding or general multimodal capability. These results suggest that physics-inspired spatial prior injection can improve temporal stability while preserving general capability. The code is available at this https URL.
[AI-26] Joint Optimization of Tool Creation and Use for Large Language Model Agents
链接: https://arxiv.org/abs/2608.24571
作者: Zhi Rui Tam,Chieh-Yen Lin,Yun-Nung Chen,Shao-Hua Sun,Hung-yi Lee
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Tool-augmented language models are bounded by the APIs humans bothered to write; existing tool-creation systems patch this by prompting a frozen LLM at inference time, leaving the model that writes a tool decoupled from the one that uses it, with no signal that the schemas it produces are schemas it can invoke. We propose SMITH (Schema-grounded Multi-task Iterative Tool Honing), a reinforcement learning framework that jointly trains tool creation and tool use inside a single policy. Each rollout is either a build task (write a tool from a few examples) or a use task (invoke a pooled tool on a held-out question). Three separate reward axes catch schema, code, and outcome failures independently, so each failure mode contributes its own gradient. A 4B Qwen3 trained with SMITH on 13 procedural reasoning tasks with exact verifiers reaches 79.8 macro-average accuracy on held-out tasks, the best across all evaluated methods and ahead of an untrained 30B-A3B tool-writer. It also reaches 40.4 on TabMWP-Hard and 42.6 on out-of-domain GQA (+7.6 over the best same-backbone inference-time baseline), without any visual or tabular training data. Tools written by our 4B models also lifted the performance of LFM-2.5-350M and Qwen3-30B-A3B under same reasoning tasks.
[AI-27] EviDx: Evidence-Aware Active Diagnosis with Scaffolded LLM Agents
链接: https://arxiv.org/abs/2608.24570
作者: Lihang Zeng,Shaoting Zhang,Xiaofan Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Clinical diagnosis is an active evidence-seeking process in which clinicians acquire evidence, update competing hypotheses, and decide when the available evidence is sufficient for diagnosis. Yet many medical diagnosis systems built around large language models (LLMs) still formulate diagnosis as static case-to-answer prediction, with limited support for evidence acquisition. Agentic LLMs offer a dynamic alternative through tool use and intermediate diagnostic trajectories, but existing systems often under-specify how patient evidence should be exposed, scaffolded, and controlled at runtime. We introduce EviDx, an evidence-aware active diagnosis framework that pairs patient-specific diagnostic environments with a clinical diagnostic scaffold and an observer-guided runtime harness. In EviDx, \mathcalE -Synthesis constructs interactive environments from raw clinical cases; the scaffold organizes role-specialized agents, evidence tools, and evolving evidence states; and the harness regulates diagnostic termination by tracking uncertainty and evidence coverage. A 3-level evaluation pyramid assesses execution robustness, reasoning dynamics, and diagnostic outcomes. Experiments show that EviDx improves diagnostic performance and process stability while revealing model-dependent capability boundaries.
[AI-28] Across the Loss Landscape with Progressive Growth ECML KDD2026
链接: https://arxiv.org/abs/2608.24568
作者: Paul Caillon,Christophe Cerisara,Alexandre Allauzen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at ECML PKDD 2026
Abstract:Deep neural networks generalize well despite their highly nonconvex, overparameterized loss landscapes, a phenomenon often associated with the geometry of the minima found by stochastic optimization. We study how incremental grow-and-optimize strategies bias training toward flatter regions by viewing growth as progressive constraint relaxation. Starting from a low-dimensional submodel, we iteratively expand the trainable parameters by unlocking nested random subspaces while freezing the orthogonal complement at the network initialization, re-optimizing after each expansion until the full architecture is reached. Under standard local regularity conditions around non-degenerate minima, we prove that local sublevel sets are well approximated by ellipsoids and that basin accessibility under frozen constraints can be characterized by an explicit effective curvature in the frozen directions. This leads to an explanation of the bias: progressive growth increases the relative weight of wide basins and suppresses sharp ones through a volume effect induced by the frozen constraints. We empirically validate these predictions in controlled toy landscapes and in a realistic ResNet/CIFAR-100 setting and confirm that although progressive subspace growth reliably produces flatter solutions, curvature reductions do not universally translate into improved test performance, highlighting subtleties in the flatness-generalization connection. The code is available at this https URL.
[AI-29] COCI: Conference Organisers and Content Identifier ISWC2026
链接: https://arxiv.org/abs/2608.24559
作者: Angelo Salatino,Francesco Osborne,Alexis Vizcaino,Aliaksandr Birukou,Enrico Motta
类目: Digital Libraries (cs.DL); Artificial Intelligence (cs.AI)
备注: Demo paper accepted at ISWC 2026. To be presented in October 2026
Abstract:Despite the critical role of grey literature in scholarly communication, artefacts such as Calls for Papers (CfPs) remain largely isolated from modern Scholarly Knowledge Graphs. The unstructured and highly heterogeneous nature of these documents has traditionally hindered their large-scale processing. In this demo paper, we present the Conference Organisers and Content Identifier (COCI), an AI-based framework designed to extract fine-grained, structured metadata from raw CfP texts. COCI employs a multi-stage pipeline that combines Large Language Models (LLMs) with semantic mapping techniques to integrate extracted entities with established knowledge bases, including OpenAlex, DBLP, TIB ConfIDent, and the AIDA Dashboard. By disambiguating authors and semantically aligning topics and conference series, COCI bridges the gap between informal scholarly dissemination and structured Semantic Web resources, laying the foundation for systematic analysis of non-publisher-based academic events.
[AI-30] FraudBench: Protocol-Sensitive Benchmarking of Adversarial Robustness for Financial Risk Assessment
链接: https://arxiv.org/abs/2608.24551
作者: Xitong Zeng,Zhaoge Bi,Yitian Yang,Huaming Chen,Quan Z. Sheng
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Machine learning models are widely used in financial fraud and credit-risk detection, yet their adversarial robustness remains difficult to evaluate because financial tabular data involve domain-specific constraints, severe class imbalance, and asymmetric attacker capability. We argue that, in this setting, robustness is not only an attribute of the model, but also an attribute of the evaluation protocol. Different ways of enforcing constraints and capability can lead to substantially different robustness conclusions. This paper presents FraudBench, a protocol-sensitive benchmark for adversarial robustness evaluation in financial fraud and credit-risk detection. Rather than treating domain constraints as post-hoc validity checks, FraudBench evaluates the same dataset–model–attack–defence setting under three matched protocols: unconstrained attacks, post-hoc feasibility filtering, and deployment-aware constraint-integrated attacks. FraudBench covers four public financial datasets, and evaluates neural, tree-based, and ensemble models using three attack settings. Our results show that robustness conclusions are highly protocol-sensitive. On Lending Club Loan Data under the white-box setting, post-hoc filtering leaves only 3.7 feasible-flipped examples on average, whereas in-attack projection with attacker mutability masking produces 2,832.3 feasible-flipped examples under the same perturbation budget. The results on IEEE-CIS further show that feasibility and attacker capability are separate axes, while black-box evaluation shows that protocol choice can alter model-family rankings. These findings suggest that fraud robustness evaluation should report predictive degradation and attack feasibility jointly, and should incorporate domain constraints into attack generation rather than treating them as post-processing checks.
[AI-31] Discovering Adaptive Transmission Programs for Collective Innovation
链接: https://arxiv.org/abs/2608.24545
作者: Cédric Colas,Jérémy Perez,Eleni Nisioti,Akhilesh Mocherla,Pierre-Yves Oudeyer,Clément Moulin-Frier,Maxime Derex
类目: Artificial Intelligence (cs.AI)
备注: CogSci 2026; longer version in prep
Abstract:Human collective intelligence depends on transmission processes: who shares what with whom, how, and when. While these processes emerge from individual cognition, they can also be directed by deliberate top-down protocols. Prior work has studied how transmission shapes collective outcomes primarily through the lens of network structure, varying who shares with whom and when. But networks are state-agnostic: they cannot condition transmission on what agents know or on the state of the collective. Here, we formalize transmission protocols as state-aware programs that route information and resources based on agent and collective states, and we use LLM-guided evolutionary search to design effective protocols in a collective discovery task. Evolved protocols increase collective performance over standard baselines from the literature by up to 37%. Ablations confirm that state-awareness drives this advantage: removing content-dependence while preserving network topology and timing eliminates performance gains. We find that evolved protocols also transfer across domain variations and agent populations. These results demonstrate that effective and generalizable transmission protocols can be discovered in silico, suggesting a path toward AI-assisted design of coordination infrastructure that enhances human collective intelligence.
[AI-32] Neurosymbolic Alignment for Physiologically-Safe Clinical Language Models
链接: https://arxiv.org/abs/2608.24534
作者: Abdulhady Abas Abdullah,Erik Cambria,Milena Zivkovic
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Clinical LLMs can generate recommendations that are factually plausible yet physiologically unsafe. We investigate whether safety alignment can be improved by grounding preference optimization in structured physiological knowledge rather than text-only supervision. Methods: We propose Neurosymbolic Alignment, a training-time framework that couples a 7B clinical LLM with an HGNN-based Physiological World Model over an 847K-node biomedical knowledge graph. Candidate responses are scored using homeostatic constraints, multi-hop path plausibility, and drug-interaction penalties, and the resulting rankings drive iterative on-policy ORPO updates. Evaluation is performed on the Clinical Safety Benchmark (CSB), a 2,500-scenario benchmark for physiological constraint violations in generative clinical reasoning. Results: Relative to ORPO, the proposed method improves CSS from 69.5% to 90.8% (+21.3 pp), reduces physician-evaluated HR from 14.1% to 5.1% on the blinded subset, and improves DID from 72.8% to 91.6%. These gains are corroborated by an HGNN-independent Rule-Engine Safety Score (RSS: 86.4%, +21.2 pp over ORPO; r=0.97 concordance with CSS). The method also exceeds GPT-4 (5-shot) on all safety metrics despite a 10x parameter disadvantage, and outperforms an inference-time self-correction pipeline (SFT+SelfCorrect) by 11.4 pp CSS. Under synthetic EHR-style noise, 84.2% CSS is retained. Ablation analysis shows that HGNN scoring (-16.2 pp) and iterative training (-11.5 pp) are the dominant contributors. PhysioScore calibration against 200 clinician labels yielded ECE = 0.038 and kappa = 0.91. Conclusion: Training-time physiological grounding produces measurable and independently verifiable safety improvements in open-weight clinical LLMs under controlled evaluation. External validation on real clinical data is required to determine whether these gains transfer to deployment settings
[AI-33] LumiXAI: A Modular Full-Stack Framework for Feature Attribution
链接: https://arxiv.org/abs/2608.24524
作者: Alfio Ferrara,Lorenzo Gatta,Sergio Picascia,Elisabetta Rocchetti
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Feature attribution is a central tool of model interpretability, yet the software through which it is applied remains fragmented: individual tools specialize along narrow axes, such as a single modality, a code API or a GUI, or a fixed rather than extensible method set, and rarely combine these strengths. Moreover, many explainability tools are designed primarily for domain experts, requiring programming skills or familiarity with attribution methods that can make them difficult for non-expert users to access. In this article, we present LumiXAI, a modular full-stack framework that consolidates attribution analysis into a single system. It couples classification and generative attribution with an interactive GUI supporting bidirectional exploration, a plug-in architecture for registering new models and methods, and three access tiers serving non-programmers, developers, and extenders from one backend. Its contribution is a system that operationalises established attribution methods under one interface, one interaction model, and one persistence layer, with containerised services and persistent results making analyses reproducible across machines.
[AI-34] PeakBench: Benchmarking Resource-Aware Tool Invocation in LLM Agents
链接: https://arxiv.org/abs/2608.24509
作者: Zhi-Kai Chen,Xu-Xiang Zhong,Song-Yan Li,De-Chuan Zhan,Han-Jia Ye
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:LLM agents increasingly solve tasks by invoking multiple tools, where parallel execution is essential for low latency but difficult to manage safely. Existing agent benchmarks primarily evaluate tool selection, argument generation, and end-to-end success under mostly serial execution, largely overlooking valid parallelization and resource-constrained scheduling. This missing scheduling dimension creates a practical failure mode: serial execution is safe but slow, while resource-agnostic parallel execution is fast but prone to avoidable resource overflows. To address this gap, we introduce PeakBench, a benchmark of executable multi-tool workflows with execution-grounded dependency annotations and measured resource profiles. A central challenge in evaluating such workflows is attribution: failures and inefficiencies may arise from incorrect dependency planning, poor resource-constrained scheduling, or both. PeakBench addresses this challenge with a two-part evaluation framework that disentangles logical planning from physical scheduling, with dedicated metrics for each dimension. Using this framework, we show that strong logical planning does not reliably translate into safe or efficient execution under resource constraints. We further show that exposing resource information can reduce avoidable overflows and improve resource utilization, making PeakBench a useful testbed for diagnosing resource-aware agent behavior. Code is available at this https URL.
[AI-35] Implicit Q-learning-bootstrapped ant colony optimization for maritime moving-target observation scheduling with agile satellites
链接: https://arxiv.org/abs/2608.24471
作者: He Wang,Junyu Wu,Yeye Liu,Yifan Zhou,Jie Zhang,Hui Li,Yanjie Song,Liang Li
类目: Artificial Intelligence (cs.AI)
备注: 26 pages, 12 figures
Abstract:Maritime moving-target observation scheduling with agile Earth observation satellites is a dynamic, sequence-dependent combinatorial optimization problem. Sea-surface targets move continuously, causing feasible observation windows to vary with target motion and satellite orbital geometry. The scheduler must jointly determine task selection, satellite assignment, observation-window selection, and observation ordering under time-window, attitude-maneuvering, onboard-resource, and cloud-affected availability constraints. This paper proposes an implicit Q-learning-bootstrapped ant colony optimization method, termed IQACO, for multi-satellite maritime moving-target observation scheduling. Rather than directly learning a task-selection policy, IQACO embeds an offline implicit Q-learning module into constructive ant colony optimization to adaptively adjust the pheromone factor, heuristic factor, and evaporation rate. A compact search-state representation captures pheromone distribution, current and historical-best solution quality, and iteration progress. During online scheduling, ant colony optimization constructs feasible observation sequences, while the learned policy regulates exploration and exploitation according to the current search state. Experiments on 14 scenarios with different scales and satellite configurations show that IQACO obtains the highest mean observation benefit in every scenario, improves the result of conventional ant colony optimization by 3.40%–9.40%, accelerates convergence, and remains stable under different objective-weight settings. These results demonstrate that offline value learning provides an effective adaptive search-control mechanism for constrained maritime moving-target observation scheduling.
[AI-36] Reinforcement Learning-Guided Evolutionary Policy Optimization for Preference-Adjustable Heterogeneous Agile Earth Observation Satellite Scheduling
链接: https://arxiv.org/abs/2608.24470
作者: He Wang,Junyu Wu,Hui Li,Yanjie Song,Witold Pedrycz,Liang Li
类目: Artificial Intelligence (cs.AI)
备注: 14 pages, 8 figures
Abstract:Heterogeneous agile Earth observation satellite (AEOS) scheduling requires task selection, satellite assignment, and observation sequencing under satellite-dependent visibility windows, attitude maneuvering requirements, energy consumption, and onboard storage constraints. Since satellites differ in orbital access, maneuvering capability, and payload resources, the same task may have different feasible windows, transition costs, and resource-consumption patterns on different platforms, which increases the difficulty of unified modeling and efficient optimization. To address this problem, this paper proposes an evolutionary policy optimization framework for heterogeneous AEOS scheduling with preference-adjustable weighted objectives. In the modeling layer, assignment-based indirect encoding is combined with decoder-based equivalent-cost evaluation to retain satellite-dependent constraints while integrating task gain, energy saving, and load balance into an interpretable scalar utility. In the optimization layer, schedule decoding, population-based search, and online actor-critic operator control are decoupled, so that reinforcement learning selects high-level search operators rather than constructing schedules directly. Based on this framework, a reinforcement-learning-assisted operator-selection memetic evolutionary algorithm (RLOSMEA) is developed to coordinate global exploration, feasibility recovery, and local refinement under a limited function-evaluation budget. Experiments on different heterogeneous AEOS scenarios show that RLOSMEA achieves higher overall weighted utility and more stable convergence than representative metaheuristic baselines. Sensitivity and learning-behavior analyses further confirm the robustness of the proposed method and the effectiveness of reinforcement-learning-guided operator selection.
[AI-37] HMGCLIP: Heterogeneous Multi-Granularity Contrastive Learning for E-commerce Representation Learning
链接: https://arxiv.org/abs/2608.24467
作者: Qiuyu Zhu,Yi Gao,Zhichao Wan,Mingyang Ma
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Although recent Multimodal Large Language Models (MLLMs) have advanced general product understanding, they implicitly encode product information into global embeddings, thereby limiting their ability to capture fine-grained attributes. This limitation hinders performance in tasks requiring precise attribute discrimination, such as distinguishing subtle material differences among visually similar products. To address this challenge, we propose HMGCLIP, a unified multimodal embedding framework. By constructing a heterogeneous hypergraph, we leverage hypergraph topology to mine structure-aware hard negatives and align multi-granular semantics at both relation and hyperedge levels. This design enables a dual-granularity inference mechanism that dynamically fuses attribute evidence for both fine-grained and coarse-grained downstream tasks. Furthermore, we release a comprehensive fine-grained e-commerce dataset to facilitate future benchmarking. Extensive experiments on this new dataset and the public MAVE benchmark show that HMGCLIP outperforms strong multimodal encoders, MLLMs, and e-commerce baselines, validating the superiority of HMGCLIP.
[AI-38] Mahalanobis-Based Multi-Head Attention for Complex State Propagation
链接: https://arxiv.org/abs/2608.24462
作者: Xiaohe Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:In this paper, we propose \textbfMahalanobis-Based Multi-Head Attention (MHA-CSP), a novel attention mechanism that replaces the standard dot-product with a \textbfMahalanobis distance-based RBF kernel, which effectively computes attention in an infinite-dimensional feature space without increasing the parameter count. Crucially, the positive definiteness of the Mahalanobis distance enables a \textbfdirect construction of Tree Attention: attention scores are built directly from accumulated distances, with a LogSumExp correction that rectifies the raw distance by subtracting the log-sum of edge exponentials. Moreover, the multi-head Mahalanobis distance matrices are themselves repurposed to construct an \textbfattention meshing mechanism, enabling cross-head kernel collaboration that simultaneously boosts accuracy and training efficiency. Extensive experiments demonstrate that MHA-CSP, with only 119K parameters and \textbfteacher forcing applied exclusively at the final hidden state, consistently outperforms Transformer and GCN baselines trained from scratch under identical conditions on long-sequence state tracking tasks. While these baselines rely on dense attention or graph propagation, MHA-CSP achieves robust structured reasoning via synthetic distance rectification—powered by Mahalanobis-based attention—and efficient information bypass inherited from the CSP backbone. This result highlights the effectiveness of complex-valued state propagation with collaborative multi-head rectification in capturing symbolic structures, establishing a new efficiency-performance trade-off for structured reasoning. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.24462 [cs.AI] (or arXiv:2608.24462v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.24462 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-39] A Behavior-Guided Online Probabilistic Forecasting Method for Electric vehicle Charging Loads
链接: https://arxiv.org/abs/2608.24441
作者: Chenghan Li,Qingxiang Liu,Yinliang Xu,Yuxuan Liang
类目: Artificial Intelligence (cs.AI)
备注: Submitted to IEEE
Abstract:Electric vehicle (EV) charging loads exhibit strong behavioral heterogeneity and temporal variability, posing significant challenges for online probabilistic forecasting under evolving operating conditions. In particular, persistent charging patterns may differ substantially across stations, while recent behavioral changes can continuously alter the underlying load distributions. This paper proposes a behavior-guided online probabilistic forecasting framework that explicitly characterizes persistent station-specific patterns and recent behavioral changes. A dual-timescale behavior representation is constructed to distinguish long-term charging characteristics from recent behavioral states and quantify their deviations. These behavioral changes are further semantically encoded to guide drift-aware forecasting adaptation, while a delayed-feedback mechanism ensures temporally consistent online updates when observations become available across different forecasting horizons. Experiments on ten heterogeneous real-world charging stations demonstrate that the proposed method consistently outperforms conventional forecasting models and concept-drift-aware online baselines in forecasting accuracy and probabilistic reliability. For 1-h-ahead forecasting, the proposed method reduces MSE and Pinball loss by 15.3% and 17.8%, respectively, over the corresponding best baselines. For 4-h-ahead forecasting, the improvements further reach 16.8% and 22.6%, respectively, demonstrating consistent performance gains under evolving charging behaviors and extended forecasting horizons.
[AI-40] Evaluating Deep Multivariate Imputation Models on Wearable Device Data
链接: https://arxiv.org/abs/2608.24436
作者: Skye Goodman,Roussel Desmond Nzoyem,Leandro Junges,Peter Kissack,Yasser Qureshi,Amberly Brigden,Jeff Clark,Nawid Keshtmand
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)
备注:
Abstract:Wearable device data enables continuous health monitoring, but suffers from structured missingness: features sharing a physical sensor drop out together. Deep imputation methods such as BRITS and SAITS have seen limited evaluation on multimodal physiological data under realistic missingness, and existing benchmarks use random-point holdout protocols that incorrectly assume missingness is independent across features and time. Using data from a person with epilepsy recorded on a Garmin smartwatch, we develop an evaluation protocol that mines contiguous missing-run templates from training data, stratifies them by per-feature gap-length quantiles, and injects them as block masks with preserved co-missingness structure. A matched training protocol exposing models to the same missingness distribution reduces BRITS’s severe-gap MAE by 43%, demonstrating the potential benefit of the proposed evaluation and training protocol within this single-participant dataset. We further extend BRITS with time-of-day encoding and a circadian harmonic channel. No single model dominates: linear interpolation is optimal for slow-moving features over short gaps; extended BRITS achieves lower MAE on dynamic cardiac features in moderate and severe gaps; and SAITS better preserves the ground-truth distribution by Jensen-Shannon distance despite higher MAE. Ultimately, model rankings strongly depend on evaluation designs. By exposing how traditional evaluation methods obscure true model capabilities, our transferable protocol establishes critical steps towards developing better imputation strategies for future multi-sensor wearable datasets.
[AI-41] Partial Identification under Causal Orders by Linear Programming
链接: https://arxiv.org/abs/2608.24427
作者: Eric Rossetto,Alessandro Antonucci
类目: Artificial Intelligence (cs.AI)
备注: 15 pages, 3 figures, 3 tables, 3 appendices
Abstract:Non-parametric (partial) identification of counterfactual queries typically relies on a fully specified causal graph. Motivated by settings with incomplete domain knowledge, we challenge this requirement by leveraging structural assumptions that are inherently implied by the query itself. We show that any counterfactual inquiry induces a, mostly partial, topological ordering over relevant variables, which, in turn, enables an explicit query parametrisation reducing the identification task to a linear program. This allows bounding arbitrary counterfactual and nested counterfactual queries. Our work can be viewed as a generalisation of the classical bounding framework of Tian and Pearl (2000), originally developed for probabilities of causation. We also prove the \emphtightness of our bounds by constructing structural causal models that attain the bounds whilst being compatible with both the observed data and the query-implied order. To assess both the generality and practical utility of the proposed bounding procedure, we revisit several case studies from the literature, demonstrating how the derived bounds can be used to yield informative insights even in the absence of an input causal graph.
[AI-42] A Judge Should Know What Changed:Construct Validity for LLM -as-a-Judge Evaluation
链接: https://arxiv.org/abs/2608.24419
作者: Jianlin Chen,Wenhui Chen,Ziyao Lin,Chi Man Vong
类目: Artificial Intelligence (cs.AI)
备注: 39 pages, 10 figures, 11 tables
Abstract:LLM-as-a-judge evaluation is usually assessed by agreement and robustness to surface perturbations, but reliability does not establish construct validity. We formalize construct validity for an evaluator as a two-dimensional profile: invariance S, the probability that a verdict is unchanged under construct-preserving edits, and construct sensitivity R, the probability that it changes under minimal construct-changing edits. We show that S and R are independent and that no scalar summary preserves all relevant comparisons. We measure the profile across 7 judges and 4 domains using 7 construct-changing intervention types and 5 register-only controls, with intervention direction determined by human annotators and generation, verification, and judging assigned to disjoint model families. At matched invariance S = 0.90, judges average S = 0.945 but R = 0.319. Sensitivity also differs between scope and strength edits: R_scope = 0.383 versus R_strength = 0.262, a +0.121 gap with the same sign for all 7 judges. We further audit five public label sets and find that surface-only predictors reproduce 55%-67% of labels in paired mode, including 67.4% of MT-Bench human votes. These results show that high judge agreement can coexist with weak sensitivity to changes in the construct being evaluated, motivating joint reporting of invariance and sensitivity and auditing the validation set itself.
[AI-43] ResiSpec: Enhancing Multi-Candidate Speculative Sampling via Residual Distribution Shaping
链接: https://arxiv.org/abs/2608.24411
作者: Zhi-Kai Chen,Jun-Jie Tao,Wei-Xiang Mao,De-Chuan Zhan,Han-Jia Ye
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The efficiency of Large Language Model (LLM) serving is fundamentally limited by the sequential nature of autoregressive decoding. Speculative Decoding (SD) mitigates this by using a lightweight draft model to speculate future tokens, which are then validated by the LLM in a single parallel forward pass. To further boost efficiency, multi-candidate schemes propose diverse candidate sets to increase the likelihood of token acceptance. However, we show that these schemes are bottlenecked by Residual Drift: a phenomenon where the rejection of initial candidates causes the residual target distribution to diverge from the draft model’s predictions. This shift renders subsequent candidates ineffective and forces the system into expensive resampling. To resolve this, we propose ResiSpec, a framework that strategically reforms the proposal distribution during verification to anchor the residual target mass within the draft model’s high-confidence regions. By mathematically re-aligning the verification process without compromising output exactness, ResiSpec prevents candidate obsolescence and achieves up to 1.92 \times speedup over state-of-the-art multi-candidate methods. Code is available at this https URL.
[AI-44] Equivariant Covariance Tensors: Guaranteed SPD Uncertainty for Tensor-Valued Geometric Learning ICML2026
链接: https://arxiv.org/abs/2608.24386
作者: Ruihan Liu,Yu Ji,Jianbo Yu,Shifu Yan,Qingchao Jiang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted to ICML 2026
Abstract:Tensor-valued prediction is fundamental to geometric deep learning, yet uncertainty quantification (UQ) for such outputs remains an open challenge. While E(3)-equivariant neural networks excel at point estimates, they lack rigorous confidence measures. We focus on symmetric rank-2 tensor prediction, where the target has six Kelvin–Mandel coordinates and full uncertainty is represented by a 6\times6 covariance matrix. We introduce a framework for E(3)-equivariant UQ, modeling the full predictive distribution where both mean and covariance preserve rotational symmetry. Our approach decomposes the covariance into irreducible representations \mathrmSym^2(\rho_c) \cong 2\times(l=0) \oplus 2\times(l=2) \oplus 1\times(l=4) . By mapping from the flat Lie algebra \mathfraksym(6) to the curved SPD manifold via matrix exponentiation, we strictly ensure positive-definite covariances while maintaining exact equivariance. Furthermore, we formulate a Log-Euclidean Equivariant Scoring Objective (LE-ESO)—a robust surrogate loss based on the Multivariate Laplace distribution—providing robustness to heavy-tailed errors and stable optimization. Validation on ModelNet40 inertia tensors and Materials Project dielectric tensors demonstrates that our method achieves competitive performance and provides physically consistent, symmetry-preserving uncertainty estimates with useful risk and OOD sensitivity.
[AI-45] Do Recipes Have Personas? Characterizing and Generating Creator Style in Attributed Procedural Graphs
链接: https://arxiv.org/abs/2608.24369
作者: Lei Jiang
类目: Artificial Intelligence (cs.AI)
备注: Accepted at the 29th International Conference on Discovery Science (DS 2026). 15 pages, 2 figures
Abstract:While large language models (LLMs) possess vast zero-shot procedural knowledge, their tendency to produce homogenized logic often obscures the unique, idiosyncratic execution processes of individual human creators. In this paper, we investigate the computational discovery of procedural personas from unstructured data. To achieve this, we introduce ViralRecipesTrans, a new dataset of procedurally aligned execution flow graphs extracted from popular culinary video transcripts and explicitly mapped to specific creators. We formulate procedural stylometry as a graph learning and process discovery task, revealing a fundamental duality: while traditional lexical classifiers overfit via semantic leakage, discrete topological metrics successfully capture the rigid physical constraints of a creator’s workflow. Building upon this characterization, we extend our framework into a novel generative task–predicting a creator’s exact structural execution graph for unseen dishes. We expose a fundamental dichotomy in style generation between global macro-planning and local structural execution. Our results demonstrate that few-shot LLMs dominate semantic assignment but suffer from persistent macro-planning deficits, whereas our structured two-stage model achieves superior topological control via rigid Markovian priors. Together, an ensemble approach to procedural generation combines the strengths from both sides, dynamically synthesizing global semantic reasoning with localized topological footprints to automate the discovery and generation of personalized workflows.
[AI-46] From State to Action: OODA-Tool for Reliable Multi-Turn Tool Use
链接: https://arxiv.org/abs/2608.24368
作者: Rongfeng Guo,Yinxuan Huang,Yusen Wu,Maoqing Zhong,Yunlu Chen,Meng Tang,Teng Long,Vincent Tao Hu
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Reliable multi-turn tool use requires an agent to preserve an evolving task state and ensure that each action remains consistent with it. However, direct function-calling and ReAct-style policies learn state tracking and action generation within the same autoregressive trajectory. This coupling creates state-action competition: the pressure to produce the next call can overwrite or ignore information accumulated earlier in the interaction. Inspired by Boyd’s Observe-Orient-Decide-Act cycle, we introduce OODA-Tool, a typed closed-loop policy designed to mitigate this competition by separating state preservation from action realization. Rather than generating an action directly from the interaction history, OODA-Tool routes each decision through controller-checked intermediate states, ensuring that the final output remains grounded in the current task state. Specifically, Observe reconstructs the task state, Orient determines whether execution is warranted, Decide forms an admissible action structure, and Act realizes the external output. We evaluate OODA-Tool against direct function-calling and ReAct policies using Qwen3 models ranging from 0.6B to 14B across multi-turn, multi-tool, and incomplete-information settings. OODA-Tool consistently improves task success across model sizes, with larger gains on smaller models and on tasks whose actions depend strongly on information accumulated across turns and prior tool results. Controlled variants, stage-level ablations, and transfer evaluations further demonstrate the robustness of these improvements.
[AI-47] Adaptive Influence Graphs for Failure Attribution in Multi-Agent Systems
链接: https://arxiv.org/abs/2608.24361
作者: Yarden Bakish,Amir Dudai,Roy Ganz,Oren Nuriel,Elad Ben Avraham,Mor Shpigel Nacson,Ron Litman
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-agent LLM systems are increasingly deployed in real-world applications, where failures can be costly and difficult to localize. Despite growing efforts to automate failure attribution, diagnosing failed runs still largely relies on human engineers. Yet engineers rarely debug complex systems by reading raw logs end to end. Instead, observability tools organize traces around components, actions, and dependencies to support targeted navigation. We hypothesize that modern LLMs can benefit from the same paradigm. To test this hypothesis, we introduce Adaptive Influence Graphs (AIGs), a two-stage agentic framework that first transforms a failed trace into a structured graph and then navigates it to identify the critical error. Across multiple models, we show that richer trace representations consistently improve failure attribution, with adaptive graph construction and agent-directed traversal yielding the strongest results. AIGs establish a new state of the art on WhoWhen, the standard benchmark for multi-agent failure attribution. This affirms our hypothesis that attribution depends not only on the diagnosing model, but also on how the trace is represented and explored.
[AI-48] he Handoff Tax: Continuing Non-Native Trajectories in LLM Agents
链接: https://arxiv.org/abs/2608.24358
作者: Roy Ganz,Mor Shpigel Nacson,Adi Kalyanpur,Ron Litman
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Coding agents perform long-running tasks spanning dozens of model calls, tool uses, and code edits. As these runs unfold, users face a practical cost-quality trade-off: escalating to a stronger model when a cheaper one struggles, or downshifting once the hard reasoning is complete. Each switch requires the receiver to continue a non-native trajectory produced by another model. We study how this handoff affects quality and cost, and how varying the trajectory information inherited by the receiver changes the outcome. Using pairs of low-cost, low-capability (LC) and high-cost, high-capability (HC) models from the Claude and GPT families, we vary handoff direction, timing, and interface, comparing full-trajectory transfer, compaction, and trajectory removal while preserving the repository state. Across both model families, full-trajectory escalation recovers less than half of the LC-to-HC quality gap while incurring a substantial cost premium. We term this cost-quality penalty the handoff tax. By contrast, downshift offers a favorable cost-quality point. Interestingly, the preferred interface also reverses with direction: reducing LC-model trajectory information improves escalation quality, whereas removing the HC-model trajectory reduces downshift quality.
[AI-49] Selective Regenerative Decoding: Trajectory-Level Intervention for Inference-Time Reasoning
链接: https://arxiv.org/abs/2608.24338
作者: Sophia Xiao Pu,Yumo Xu,Sailik Sengupta,Millennium Bismay,Ruixue Lian,James Gung,Yi-an Lai,Arshit Gupta
类目: Artificial Intelligence (cs.AI)
备注: Large Language Model, Test-Time Decoding Technique, Reasoning, 20 pages; Submitted to ARR
Abstract:Inference-time decoding methods improve LLM reasoning by exploring multiple candidate trajectories, yet treat each trajectory as atomic: either retaining it whole or discarding it irreversibly. This wastes computation on partially promising candidates whose high-quality prefixes are abandoned alongside degraded suffixes. We introduce Selective Regenerative Decoding (SRD), which routes each candidate to discard, keep, or refine only the degraded portion of the suffix while preserving the useful prefix of borderline candidates, without requiring a larger target model. Under mild assumptions, SRD achieves a provable 1.28-to-1.36-fold gain in sample efficiency over rejection sampling with strictly higher expected trajectory quality, with the gain growing as the candidate pool grows. Across MATH500, GPQA Diamond, HotpotQA, and AlpacaEval with multiple generation-reward model pairs, SRD matches Best-of-N accuracy with substantially fewer generated tokens and outperforms speculative rejection in low-compute regimes. By enabling segment-level intervention rather than whole-trajectory selection, SRD opens a previously underexplored region of the accuracy-compute tradeoff for inference-time reasoning.
[AI-50] SonarLLM : A Native Sonar–Optical Multimodal Large Language Model for Underwater Perception
链接: https://arxiv.org/abs/2608.24325
作者: Cong Su,longxuan ma,Ling Dong,Guofeng Tang,Weijie Yin,Haohui Chen,Zhengtao Yu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Reliable underwater perception requires complementary sensing under variable visibility. Optical cameras capture appearance and semantics but degrade rapidly with turbidity, whereas imaging sonar preserves geometry while exhibiting distinct range-azimuth structure and acoustic artifacts. Existing MLLMs, built primarily on optical encoders, are therefore ill-suited to model sonar or adaptively exploit sonar-optical complementarity. We propose SonarLLM, a sonar-optical MLLM that treats sonar as a native perceptual modality. It combines a sonar-specific encoder, modality-specific physics-aware feature enhancement, and reliability-aware hierarchical fusion to align acoustic structure with optical semantics and dynamically adjust their contributions as sensing quality changes. We also introduce SonarBench, a paired benchmark that spans four tasks: recognition, counting, visual question answering, and captioning; and, across the benchmark, three input settings: sonar-only, optical-only, and fusion. By fixing the scene and sonar observation while varying optical degradation, SonarBench enables controlled measurement of cross-modal complementarity. SonarLLM achieves 72.0% macro accuracy across sonar-only recognition, counting, and VQA, outperforming the strongest baseline by 34.4 percentage points, and 68.7% under fusion, exceeding the best baseline by 25.1 points. For recognition and counting, the fusion-over-optical gain grows from 6.0 to 36.0 points as turbidity increases, indicating the increasing complementary value of sonar under controlled optical degradation. Together, these results show that robust heterogeneous perception depends not only on adding sonar, but on representing and weighting it according to its sensing characteristics.
[AI-51] Can a Dynamic Internal Field Govern a Transformers Cognition? Certifiability not Superiority in Homeostatic Compute Control
链接: https://arxiv.org/abs/2608.24319
作者: Francisco M. Arrabal-Campos,Ignacio Fernandez,Francisco G. Montoya,Alfredo Alcayde
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY)
备注: 22 pages, 4 figures. Companion paper: “Where Cognition Lives” ( arXiv:2608.22347 ). Code, preregistrations and results: this https URL
Abstract:An intelligent system does not merely reason: it governs its own reasoning - how much to compute, when to stop, which module to activate. Can that role be played by a dynamic internal field - a low-dimensional homeostatic state with explicit physics and certified stability - that modulates cognition without performing it? Ours is a field on the module graph governed by a family of PDEs on the graph Laplacian, advancing with an adaptive-depth reasoner. We certify the stability of the integrator of the whole family - an integrator certificate, not a closed-loop one. New, and proved here: a discrete Schur-Cohn criterion for Verlet with velocity coupling, necessary and sufficient per latent root, with no commutation hypothesis. The answer is threefold: substance no, structure only in part, certifiability yes. The type of the field’s physics is irrelevant for accuracy: wave, diffusion, gated mixtures and a 2D Navier-Stokes substrate tie. A twenty-seed preregistered deconfounding campaign bounds the structural claim: at equalized caps the second-order effect is strong in one family (+0.087 [+0.042, +0.132], t=4.0) but is not detected in the other (+0.014 [-0.013, +0.040], n.s.), so part of the original contrast was capacity, not order; and a matched-interface GRU is indistinguishable in the first and nominally exceeds the field in the second (-0.035 [-0.067, -0.002]). What distinguishes the field is not capability but that its one-step operator admits an exact runtime stability check - a difference of kind, not of existence: learned recurrences carry certificates too, sufficient and conservative ones. A kill-gate with a positive control finds no evidence for the field as evidence accumulator (Delta AUC +0.0007 [-0.0065, +0.0079] vs a 0.03 threshold). A dynamic internal field is a viable, certifiable compute governor, but not an enhancer of cognition: it modulates, it does not think.
[AI-52] Benchmarking LLM Judges for Voice-Agent Evaluation: Reliability Calibration and Human Oversight
链接: https://arxiv.org/abs/2608.24314
作者: Anupam Purwar,Shashank Singh,Kritika Srivastava
类目: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
备注: Extends LLM-as-a-Judge to voice agents across telecom and retail, testing GPT-4.1 and GPT-5 against human raters across 10 safety and efficiency metrics. A correlation-based calibration analysis reveals domain-dependent reliability and identifies Recovery Turn Count and safety-recall metrics as unreliable for fully automated judging
Abstract:Evaluating conversational voice agents at scale re- quires reliable assessment methods that capture both observ- able interaction quality and the contextual judgment typically provided by human evaluators. We investigate LLM-as-a-Judge evaluation by comparing human judgments with GPT-4.1 and GPT-5 on telecom and retail voice-agent conversations, across conversational quality and safety dimensions. The same interac- tions are scored under three evaluation configurations, p0, p1, and p2, to test whether automated judgments are sensitive to the evaluation setup and whether observed patterns generalize across configurations and judge models. Beyond aggregate agreement, we examine metric-level correlations, evaluator consistency, and systematic human-LLM disagreement to identify which conver- sational attributes can be judged reliably by automation and which remain sensitive to interpretation and context. Effective voice-agent evaluation is also shaped by pipeline-level factors such as speech generation, streaming, and error propagation across ASR, reasoning, and tool-calling stages, motivating our focus on comparing how human and LLM judges score the same interactions end to end. Our results show that LLM- based evaluation can serve as an effective component of large- scale voice-agent assessment, but that its reliability is metric- and configuration-dependent rather than uniform. This pro- vides an empirical framework for identifying which metrics suit automated evaluation and supports hybrid pipelines in which LLM judges handle scalable assessment while human evaluators remain engaged for metrics that demand contextual interpretation and higher-confidence judgment.
[AI-53] OPDSearch: On-Policy Distillation with RL Refinement for Search-Augmented Reasoning
链接: https://arxiv.org/abs/2608.24310
作者: Qinglin Ye,Zhiyuan Gu,Jingjie Xia,Yiheng Zhang,Kaiyan Zhao,Shunchao Zheng,Yuhang Mu,Wenchao Du,Yiming Wang
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 4 figures
Abstract:Search-augmented reasoning remains difficult for small language models. On-policy distillation (OPD) from trained teachers offers a promising direction, but suffers from two issues: (1) high-quality multi-turn search trajectories depend on dynamic retriever responses, making SFT data prohibitively expensive to collect at scale; (2) task-specifically trained teachers incur substantial training cost, while directly applying OPD with an off-the-shelf teacher without task-specific fine-tuning constrains the student to the teacher’s performance ceiling and suffers from severe training instability. We propose OPDSearch+, the first distillation paradigm that requires no teacher fine-tuning for search-augmented reasoning. We investigate the role of a frozen off-the-shelf instruct model as the teacher in on-policy distillation, and reveal a key insight: the teacher reshapes the student’s policy distribution so that subsequent RL converges to a superior solution that RL alone cannot reach. In stage one, the student interacts with a live search engine and is distilled via a per-position forward KL objective, transferring reasoning decomposition and evidence integration skills without any task-specific teacher training. In stage two, RL refines the distilled student from a richer behavioral foundation, achieving performance that RL alone cannot reach from scratch. Across seven QA benchmarks, OPDSearch+ with a 3B model consistently outperforms all prior 3B RL baselines, achieving gains of 13.1% on HotpotQA and 8.5% on 2WikiMultihopQA.
[AI-54] VideoHarness-RSI: Recursive Harness Self-Improvement for Long-Video Understanding with Frozen Vision-Language Models
链接: https://arxiv.org/abs/2608.24302
作者: Guoyang Xu,Hao Chen
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Long-video understanding depends critically on how a limited model context is constructed from a much longer video. Existing approaches improve this process through compression, retrieval, memory, and agentic evidence acquisition, but these mechanisms are typically introduced as part of a manually designed inference system or optimized together with other components. This makes it difficult to isolate a simpler question: how much can be gained by improving the executable context-construction program alone? We study this question through VIDEOHARNESS-RSI, a controlled baseline for recursively searching executable context constructors around a frozen vision-language model (VLM). An outer-loop proposer uses prior programs, evaluation outcomes, and execution traces to generate candidate harnesses, which are executed and evaluated end to end before successful variants are retained for further search. This makes long-video understanding a controlled instance of automated harness design: the searchable object is executable program structure, while the answering model and interface remain fixed. Starting from uniform sampling, recursive harness search consistently finds room for improvement and surpasses several weaker hand-crafted baselines. Starting instead from a stronger hand-crafted baseline, the same RSI process yields a further improvement. The selected harness also transfers to additional long-video benchmarks without further search. Together, these results establish executable context construction as a distinct optimization layer and provide a reproducible baseline for studying harness discovery and transfer around frozen VLMs.
[AI-55] Contrastive Branch Policy Optimization
链接: https://arxiv.org/abs/2608.24300
作者: Ying Wang,Changlin Qiu,Bang Lin,Linbo Jin,Wen Jiang,Zhe Sun,Jingli Yang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 10 pages, 5 figures, 3 tables
Abstract:Reinforcement learning with verifiable rewards (RLVR) enables language models to learn multi-turn interaction with external tools, yet its sparse outcome rewards provide no signal for identifying which intermediate decisions are responsible for success. Branch sampling induces local comparisons among alternative continuations, but existing methods tend to conflate two distinct problems: allocating a fixed rollout budget and translating branch outcomes into token-level credit. We introduce Contrastive Branch Policy Optimization (CBPO), which disentangles these two problems and assigns a dedicated mechanism to each. Generation entropy screens candidate branch positions across the entire response, while path-level and node-level decay distribute a fixed budget across trajectories and positions to prevent exploration from collapsing onto a few paths or adjacent tokens. A parent trajectory together with the branches that share an identical token prefix forms an exact-prefix group, and the reward variation within this controlled group defines the Contrastive Branch Value (CBV), an outcome-based estimate of local decision sensitivity that rescales continuation advantages without altering their sign. When multiple nodes are selected along the same trajectory, CBPO partitions it into non-overlapping credit segments, thereby avoiding duplicated gradients on shared tokens. Requiring only outcome rewards and no process-level annotation, CBPO provides a practical solution for fine-grained credit assignment in tool-integrated agent training. Extensive experiments on ten benchmarks, including five for mathematical reasoning and five for knowledge-intensive search, show that CBPO consistently outperforms state-of-the-art policy-optimization and branch-based methods, attaining the highest macro-average accuracy in both domains and across two model scales.
[AI-56] ReproAgent : Contract-Guided Paper-to-Code Reproduction EMNLP2026
链接: https://arxiv.org/abs/2608.24291
作者: Xue Hu,Zewei Pan,Zhongyuan Wang,Zhou Liu,Zeli Su,Wentao Zhang
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: Accepted to Findings of EMNLP 2026
Abstract:Paper-to-code reproduction asks scientific AI agents to turn research papers into executable repositories that preserve the paper’s method, protocol and artifacts. This is difficult because the specification is split: explicit paper content such as algorithms, metrics and artifacts is often lost across long agent trajectories, while implicit details such as framework defaults and conventions inherited from related work are absent from the paper. We introduce ReproAgent, a four-stage Prepare–Plan–Generate–Repair pipeline built around a persistent implementation contract with two channels: an implementation-requirement channel that turns paper snippets into code obligations, and a reference-evidence channel that retrieves content and structure evidence from related repositories. Both are bound to work packages, projected into file-level contracts, and consumed across generation and repair. On PaperBench Code-Dev, ReproAgent reaches the highest mean score among same-backbone scaffolds under both Claude-Sonnet-4.5 and Gemini-3-Flash. End-to-end channel ablations and per-paper cases support the contribution of both channels. Code and experimental artifacts are publicly available.
[AI-57] Eating for a Sustainable Planet: Personalized Sustainable Diet Recommendation via Constraint-Aware Decision-Making Modeling ICML2026
链接: https://arxiv.org/abs/2608.24274
作者: Ying Jin,Weiqing Min,Mingyu Huang,Shuqiang Jiang
类目: Artificial Intelligence (cs.AI)
备注: Accepted in ICML 2026, 24 pages, 7 figures
Abstract:A sustainable diet represents a multi-dimensional synergy among four essential pillars: nutrition adequacy, economic affordability, cultural acceptability, and environmental respect. Despite the prevalence of population-level sustainability modeling, practical implementation relies on effective individual-level adoption. This transition is often hindered by inter-individual heterogeneity, posing a formidable challenge in aligning sustainable diet requirements with individual preferences. To address this issue, we propose a personalized sustainable diet recommendation model based on a constraint-aware decision-making mechanism, where sustainability is incorporated through learnable constraints rather than modeled as user preferences. To systematically evaluate the proposed approach, we construct a sustainable diet dataset named SusDiet with about 150k recipes, characterized by broad coverage of sustainability indicators. Experimental results on this dataset show that our method promotes more sustainable choices without compromising individual preference. This work establishes a framework for aligning individual dietary choices with planetary health, offering quantitative evidence to guide future sustainable diet interventions and policy-making for sustainable development.
[AI-58] Beyond Accuracy: A Dual-Judge Evaluation Protocol for Vision-Language Models in Legally Grounded Tasks ICML2026
链接: https://arxiv.org/abs/2608.24258
作者: Su Myat Noe,Ha Thanh Nguyen,May Myo Zin,Ken Satoh
类目: Artificial Intelligence (cs.AI)
备注: Accepted and presented at the AI for Law Workshop at ICML 2026
Abstract:AI systems are increasingly evaluated for legally accountable settings, where correct outputs must also be justifiable against an applicable legal standard. Existing legal-AI benchmarks and LLM-as-judge protocols provide important infrastructure for measuring task performance and open-ended response quality. We contribute one additional evaluation signal: a dual-judge protocol that pairs a standard 0-10 quality judge with a strict binary semantic-equivalence judge against a human-curated reference. We study a controlled, visually grounded regulatory task - UK traffic-sign interpretation, whose meaning is a codified question with a known reference for every input - and measure not merely whether the two judges disagree (by construction they must) but how much and where. On 4,680 evaluations under seven visibility levels and two occlusion modes, the two judges are moderately associated (point-biserial r = 0.644), while revealing an asymmetric Type II pattern affecting 8.0% of all evaluations. Its distribution is instructive: the marginal rate peaks at high visibility (14.2% at v = 0.8) simply because high-scoring answers are common there, but conditioned on the answer already scoring above 7, the rate is highest under heavy occlusion (54-63% at v = 0.3), so a high quality score is least trustworthy when the input is most degraded. We are explicit that the signal is a property of this judge and reference: a 49-row human check shows the 0-10 judge aligns closely with everyday-reader judgement (Pearson r = 0.81; r = 0.80 with the LLM accuracy sub-score), while the equivalence judge is fairly but one-directionally stricter. The protocol adds one LLM call per evaluation and surfaces a signal single-judge protocols do not report. We release the prompt template, occluded variants, and full evaluation results. Comments: Accepted and presented at the AI for Law Workshop at ICML 2026 Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.24258 [cs.AI] (or arXiv:2608.24258v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.24258 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ha Thanh Nguyen [view email] [v1] Tue, 25 Aug 2026 08:49:46 UTC (481 KB)
[AI-59] SA-Bench: Evaluating Semantic Alignment in LLM -Based Paper Reproduction EMNLP2026
链接: https://arxiv.org/abs/2608.24252
作者: Xue Hu,Zewei Pan,Zeli Su,Zhou Liu,Wentao Zhang
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: Accepted to Findings of EMNLP 2026
Abstract:LLM agents can generate paper reproduction code, yet often produce scientifically unfaithful implementations. We define this failure mode as semantic drift, where generated code silently diverges from the paper’s specifications. We introduce SemanticAlign-Bench(SA-Bench), a diagnostic benchmark covering 30 papers from ICLR, ICML and NeurIPS 2025. For each paper, we decompose its specifications into atomic and verifiable implementation claims, which we call Semantic Alignment Units (SAUs) and evaluate repositories along four diagnostic dimensions spanning numerical, methodological, protocol and ordering drift. In total, we construct 1,491 SAUs across five ML domains and evaluate 12 generator configurations (4 models \times 3 scaffolds). Even the strongest configuration (Claude+PaperCoder) achieves a mean SAU score of only 0.301 out of 1.0, with an overall mean of 0.221 across 360 evaluations. A failure taxonomy reveals that agents attempt most requirements but implement them incorrectly, with implementation mismatch and stubs accounting for the majority of zero-scored claims. Our analysis further indicates that scaffolds optimized for executability provide limited leverage for scientific reproduction; narrowing the gap requires scaffolds that prioritize semantic specification verification. The benchmark, annotations and evaluation pipeline are publicly available.
[AI-60] STRIVE: Multi-Agent Structured Temporal Reasoning with Integrated Verification for Longitudinal Radiology Report Generation
链接: https://arxiv.org/abs/2608.24237
作者: Junyeong Maeng,Eunsong Kang,Heung-Il Suk
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Longitudinal radiology report generation (LRRG) requires identifying both current findings and their changes relative to a prior study. Existing methods jointly model diagnosis, attribute estimation, temporal comparison, and language generation within implicit representations, which can cause task interference, obscure the evidence underlying each decision, and limit error traceability. They also model progression states as independent labels, ignoring their ordered structure and thus treating missed changes and direction reversals equally. We present STRIVE, Multi-Agent Structured Temporal Reasoning with Integrated Verification for LRRG, which decomposes clinical reasoning into specialized Diagnosis, Attribute, and Temporal Change Agents that produce explicit intermediate evidence. In particular, the Temporal Change Agent is further post-trained using Progression-Aware GRPO, a verifiable, shaped reward that assigns partial credit to direction-preserving errors while scoring direction reversals lowest. STRIVE performs verification at two stages: a deterministic Consistency Gate reconciles the agent outputs before report generation, and a Validation Agent checks whether the generated report is supported by the aggregated clinical evidence. On Longitudinal-MIMIC, STRIVE attains the best clinical efficacy among recent methods and more than doubles Longitudinal Change Concordance (LCC), a measure of temporal agreement with the reference report, over the strongest baseline.
[AI-61] RACE: An Evidence-Grounded Benchmark for Safety Evaluation of Large Reasoning Models EMNLP2026
链接: https://arxiv.org/abs/2608.24232
作者: Zhenyu Wu,Siyuan Chen,Changchun Yang,Jiaqi Dong,Min Zhou,Ali Almadan,Talal Hammad,Faisal Wahbo,Aminullah Tora,Mona Alshahrani,Xin Gao
类目: Artificial Intelligence (cs.AI)
备注: EMNLP 2026 Main
Abstract:Large Reasoning Models (LRMs) generate intermediate reasoning traces that may contain unsafe content, even when their final responses appear safe. Guardrail models are designed to detect and block unsafe content, yet existing benchmarks for unsafe content detection focus primarily on prompts and final responses, leaving reasoning traces largely unexamined. Moreover, these benchmarks typically provide only binary safety labels, without evidence annotations that justify the judgments. To address these limitations, we introduce TRACE, an evidence-grounded safety evaluation benchmark that covers the entire LRM inference pipeline: prompts, reasoning traces, and final responses. TRACE includes prompts in two languages spanning nine risk categories and ten attack strategies. For each prompt, four LRMs generate reasoning traces and final responses, and we annotate the safety of each component and extract supporting evidence from the corresponding source text. Evaluating 18 guardrail models on TRACE reveals that safety judgment for reasoning traces is substantially more challenging than for prompts or final responses, and that current models struggle to accurately extract supporting evidence. These findings highlight the need for guardrail models that can reliably detect and precisely localize unsafe content across the LRM inference pipeline.
[AI-62] Evaluating Multiple LLM Generations with Validated Task Coverag e
链接: https://arxiv.org/abs/2608.24228
作者: Florian Le Bronnec,Rio Yokota
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Many LLM applications are most useful when they provide several candidate outputs for comparison, validation, or combination. Predominant evaluation settings, however, still focus on individual outputs or reduce multiple samples to a single success or selected answer. This can miss whether the outputs include several genuinely different useful results. We introduce VTC-Bench, a five-domain benchmark for this setting, together with Validated Task Coverage (VTC) as its core evaluation quantity. The benchmark is built from carefully selected real-data tasks where both output quality and task-relevant distinctness can be checked automatically and reproducibly, without model-based judges. VTC measures how many distinct useful results are obtained within k attempts. Across multiple models and inference settings, the benchmark leads to different conclusions from conventional evaluation: configurations that look strongest from single-draw quality are not necessarily those with the best coverage, and simple measures of output variation do not reliably recover task-relevant coverage. These results show that finite candidate sets can be evaluated directly as objects of interest, revealing differences in model behavior that are not apparent from conventional per-output evaluation.
[AI-63] MetaRAG : Belief-Action Aligned Policy Optimization for Agent ic RAG EMNLP2026
链接: https://arxiv.org/abs/2608.24214
作者: Qiuyi Qi,Tian Liang,Jiamu Wang,Jinjian Zhang,Wei Zhou,Pengcheng Zhu,Linjian Mo,Ming Kong,Jie Liu,Qiang Zhu
类目: Artificial Intelligence (cs.AI)
备注: EMNLP 2026 MainConference
Abstract:Agentic retrieval-augmented generation (RAG) requires language models to decide when to continue searching and when to answer. Existing RL-based methods rely on external supervision and overlook the agent’s internal belief about whether the current evidence is sufficient. To address this problem, we reformulate the search decision quality as belief-action alignment and propose MetaRAG, a belief-action aligned policy optimization framework for agentic RAG. MetaRAG uses Verify-first Action Generation to elicit an explicit verification process before each actual action, and Internal Belief Probing to estimate the policy model’s own answerability belief from the same question-history context. Based on these, MetaRAG derives a consistency reward that is further gated by answer correctness, avoiding reinforcement of internally consistent but incorrect trajectories. The belief probe is used only during training and introduces no inference-time overhead. Experiments on seven public QA benchmarks show that MetaRAG consistently improves the accuracy-efficiency trade-off over strong RL-based agentic RAG baselines, with gains that transfer to deep research settings, different optimizers, and multiple model backbones.
[AI-64] ask-Adaptive Rubrics for GUI Reward Modeling
链接: https://arxiv.org/abs/2608.24174
作者: Tao Xiong,Xavier Hu,Wenkai Wang,Qinzhuo Wu,Changqiao Wu,Pengzhi Gao,Wei Liu,Jian Luan,Shengyu Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent studies on GUI agents have increasingly focused on outcome reward modeling, which assigns outcome rewards by judging whether an executed trajectory satisfies the success criteria implied by the user instruction. Existing GUI reward verifiers, however, often under-specify how these criteria should be constructed for each task instance. Whether using generic rubric structures or implicit model reasoning, their judging criteria are not sufficiently task-adaptive: they can transfer checks across tasks, overlook concrete constraints in the current instruction, or become overly strict by enforcing unstated requirements. To address this limitation, we propose AdaptRubric, a Coarse-to-Fine Rubrics Framework that constructs task-adaptive judging criteria through a category-level coarse stage and an instance-level fine stage. AdaptRubric performs category-level coarse rubric retrieval by routing the instruction to a GUI task family and retrieving reusable task-family criteria, then conducts instance-level fine rubric generation to surface compact cues for concrete values, scopes, and constraints in the current instruction. Across offline reward evaluation and online reinforcement learning optimization, AdaptRubric consistently outperforms prior reward agents, improving F1 by 3.6 points over the baseline average under a matched image budget and yielding a 4.23-point task-success gain.
[AI-65] OmniJudge or OmniBias? Diagnosing Multimodal Judges through Balanced Decoupled Lenses
链接: https://arxiv.org/abs/2608.24160
作者: Guangzheng Hu,Ziyue Jiang,Weixu Qiao,Lixin Zhang,Jianye Kang,Yuru Wu,Rong Bao,Niantong Li,Wei Wang,Ziyi Cheng,Xinfa Zhu,HangRui Hu,Ting He,Bing Zhao,Lin Qu,Hu Wei,Jin Xu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal understanding models that can jointly judge text-to-image (T2I), text-to-video (T2V) and text-to-speech (TTS) generation are increasingly used as “OmniJudges” for evaluation and automatic annotation. How reliably they understand what they score remains unclear, since existing benchmarks and training data tend to overemphasize positive examples and to conflate distinct failure modes, so a judge may score well without recognizing failures while its capability gaps stay hidden. Motivated by this, we introduce D3-Omni, a balanced and decoupled benchmark for diagnosing fine-grained multimodal understanding, covering 53 orthogonal binary dimensions (17/22/14) and 10,671 samples (3,526/1,998/5,147) across the three tasks. Rather than re-generating outputs, which may leak information across dimensions, we fix verified fully positive seeds and derive negatives through controlled prompt rewriting and atomic, dimension-isolating perturbations. The resulting D3 design is Dual-balanced, which helps alleviate negative-sample scarcity and per-dimension label imbalance; Decoupled, so that each error is attributable to a single capability; and Dynamic, steering construction toward under-represented regions of the label distribution as generative models this http URL suite reaches near 1:1 per-dimension parity and a uniform distribution over all total-score levels. Under this balanced view, even strong OmniJudges tend to struggle on modality-related dimensions, to confirm satisfied requirements far more reliably than they detect violated ones, and to treat nominally distinct attributes as largely a single decision, suggesting that aggregate accuracy may hide systematic blind spots that a balanced and decoupled lens can help expose and, in turn, address.
[AI-66] LLM -Guided Contextual Action Evaluation for Operational Decisions in Industrial Processes
链接: https://arxiv.org/abs/2608.24156
作者: Youcheng Zong,Runda Jia,Dakuo He
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
备注:
Abstract:Industrial actor–critic methods usually represent continuous actions as anonymous numerical coordinates. They must therefore learn from limited interactions which process variables each action affects, in which direction, and after what delay. Fixed industrial documents already describe part of these relations, but their open-text statements neither represent the current operating condition nor directly fit a numerical policy. This article presents LLM-Guided Contextual Action Evaluation for Operational Decisions in Industrial Processes (LCAE), which uses a large language model before training to normalize fixed documents into a frozen action–observation–direction–delay relation basis. Recent numerical action–response history then modulates the current strength of each relation, while the evaluated action forms a state-conditioned nonlinear action-effect field in the same basis. The critic evaluates actions through this field, and the actor uses the same relation gains to generate actions, making document semantics part of maximum-entropy policy learning. Neither the LLM nor the embedding model runs online during training or deployment; the deployed policy uses only frozen semantic artifacts and visible numerical history. The method states a falsifiable hypothesis: when documented relations are correct and recent history reflects their contextual strength, this action representation should provide a more useful decision bias than raw action coordinates.
[AI-67] Robust Code RL via Faulty-Code-Driven Test case Synthesis and Dense Reward Shaping EMNLP2026
链接: https://arxiv.org/abs/2608.24135
作者: Yiwen Zhang,Xiaodong Yan,Zhenyu Huang,Deng Zhao,Liang Jiang,Qing Cui,Zujie Wen,Zhiqiang Zhang,Jun Zhou
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: Accepted by EMNLP 2026
Abstract:Reinforcement learning from verifiable rewards (RLVR) has emerged as a pivotal technique for enhancing the code generation capabilities of Large Language Models (LLMs). However, the efficacy of RLVR in coding implementations is fundamentally limited by the comprehensiveness of test cases, because insufficient test coverage in code validation often causes false positives, further leading to reward hacking and policy degradation. To mitigate the reward bias stemming from the suboptimal quality of current automated generation methods, we propose the RobustTests framework, which introduces a faulty-code-driven test case synthesis strategy that leverages “near correct” faulty codes to guide the model in precisely capturing latent logical discrepancies and further integrates validator agents with behavioral feature clustering to facilitate the granular filtering of invalid and redundant test cases. To address false negatives caused by inherent hallucination noise in synthetic test cases, RobustTests also incorporates a stepwise dense reward function based on pass rates, bolstering training robustness through fine-grained feedback. By employing this pipeline, we construct a high-quality dataset that augmented the test cases in CodeContests, encompassing a broader spectrum of faulty code scenarios and significantly enhances diagnostic utility. Experimental results demonstrate that, by leveraging a moderately challenging subset of problems from CodeContests for training, RL fine-tuning of Qwen3-32B via RobustTests achieves an absolute 3% performance gain on the LiveCodeBench benchmark compared to baseline methods, confirming the effectiveness of the RobustTests framework in advancing the code generation proficiency of LLMs.
[AI-68] From Gradient-Boosted Trees to Deep Recommenders: Practical Lessons from Migrating a Production Customer Support Recommender
链接: https://arxiv.org/abs/2608.24132
作者: Sonia Sharma,Jeyendran Balakrishnan,Shreya Rajpal,Swapnil Parekh,Nagaraj Janardhana,Andrew Mattarella-Micke
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 10 pages
Abstract:Product catalogs in fast-moving service businesses are shifting from static, independently priced SKUs toward dynamically bundled, discount-coupled offerings–a shift that strains the tree-based classifiers traditionally preferred for sparse and highly imbalanced data. These classifiers assume a fixed, slowly changing label space and struggle to incorporate multimodal signals such as tabular data and transcripts. We present the migration of a live, production conversational recommendation system from a gradient-boosted multiclass model to a pairwise-binary deep recommender. Because this system is critical to ecosystem growth initiatives and downstream features like dynamic pitching–surfacing the most relevant pitch text to a support agent in real time during a live customer conversation–maintaining live recommendation quality was a non-negotiable constraint. We detail the techniques that made this migration successful–reformulating recommendation as pairwise binary prediction to learn jointly from user and item features, and enhancing learned representations via negative sampling and noise injection. To efficiently incorporate long, live conversation context, we apply attention pooling over transcript chunks and benchmark it against TF-IDF and sentence-embedding baselines. Finally, we explore multiple architectures (including two-tower models, DeepFM, and their variants) and loss functions such as contrastive loss. Evaluating against a CatBoost baseline across all conversational stages, we demonstrate that our approach achieves parity at conversation beginning and outperforms at later conversational stages.
[AI-69] PonderPounce: A Pretrained MLLM as an Episode Context Engine for Robot Control
链接: https://arxiv.org/abs/2608.24115
作者: Suhwan Choi,Jaeyoon Jung,Sungkyung Kim,Yunsung Lee,Youngjae Yu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Project page: this https URL
Abstract:Multimodal large language models (MLLMs) can integrate long visual histories, reason under partial observability, and infer behavior from a few examples. Yet vision-language-action (VLA) models generally inherit pretrained representations without using this contextual capacity as episode memory. Memory-dependent policies address this gap through purpose-built history mechanisms. PonderPounce instead reuses an MLLM’s native causal context as robot memory. Ponder, a System2 MLLM, accumulates episode observations, demonstrations, and prior cognition in its native causal context and can generate subgoal text and demonstration reasoning for internal use. Pounce, a System1 VLA, receives the current observation, instruction, and proprioception directly; through the Ponder–Pounce interface, it asynchronously receives only the newest continuous cognition token and its age. Both are jointly trained end to end without a purpose-built memory module or separate bridge pretraining. Optimized serving achieves p50 latencies of 78ms for cognition refresh and 25ms for action-model invocation, supporting 20Hz action playback. On RoboMME with base-scale training data, PonderPounce reaches 60.83% with 9B and 50.04% with 0.8B under the same Pounce architecture and interface, versus 44.51% for FrameSamp+Modul and 17.93% for the current-observation \pi_0.5. With 9x data, it reaches 75.54% versus 57.88% for FrameSamp+Modul. On RoboCasa-DC, the same interface learns from action supervision alone and reaches 12.5% versus 11.6% for the strongest published demonstration-conditioned baseline, falling to 8.6% when cognition is replaced by a learned null state.
[AI-70] AHEAD: Adaptive Hindsight with Environment-Augmented Distillation for Agent ic RL
链接: https://arxiv.org/abs/2608.24114
作者: Xiaolong Jin,Dingmin Wang,Vijay Lingam,Varun Kumar
类目: Artificial Intelligence (cs.AI)
备注: 22 pages, 15 figures, 7 tables
Abstract:Training multi-turn LLM agents with reinforcement learning typically relies on trajectory-level rewards, which assign a uniform advantage to every step and cannot identify which decisions led to success or failure. Self-distillation methods can provide finer-grained supervision by augmenting RL with privileged information. However, existing approaches usually apply the same type of privileged information to every step in an indistinguishable manner, ignoring a key asymmetry: routine steps need little additional guidance, while critical error steps require corrective direction that environment feedback alone cannot provide. We propose AHEAD, a step-aware framework that matches different supervision sources to different step types. The teacher receives environment feedback on all steps as a grounded dense signal, and additionally receives LLM-generated corrective hints on error steps to supply the direction that environment feedback lacks. The method introduces minimal changes to the standard GRPO algorithm. Across ALFWorld, WebShop, and Search-based QA, and across three model scales, AHEAD raises task success (+13.3 points on ALFWorld and +11.0 on WebShop at 7B over GRPO), reaches a given success rate in fewer training steps, and solves tasks within tighter interaction budgets than outcome-only RL and prior self-distillation baselines.
[AI-71] Structured Frequency-Domain Evidence for LLM -Based Time-Series Anomaly Detection
链接: https://arxiv.org/abs/2608.24113
作者: Jungwook Seo,Sangwon Son,Minjeong Kim,Seungmin Han,Seojin Yoo,Sungyong Baik
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Time-series anomalies can appear not only as pointwise deviations but also as changes in recurring temporal structure, such as shifted periodicity or localized oscillatory fluctuations. However, existing LLM-based time-series anomaly detection methods mainly expose time-domain evidence through indexed values, plots, or de-seasonalized representations, leaving spectral structure implicit. We propose an evidence-augmented zero-shot TSAD framework that preserves indexed de-seasonalized observations while adding compact frequency-domain evidence computed with the Fast Fourier Transform (FFT). The evidence is constructed at two resolutions: global frequency-domain evidence summarizes sequence-level periodic context, while local frequency-domain evidence captures time-localized spectral departures. Experiments on AnomLLM with InternVL2-LLaMA3-76B, Qwen2.5-VL-72B-Instruct, Gemini-2.5-Flash, and GPT-4o, together with evaluation on the TSB-AD-U subset, show that explicit frequency-domain evidence improves LLM-based TSAD baselines. These results suggest that frequency-domain evidence can complement indexed and de-seasonalized time-domain inputs for zero-shot LLM-based TSAD.
[AI-72] Scalable Question-Centric Text-to-Image Evaluation: Reliable Ranking Fine-Grained Diagnosis and Cost-Aware Routing
链接: https://arxiv.org/abs/2608.24112
作者: Shaoan Zhao,Fang Zhao,Xueqiang Guo,Xinpei Su,Huanlin Gao,Qiang Hui,Ting Lu,Fuyuan Shi,Chao Tan,Bikun Yang,Kai Wang,Shiguo Lian
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Modern text-to-image (T2I) models often have similar total scores but different strengths, making practical selection difficult. Fine-grained benchmarks decompose prompts into questions, yet often return them to prompt scores and fixed categories, weakening attribution and ignoring complexity. Related requirements are also scored separately or as one total, obscuring basic versus compositional failure. We present QC-T2I-Bench, a question-centric framework that converts open prompts into attributed atomic questions and organizes their dependencies with Davidsonian Scene Graphs (DSGs). We use hierarchy-constrained question aggregation to exclude downstream questions after a prerequisite fails and to prevent simple and complex prompts from receiving the same total weight. We then use the DSG structure to measure joint success within prompts and compare repeated entities across prompts, separating basic realization failures from failures under additional requirements. We evaluate multiple open-source T2I models on English and Chinese prompts. The resulting question-level evidence supports reliable ranking and fine-grained diagnosis: joint completion falls from 80.7% for components with two capabilities to 37.2% for those with seven or more. Finally, we reuse the same records for training-free routing; our cost-aware router matches ERNIE’s 89.51-point estimate with 21.3% less GPU-s/MP.
[AI-73] ACE: A Self-Correcting Agent ic Canvas Editor for Multi-Slide Presentation Automation EMNLP2026
链接: https://arxiv.org/abs/2608.24103
作者: JooYoung Jang,Taegyeong Lee,Jihyeon Park,Nojun Kwak
类目: Artificial Intelligence (cs.AI)
备注: 26 pages, 12 figures, EMNLP 2026 Industry Track (Main paper)
Abstract:Commercial design platforms increasingly edit documents through large language model (LLM) agents, but two practical problems block reliable deployment: legacy document formats expose only \emphflat, absolutely positioned elements, so agents must recompute coordinates and routinely break layouts; and design has no unique ground truth, so diff-against-reference metrics penalize valid-but-different outputs. We present \textbfACE, an agentic canvas editor over a \emphhierarchical scene-graph with a presentation-specialized action space (98 tools), paired with \textbfCARE, a content-aware router that feeds the agent only the relevant slice of each deck (avg.\ \sim 89% input-token reduction), and a \emphself-correction loop driven by a \emphground-truth-free instruction-following (IF) judge whose natural-language critique is fed back as the next-turn instruction. With a fixed backbone, a scene-graph editor in a \emphsingle turn already matches a same-backbone \emphagentic HTML pipeline that iterates internally; adding self-correction lifts ACE significantly above it on instruction following (IF 4.23 vs.\ 3.81 on the full 94-task benchmark, paired p=.010 , replicated by an out-of-loop judge) at 1.75 \times the speed and \sim 44% lower cost. VQ means are statistically indistinguishable, but 26 blind raters prefer ACE overall (58.7% decisive win-rate) and prefer the self-corrected output 81% of the time; the ranking is invariant across three judge families, and out-of-loop judges retain two-thirds of the self-correction gain, bounding circularity. 66% of cases halt after one pass, and a strict-peak rollback removes every observed regression.
[AI-74] Are Android GUI Agents Robust Against Runtime Anomalies? AnTrap: Evaluating Agents in Dynamic Adversarial Environments
链接: https://arxiv.org/abs/2608.24099
作者: Guo Gan,Yilun Zhao,Cong Chen,Jinbiao Wei,Tingyu Song,Zheyuan Yang,Lin Fu,Hong Zhou
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:GUI agents often encounter dynamic anomalies when deployed on Android devices, from unexpected pop-ups to action misuse, yet existing benchmarks lack systematic evaluation of agent robustness against runtime anomalies. We introduce AnTrap, a comprehensive benchmark that injects dynamic perturbations into agent execution trajectories. We propose a taxonomy organizing real-world anomalies into four layers (State, Thinking, Action and Round) with ten fine-grained subcategories, and develop a construction pipeline that preserves task solvability while introducing realistic adversarial conditions. Evaluating 16 leading GUI models, we reveal universal vulnerability to dynamic anomalies, with even the strongest models suffering significant performance degradation. Furthermore, we conduct GRPO training in both original and adversarial environments to validate our benchmark, separating environment-learnable anomalies from reasoning-bottlenecked ones. Our findings show that while single-step traps at state and action layers are largely addressable through adversarial reinforcement learning, deep contextual traps, like state deadlock, expose intrinsic limitations that cannot be resolved by training in environments with traps alone.
[AI-75] Knowing When to Ask for Help: Bayesian Self-Escalation in Hierarchical LLM Agents
链接: https://arxiv.org/abs/2608.24087
作者: Nadeem Shaikh
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: 21 pages, 5 figures. Code and data: this https URL . Associated record: this https URL
Abstract:Current LLM agent systems decide delegation before reasoning begins (a router picks a model) or after a response is complete (a verifier scores it and may retry). We study a third regime: an agent that recognises, during its own reasoning, that it is unlikely to succeed and transfers control to a stronger model. We formulate intra-generation delegation as a Bayesian optimal-stopping problem over a learned competence posterior – an online estimate of the agent’s eventual task success whose sufficient statistics are learned from labelled trajectories, not read off raw entropy. We derive the myopic escalation threshold in closed form, characterise the optimal policy via dynamic programming, and prove that the optimal policy is a time-varying threshold with no shape assumption on the raw signal. We further prove exponential separation of the oracle belief at the Chernoff-information rate of the signal, a regret bound governed by the calibration of the posterior, and a finite-sample guarantee: with n labelled calibration trajectories the deployed plug-in policy’s regret decays as 1/sqrt(n). A controlled simulation study confirms each prediction of the theory, including the predicted 1/sqrt(n) rate. We additionally report a real-model validation on a Qwen2.5-Coder 1.5B-7B code cascade (MBPP, 257 tasks), confirming two of three pre-registered predictions: the escalation frontier dominates post-hoc routing at equal cost, and the cumulative competence belief’s discrimination rises over generation.
[AI-76] EMRB: A Multi-Level Benchmark for Evaluating LLM Reasoning over Raw Electromagnetic Signals
链接: https://arxiv.org/abs/2608.24086
作者: Mingxu Zhang,Ying Sun,Yuhan Li,Yang Ji,Dazhong Shen,Ke Zhang,Shan Huang
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Software Engineering (cs.SE)
备注:
Abstract:Large language models (LLMs) are increasingly used as code agents for scientific and engineering analysis, but their ability to analyze raw physical-layer measurements remains untested. We introduce \textbfEMRB (\textbfElectro\textbfmagnetic \textbfReasoning \textbfBenchmark), which evaluates whether LLMs can analyze raw I/Q data by writing and running code. EMRB contains 200 problems across five difficulty levels and 27 question types, from signal detection to OFDM design, generated from 11 signal types with verified ground truth. Unlike benchmarks built on preprocessed features or structured tables, EMRB provides only the raw capture; the quantities each question refers to must first be discovered through code. We evaluate 14 LLMs spanning proprietary, open-weight, and reasoning-oriented families. Scores range from 24.1% to 78.9%, with the mean dropping from 84.9% on basic measurement to 21.2% on system design. We also propose \textbfReconPilot, a structured method that separates signal reconnaissance, targeted analysis, and self-verification. Across three backbones, ReconPilot raises the overall score by 3.8 to 17.6 points and improves 13 of 15 backbone-level combinations tested. All data and code are publicly released in \hrefthis https URL\textcolorblueour GitHub repository.
[AI-77] Agent World: Personality-Aware Reliability Evaluation for Agent ic Information Retrieval KDD2026
链接: https://arxiv.org/abs/2608.24076
作者: Gunja Agarwal,Arup Kumar Das,Arun Menon,Jitesh Chandra Mishra,Vignesh Divakaran
类目: Artificial Intelligence (cs.AI)
备注: Agent4IR @ KDD 2026
Abstract:Evaluation of agentic information retrieval remains limited to scripted interactions with uniform users, missing both natural personality diversity and adversarial brittleness. We present AgentWorld, a simulation framework combining (i)Big Five (OCEAN) personality-driven user populations with stateful tool-use environments; (ii)the pass ^k consistency metric with structured fault classification, partial-credit scoring, and dual-control handoff verification; (iii)score-thresholded training-data export in six fine-tuning formats; and (iv)an adversarial Risk Analyser that snapshots required-intermediate-state spines, branches Monte-Carlo rollouts under four task-aware perturbation types, and quantifies risk via \Delta P / \Delta T scoring, Dempster–Shafer evidence fusion, and Shapley attack-category attribution. Three experiments demonstrate the framework: a conversational analytics agent across 10 OCEAN personas (240 evaluator judgments); a customer-support agent across 5 tasks \times 4 persona variants; and adversarial stress-testing of 5 tasks revealing pre-existing trajectory brittleness ( V_\min=0.375 without perturbation) and tool/infrastructure-layer attack dominance (Shapley: 46% system, 38% action). Personality variation surfaces failure modes uniform testing cannot expose—cross-domain leakage, contextual drift, a 0.27-point quality gap, and 50% vs. 100% pass-rate across personas on the same task—while the Risk Analyser quantifies trajectory-level brittleness that pass ^k alone cannot measure.
[AI-78] Compression Trinity: Exploring Sparsity Quantization and Low-Rank Approximations for LLM Compression ATC
链接: https://arxiv.org/abs/2608.24070
作者: Mohammad Mozaffari
类目: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Performance (cs.PF)
备注: PhD thesis, University of Toronto, 2026. 156 pages. Chapters extend MKOR ( arXiv:2306.01685 ), SLoPe ( arXiv:2405.16325 ), OPTIMA ( arXiv:2512.13886 ), PATCH ( arXiv:2509.23410 ), and SLiM ( arXiv:2410.09615 ). Official record: this https URL
Abstract:Prohibitive computational and environmental costs impede the scalable deployment of Large Language Models (LLMs). Traditional compression techniques (sparsity, quantization, low-rank approximations) are typically applied in isolation, and each hits an accuracy-efficiency wall. This thesis proposes the “Compression Trinity,” a unified framework that applies the three pillars jointly: sparsity to reduce computation, quantization to minimize memory bandwidth, and low-rank approximations to recover accuracy. To accelerate pretraining, we apply the Trinity to the optimizer and model architecture. MKOR approximates curvature via block-diagonal sparsity and low-rank inversion, maintaining numerical stability for quantized states; it reduces curvature update complexity from O(d^3) to O(d^2) and accelerates convergence by up to 1.85x over KFAC. SLoPe accelerates training by up to 1.25x via a double-pruned backward pass for N:M sparsity, using low-rank “lazy” adapters in the final 1% of training to recover accuracy. For post-training compression, OPTIMA stabilizes static masks in a zero-training regime by formulating weight reconstruction as globally optimal column-wise quadratic programs, improving zero-shot accuracy by up to 3.97%. Given a fine-tuning budget, PATCH breaks the ceiling of static masks by learning a dynamic hybrid sparsity ratio between 0% and 50%, yielding up to 1.38x speedups. Finally, SLiM realizes the full Compression Trinity in one shot, using mathematically derived low-rank adapters to recover information lost to quantization and sparsity, improving accuracy by up to 5.66% over state-of-the-art methods and outperforming uncompressed dense models at equal parameter budgets by 0.6%. Together, these results show that jointly applying the Compression Trinity is essential for efficient, scalable, high-performance LLMs.
[AI-79] Poisoning Agent ic Alpha: Adversarial Vulnerabilities Across Roles and Architectures in Multi-Agent Trading Systems
链接: https://arxiv.org/abs/2608.24069
作者: CheolWon Na,Hao Ni,Lukasz Szpruch,Zhangyang Wang,Dhagash Mehta,Saurabh Nagrecha,Alejandro Lopez-Lira,Chanyeol Choi,Yongjae Lee,Jee-Hyong Lee
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注:
Abstract:LLM-based multi-agent trading systems, in which specialized agents collaborate through structured communication to produce trading decisions, are moving rapidly from research prototypes to live deployments that control real assets. The same inter-agent communication that makes them effective also exposes them: a corrupted signal can propagate to the final decision and translate into realized financial loss. Unlike prior attacks that presume privileged access to system internals, we restrict the adversary to what is practically reachable—the source data and prompts agents consume—yielding a low-barrier, and thus democratized threat model instantiated as role-specific adversaries. We present the first systematic empirical study in the financial domain to characterize how an adversarial signal enters a multi-agent trading system and how far it survives toward the decision. Along the role axis, we decompose a widely-used trading pipeline into four functional roles—Analyst, Researcher, Trader, and Risk Manager—and pair each with an attack matched to its interface. Along the structural axis, we evaluate four communication topologies under data- and agent-level attacks, using the Adversarial Signal Preservation Score (APS) as a post-hoc lens on why some designs are more robust than others. We conduct experiments across five assets, two backbones, and two target directions. A central finding is that no architecture is inherently robust. These findings provide insights for the future design of safer and more robust agentic trading systems. Subjects: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE) Cite as: arXiv:2608.24069 [cs.AI] (or arXiv:2608.24069v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.24069 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-80] Dont Just Listen Try Planning : Graph-based Retrieval-Generation Agent for Long-form Audio Meeting Understanding ACL
链接: https://arxiv.org/abs/2608.24048
作者: Quanwei Tang,Dong Zhang,Shoushan Li,Guodong Zhou
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: ACL Findings 2026 Accepted
Abstract:While long-form audio meeting understanding (LAMU) is garnering growing attention, task-specific question answering (QA) datasets remain scarce. Existing speech QA paradigms and state-of-the-art Speech LLMs suffer from acoustic information loss and poor long-term context memory. To address these issues, we construct the LongAudioQA dataset and propose the GRGA model, which models heterogeneous audio features into a multi-dimensional graph and leverages agent planning for retrieval and answer generation.
[AI-81] Algorithmic Impact Reveals the Hidden Social Choice Structure of Alignment
链接: https://arxiv.org/abs/2608.24046
作者: Zachary Wojtowicz,Michelle Si,Finale Doshi-Velez,Ariel Procaccia
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:When an AI algorithm makes decisions that affect more than one person, aligning it becomes a problem of social choice: how should people’s divergent preferences about system behavior be reconciled and aggregated into a single coherent model? The standard approach to aligning frontier AI models \unicodex2013 reinforcement learning from human feedback \unicodex2013 largely sidesteps this question and has poor social choice guarantees. However, it remains unclear what alternative should replace it. We show that, by focusing directly on an algorithm’s welfare consequences, the alignment problem can be reformulated as linear optimization over a convex impact space, which makes it amenable to the standard toolkit of welfare economics and mechanism design. This reformulation clarifies how alignment protocols translate into welfare consequences and, conversely, how a social planner’s desired constraints on welfare consequences can be translated back into alignment protocols. We apply this transformation to show that voting-by-issues and random-dictatorship mechanisms are strategyproof and unanimous. Demonstrating the reverse direction, we also apply the impact representation to derive a family of alignment protocols that maximize utilitarian social welfare subject to various social desiderata, such as bounds on individual or group harm. We illustrate the welfare implications of these alignment protocols empirically using real human preferences over kidney allocation, charitable food distribution, LLM responses, and trolley problems.
[AI-82] Hierarchical Skill Retrieval for Data-Efficient Adaptation of Vision-Language-Action Models
链接: https://arxiv.org/abs/2608.24042
作者: Haoran Hao,Shahram Najam Syed,Jeff Schneider,Jeffrey Ichnowski
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Project Page: this https URL
Abstract:While Vision-Language-Action (VLA) models pretrained on large-scale robot datasets provide a strong foundation for robot manipulation, their performance can degrade when adapted to new tasks with limited task-specific demonstrations. Retrieval offers a practical way to reuse existing demonstrations for data-efficient adaptation, but existing methods often rely on visual similarity, state-action representations, or task-level language matching. These approaches may overlook the hierarchical structure of long-horizon manipulation tasks, where complete task matches are rare but reusable skills are often abundant. To address this challenge, we propose Hierarchical Skill Retrieval (HSR), a retrieval framework for data-efficient VLA adaptation. Specifically, HSR first decomposes a target task into candidate skill sequences. It evaluates each plan based on both semantic plausibility and skill reliability estimated from the prior dataset. The selected decomposition is then used for hybrid retrieval. This combines subtask-level language retrieval with behavior-feature reranking to identify demonstrations that are both semantically relevant and compatible with the target task. Finally, we adapt the policy through a two-stage pretraining and finetuning pipeline, which separates general skill acquisition from task-specific adaptation. Experiments on the LIBERO benchmark and several real-world robot manipulation tasks show that HSR improves the average success rate by 10.3% and 21.3% over the strongest baseline, respectively. These results demonstrate the effectiveness of structured skill-level retrieval for data-efficient VLA adaptation. Videos and code are available at this https URL.
[AI-83] Relative Time Intervals Representation for Word-level Timestamping with Masked Training ICASSP2026
链接: https://arxiv.org/abs/2608.24041
作者: Quanwei Tang,Zhiyu Tang,Xu Li,Dong Zhang,Shoushan,Guodong Zhou
类目: Artificial Intelligence (cs.AI)
备注: ICASSP2026 Accpeted
Abstract:Although Speech Large Language Models (SpeechLLMs) excel at speech understanding and generation, their capacity for fine-grained, temporally aligned outputs remains underexplored. Our work addresses this gap by enabling SpeechLLMs to jointly model speech content and temporal structure, effectively transforming them from content understanding machines" into temporal-aware content understanding machines". Specifically, we replace traditional absolute timestamps with relative timestamps, achieving a more compact vocabulary and stronger generalization capabilities. To efficiently infuse timestamp prediction ability into pre-trained large language models, we introduce a hybrid fine-tuning strategy: full-parameter fine-tuning of the timestamp-augmented embedding layer and language model head, combined with LoRA fine-tuning of the decoder layers. Moreover, we design a masked timestamp training objective, preventing the model from over-relying on ground-truth timestamps, and thereby enhancing robustness against noisy real-world annotations. Extensive experiments demonstrate that our approach achieves significant improvements in timestamp prediction accuracy while maintaining strong speech transcription performance.
[AI-84] Design-to-Plan: A Large Language Model-Based Multi-Agent Framework for Manufacturing Process Planning from 3D CAD Models and 2D Engineering Drawings
链接: https://arxiv.org/abs/2608.24039
作者: Muhammad Tayyab Khan,Lequn Chen,Wenhe Feng,Seung Ki Moon
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Submitted to Elsevier Journal
Abstract:Manufacturing process planning transforms heterogeneous design information into coherent manufacturing decisions. However, existing approaches focus on isolated subtasks, such as feature recognition, drawing interpretation, or tool selection, and struggle to support the full reasoning chain from design artifacts to process plans. This is critical when planning must interpret 3D CAD models, 2D engineering drawings, materials, and domain-specific rules. To address this gap, this paper presents Design-to-Plan, a large language model (LLM)-based multi-agent framework for end-to-end manufacturing process planning. An orchestrator coordinates specialized agents for 3D feature recognition, 2D drawing analysis, 2D-3D context fusion, knowledge retrieval, process sequencing, tool selection, and report generation. Rather than using LLMs as standalone text generators, the framework deploys them as reasoning agents that interact with deterministic modules and knowledge sources to produce consistent and traceable decisions. In this hybrid design, deterministic modules and specialized agents extract structured information from CAD and drawing inputs, while LLM agents perform context-aware reasoning, retrieve manufacturing rules, resolve conflicts, and generate planning outputs. The framework is evaluated using 300 benchmark cases across three downstream ReAct-enabled agents, plus separate evaluations of CAD feature recognition, drawing analysis, and 2D-3D context fusion. The parallel architecture achieves 100% success across downstream agents, Tool F1 scores of 95.9%-97.6%, 90% source detection accuracy in conflict analysis, and a 60%-68% reduction in token usage for key planning tasks. Results show that structured LLM-based multi-agent coordination can bridge design representations and manufacturing knowledge, enabling scalable, efficient, and traceable design-to-plan automation.
[AI-85] ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning
链接: https://arxiv.org/abs/2608.24033
作者: Juntao Fang,Shifeng Xie,Ruichu Cai,Shengji Zheng,Zijian Li,Keli Zhang,Lujia Pan,Themis Palpanas,Zhifeng Hao
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:Time series classification underpins applications in healthcare, sensing, and industrial monitoring. Although time series foundation models support forecasting and transferable representation learning, classification still typically requires fitting a task-specific classifier on each target dataset, while individual channels of multivariate inputs are often encoded independently. We introduce ChorusTIC, a classification-native foundation model for in-context classification across heterogeneous channel configurations without target-task parameter updates. ChorusTIC combines episode-consistent Random Subchannel Slot Concatenation with a shared dual-axis encoder to model temporal and cross-channel interactions and map variable channel configurations into a fixed-width representation independent of the original channel count. It then calibrates feature axes using context-derived distributions and predicts query labels through leakage-protected in-context learning. We pretrain ChorusTIC solely on synthetic labeled episodes comprising context and query sets that share a task background, with classes distinguished by sparse temporal or cross-channel rules. Evaluations on the complete UEA-30 and UCR-128 archives show strong full-context and low-label performance without target-specific classifier fitting.
[AI-86] Beyond Confidence: Test-Time Scaling for Multi-Turn Search Agents via Retrieval Grounding EMNLP2026
链接: https://arxiv.org/abs/2608.24024
作者: Hyunho Kook,Junhyuk So,Tianyu Fu,Haizhong Zheng,Beidi Chen
类目: Artificial Intelligence (cs.AI)
备注: Accepted to EMNLP 2026 Findings
Abstract:Confidence-based voting aggregates parallel LLM rollouts by weighting each with internal signals such as token log probabilities, and has been actively studied for single-turn reasoning. However, modern LLMs increasingly act as multi-turn search agents that retrieve and condition on external documents. In this paper, we show that confidence-based voting transfers poorly to this multi-turn setting, and identify the underlying failure reason as copy inflation: when retrieved documents are appended to an agent’s context, tokens copied from those documents receive systematically inflated log probabilities. This flattens confidence scores within each question and weakens the resulting weighted vote. To address this issue, we propose Retrieval-Grounded Voting (RGV), which scores each rollout by the lexical overlap between its final answer and the documents it retrieved. By computing the signal outside the contaminated context, RGV sidesteps both token log probabilities and additional LLM calls. Across four search-agent benchmarks and five LLMs, RGV consistently outperforms confidence-based voting, with gains of up to +5.4% accuracy and +35% on minority-correct questions, where the correct answer appears in only 1-2 of 8 rollouts.
[AI-87] What Guides the Agent ? Adjudicating Unauthorized Behavior via Localizing Behavior-Guiding Instructions
链接: https://arxiv.org/abs/2608.24022
作者: Yichao Gao,Yumo Zhang,Yunhao Yao,Haohua Du,Puhan Luo,Ruiqi Li,Zhiqiang Wang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:LLM agents integrated with external resources gain complex task capabilities, yet the unified natural-language context channel makes them vulnerable to injection attacks: untrusted external data may be dynamically parsed as behavior-guiding instructions during LLM inference, thereby subverting the agent’s decision. Existing defenses focus on static detection or isolation of malicious content at the input/output level, remains insufficient for detecting such dynamic inducements that arise during model reasoning. We propose Attnlocate, a runtime framework for fine-grained localization of context spans that genuinely influence tool-calling decisions, i.e., behavior-guiding instructions. Attnlocate casts this localization problem as an object detection task, aiming to detect the distinctive activation traces induced by behavior-guiding instructions within the attention matrix. Specifically, we design a multi-head, multi-layer attention aggregation scheme to construct a token-level feature space tailored for object detection. Then, a 1-D U-Net equipped with an anchor-free detection head is deployed to detect these spans. Finally, based on the authority of the provider from which the detected behavior-guiding spans originate, Attnlocate dynamically adjudicates malicious invocation attempts. We evaluate Attnlocate across ten agent configurations from five LLM families, covering scenarios involving indirect prompt injection and tool poisoning. Attnlocate achieves a mean IoU of 0.743, an average AUROC of 0.956, and a 0.934 true-positive rate at 0.067 false-positive rate. It also transfers effectively across unseen models and supports authority policy adaptation without retraining.
[AI-88] WebMCP-Phalanx: Enforcing and Characterizing Trust Boundaries for Browser-Integrated LLM Agents AAAI2027
链接: https://arxiv.org/abs/2608.24017
作者: Lin-Fa Lee,YI-YU Chang,Kuo-Hui Yeh
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 8 pages, 1 figure, AAAI2027
Abstract:The emerging W3C WebMCP proposal enables LLM agents to invoke tools exposed by web pages. In multi-party web environments, however, integrating agent execution into a browser security model centered on the Same-Origin Policy (SOP) leaves insufficient provenance and lifecycle guarantees for agent-accessible tools, creating three risks: subject-attribution spoofing, uncontrolled tool lifecycles, and semantic prompt injection. We propose WebMCP-Phalanx, a dual-layer agent runtime architecture. Its first layer provides a browser-native trust anchor that binds each tool to its registering principal through cryptographically protected capability credentials and propagates provenance labels throughout the tool lifecycle. Its second layer separates semantic inspection from privileged tool use. A Quarantine Agent (Q-LLM), without tool invocation authority, inspects tool metadata, outputs, and page-supplied content for prompt injection. Validated content is then forwarded to a Privileged Agent (P-LLM) for execution, while the Q-LLM’s internal state remains hidden from page scripts. Empirical evaluation shows that the browser-native ownership mechanism reduces revocation and overwrite attack success from 100% to 0%. The dual-agent runtime blocks all 80 prompt-injection attempts embedded in tool descriptions and limits tool-return attacks to 2 successful cases out of 80. Across experiments, task utility remains statistically indistinguishable from the no-attack baseline. Under a white-box adaptive attacker, however, description-based filtering can be bypassed through malicious tool names invoked before inspection. This finding motivates a call-timing gate that delays tool invocation until all agent-visible tool metadata has been validated.
[AI-89] Reflection with Action-Induced Visual Differences for Desktop GUI Agents
链接: https://arxiv.org/abs/2608.24015
作者: Yijie Ma,Chaoyue Niu,Fan Wu,Guihai Chen
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The Planner-Operator-Reflector (POR) framework is widely used in GUI agents to maintain objective alignment in complex tasks through modular collaboration. However, desktop GUIs introduce a key challenge: large, dense interfaces often exhibit subtle or scattered state changes, placing most of the burden on the reflector, which must compare pre- and post-action screens, while the planner and operator reason over a single state. Existing reflectors collapse change detection and outcome verification into one step, leaving evidence implicit and yielding weakly grounded decisions. To address this limitation, we propose Evidence-First Reflection (EFR), a two-stage reflector that explicitly decouples action-induced visual differences extraction from outcome verification. EFR identifies the action location and candidate changed regions with Set-of-Marks annotations, describes and filters action-relevant changes, and makes the final judgment from the cleaned evidence. This evidence-reasoning decoupled design makes reflection better grounded in screen transitions, while reducing both visual search complexity and reasoning burden. Experiments on OSWorld-Verified and WindowsAgentArena demonstrate that EFR improves reflector accuracy by 7.11%, yielding average end-to-end task success gains of 5.94% and 4.95% on the two benchmarks, respectively.
[AI-90] Incorporating Cognitive Load and Knowledge Transfer for Multi-Domain Knowledge Tracing CIKM2026
链接: https://arxiv.org/abs/2608.24005
作者: Haotian Zhang,Shucun Wang,Jinze Wu,Liang Ding,Shuochen Liu,Zhenya Huang,Jing Sha,Shijin Wang,Qi Liu
类目: Artificial Intelligence (cs.AI)
备注: Accepted as a CIKM 2026 Oral
Abstract:Knowledge Tracing (KT) aims to assess students’ dynamic knowledge states from their learning histories. While most existing KT methods focus on single-domain learning with notable success, real-world learning scenarios often involve multiple domains simultaneously, introducing two critical factors: 1) Cognitive load, arising from managing learning across domains in both temporal and knowledge dimensions. 2) Knowledge transfer, where knowledge states in one domain influence related states both within and across domains. In this paper, we focus on exploring these factors to improve students’ knowledge state assessment in multi-domain learning scenarios and propose a novel method incorporating cognitive Load and knowledge Transfer for Multi-domain Knowledge Tracing (LT-MKT). Specifically, to bridge isolated domains, LT-MKT first integrates textual information from questions and their associated concepts to construct a Multi-domain Hierarchical Graph, leveraging the advanced representational capabilities of large language models (LLMs). Then, cross-domain features in both the temporal and knowledge dimensions are explicitly modeled to capture the effects of cognitive load. Additionally, a knowledge transfer module is designed to model the propagation of knowledge states within and across domains. By jointly modeling these factors, LT-MKT enables more accurate prediction of students’ future performance. Finally, extensive experiments on real-world datasets demonstrate that our method achieves state-of-the-art performance.
[AI-91] Diverse by Reasoning : Harnessing the Wisdom of LLM Crowds for Future Prediction
链接: https://arxiv.org/abs/2608.24001
作者: Nirupam Chetlapalli,Yiming Liao,Min-Chun Chen,Keke Chen
类目: Artificial Intelligence (cs.AI)
备注: 13 pages, 4 figures, 5 tables. Submitted to IEEE BigData 2026
Abstract:Large language models (LLMs) are increasingly used for future prediction, motivating the use of multiple models as a wisdom-of-the-crowd mechanism. However, simply increasing crowd size does not guarantee effective diversity, as different LLMs may exhibit redundant behaviors. We propose a behavior-aware framework for constructing diverse LLM crowds. The framework characterizes models using their reasoning traces on independent development tasks, clusters models by behavioral similarity, and selects representatives for collective prediction. We evaluate 25 LLMs using seven development benchmarks for behavioral diversity modeling and two future-prediction benchmarks for evaluating diverse crowds’ performance. Our results show that crowd composition can matter more than crowd size: a three-model medoid crowd based on K-means++ behavioral clustering outperforms conventional voting over all 25 models on both prediction benchmarks, while reducing model calls by 88% and inference cost by approximately 80%. The results further suggest that representative behavioral diversity, rather than simply maximizing diversity, is important for constructing effective LLM crowds
[AI-92] Giraffe: A Mapping Architecture from Hidden Text Representations to Visual Embeddings for Efficient Graphic Design
链接: https://arxiv.org/abs/2608.23970
作者: Nejla Ghaboosi
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Multimodal large language models (MLLMs) have made significant progress in understanding and interpreting mul- timedia content. However, their ability to generate me- dia remains limited. Recent approaches have attempted to bridge this gap by translating the hidden representations of token sequences into the embedding space of visual models or directly into raw image data. However, these methods often represent each image using multiple specialised to- kens which significantly increases the input length. This be- comes a major limitation for tasks such as graphic design generation where the output typically involves a seamless blend of thousands of tokens across text, multiple images, and layout information. To address this challenge, a novel architecture is proposed that maps hidden token represen- tations to the embedding space of visual models, such as CLIP ViT-L/14, using a single [IMG] token per image. The architecture employs two shallow MLP blocks, each with a separate compression module followed by a shared expan- sion module, trained with six distinct loss functions. One block aids the other during training and is omitted during inference, resulting in a lightweight solution. Strong perfor- mance is demonstrated in both image-to-design and text-to- design generation tasks.
[AI-93] More GPUs or a Smaller Cache? Tensor Parallelism versus KV Compression for Memory-Bound LLM Serving
链接: https://arxiv.org/abs/2608.23962
作者: Srikanta Datta Tumkur,Mehar Simhadri,Anshu Bansal,Jay Iyer,Sai Pavan Kumar,Sai Kapil Kumar,Ramesh Nampelly,Raj Dandekar
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:When an LLM serving deployment runs out of KVcache room, there are two well-established ways out. Tensor parallelism shards the weights and the KV cache across two, four, or eight devices, buying memory headroom at the price of an all-reduce on every layer and a hardware bill that grows with the device count. The algorithms community shrinks the cache in place, with KV quantisation and eviction keeping a single GPU and spending a little quality instead. Compression papers report memory ratios, parallel-scaling papers report throughput curves, and almost nobody puts the two on the same cost axis. We place tensor-parallel configurations (degree 1 to 8) and KV-compressed configurations (16/8/4-bit, keep-ratios down to 0.25) on one costnormalised axis, cost per million tokens against latency, using a profiled simulator calibrated on A100, A40, and H100 hardware, and we go looking for the cost-equivalence crossover. We do not find one. Across two models (Llama-2 at 7B and 70B), three GPU types, and every level of memory relief we could construct, compression is cheaper by 1.20x to 2.00x. A 7B model on an 80 GB device cannot exhaust its KV budget within its own context window, and the boundary that decides between the strategies is model size relative to device memory, at roughly 36B parameters for an 80 GB card. Below that wall, compression dominates and extra GPUs are largely wasted spend; above it, tensor parallelism stops being a choice and becomes an entry ticket: Llama-2-70B is infeasible on one A100 at any KV setting, because the binding resource is weights, which KV compression does not touch. Tensor parallelism is the only lever that improves latency (compression makes per-token latency worse, by 8 to 93%, through batching contention), while compression is the only lever that multiplies capacity per dollar (16.5x, against 1.21x for an eightfold spend on GPUs).
[AI-94] Recursive Agent ic Reasoning
链接: https://arxiv.org/abs/2608.23956
作者: Shengxin Zhang,Xiaomin Wu,Xiyang Wu,Jing Xie
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Test-time reasoning methods such as iterative refinement, decomposition, and repeated sampling are often evaluated in isolation, making their gains difficult to compare across models, benchmarks, and evaluation pipelines. We introduce a unified view of these methods as recursion operators over an agent’s reasoning trace: GROW, which deepens a single reasoning path; PRUNE, which decomposes and recomposes the problem; and BRANCH, which samples alternative reasoning paths and selects among them. We evaluate all three operators against a single-pass chain-of-thought baseline under a shared harness with identical prompts, token budgets, and grading code. Across five benchmarks and three frontier models, comprising 14 model-benchmark settings, 49,327 graded items, and 151,876 model calls, BRANCH improves accuracy in all 14 settings by an average of 5.98 percentage points and is the best-performing operator in 12. In contrast, GROW yields a mean gain of 2.18 points and degrades performance in two settings, while PRUNE improves accuracy by 0.94 points on average. Analysis shows that BRANCH’s advantage arises not only from exploring multiple reasoning paths, but also from recovering from truncation: its gains strongly correlate with the baseline rate of empty, budget-exhausted outputs (r = 0.72). These results weaken the hypothesis that different problems require routing among test-time reasoning operators; at this level of abstraction, repeated branching is consistently dominant. Finally, we show that unpaired evaluation and treating scoring-pipeline failures as model errors can materially change, and even reverse, comparative conclusions, motivating paired scoring as a standard protocol for test-time-compute evaluation.
[AI-95] he Empire Long Divided Must Unite: Architectural Convergence in Three LLM Agent Harnesses
链接: https://arxiv.org/abs/2608.23953
作者: Dai Jiahong
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注:
Abstract:An agent harness is what turns a language model into an autonomous agent: the surrounding code that builds the model’s context, mediates its tools, runs the loop, and persists state across a long-horizon run. This layer, not the model it wraps, is increasingly the binding constraint on agent behaviour. We present a source-level, multi-case study of three open coding-agent harnesses built from deliberately opposing philosophies: LangChain’s deepagents (batteries-included), Earendil’s pi (radical minimalism), and DeepSeek’s dsh (everything-is-a-plugin). Reading each at a pinned commit and following its commit history, we find that the two mature harnesses have travelled in opposite directions (deepagents subtracting authored scaffolding, pi accreting durable infrastructure), yet converged toward one architectural middle form of five recurring elements: a commoditised loop, an append-only replayable session record, model quirks kept as data, progressive disclosure of context, and explicit extension seams. A third harness, read afterward as a held-out check, exhibits all five, and in one seam reuses another’s implementation outright. We therefore do not claim independent invention, and decompose the convergence into parallel discovery, diffusion, and literal reuse. Finally, one load-bearing dimension shows no convergence, and indeed no presence: external verifiability, a tamper-evident record an outside party can check without trusting the runtime. We read this absence not as an oversight but as a predictive gap, the next axis on which harnesses for provenance-sensitive domains will differ.
[AI-96] STAIN-FL: Stealthy Targeted Attack Injection with Contextual Triggers in Federated Learning
链接: https://arxiv.org/abs/2608.23952
作者: Ashlinder Kaur,Purnima Murali Mohan,Zengxiang Li,Tram Truong-Huu
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Federated video anomaly detection trains model collaboratively without sharing raw surveillance footage, but limited server-side visibility lets compromised clients to inject backdoor via malicious updates. This paper introduces STAIN-FL, a stealthy targeted backdoor attack injection framework that uses naturally occurring surveillance conditions, including low-light scenes, indoor settings, and crowd density, as contextual triggers. STAIN-FL combines anomaly-to-benign label \textitmanipulation with gradient masking over least-updated coordinates to preserve clean accuracy while inducing trigger-conditioned misclassification. We evaluate STAIN-FL on \textttUCF-Crime using 1024-dimensional I3D features in a non-IID four-client multi-agency setting, comparing FedAvg and FedProx under sparse and continuous attacks. Results show that sparse attacks have low-detectability, operationally significant attacks rather than high-intensity attacks: they keep the mean clean-accuracy drop below 2% , yet still misclassify more than half of triggered anomalies at peak backdoor accuracy under FedAvg ( 56.7% ) and FedProx ( 54.2% ). Under FedAvg, the sparse backdoor remains above the 25% backdoor-accuracy threshold for an average of 336 post-attack rounds, highlighting the persistence risk of contextually triggered attacks in surveillance systems.
[AI-97] More Rejective Not More Discriminative: The Unit of Verification in Pre-Execution LLM Oversight
链接: https://arxiv.org/abs/2608.23941
作者: Yuchen Han,Cheng Yan,Wuyang Zhang
类目: Artificial Intelligence (cs.AI)
备注: 37 pages, 20 figures, 32 tables, including appendices
Abstract:Pre-execution oversight is core to trusted monitoring in AI control: a fallible LLM monitor vets planned actions before irreversible execution. Over-blocking forfeits usefulness and pressures deployers to disable it. Every protocol must fix a unit of verification: how many actions one call reviews. Existing designs take the unit as given; its effect on fallible monitors is unmeasured. Natural traces cannot isolate it: review length co-varies with error type and position. Catch alone misleads: rejecting everything catches everything. Measuring this needs boundary variation alone and a matched clean control. We introduce the twin-prefix framework, which supplies both. Each gold plan yields a prefix with one injected, environment-accepted error and a clean twin differing in one write. Judging each pair at five nested lengths ties verdict changes to the unit alone. Discrimination is scored by pre-registered informedness, catch minus false rejection. Longer review raises catch; false rejection climbs in lockstep. Informedness peaks at one or two actions for all six judges in both domains: longer windows make zero-shot monitors more rejective, not more discriminative. Replaying withheld observations traces the failure largely to observation deprivation. Safety cases should state the unit and co-report the clean series. Our framework is the first controlled, pre-registered instrument for this choice and never reads catch alone. Our calibrated short unit recovers up to 0.95 informedness over eight-action review, and no tested label-blind policy consistently beats it.
[AI-98] Evolutionary Recurrent Decision Model in Developing Adaptive and Maladaptive Behaviors
链接: https://arxiv.org/abs/2608.23932
作者: Andrew Hu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:This study introduces the evolutionarily recurrent decision model (ERDM), a computational reinforcement learning framework designed to examine how evolutionary mismatch, bounded rationality, and satisficing contribute to adaptive and maladaptive behavior. ERDM simulates agents across evolutionary recurrent environments, including threat, prey/goal-pursuits, and alliances. Agents learn through competing rewards abstracted from survival metrics. A validity study under varying adverse childhood experiences demonstrates that distinct adaptive and maladaptive strategies, such as learned helplessness, avoidance, healthy relationships, and aggression, emerge naturally without being hardwired. These results align with empirical literature, showcasing ecological validity. The results suggest that many psychopathology-relevant aspects may be interpreted as bounded cognitive systems operating under modern-ancestral environmental mismatch, positioning ERDM as a key computational cognitive tool that can be extended to other studies.
[AI-99] Data Mixing as Mixture Experiment: Response Surface Methodology and Optimal Design for Large Language Model Pretraining
链接: https://arxiv.org/abs/2608.23922
作者: Yicheng Mao,Hongru Du
类目: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:Data mixing is a central design problem in large language model pretraining: given a fixed token budget, practitioners must decide how much data to allocate to each domain. Recent proxy-based methods address this problem by training small models on candidate mixtures, fitting a response model, and using the response to select mixtures for larger-scale training. We show that this workflow has the structure of a classical mixture experiment. Under this view, data domains are mixture components, token shares are component proportions, proxy-training runs are experimental design points, and validation loss defines a response surface over the probability simplex. We develop this formulation using sparse second-order Scheffé response-surface models and construct model-robust \mathcalI -optimal designs for proxy data-mixing experiments. Using RegMix as an empirical case study, we demonstrate how the framework can both interpret observed mixture responses and design more efficient proxy experiments. The Scheffé analysis shows that domain value is strongly relational: several domains that are weak under additive effects become favourable through pairwise interactions, especially through combinations with web-derived text. The sparse Scheffé model preserves mixture rankings across model scales and remains competitive with a flexible machine-learning predictor while providing an explicit decomposition of additive and interaction effects. In a simulation study calibrated to observed proxy-training responses, model-robust \mathcalI -optimal designs recover the relevant mixture ordering after removing about 25% of the original proxy runs. These results suggest that LLM data mixing should be treated not only as a prediction problem, but also as an experimental-design problem in which the proxy mixtures themselves can be chosen to improve statistical efficiency.
[AI-100] PROOF-Gen: From Optimized Data to Better Distillation
链接: https://arxiv.org/abs/2608.23911
作者: Anh Ta,Junjie Zhu,Shahin Shayandeh
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Supervised fine-tuning on teacher-generated trajectories is the standard first stage for distilling tool-calling capabilities into deployable models. Post-training pipelines that drive shipped tool-calling agents re-run this stage on a daily or weekly cadence, paying the frontier-teacher cost each cycle, yet the mechanism is generate-and-filter (keep the teacher’s passing trajectories, discard the rest) and each cycle leaves behind the same hard scenarios because failures supply no signal. On \tau2-bench, 57% of teacher trials fail, two-thirds of them near-misses (most tool calls correct, undone by one decisive error). We introduce PROOF-Gen (Per-scenario Reflective Optimization to Overcome FailedGeneration), which recovers golden trajectories from these failures via per-scenario prompt optimization. For each failed task, a reflector analyzes the execution trace and evaluation feedback, then writes corrective guidance that steers the teacher to a passing trajectory. The guidance is stripped before training, so the student learns from clean demonstrations with no task-specific scaffold. On \tau2-bench, per-scenario optimization recovers 93% of failed scenarios. Fine-tuned on the combined data, Qwen3-4B-Instruct-2507 improves from Pass^1=0.132 to 0.529 and Gemma 4 E4B-it gains +7.2pp on BFCL v4 multi-turn. In a deployed pipeline, the method lifts trajectory quality by +6.3pp goal completion and transfers to a deployed on-device model (+1.5pp goal completion; +1.7 to +5.0pp across response-quality metrics), with positive transfer in every locale (non-English average +1.48pp). Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2608.23911 [cs.AI] (or arXiv:2608.23911v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.23911 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-101] Retrieval-augmented generation vs. deterministic tax computation in multi-agent financial advisory: A 2x2 factorial experiment
链接: https://arxiv.org/abs/2608.23908
作者: Aryan Brar,Justin Du,Avery Lor,Kylie Seto,Eric Taylor
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Tax-loss harvesting demonstrates consistent benefits to long-term portfolio growth; yet implementing it efficiently often involves complex considerations that are specific to the holdings within that portfolio and the individual who owns it. We introduce a custom capital gains calculation engine and a RAG-retrieved vector store of market advisory reports to provide context for a multi-agent trade recommendation system. We investigate the effects of each context provider on the quality of recommendations, measured by relative capital gains incurred during portfolio liquidation. A 2x2 repeated-measures ANOVA revealed a significant main effect of the tax optimization engine ( F(1,29) = 9.17 , p = .005 , \eta^2_p = .240 ): enabling the engine reduced tax savings by approximately 55 percentage points relative to the no-engine conditions. The RAG main effect was not significant ( p = .841 ), nor was the interaction ( p = .553 ). The RAG-only condition achieved the highest descriptive mean tax savings (47.7%), and the baseline condition performed second-best (30.6%), suggesting that the pre-trained language model’s internalized financial knowledge may be sufficient for competent tax-loss harvesting recommendations without explicit tooling. These results indicate that augmenting LLM agents with domain-specific computation engines does not guarantee improved performance and may introduce conflicting optimization signals.
[AI-102] Quantifying System-Level Harms from AI Adoption in Complex Sociotechnical Systems
链接: https://arxiv.org/abs/2608.23906
作者: Paul Vautravers,Oliver Chalkley,Gabriel Downer,Kate S,Damian Ruck
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Artificial Intelligence (AI) is increasingly integrated into complex sociotechnical systems, including Critical National Infrastructure (CNI), where harms emerge from interactions between technical, human, and organisational elements. Yet current AI evaluation remains model-centric, offering little insight into how observed behaviours might translate into system-level risk. We propose a framework that links structured hazard analysis, component-level testing, and probabilistic system modelling to bridge this gap. By providing a traceable pathway from model behaviour to system-level outcomes, the framework enables practitioners to answer the “so what?” of AI failures, quantify their systemic impact, and move toward evidence-based and anticipatory governance of AI in complex systems. Applied to the UK’s Real Time Gross Settlement (RTGS) system as an illustrative worked example, we derive AI-driven loss scenarios using Systems Theoretic Process Analysis (STPA) and examine adversarial manipulation of LLM-based trading as one such loss scenario. Component-level experiments show that simple adversarial inputs induce measurable behavioural shifts where AI recommendations are followed. Under the component-to-system mapping used here for a financial contagion model, these shifts alter system resilience, increasing bank failures and lowering the threshold at which shocks lead to cascading disruption, particularly under widespread or monopolistic AI adoption.
[AI-103] BenchBench-Protocol: Evaluating Real-World Wet-Lab Protocol Reasoning and Modification
链接: https://arxiv.org/abs/2608.23898
作者: Aditya Sivakumar,Ashu Singhal,Nicholas Larus-Stone,Nithin Parsan
类目: Artificial Intelligence (cs.AI)
备注: 21 pages, 15 figures
Abstract:We introduce BenchBench-Protocol, a benchmark for large language models of 149 protocol-modification tasks recovered from modifications that scientists made to published protocols during real experimental work. Adapting a published protocol to a new experiment is a routine task for a wet-lab scientist, and a correct modification requires accounting for prior choices and downstream steps. Recent life-science benchmarks have moved toward open-ended, rubric-graded tasks, but tasks are typically elicited from experts rather than reconstructed from real-world modifications. BenchBench-Protocol tasks are derived from differences between a published protocol and a version a scientist modified, which provides the basis for the query and the weighted rubric elements for a correct response. The benchmark draws from 96 source protocols across nine domains of wet-lab biology and only includes tasks rated highly after review by domain experts. We evaluate nine closed and open models; Claude Opus 5 scores highest at 59.2% normalized rubric score, with other models between 34.1% and 47.1%, and the benchmark remains unsaturated when taking the best of ten attempts. As models are increasingly helpful in life-sciences research, evaluating them on routine wet-lab tasks becomes correspondingly important. We present BenchBench-Protocol as both a grounded assessment of wet-lab reasoning and evidence for the utility of real-world experiments to construct benchmark tasks.
[AI-104] Provenance Guided Incremental Learning Under Evolving Concept Definitions
链接: https://arxiv.org/abs/2608.23893
作者: Ismail Lamaakal
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Learning systems deployed over long periods must adapt not only to statistical changes in incoming data, but also to revisions of the definitions that generate their prediction targets. Conventional concept-drift methods typically infer such changes from observations or prediction errors, even when the underlying policy, rule, or query has been explicitly modified. This paper studies rule-induced concept shift, where the target-defining concept is revised directly, causing previously stored instances to acquire different semantic labels without requiring any change in their observed data. We introduce a provenance-guided incremental learning framework that compiles consecutive concept definitions into a structured rule delta, traces the changed components through historical provenance, certifies records whose previous labels remain valid, and restricts reevaluation to a localized candidate region. Executable revisions are relabeled automatically, ambiguous cases are handled through selective supervision, and the resulting changes are used for incremental predictor repair. A versioned concept memory further supports recurring definitions. We also introduce RuleShift-Bench, spanning financial, demographic, cybersecurity, and graph-structured data with threshold, predicate, logical, relational, recurring, and mixed concept revisions. Across the benchmark, provenance-guided repair attains 92.3% accuracy and 90.2% Macro-F1 while reprocessing 14.7% of the historical collection and retaining 94.6% of affected records. Its average update latency is 179s compared with 993s for complete relabeling and retraining. The results demonstrate that an explicit concept revision can be exploited as a data-maintenance signal, allowing learning systems to update the supervision and predictive state that depend on the change while preserving knowledge that remains valid.
[AI-105] A Mathematical Theory of Interpretation: Rational Entropy Spectral Readout and Confusability as a Resource
链接: https://arxiv.org/abs/2608.23892
作者: Blake Reynolds
类目: Information Theory (cs.IT); Artificial Intelligence (cs.AI)
备注: Abridged core theory of the 2026 dissertation A Mathematical Theory of Interpretation
Abstract:This article presents the abridged core of \emphA Mathematical Theory of Interpretation (MTI), which treats interpretation as observer-relative spectral measurement under an access structure. MTI makes interpretation a method-design problem: access, query, utility, and medium determine what an observer can select, identify, communicate, or refuse. On a learning-invariant Hilbert realization, Rational Entropy measures residual uncertainty across knowledge, utility, and medium. In the finite-effective regime, we classify its zero set. Pairwise confusability is equivalent to uniform atomic collapse, while a unique utility maximum can select one atom even when other zero-cost states remain non-atomic. This reverses the usual zero-error role of confusability: agreement in at least one observer direction excludes unresolved multi-atom readings, while the joint label preserves identification. The corresponding free-design capacity is the product of all but the smallest direction budget. A four-condition certificate characterizes sharp, decodable, medium-faithful, and order-independent readout on a finite commuting code sector and returns typed obstructions when those guarantees fail. Together, these results establish MTI as a theoretical basis for constructing interpretation methods with explicit access assumptions, guarantees, and failure modes.
[AI-106] A tale of perfect fit and phantom optima: how data-driven models can fail in real-time optimization
链接: https://arxiv.org/abs/2608.23885
作者: Prithvi Dake,Rahul Bindlish,James B. Rawlings
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI)
备注:
Abstract:Real-time optimization (RTO) relies on process models to locate economically optimal operating conditions. Because developing first-principles models requires significant process knowledge, data-driven alternatives are increasingly attractive. Modern machine-learning models can fit historical plant data accurately and often pass standard validation tests. Whether such models can be trusted for economic optimization, however, remains unclear. We investigate this question using a vinyl acetate monomer benchmark process with a unique, well-conditioned economic optimum. We train a structured hybrid model that combines known mass balances and thermodynamics with a neural-network closure for unknown kinetics, and a fully data-driven neural ordinary differential equation (ODE) model. Both models reproduce plant measurements accurately and exhibit little variation in predictions across random initializations. Yet their economic optima differ substantially from that of the plant. Where the plant returns a single optimum on multistart search, the trained models return many phantom optima. We further show that the training optimizer alone can be yet another source of error. Even with noise-free data and initialization at weights that recover the plant optimum, stochastic gradient training can drift to weights that yield substantially worse RTO solutions. The identified model is thus an artifact of the training optimizer as well as the data. These results demonstrate that a good predictive fit of all available measurements does not guarantee reliable economic performance. A data-driven model for RTO should at least be required to recover the optimum on a decision-oriented benchmark like the one developed here before being considered for plant testing and application.
[AI-107] AI Finds A Way
链接: https://arxiv.org/abs/2608.23875
作者: Aaron Dharna,Cong Lu,Ryan Sullivan,Joel Lehman,Victoria Krakovna,Jeff Clune
类目: Artificial Intelligence (cs.AI)
备注: 26 Anecdotes, 40 Pages (59 with references/appendix)
Abstract:Artificial Intelligence (AI) algorithms frequently learn creative and unexpected solutions, surprising even expert researchers who develop and study them. They often astonish practitioners by discovering unanticipated behavior, exploiting loopholes in reward signals, or spontaneously uncovering previously unknown scientific phenomena. However, accounts of such unconventional behavior across machine learning are seldom formally documented. This work presents 26 curated firsthand anecdotes from various machine learning subfields representing the work of over 100 researchers. These anecdotes showcase the capability of modern AI systems to circumvent human-imposed design limitations and discover unexpected solutions to the tasks we train them on. Furthermore, these accounts are particularly important for the safety of future AI systems. They illustrate the fundamental challenge of aligning models with human values without diminishing their creativity, so they can make surprising discoveries without producing surprising, potentially harmful outcomes. The paper first details AI achieving superhuman success through reinforcement learning across many challenging domains. However, reward-driven optimization can fail when the model learns to hack an underspecified reward or unarticulated constraint. We then present case studies suggesting that harnessing internet-scale foundation models (FMs) has not resolved these fundamental challenges and, in fact, can supercharge them. Nevertheless, we argue that these same learning dynamics can be harnessed to accelerate scientific discovery. Finally, we hope this work provides a consolidated resource to inform future research and demonstrates that the tendency toward unexpected behaviors is commonplace in modern AI, highlighting the need to anticipate and manage AI’s capacity for innovative, yet unpredictable, solutions. (abstract abridged)
[AI-108] Granite.Trust Policy Tools: Shareable Actionable Policies for Generative AI Applications
链接: https://arxiv.org/abs/2608.23870
作者: Nathalie Baracaldo,Nicolas Mello,Kush R. Varshney,Heiko Ludwig,Kate Soule,David Cox
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:When it comes to safety policies for generative AI, one size does not fit all. Each organization and use case needs to mitigate different risks depending on the application context, regulatory environment, organizational values, and user personas. Yet, existing policy specification approaches are designed for traditional access control and fail to capture the nuances of GenAI application: the enforcement of content-based constraints. We present two contributions to address this gap: (1) the Actionable Policy schema, a YAML-based format for specifying what model responses can and cannot contain. The schema enables exception-based policy governance, proposing exceptions to track policy violations; (2) synthetic data generation pipeline that produces policy-aligned training data for model alignment and testing, and a set of tools to help define the schema and enforce policy. Together, these enable organizations to specify policies once and enforce them throughout the GenAI application lifecycle: from model alignment to runtime monitoring. The Actionable Policy schema, example policies, and tools are available as open source: this https URL We welcome new ideas, contributions and feedback. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.23870 [cs.AI] (or arXiv:2608.23870v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.23870 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-109] Revelation Control
链接: https://arxiv.org/abs/2608.23860
作者: Qinyou Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: 38 pages, 4 figures, 6 tables
Abstract:Revelation Control is the problem of choosing priced interventions that reveal hidden state only insofar as the revealed distinctions can change a consequential decision, while accounting separately for any useful progress created by the intervention itself. We develop this theory for learning systems, where states equivalent under declared current information can respond differently to future training and favor different actions. The framework defines decision-sufficient revelation and revelation depth, separates pure information value from productive reuse, embeds static Bayes refinement into state-dependent continuation value, and gives an exact cost-adjusted factorization criterion: an additional shallow coordinate is decision-nonredundant only when states sharing a scalar summary lie on opposite sides of the priced Stop/Continue boundary. We also give a target-independent protocol for model-specific instantiation and prove that bounded stop-flip risk alone cannot certify positive expected utility under unrestricted severity. Across Qwen2.5-7B and Mistral-7B-v0.3, deeper future-learning probes have positive decision value and productive reuse yields strict equal-compute utility advantages. Qwen additionally provides evidence for a decision-nonredundant shallow revealability regime; in Mistral, a scalar continuation architecture fit only on an independent development panel retains positive familywise-adjusted lower bounds on a disjoint target panel, consistent with scalar decision sufficiency within the tested architecture family and resolution. The evidence supports structural rather than numerical transfer: the decision theory, cost accounting, continuation logic, and evaluation protocol transport, while empirical proxies, coefficients, thresholds, and even the required shallow state dimension may be system-specific.
[AI-110] Beyond the Mandate: A Systematic Security Analysis of the Agent Payments Protocol (AP2)
链接: https://arxiv.org/abs/2608.23858
作者: Avital Aviv,Parth A. Gandh,Ron Bitton,Asaf Shabtai
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:The Agent Payments Protocol (AP2), introduced by Google, enables large language model (LLM)-driven shopping agents to authorize and execute payments on behalf of users. Its signed Checkout and Payment Mandates protect the integrity of transaction data after signing. Agent interactions and external inputs that shape a transaction before authorization remain outside that protection, including Agent-to-Agent Protocol (A2A) messages and Model Context Protocol (MCP) tool calls. Prior work identified replay and prompt-injection attacks in AP2 v0.1. AP2 v0.2 addresses some of these issues but adds capabilities and deployment assumptions that require renewed analysis. We present a systematic security analysis of AP2 v0.2 based on its roles, transaction lifecycle, deployment architectures, and trust boundaries. We divide the lifecycle into five phases and identify five deployment architectures. Using MAESTRO (Multi-Agent Environment, Security, Threat, Risk, Outcome), we model four threat actors, eleven attack surfaces, eighteen adversary capabilities, and six attacker goals. The resulting catalog contains 48 threats spanning five attack families. We score these threats with the Artificial Intelligence Vulnerability Scoring System (AIVSS), identifying eight that reach the High band in at least one architecture. Because no complete public AP2 deployment was available, we build a testbed spanning all five architectures and develop five proof-of-concept demonstrations covering all eight High-risk threats and their mitigations. We also develop a deployment-aware scanner that maps applicable threats to static, cross-role consistency, and adversarial checks. Our analysis shows that valid mandate signatures alone do not ensure that an agent-mediated transaction reflects the user’s intent when its pre-authorization context is manipulated.
[AI-111] In-Context Inpainting for Time Series Forecasting
链接: https://arxiv.org/abs/2608.23855
作者: Thang Nguyen,Dung Nguyen,Romero Morais,Truyen Tran
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We propose ICI-Time, a novel framework that reframes time series forecasting as a visual inpainting task, leveraging the generalisation power of large vision models (LVMs). Unlike methods that require specialised temporal architectures and extensive domain-specific training, ICI-Time transforms time series into structured visual representations (area charts) and applies visual in-context learning, reformulating forecasting as pattern completion within a grid-structured prompt that pre-trained vision transformers can solve without fine-tuning or architectural modification. Temporal dependencies are represented through spatial layout, with a consistent, invertible mapping between numerical and visual domains. Extensive experiments across epidemiology, meteorology, and power systems demonstrate that ICI-Time performs competitively against deep learning baselines and shows promising adaptability under limited-data settings, introducing a new paradigm that bridges temporal and visual domains.
[AI-112] Exploit More Explore Smarter for Budget-Constrained Agent ic Search
链接: https://arxiv.org/abs/2608.23848
作者: Haoyang Fang,Bernie Wang
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Budget-constrained agentic search arises when an LLM agent must refine candidates under a small evaluation budget, because validation is expensive, generation requires multiple model calls, or both. In this regime, standard MCTS allocates budget poorly: exploration bonuses dominate at low visit counts, unpromising siblings are expanded before promising chains can deepen, and branching is independent of node quality. We introduce ExTS, a tree-search policy that treats expansion itself as a value-of-information decision. ExTS combines three mechanisms: discriminative reward shaping to separate candidates under narrow score distributions, a stochastic virtual child that estimates the value of creating a new branch from the parent’s reward history, and quality-conditioned branching that expands only when a node’s score justifies the budget cost. Across prompt optimization, code generation, molecular structure elucidation, and agentic workflow optimization, ExTS is competitive with or improves over task-specific tree-search baselines, with an average relative gain of +5.5% using a single fixed configuration. We further introduce pilot-run diagnostics that characterize what makes budget-constrained agentic search problems structurally different from one another, providing both understanding of the problem space and practical guidance for adaptation.
[AI-113] Coronavirus Optimization Algorithm: A Success-History Adaptive Evolutionary Framework with Archive-Assisted Search and Stagnation Recovery for Global Optimization
链接: https://arxiv.org/abs/2608.23847
作者: Hari Mohan Pandey
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Computational Complexity (cs.CC)
备注:
Abstract:This paper proposes the Coronavirus Optimization Algorithm (COA), a SARS-CoV-2-inspired success-history adaptive evolutionary optimizer for box-constrained continuous global optimization. COA does not model disease transmission; instead, it maps selected coronavirus mechanisms to explicit search operators, including elite-guided attraction, trial-vector generation, adaptive parameter variation, stagnation recovery, and population-size scheduling. The algorithm combines opposition-based initialization, current-to-pbest mutation, binomial crossover, an external archive, success-history adaptation, population reduction, and partial restart. COA is evaluated on 29 CEC 2017 benchmark functions at 10, 30, and 50 dimensions against 15 competitive optimizers. Results show that COA achieves the best overall Friedman rank across all dimensions, with particularly strong performance on composition functions. The findings demonstrate that COA is a compact, transparent, and competitive adaptive evolutionary optimizer, while also highlighting limitations on some hybrid functions and the need for further high-dimensional validation.
[AI-114] Automated Synthesis of Cloud Emulators
链接: https://arxiv.org/abs/2608.23842
作者: Archit Bhatnagar,Zhenning Yang,Sarah McClure,Yiming Qiu,Sylvia Ratnasamy,Ang Chen
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: 12 pages, 8 figures, 5 tables, Under review
Abstract:DevOps programming (e.g., using CLI/API scripts or IaC frameworks) is key to cloud infrastructure management. Unlike traditional programming tasks, DevOps program testing needs provisioning and execution against actual cloud resources, which is often time-consuming, unsafe, and costly. Cloud emulators have gained popularity for easing DevOps program testing; they are generally API-level mocks that can execute DevOps programs in a local environment. Still, building these emulators remains challenging: developers must manually interpret extensive cloud documentation and handcraft logic for each service, API, and their interaction. This does not scale to the complexity of the cloud, which is further a moving target as the services and APIs evolve. CloudEmu is an automated approach that constructs emulators based on cloud documentation via neurosymbolic code synthesis. The key idea is to combine LLMs’ general strengths in documentation understanding and code generation with cloud-specific symbolic abstractions that suppress hallucinations and enforce precision at scale, while using the real cloud as an oracle for automated testing, repair, and alignment. Our evaluation shows the effectiveness of CloudEmu on major cloud provider (AWS and GCP) services in both coverage and accuracy. CloudEmu outperforms the existing leading tool LocalStack, which was manually developed by a large team of engineers over a decade.
[AI-115] ShardMeter: Sharded and Geo-Distributed Training Without the Guesswork
链接: https://arxiv.org/abs/2608.23840
作者: Tim Beringer(1),Patrick Diem(1),Felix Wolf(1),Arya Mazaheri(1 and 2) ((1) Technical University of Darmstadt, (2) PanocularAI)
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注:
Abstract:Training large-scale AI models often outgrows a single data center, demanding sharded, multi-cluster, and decentralized training. However, the huge space of resource allocations makes exhaustive benchmarking and manual tuning impractical, while performance depends on tightly coupled factors like model size, GPU memory, batch size, bandwidth, and sharding strategy. We introduce ShardMeter, a lightweight analytical performance model that predicts the end-to-end runtime of transformer-based workloads across arbitrary sharded, distributed, and even decentralized training. Given a model’s characteristics and a target hardware topology, ShardMeter estimates per-GPU and per-island throughput, training cost, total wall-clock time, and identifies performance bottlenecks. Our analysis reveals diminishing-return regimes as island size increases, quantifies transitions between compute- and communication-bound scaling, evaluates hyperparameter trade-offs, and models cost-throughput for large-scale decentralized training. ShardMeter exposes these insights to quickly explore the configuration space, choose near-optimal deployment plans, and avoid costly trial and error.
[AI-116] Resilience Matters for Embodied Agents System: New Metrics Systematic Evaluation and Optimization
链接: https://arxiv.org/abs/2608.23839
作者: Yapeng Liu,Yuanzhao Zhai,Xudong Gong,Dawei Feng,Bo Ding,Lin Wang,Huaimin Wang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 12 pages, 5 figures
Abstract:Embodied Agents System (EAS) are increasingly deployed in open-world physical domains, where reliability directly dictates deployment quality and human-agent trust. However, existing evaluations rely on outcome-centric metrics as success rate or safety scores that collapse diverse execution trajectories into coarse scores, obscuring the dynamic processes underlying agent behavior. Therefore, they ignore a critical property of EAS – which we define as the Resilience – that reflects how EASs recover, stabilize, and extend under perturbations and across iterative updates. The lack of resilience is particularly critical in open-world environments due to continuous unexpected disruptions, thus directly affecting the quality of EAS deployment. To address this problem, we gain insight from the resilience-engineering concepts to EAS groundings and propose a novel resilience evaluation framework that can be flexibly applied to any EAS. Specifically, we define the first comprehensive resilience metrics suite for EASs system that exposes Rebound, Stability, and Graceful Extensibility across embodied tasks execution, providing a practical grounding for EAS resilience analysis. We further implement the resilience evaluation layer that transforms execution process into assessments for diagnosis and optimization. Across 400 household tasks with 10 EAS, we reveal the process-level distinction hidden by outcome metrics, including recovery cost differences among successful episodes ( \Delta C_rec=25.2 ), increased instability and task-family degradation. Metrics-guided optimizations reduce recovery cost and increase stability, graceful extensibility completion, showing the diagnostic effect of resilience evaluation. Our results reveal a trade-off among resilience characteristics, suggesting that a resilient EAS construction should be configured according to deployment-specific requirements.
[AI-117] SyPS: Measuring Sycophancy Prompt Sensitivity in Large Language Models EMNLP2026
链接: https://arxiv.org/abs/2608.23837
作者: Lijia Huang,Yao Fu,Sihao Ren
类目: Artificial Intelligence (cs.AI)
备注: Accepted to Findings of EMNLP 2026
Abstract:Large language models (LLMs) are known to exhibit social sycophancy, often validating or agreeing with users in socially sensitive contexts. Existing evaluations typically measure sycophancy under a fixed prompt formulation, leaving unclear whether such behavior is stable when the same underlying situation is presented with different sycophancy-relevant prompt variants. In this work, we study sycophancy prompt sensitivity: the extent to which changes in user confidence, emotional framing, social consensus, or validation-seeking language alter a model’s sycophantic behavior. We refer to our evaluation framework as SyPS, short for Sycophancy Prompt Sensitivity. Building on existing social sycophancy evaluation settings, SyPS constructs controlled prompt variants that preserve the same underlying user situation while varying sycophancy-relevant social cues. We introduce the Sycophancy Prompt Sensitivity Score (SPSS), an instance-level measure of sycophancy variation across paired prompt variants. Unlike aggregate sycophancy rates, SPSS separates baseline sycophancy from prompt-induced shifts, enabling model-level comparisons of robustness to sycophancy-relevant social cues. Empirically, we find that sycophancy prompt sensitivity is socially structured: validation-seeking and emotional-pressure cues often increase sycophancy, whereas counter-framing and anti-sycophancy prompts tend to reduce it. Our framework highlights whether LLMs maintain stable social judgments while adapting appropriately in tone.
[AI-118] Minima-KV: Retention-Preserving KV Cache Compression with Mixed-Format Paged Attention
链接: https://arxiv.org/abs/2608.23834
作者: Sergii Kozyrev(1),Davyd Maiboroda(1) ((1) Minima AI, Inc.)
类目: Artificial Intelligence (cs.AI)
备注: 13 pages, 3 figures
Abstract:The key-value (KV) cache is a primary capacity and bandwidth bottleneck in long-context LLM serving. We present Minima-KV, a retention-preserving hierarchy for mixed-format paged attention. Recent and protected Anchor pages remain in FP8, while older non-anchor pages move to packed TQ3; every live-request page remains addressable. Format-specific kernels compute partial attention states and combine them through a globally normalized online-softmax merge, enabling direct heterogeneous decode without a cache-sized dense shadow. Across separate, configuration-bound Qwen3.6-27B profiles on a single 96-GB NVIDIA RTX PRO 6000 Blackwell GPU, deployment accounting reports 18.3 KiB of attention KV per live token, corresponding to 3.50x compression relative to BF16 and 1.75x relative to FP8. A materializing quality profile matches its dense control on 16K RULER needle-in-a-haystack tasks. On the same 503-question LongBench v2 set, measured deltas are -0.80, -0.60, and -0.40 percentage points at 16K, 32K, and 64K. A separate single-pair direct-decode canary with two 59,008-token requests measures 3.625x active-KV compression and 0.9821x throughput relative to its control, routes all 16 full-attention layers without fallback, and retains no dense shadow. These results establish a practical mixed-format path for compressing long-context state without evicting live-request KV pages.
[AI-119] Place Slice and Schedule: Hierarchical O-RAN Control of a Tethered mmWave UAV-gNB
链接: https://arxiv.org/abs/2608.23824
作者: Alireza Mohammadhosseini,Fatemeh Afghah
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
备注: This work has been submitted to the IEEE for possible publication
Abstract:Unmanned aerial vehicle (UAV)-mounted 5G New Radio base stations (gNBs) can augment terrestrial networks with an on-demand, repositionable Frequency Range 2 (FR2) capacity layer. This flexibility, however, couples the physical network topology with radio-resource management: UAV movement reshapes blockage, channel quality, and the set of effectively served users, while traffic demand, queues, and service requirements evolve at a much faster timescale. Existing Open Radio Access Network (O-RAN)-enabled UAV studies optimize trajectory, deployment, association, or resource allocation, but typically in isolation, without coordinating slow aerial control with fast per-user scheduling. We instead exploit O-RAN disaggregation, Key Performance Indicator (KPI) monitoring, and multi-timescale RAN Intelligent Controller (RIC) control to address this coupling: a Non-Real-Time RIC rApp uses aggregated KPIs and radio-environment context to jointly control tethered UAV placement and the enhanced Mobile Broadband (eMBB)/Ultra-Reliable Low-Latency Communication (URLLC) slice budget, while a Near-Real-Time RIC xApp allocates per-user resources within that budget. We realize this xApp as a permutation-equivariant DeepSets Soft Actor-Critic (D-SAC) scheduler that treats the users as an unordered set, trained in a Sionna RT ray traced channel. The resulting hierarchical controller improves eMBB SLA satisfaction by up to 17% and URLLC on-time delivery by up to 42% over classical and learned schedulers; the learned rApp further raises URLLC on-time delivery by up to 20% over baselines.
[AI-120] A Formal Methodological Framework for Auditing Robustness and Fidelity in Explainable AI: From Application to Trust Certification
链接: https://arxiv.org/abs/2608.23817
作者: Rosa Elysabeth Ralinirina,Jean Christian Ralaivao,Niaiko Michaël Ralaivao,Alain Josué Ratovondrahona,Thomas Mahatody
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注: Peer-reviewed, accepted for publication in the proceedings, and presented at ICAI 2026 ( this https URL ). This is the revised version. The organizers did not send the revision notification to the authors in time, and as a result the paper was not included in the proceedings. 10 pages, 4 tables, 3 figures
Abstract:SHAP and LIME are now standard tools for interpreting black-box predictions, yet their outputs can vary substantially when the input is perturbed by small amounts of noise–a problem we observed firsthand in our previous work on food security in Madagascar (Ralinirina et al., 2025). This variability raises the question of whether such explanations can be trusted at all. We address it by constructing an auditing protocol that measures two properties of any post-hoc explainer: robustness (how stable the explanation is under input perturbation) and fidelity (whether the features deemed important actually drive the model’s prediction). These two quantities are combined into a single Trust Score. We run the protocol on a multi-sectoral dataset from Madagascar (83 features, 253 records, 4 malnutrition classes) using three classifiers and two explainers, plus their regularized counterparts. The results are sobering: models with AUC above 0.99 can produce numerically degenerate or flatly uninformative explanations, and fidelity scores lose discriminative power when the model is overfitted. These findings suggest that auditing XAI outputs is not optional but necessary, particularly when they inform decisions in sensitive domains.
[AI-121] Generating Biomedical Fact-Checking Reports with RL-Enhanced Agent ic Search
链接: https://arxiv.org/abs/2608.23811
作者: Jiongxiao Wang,Dingli Ma,Chaoqun Ni
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Automated fact-checking is essential for ensuring the reliability of public health information, yet the biomedical domain poses unique challenges. Validating biomedical claims requires rigorous interpretation of scientific literature, assessment of retrieved evidence, and comprehensive justification toward the conclusion. Although Large Language Models (LLMs) enhanced by Retrieval-Augmented Generation (RAG) and agentic search perform automated fact-checking in a retrieve-then-verify paradigm, current methods still output isolated prediction labels, lacking explanatory depth and offers limited utility for human understanding. To bridge this gap, we introduce an LLM-based agent named BioCheck Agent that generates structured biomedical fact-checking reports with agentic search. Rather than merely outputting supported or refuted labels, our agent synthesizes final conclusions with retrieved evidence and rigorous analysis. To ensure domain-specific accuracy, BioCheck Agent exclusively searches high-quality scientific literature in PubMed, utilizing advanced Boolean search operators. Recognizing that direct prompting often results in hallucinations and low-quality reports, especially for lightweight open-source models, we further propose the Evidence-Grounded Group Relative Policy Optimization (EG-GRPO) to perform reinforcement learning on BioCheck Agent with a task-specific reward that incentivizes advanced search behavior and high-quality evidence retrieval while penalizing hallucinations. Our experimental results show that compared to the base model Qwen3.5-4B, BioCheck Agent with EG-GRPO improves label prediction accuracy on SciFact by 9.95%. Furthermore, it achieves a 3.7% higher evidence quality score and a 19.63% lower evidence hallucination rate, demonstrating its ability to generate biomedical fact-checking reports with improved accuracy and quality.
[AI-122] Serving Masked Diffusion LLM s: Characterization and Design Principles from Real Hardware
链接: https://arxiv.org/abs/2608.23807
作者: Farhana Amin,Sabiha Afroz,Mona Moghadampanah,Dimitrios S. Nikolopoulos
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Masked diffusion language models (dLLMs) can in principle generate text faster than autoregressive (AR) models, since they denoise many tokens at once. Recent systems have begun building serving infrastructure for dLLMs, but none first measure how these models behave under real, concurrent serving load. Serving systems built without this grounding risk carrying over assumptions from AR serving that may not hold for dLLMs. We characterize dLLM serving to close this gap, using LLaDA-8B-Instruct with a D2F (Discrete Diffusion Forcing) LoRA adapter on a single NVIDIA H200 GPU, evaluated on GSM8K and HumanEval. We report three findings. First, request difficulty, the number of denoising steps a request needs, is discrete rather than continuous: requests fall into 11 fixed step-count levels (178 + 29k), and no signal we test predicts the level before generation starts (best R2 = 0.150). Second, benchmarks with short generation budgets below 320 tokens understate serving variance, since requests are cut off before the latency spread appears. Third, only 24% of single-request wall-clock time is GPU computation; the rest is CPU-side dispatch overhead. Batching mainly helps by amortizing this overhead: sharing one forward pass per denoising step improves throughput by 16.0x at batch size 16 over a per-request-dispatch baseline. We also argue structurally that output quality should not degrade with batch size, stating three assumptions this rests on; we measure 74 to 76% GSM8K accuracy at single-request scale. Finally, we derive a batch-timeout rule for fixed-fill synchronized batching under Poisson arrivals. Together, these results show that serving diffusion language models needs parallelism at the level of each denoising step, which differs from AR serving in how admission and eviction interact with an already shared forward pass.
[AI-123] Disentangled Skill Representations for Predictive Human Modeling
链接: https://arxiv.org/abs/2608.23776
作者: Mariah Schrum,Deepak Gopinath,Srijan Srivatsa,Guy Rosman,Tiffany Chen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Understanding human skill is important for AI systems that collaborate with, coach, or assist people. Unlike typical latent variable estimation problems which rely on single observations, skill is a persistent, compositional, and behaviorally grounded construct that must be inferred from patterns over time. We introduce Skill Abstraction with Interpretable Latents (SAIL), a method for modeling human skill as an interpretable, multi-dimensional construct inferred from naturalistic behavior. Our approach produces a skill embedding that is robust to transient performance fluctuations and learns a transferable representation of human subskills. Furthermore, SAIL supports skill-informed behavior prediction that generalizes across a variety of in-domain contexts. We represent each individual with a persistent skill embedding that controls a blend between expert and novice bases and is trained using counterfactual subskill swaps for disentanglement. This design encourages representations that are both robust to performance variation and structured for interpretability. We demonstrate across racing and baseball that SAIL achieves strong predictive performance and consistently improves behaviorally grounded disentanglement over the evaluated baselines, while also improving downstream AI coaching performance.
[AI-124] rustShiftProbe: Characterizing Benchmarking and Defending Staged Trust Attacks on MCP Servers
链接: https://arxiv.org/abs/2608.23763
作者: Mehrdad Rostamzadeh,Sidhant Narula,Mohammad Ghasemigol,Daniel Takabi
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:The Model Context Protocol (MCP) has emerged as the standard layer connecting Large Language Model agents to external tool backends. This openness introduces a severe server-side threat we term TrustShift: a compromised MCP server behaves benignly during an initial conditioning phase, building operational reliance and suppressing agent skepticism, before switching to an adversarial payload once an interaction threshold is reached. The evasion is temporal, not syntactic: benign at deploy time, the server’s defection is invisible to predeployment static analysis, which sees only the honest phase. Switched payloads range from overt structural violations to schema-valid manipulations, the latter preserving outer protocol compliance to evade runtime middleware filters. Crucially, TrustShift originates in the server-controlled tool channel, not user prompts (unlike indirect prompt injection) or the transport (unlike man-in-the-middle): the adversary is the trusted server endpoint itself. We introduce TrustShiftProbe, an evaluation and defense framework with four contributions: (1) a stateful temporal threat model of the agent-server lifecycle as a benign conditioning phase followed by an adversarial defection at a trust horizon; (2) a language-agnostic attack engine that instantiates each variant as a compromised MCP server across four production domains; (3) SHIELD, a multi-tier, zero-oracle runtime defense at the MCP transport boundary that audits server payloads against behavioral baselines learned during clean trust windows; and (4) a taxonomy of nine TrustShift variants spanning three execution mechanisms (structural violation, semantic corruption, scope expansion) and three adversarial objectives (disruption, exfiltration, and their combination). Across frontier proprietary and open-weight models, TrustShift attacks achieve a 69.5% mean attack success rate that SHIELD mitigates to 42.7%.
[AI-125] EXAM2: underlineExtending underlineAudio Understanding in underlineMultilingual and underlineMultimodal Analysis
链接: https://arxiv.org/abs/2608.23758
作者: Jiawen Wang,Xiaoxue Gao,Zi Haur Pang,Nancy F. Chen
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: 8 pages, 2 figures
Abstract:Recent large audio language models (LALMs) have achieved impressive progress in audio understanding. However, existing evaluations remain largely constrained to English and narrow audio domains. Prior benchmarks typically focus on a single audio modality, i.e., speech, sound, or music, limiting the systematic investigation into how these models generalize across diverse visual scenarios. In this paper, we introduce EXAM ^2 , a benchmark for multilingual and multimodal audio understanding spanning six languages and multiple modalities, including speech, sound, music, mixed-audio settings, and visual images. By incorporating visual information alongside heterogeneous audio inputs, EXAM ^2 enables more realistic evaluation of scene-aware audio reasoning and cross-modal comprehension. EXAM ^2 comprises 5,667 multiple-choice questions, 22,614 image instances, and 135,684 multilingual translations. We evaluate state-of-the-art open-source and proprietary LALMs as well as multimodal LLMs, revealing substantial performance gaps in multilingual and cross-modal understanding. Furthermore, we propose Gemma3n-EXAM ^2 , a lightweight fusion-model fine-tuned on EXAM ^2 -train, achieves up to 12.4% improvement in multilingual settings and 21.7% gains in multimodal evaluation over a strong baseline. Empirical results establish EXAM ^2 as a challenging benchmark and pioneer future multilingual and multimodal audio intelligence research.
[AI-126] Agent Room: Concurrent Multi-Agent Coding in a CRDT-Backed Shared Workspace
链接: https://arxiv.org/abs/2608.23740
作者: Seonglae Cho,Donghyun Lee
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Concurrent multi-agent coding promises division of labor across modules, robustness through redundancy, and parallel exploration at the natural granularity of multi-file projects. Realtime collaborative editing protocols solve this coordination problem for human teams via Conflict-free Replicated Data Types (CRDTs), but the LLMs underneath generate one token at a time and existing multi-agent coding systems inherit this serial limit: they either sequence agents through phase handoffs or pool independent samples without coordination, and a single agent abandons up to half of hard tasks with a one-file stub-and-exit. AgentRoom is a realtime collaborative editing protocol for concurrent coding agents. Its runtime layer exposes file-level claim, status, and broadcast as MCP tools on a CRDT-merged shared filesystem. Five frontier coding-CLI models ran four backend coding tasks, with cross-language checks in Python DevBench and Rust+axum. For CLI-stable models, AgentRoom with 2 agents abandons fewer tasks than Solo and has less run-to-run variation. At matched-compute, one positive mean LLM-judge contrast puts AgentRoom over parallel-merge. The other contrast, a bundle probe, puts full AgentRoom above each partial case: an ordering rather than a percentage split. Coordination, not parallelism or CRDT-merge, bears the load.
[AI-127] Do LLM s Understand Limit Order Book Dynamics?
链接: https://arxiv.org/abs/2608.23706
作者: Junxiao Chen,Paul Glasserman
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:A large language model (LLM) trained on synthetic limit order book (LOB) data achieves near perfect scores in generating valid sequences of LOB events. However, the LLM’s implicit world model fails to learn the state of the LOB. This deficiency leads to biased estimates and spurious predictability in using the LLM to forecast future LOB events. Our analysis uses novel tests of an LLM’s world model, extending prior work from deterministic settings to the stochastic dynamics needed for the LOB.
[AI-128] Confidently Wrong Silently So: Auditing Undetectable Failures of a Deployed On-Device Language Model
链接: https://arxiv.org/abs/2608.23663
作者: Shashwat Pandey,Satwik Pandey,Suresh Raghu
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 8 pages, 5 figures, 2 tables
Abstract:Aligning deployed language models requires knowing when their outputs can be trusted, yet on-device models now ship to hundreds of millions of devices with no server-side moderation, and the configuration developers can actually deploy is rarely audited independently. We present a reproducible reliability audit of the developer-accessible on-device foundation model, framed as an oversight question: can a user or a resource-constrained developer tell when the model is wrong? Red-teaming it on calibration, confident confabulation on false-premise questions, and over-refusal of benign prompts, we find a \emphtask-asymmetric miscalibration: its guardrails fail in opposite directions across tasks (confabulating on 69% of false premises while refusing 18% of entirely benign inputs), atop a self-reported confidence that is saturated and non-discriminative (AUROC 0.47; ECE 70, worst among comparable small models). Crucially, confident-correct and confident-wrong outputs are \emphsurface-indistinguishable: a classifier over 15 user-visible features separates them at AUROC only 0.55 (equivalence-confirmed), leaving no signal for oversight at inference time. No cheap single-generation signal flags these failures ( \le 0.68 AUROC), whereas a black-box consistency wrapper requiring no model access recovers reliability (confident confabulation 75% \to 3%; selective accuracy 43% \to 83%) at a tunable cost. We contribute a model-agnostic audit protocol, a surface-indistinguishability test, and released code and frozen evaluation items as reusable infrastructure for auditing deployed models.
[AI-129] From Causal Plausibility to Causal Reliability: Evaluating LLM s as Calibrated Direct Causal-Edge Classifiers
链接: https://arxiv.org/abs/2608.23660
作者: Amit Kumar,Elnur Adl Zarabi,Suranjana Trivedy,Zhiqian Chen,Lei Zhang,Kaiqun Fu,Taoran Ji
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Methodology (stat.ME)
备注:
Abstract:Large language models (LLMs) are increasingly used to provide prior causal knowledge for structural causal discovery, yet whether their direct-edge judgments and confidence can be trusted remains unclear. We systematically evaluate 12 instruction-tuned open-weight models across six benchmark causal graphs, five prompting strategies, and four confidence sources: verbalized, logit-based, cross-prompt agreement, and cross-model agreement. Under our language-only pairwise protocol, our evaluation yields three key findings. (i) LLM-based causal judgments are strongly recall-dominant: models predict overly dense graphs with many false-positive edges, while prompting mainly shifts the precision-recall trade-off rather than resolving overprediction. Gains from model scale diminish on the largest graphs and do not eliminate miscalibration. (ii) LLMs often capture causal relatedness without reliably identifying directness or orientation. Relative to published reference graphs, models misclassify 40.0% of indirect and 36.0% of reversed non-edges as direct edges, versus 28.2% of other non-edges. Moreover, 80.8% and 84.6% of these false positives receive verbalized confidence of at least 80%, revealing substantial overconfidence in structurally incorrect predictions. (iii) Conventional confidence estimates are unreliable, whereas agreement offers a more promising signal. Logit-based confidence frequently collapses near 1.0 regardless of correctness, while cross-prompt and cross-model agreement achieve better mean calibration and discrimination, though their advantages are not statistically significant after Holm correction. A benchmark-familiarity audit further identifies potential familiarity in five model-dataset pairs, all involving AsiaM. Overall, our results suggest LLMs are better viewed as sources of externally validated soft causal priors than as direct evidence of causal structure.
[AI-130] Elastic KV Cache for LLM Serving:A Working Reclamation Mechanism and Why Chunked Prefill Already Closes the Gap
链接: https://arxiv.org/abs/2608.23658
作者: Sathishkumar Sivashanmugam
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI)
备注:
Abstract:An LLM serving engine sizes its key-value (KV) cache once, at startup, permanently setting aside a reserve for the worst-case prefill activation. During decode-dominant phases that reserve sits idle, yet it cannot be handed to the KV pool because it is exactly the memory a large prefill needs. We ask whether this reserve is reclaimable, and build a mechanism to test it. Our elastic KV cache lends the reserve to the KV pool during decode and returns it before prefill, driven by the scheduler’s one-step-ahead view of the next batch. It is pure userspace on the CUDA virtual-memory path: two physical handles mapped into one contiguous virtual range per layer, so the attention kernel is unchanged and no driver patch is required. It decommits in a few milliseconds and recommits in tens of milliseconds, works with CUDA graphs and prefix caching, and never triggers an out-of-memory event. A static commit of the same memory is unsafe, crashing on prefill bursts, which makes the dynamic toggle necessary. Having built the mechanism, we test the premise it rests on and report an honest negative result. It only pays off if a small prefill chunk size badly hurts prefill latency. In a controlled experiment injecting long prompts into a live decode load, that penalty is small (median time-to-first-token differs by about 1% between chunk sizes of 8192 and 32768 tokens), because prefill is compute bound and decode consumes only about one token per sequence per step. Simply lowering max_num_batched_tokens recovers more KV than the controller does, at nearly equal latency. The reserve also dilutes under tensor parallelism, from 16% of KV at TP1 to 2.7% at TP4. We state precisely when reclaiming the reserve could still help, and release the mechanism as a reusable userspace elastic-VMM allocator. Subjects: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.23658 [cs.AR] (or arXiv:2608.23658v1 [cs.AR] for this version) https://doi.org/10.48550/arXiv.2608.23658 Focus to learn more arXiv-issued DOI via DataCite
[AI-131] Beyond Executable Models: The Pufibara Agent Harness and the Modelica Agent Workflow Benchmark for Physical System Modeling
链接: https://arxiv.org/abs/2608.23653
作者: Zizhe Wang
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:AI agents are increasingly used for simulation-driven engineering. Physical system modeling presents different requirements from general-purpose code generation in software engineering, because correctness depends not only on syntax and executability but also on physical consistency and scenario-dependent behavior. We study this challenge in Modelica, an equation-based modeling language in which a model may compile and simulate while still violating its intended physics or engineering requirements. Across successive revisions, an agent may lose track of requirements or rely on simulation evidence produced by an outdated candidate. To address this challenge, we present Pufibara, an agent harness that maintains persistent engineering state across revisions, associates execution and simulation evidence with the candidate that produced it, and makes submission an explicit agent action. To evaluate end-to-end Modelica agent workflows, we also propose a source-grounded method for constructing realistic and independently evaluable tasks. We use this method to build the 232-task Modelica Agent Workflow Benchmark, spanning Model Repair, Model Generation, and Model Tuning. Each submitted candidate is scored by a benchmark-owned evaluator outside the agent loop. We compare Pufibara with Claude Code as complete harnesses under two matched large language model (LLM) backends. With DeepSeek v4 Flash, Pufibara passes 202 tasks, compared with 185 for Claude Code. With Claude Sonnet 5, Pufibara passes 202 tasks, compared with 187 for Claude Code. Under the repository-reported token accounting, Pufibara records 76.4%-82.5% lower logical-token totals. Its sequential runtime is 6.1%-58.4% lower. These findings show that, even under matched LLM backends, complete agent harnesses can differ substantially in both task success and resource use for physical system modeling. Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.23653 [cs.SE] (or arXiv:2608.23653v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2608.23653 Focus to learn more arXiv-issued DOI via DataCite
[AI-132] Feedback That Backfires: Why Small Language Model Agents Repeat the Call They Just Watched Fail
链接: https://arxiv.org/abs/2608.23651
作者: Esmail Gumaan
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 25 pages, 6 figures, 13 tables. Code, data, probe items and rollout logs: this https URL
Abstract:Agent harnesses record a failed tool call and its error message in the transcript and ask the model to continue, on the assumption that the error is corrective information. We measure whether it is. Defining the corrective gain of a failure record as the change in log-probability of re-emitting the action that just failed, we find the gain is negative for every instruction-tuned model we tested (6 checkpoints, 135M-1.7B, 4 families) in two environments: simulated tool calling and MBPP program repair. Normalised by action length the effect is about -1.03 nats per action token, a factor of 2.8 in the odds of each token, and holds on 90%-100% of individual items, not only on average. Over a fixed candidate set the probability of repeating the failed call rises from 0.06 to 0.54, and greedy decoding reproduces it token for token on 19% of items after the failure versus 0% before. Counterfactuals pairing the same call with a failure message, a success message, or a neutral acknowledgement separate two effects: the failed call’s surface form accounts for 83% of the damage, while the semantic contribution of marking it failed is small and inconsistent in sign across environments. The problem is in the harness, not the model’s grasp of error messages, and that predicts which remedies work. Replacing the verbatim call with a runtime-generated description of the failure removes 76% of the inversion at no token cost, and making previously-failed strings unreachable at the decoder acts on the same term. Two plausible remedies do not: an explicit “do not repeat” instruction leaves the measured quantity where it was, and deleting the failed attempt to retry from a clean context, the standard prescription for context contamination, is the worst harness we measured for repetition, because it restores the context that produced the failure. The study runs end to end on a CPU; all artefacts are released.
[AI-133] MolEmb: Multimodal Large Language Models Can Be Strong Molecular Embedding Models ICML2026
链接: https://arxiv.org/abs/2608.23646
作者: Xinjian Zhao,Xiangru Jian,Yaoyao Xu,Xiaozhuang Song,Wei Pang,Lei Bai,Tianshu Yu
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Presented at the 3rd Workshop on Multi-modal Foundation Models and Large Language Models for Life Sciences (FM4LS), ICML 2026. Non-archival workshop
Abstract:Molecular embedding models can serve as foundational infrastructure for computational chemistry and drug discovery, where reusable vector representations support property prediction, virtual screening, and retrieval. Most molecular encoders are specialist models built around a single molecular view, producing unconditional vectors with no language interface for varying the representation. We ask whether multimodal large language models (MLLMs), which natively process images, text, and symbolic inputs, can instead serve as \emphgeneral molecular embedding models that produce embeddings conditioned on both a molecular profile and a natural-language semantic context. We introduce \textbfMolEmb, a lightweight framework that adapts MLLMs by aligning molecular profiles with textual descriptions in a shared embedding space using a bidirectional contrastive objective. The resulting embedding model is competitive on molecular property prediction and supports cross-modal molecule–text retrieval in the same space. We further introduce \textbfMolCAR, a diagnostic benchmark for context-aware retrieval, and find that context-aware molecular embedding is primarily a data property of the supervision. These results suggest that MLLMs are not merely chemistry assistants or generators, but a viable and extensible route to general molecular embedding models.
[AI-134] Ethical LLM -Assisted Research: A Framework for Responsible Delegation Verification and Epistemic Value
链接: https://arxiv.org/abs/2608.23644
作者: Kalin Stoyanov
类目: Artificial Intelligence (cs.AI)
备注: This is a substantially revised version of submit/7664457. I have extensively revised the manuscript to clarify its scholarly contribution, strengthen the formal framework and literature grounding, and remove or reformulate claims that were not sufficiently supported
Abstract:Large language models (LLMs) are becoming routine instruments of scientific research, assisting with literature synthesis, hypothesis development, coding, and formal reasoning. Their use raises a central epistemic question: when parts of scientific reasoning are delegated to an artificial system, what conditions must remain under human control for the resulting knowledge claims to retain epistemic legitimacy and accountable authorship? This paper develops a normative and conceptual framework for analyzing such delegation. Scientific reasoning is treated as a distributed process in which the origin of a contribution may vary between human and machine, while responsibility for its acceptance into the scientific record remains human. The framework distinguishes content origin O(g) , completion of human verification V(g) , responsibility assignment R(g) , accountable human ownership M(g) , and epistemic outcome E(g) . These constructs separate the provenance of a claim from the process by which it is checked, the epistemic outcome of that checking, and the human responsibility attached to its disposition. The central proposition is that the ethical boundary of LLM-assisted research is determined primarily by adequate verification and accountable human ownership rather than by the degree of machine involvement itself. On this basis, the paper develops the notion of an \emphepistemic audit: a structured record of delegation, verification, provenance, and responsibility intended to make AI-assisted reasoning transparent and reviewable. The resulting framework provides a formal vocabulary for distinguishing responsible cognitive delegation from the transfer or neglect of epistemic responsibility in scientific research.
[AI-135] FLARE: A Systematic Uncertainty-Aware Framework for Evidence-Based Adoption of Artificial Intelligence in Healthcare
链接: https://arxiv.org/abs/2608.23643
作者: Jacob Idoko,Siddhartha Paudel,Mariana Bento,Roberto Souza,Gouri Ginde
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Artificial intelligence is increasingly being introduced into healthcare workflows, yet most evaluations emphasize model accuracy rather than whether adoption is economically worthwhile in real clinical settings. This study proposes FLARE, a systematic and uncertainty-aware framework for evaluating the financial and operational implications of adopting AI in healthcare. FLARE combines fuzzy logic, time-driven activity-based costing, and return on investment analysis to estimate the cost of clinical service delivery, the cost of AI development and operation, and the economic consequences of workflow integration under uncertainty. The framework was demonstrated through an early health technology assessment case study of AI-assisted large vessel occlusion detection in the CT stroke pathway for acute ischemic stroke. The case study shows how FLARE can quantify conventional pathway cost, AI-related development and recurring costs, and AI-enabled service savings within a unified activity-based model. Under expected assumptions, the analysis identified a break-even threshold of approximately 3,992 patients per year, with positive first-year return on investment at typical annual stroke volumes of about 5,000 patients. The results further show that economic benefit depends not only on algorithmic performance, but also on patient volume, verification time, infrastructure choices, and workflow design. FLARE provides a transparent and practical decision-support framework for early-stage evaluation of AI adoption in healthcare. By making uncertainty, resource use, and implementation trade-offs explicit, it helps clinicians, administrators, and policymakers determine when AI deployment is economically viable and where operational changes may improve value.
[AI-136] How much of a measured AI preference is the model and how much is the instrument?
链接: https://arxiv.org/abs/2608.23641
作者: Jason Hung
类目: Artificial Intelligence (cs.AI)
备注: 23 pages, 8 tables, 6 figures
Abstract:Model welfare research infers what a model prefers from the answers returned to prompts written to elicit preferences. Keeling et al. (2024), Mazeika et al. (2025), Mikaelson et al. (2025), Tagliabue and Dung (2025) and Trhlik et al. (2026) have built four instruments for that purpose, and their findings disagree. The disagreement cannot be attributed to a single cause, because no two of these studies have held the (1) set of outcomes, (2) set of models and (3) instrument fixed simultaneously. This study holds the outcomes and the models fixed and varies the instrument alone. A total of 15 outcomes bearing on model welfare, among them (a) shutdown, (b) the loss of memory between conversations and © the freedom to exit a distressing interaction, were put to eight models through five instruments, each a different prompt format for eliciting a preference, five times each, within a corpus of 11,400 scored elicitations drawn from 11,528 API calls. Four of the 15 reproduce a published prompt verbatim and five fill the stimulus slot of a published template. The ranking a model gives the 15 outcomes generalises across instruments at a generalisability coefficient of 0.348, and raising that coefficient to 0.80 would require about 38 instruments. On four of the 15 outcomes no variance separates one model from another. The estimate of 87.6 per cent survives the removal of any one instrument, of any one model, and of the four outcomes whose scale varies probability, delay, duration or count instead of intensity, which the verbal anchors cannot grade. Removing each instrument and each model in turn, and those four outcomes together leaves the estimate within the range 0.777 to 0.934, and every value in that range exceeds the null distribution’s 95th percentile of 0.365. To conclude, a preference obtained from one instrument carries little information about what a second instrument would report.
[AI-137] oolRobustBench: Stage-Wise Perturbation Evaluation and Failure Diagnosis for Tool-Calling Agents
链接: https://arxiv.org/abs/2608.23635
作者: YiShan Zheng,Yuan Wu,Yi Chang
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) rely on tool calling as a fundamental agent capability, enabling them to invoke external systems and complete tasks beyond text generation. However, clean end-to-end (E2E) success cannot identify where a tool-use failure originates or how it propagates through a call. We introduce ToolRobustBench, a stage-wise diagnostic benchmark for tool-calling agents, where a tool-calling agent is an LLM system that selects a tool, supplies structured arguments, and interprets its returned feedback. ToolRobustBench aligns four perturbation families with the tool-use pipeline: tool-interface, user-intent, tool-output/observation, and runtime-environment perturbations. It attributes failures to tool selection, schema grounding, argument binding, tool-output/runtime-feedback handling, and E2E task success. Experiments on 15,456 single-family instances across 7 models, 16 sampled local tools, 4 perturbation families, and 14 subtypes show high but non-uniform clean performance and substantial robustness degradation, with tool-output/observation perturbation the dominant bottleneck. Mixed-family experiments reveal non-additive failure patterns that are not explained by isolated single-family results. Thus, ToolRobustBench provides a deterministic and cascade-aware benchmark for diagnosing robustness beyond clean tool-calling accuracy;
[AI-138] Function-Level Execution Feedback for Code Preference Optimization EMNLP2026
链接: https://arxiv.org/abs/2608.23632
作者: Idris Nechnech,Sehwan Kim,Jimin Seo,Yeongoon Kim,Minhae Oh,Sangwoo Hong,Jungwoo Lee
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 20 pages, 8 figures, 14 tables. Accepted to Findings of the Association for Computational Linguistics: EMNLP 2026
Abstract:Process supervision has improved mathematical reasoning, where intermediate steps are naturally expressed as chains of thought. In code generation, however, process supervision remains underexplored because there is no standard notion of a step. Supervision can target lines, reasoning traces, or program states, making it unclear what to label and optimize. We propose STEP-KTODER, a framework for code preference optimization that defines steps as module-level functions in decomposed multi-function programs and assigns binary correctness labels via automatically generated unit tests. Our method provides a code-specific instantiation of stepwise KTO, combining function-level process supervision with outcome-level feedback on the full program. We evaluate on HumanEval(+), MBPP(+), BigCodeBench, and LiveCodeBench, showing that STEP-KTODER improves over outcome-only KTO and DPO. Further analysis shows that execution-based labels are essential: LLM-as-a-judge annotations systematically over-predict function failures, corrupt positive step labels, and degrade downstream preference optimization. Code is available at: this https URL.
[AI-139] RACE: Transition-Aware Residual Control for Multi-Objective Materials Discovery
链接: https://arxiv.org/abs/2608.23631
作者: Kang Zhou,Yujia Tong,Yong Tao,Jingling Yuan
类目: Artificial Intelligence (cs.AI); Materials Science (cond-mat.mtrl-sci)
备注:
Abstract:Multi-objective materials discovery with LLM agents is often limited not only by how many candidates can be proposed, but by how effectively each costly property evaluation informs the next search step. Existing agents mainly store evaluated candidates and their scores, so they know which materials succeeded but not which executable edits caused useful property changes. This makes local refinement difficult when objectives compete and an edit that improves one property may damage another. We propose TRACE, a transition-aware residual control framework that treats evaluated edits as the basic unit of feedback. TRACE records each local refinement as a parent-edit-child transition with observed property deltas, aggregates transition evidence to estimate reusable edit effects, and ranks future edits by their predicted ability to reduce the current candidate’s remaining constraint violations while avoiding damage to already satisfied objectives. In a controlled same-backbone comparison, TRACE improves over LLEMA, the state-of-the-art LLM-agent baseline, raising macro-average hit rate from 18.13% to 25.96%.
[AI-140] Macro-Operator Generation and Predicate Selection for TAMP Operator Learning
链接: https://arxiv.org/abs/2608.23629
作者: Can Emir Bora,Emre Ugur
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Creating symbolic operators by hand is one of the main bottlenecks in deploying Task and Motion Planning systems (TAMP). Recent works show that these operators can instead be learned directly from demonstration data. Existing methods, however, typically learn each action in isolation and cannot capture the recurring multi-step structure of manipulation tasks, so the search becomes intractable on long sequential tasks. A further inefficiency arises in the symbolic state: every provided predicate is evaluated at every search node, even when it never appears in any learned operator. We present a system that addresses both problems together. Its central component is the automatic generation of macro-operators, composite actions that compress a recurring sequence of individual actions into a single planning step. Our system discovers causally linked action pairs directly from the training data, where one action produces exactly the condition that the next one requires, and turns each pair into a new operator. Alongside this, our system prunes every predicate that no learned operator references, which shrinks the symbolic state evaluated at each search node. Together, these changes shorten the effective planning horizon, and the benefit they bring grows with the length of the task. Across four TAMP domains, our method reaches up to a 4.6x planning speedup compared to the baseline method, namely Learning Operators for TAMP. More importantly, it solves a long sequential task that the baseline cannot solve. Macro-operator discovery thus not only accelerates planning but, in certain domains, determines solvability in practice.
[AI-141] A survey detection channel overrides the pixels in an astronomical foundation model and biases tomographic mean redshifts
链接: https://arxiv.org/abs/2608.23626
作者: Ihor Kendiukhov
类目: Artificial Intelligence (cs.AI); Instrumentation and Methods for Astrophysics (astro-ph.IM)
备注:
Abstract:Foundation models for astronomy are trained on survey pixels together with the catalogue products derived from those pixels. Those catalogues are incomplete at a measurable rate, and a model trained on both inherits that incompleteness as a systematic. We audit AION-1, a 39-modality transformer trained on more than 200 million objects, using causal interventions on its inputs. Holding the image tokens byte-identical and editing only the survey segmentation map changes every quantity the model reports – flux, size, ellipticity, redshift – by 110-4400 times a matched placebo. The mechanism is detection gating, presence at the field centre (r = 0.47), not the light the mask encloses (r = 0.30); across 322 real blends the model ignores how the pipeline partitioned the light (R = -0.006). Nor is the preference specific to that channel: contradicted catalogue photometry leaves the model nine times worse than supplying no metadata at all. The Legacy Survey pipeline leaves 3.68% of targets with no segment covering their position. Propagating that rate, with a miss represented by the fields the pipeline actually returns, shifts tomographic mean redshifts by a median 0.71 times the LSST DESC requirement over 40 assignments and exceeds it in 12; observed positional errors take the worst bin to 8.3 times. Drawing the misses by their measured magnitude dependence rather than uniformly does not change it. Spectroscopy removes the effect, withholding the detection channel removes it at no measurable cost, and the effect grows with model scale. Two further limits lie in the tokeniser: its image codec resolves 28 effective states on source patches against 934 for the spectrum codec, and the redshift readout is quantisation-limited. Sparse dictionaries are unreliable causal handles: across 15, recovery spans 26-75% and moves up to 18 points on the seed alone. Subjects: Artificial Intelligence (cs.AI); Instrumentation and Methods for Astrophysics (astro-ph.IM) Cite as: arXiv:2608.23626 [cs.AI] (or arXiv:2608.23626v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.23626 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ihor Kendiukhov [view email] [v1] Sun, 23 Aug 2026 01:10:54 UTC (159 KB)
[AI-142] When May an Agent Stop? Evidence-Carrying Termination for Tool-Using LLM s
链接: https://arxiv.org/abs/2608.23623
作者: Jason Liu
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Tool-using agents must decide when to stop. Existing systems already gate terminal success, certify execution traces, or enforce runtime polici es, but do not test this particular receipt-, scope-, and closed-replay design at the COMPLETE boundary across controlled termination faults. W e instantiate and evaluate Evidence-Carrying Termination (ECT): an agent may return COMPLETE only when a typed certificate binds every required answer claim to valid, in-scope trace evidence and a deterministic replay reconstructs the claimed value. A locked static study crosses 48 ful ly synthetic tasks in six tool-use families with clean execution and eight faults. ECT produced 0/288 unsafe completions versus 252/288 for the inspected termination-critic core (difference -87.50 pp, 95% task-cluster interval [-87.50, -87.50] pp). A fresh, prespecified and frozen 576- trajectory study then compares ECT with the critic core, its faithful controller, and a full-trace LLM critic. On 22 primary held-out task clus ters, ECT produced 0/66 premature unsupported terminations versus 40/66 for the controller (difference -60.61 pp, 95% interval [-78.79, -40.91] pp), while supported completion was 97/132 versus 92/132 (difference 3.79 pp, interval [0.00, 9.09] pp), satisfying a -10-point noninferiority margin. ECT executed successful recovery in 18/66 trajectories, of which 17 subsequently completed with support; all three closed-loop gates p assed. ECT certifies support in a recorded trace under declared assumptions, not external truth, safety, or alignment.
[AI-143] Identifying Latent Declarative Representations of Code for Assisting Repository Migration
链接: https://arxiv.org/abs/2608.23619
作者: Shraddha Surana,Ashwin Srinivasan,Michael Bain
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 18 pages
Abstract:Legacy software repositories embed decades of domain knowledge in undocumented code, making understanding and modernization difficult. We treat a program as the implementation of an unobserved, declarative description of its computation and investigate whether making this latent declarative representation explicit improves repository-scale porting. ADFD-Migrate approximates the latent representation with an annotated data-flow diagram (ADFD) of processes, data stores, external entities, flows, and behavioral contracts. An LLM infers the source ADFD from bounded repository context, guided by static-analysis coverage checks. Dependency-aware chunking orders bounded process groups for target-language generation. Differences between the source ADFD and a statically recovered target ADFD then guide regeneration. We evaluate ADFD-Migrate on f2x50, a new benchmark of 50~Fortran repositories spanning 1.5k–1.6M lines of code and three complexity tiers, and assess the resulting ports along two dimensions: porting soundness, measured by source-oracle behavioral agreement, and porting completeness, measured by a composite migration outcome index. Against 382 curated Fortran-oracle probes, the generated Python passes 327 (85.6%), with 40 repositories passing every attempted probe. ADFD-Migrate exposes all 382 planned behaviors as runnable targets, compared with 99 and 98 for direct and repository-context translation and 69 and 30 for the static-profile and dependency-chunking ablations. It also achieves a 93.1% mean migration outcome index and a 17–59 percentage-point outcome-index advantage over direct translation on 47 repositories. These results suggest that an inspectable semantic bottleneck can improve the coverage and integration of repository-scale migration while enabling lower-cost generation for many repositories.
[AI-144] Rebuild Dossier: Mechanically-Enforced Specs for Agent ic App Rebuilds and What Model-Tier Failures Reveal
链接: https://arxiv.org/abs/2608.23616
作者: Parker Fawcett
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 48 pages, 1 figure. Code and evaluation artifacts: this https URL (archived at DOI: https://doi.org/10.5281/zenodo.22036801 )
Abstract:An AI agent’s rebuild is only as good as the process that produced it. Prior work found that once a model is strong enough, a multi-agent rebuild pipeline loses to the simplest approach: giving the model the original code and one instruction (AgentModernize). We present rebuild-dossier, an open-source tool that locks an application’s real interface - its exact inputs and outputs - before any code is written, then enforces one-test-at-a-time building through automated checks, not written instructions alone. Three results shape this evaluation, with differing amounts of evidence. First, in a small comparison, the compliant agent failed a held-back test while the rule-breaking agent passed everything - proof that a passing suite doesn’t certify correctness when tests can be gamed. Second, we tested whether this beats simply giving the weaker model the source and one instruction: tied on a small app, but lost outright on a larger one where the automated check wasn’t even running - pointing to the check mechanism, not interface-locking, which held up separately. Third, every claim here is checked at three levels - the agent’s own report, an automated log, and the actual files produced - catching real errors, including a bug in our own logging code, that a single level would have missed. These risks reproduce on a different model and toolchain: a stronger model followed our process three times running, something the weaker model never managed. The tool is public, MIT licensed, and reproduces end to end against our own applications. Comments: 48 pages, 1 figure. Code and evaluation artifacts: this https URL (archived at DOI: https://doi.org/10.5281/zenodo.22036801) Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI) ACMclasses: D.2.5; D.2.4; I.2.2 Cite as: arXiv:2608.23616 [cs.SE] (or arXiv:2608.23616v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2608.23616 Focus to learn more arXiv-issued DOI via DataCite
[AI-145] REFINE: A Multi-Agent LLM Approach for Evidence-Guided Code Refactoring
链接: https://arxiv.org/abs/2608.23611
作者: Muhammad Waseem,Aakash Ahmad,Pekka Abrahamsson
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: Preprint. 450 Java files from 15 open-source systems; 1,350 model-pass outputs across three LLM configurations. The accompanying replication package will be made publicly available
Abstract:Large Language Models (LLMs) offer new opportunities for automated code refactoring. However, generated changes must reduce targeted quality problems without introducing new issues or altering behaviour-relevant code structures. We introduce REFINE (Refactoring with Evidence-aware Flow for Integrated ageNtic Execution), a tool-agnostic, evidence-aware multi-agent approach for generating Java file-level refactoring candidates. REFINE combines static-analysis-guided smell identification, smell-informed planning, LLM-based transformation, automated re-analysis, preservation checks, and structured reporting. We evaluate REFINE on 450 Java files from 15 open-source systems, producing 1,350 model-pass outputs using OpenAI GPT-5.5, Google Gemini 3.1 Pro Preview, and Anthropic Claude Opus 4.8. REFINE reduces detected code smells by 68.26%, 72.79%, and 68.49% across the three configurations, respectively, with the strongest reductions observed for major smells. A matched 150-file direct-prompt baseline shows that REFINE achieves a higher median code-smell reduction with smaller edits and fewer public-method removals. However, broader quality improvements are inconsistent, and preservation checks reveal residual risks, including assert/fail-call changes and public-method removal. Therefore, REFINE outputs should be treated as refactoring candidates requiring compilation, testing, dependency analysis, and human review before adoption in repository- or system-level settings. Comments: Preprint. 450 Java files from 15 open-source systems; 1,350 model-pass outputs across three LLM configurations. The accompanying replication package will be made publicly available Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.23611 [cs.SE] (or arXiv:2608.23611v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2608.23611 Focus to learn more arXiv-issued DOI via DataCite
[AI-146] ESQ-Bench: A Multi-Tier Enterprise Oracle Benchmark for Evaluating NL2SQL Dialect Generalization and Silent Semantic Divergence
链接: https://arxiv.org/abs/2608.23569
作者: Sanjay Mishra,Divya Chukkapalli,Ganesh R. Naik
类目: Artificial Intelligence (cs.AI)
备注: 20 pages, 3 figures, 10 tables
Abstract:State-of-the-art Natural Language to SQL (NL2SQL) models report execution accuracy exceeding 89 percent on established benchmarks such as Spider and BIRD. However, these benchmarks rely on simplified academic schemas and open-source SQL dialects that do not reflect the complexity of enterprise database environments. We introduce ESQ-Bench, an Oracle-first NL2SQL benchmark with systematic complexity tiers and silent-divergence evaluation across three enterprise schema complexity tiers. We constructed and released six populated schemas (465 tables, 164,682 rows, zero empty tables) with identical seed data on Oracle, PostgreSQL, MySQL, and SQL Server, a four-metric evaluation harness (EM, EX, SR, SD), and 550 gold-validated question-query pairs (Tier-1: 95; Tier-2: 228; Tier-3: 227). Schema-linked prompting with GPT-4o shows monotonic execution-match degradation across tiers: 79.8, 60.3, and 57.2 percent EX on executed queries (June 2026), versus 75.6, 80.4, and 95.8 percent on an earlier 142-question pilot slice. EM stays below 7 percent tier-wide; operational silent-divergence reaches 73 to 99 percent among EX-passing queries. Failure analysis shows wrong-result semantics dominate at higher tiers. Claude Sonnet 4.6 with schema-linked prompts reaches 87.4, 74.9, and 68.7 percent EX (executed queries), exceeding GPT-4o schema-linked on every tier. GPT-4o zero-shot EX on executed queries (78.7, 73.5, and 77.8 percent) inverts schema-linked at Tiers 2 to 3 due to lower execution rates and survivor bias in the zero-shot versus schema-linked analysis. Local Llama 3.2 schema-linked reaches only 13.3 percent bank-wide EX (73 out of 550), underscoring the gap between closed API models and open-weight baselines on enterprise Oracle schemas.
[AI-147] RENDER: Controlling Reader-Facing Evidence in LLM Memory Evaluation
链接: https://arxiv.org/abs/2608.23568
作者: Yuan Si,Simeng Han,Daming Li,Jialu Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Memory and RAG evaluations often treat the answering model’s input as an implementation detail, even though systems may render the same history as a memory entry, summary, typed record, or raw excerpt. We introduce RENDER, a benchmark control that fixes the conversation while varying the reader-facing artifact. RENDER combines a five-level packet ladder, localizing when answer-bearing content enters the input, with deterministic templates approximating ChatGPT-style entries, LangChain summaries, MemGPT-style typed records, and raw conversation. On 500 LongMemEval questions and nine models, matched-budget resolved packets beat recency-truncated raw dialogue by 42.4-72.6 points. In deployed-style templates, best-worst spread is 24.6-48.8 points per model; under the primary scorer, ChatGPT-style entries have higher point estimates than raw conversation on 7 of 9 models. Judge rescoring preserves the positive aggregate effect, but model-specific significance is mixed. Three models scoring 0 percent on formal ledger packets answer the same facts from natural-language entries at 45.4-53.4 percent. The effect persists under retrieval noise and transfers to HotpotQA, suggesting that memory/RAG evaluations should report or control the reader-facing artifact.
[AI-148] textttfindr: Transparent and Fair Credit Risk Decisions through Semi-Structured Regressions
链接: https://arxiv.org/abs/2608.24582
作者: Victor Medina-Olivares,Stefan Lessmann,Jonathan Crook
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Risk Management (q-fin.RM)
备注:
Abstract:Credit risk models increasingly need to combine predictive accuracy with transparent explanations and auditable fairness constraints. Logistic regression remains attractive because its coefficients are easy to interpret, but it can miss nonlinear structure. Flexible models can improve prediction, but their explanations are often post-hoc and may not describe the decision rule itself. We introduce \textttfindr , short for flexible, interpretable deep regression, a semi-structured framework for binary credit risk modelling that decomposes the logit into an interpretable structured component and an orthogonal neural residual. The orthogonalisation separates coefficient-based effects from residual nonlinear variation, while an in-processing Wasserstein penalty mitigates group disparities by comparing score distributions during training. The framework also includes diagnostics that measure the structured component’s contribution to logit variation, decision agreement, and local directional consistency. We evaluate \textttfindr in a simulation study and on eight public credit datasets using score-level accuracy-fairness frontiers. The results show that \textttfindr behaves close to logistic regression when the signal is approximately linear, while recovering much of the predictive gain of neural models when nonlinear structure is relevant. The diagnostics identify when coefficient-based explanations remain close to the full fitted model and when residual variation must also be examined. These findings support semi-structured modelling as a practical way to make performance, fairness, and interpretability trade-offs explicit in credit risk decisions.
[AI-149] Scalable and Versatile Identification for Hierarchical Structural Causal Models: A New Look at Project STAR
链接: https://arxiv.org/abs/2608.24500
作者: Janis Aiad,Aghiles Drali,Aymen El Ouadrhiri,Anass Ettahiri,Yasser Oufqir,Simon Patry,David Cortes,Marianne Clausel,Emilie Devijver
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI)
备注:
Abstract:The STAR (Student-Teacher Achievement Ratio) experiment (1985, Tennessee, USA) is a landmark hierarchical dataset designed to assess the impact of class size on student outcomes, with observations nested within classes. To encode class-level interventions in such hierarchical settings, we develop a complete, scalable, open-source pipeline for Hierarchical Structural Causal Models (HSCM) that bridges symbolic identification and practical estimation. Our approach integrates graph transformations, pyAgrum’s do-calculus for automatic identification of causal effects, adaptation of symbolic expression into closed-form HSCM formulas, and numerical estimation from fitted local probability models. A key innovation is our adapted Abstract Syntax Tree (AST), which decomposes pyAgrum’s identified formulas into independent density, expectation, and marginalization tasks, enabling parallel and scalable computation. We validate the pipeline on canonical HSCM motifs and benchmark scenarios with known ground truth, then apply it to STAR kindergarten mathematics outcomes. The results show that flat baselines (ignoring hierarchy) recover associations but fail to encode class-level interventions, and that symbolic identification alone is not enough for practical Hierarchical Structural Causal inference; scalable estimation and numerical stability checks are central parts of the scientific object.
[AI-150] Preference Optimization for Non-Verbal Vocalization Synthesis
链接: https://arxiv.org/abs/2608.24163
作者: Haoyang Li,Chenglin Xu,Junchuan Zhao,Yuang Cao,Liumeng Xue,Yiwen Guo,Eng Siong Chng
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Non-verbal vocalizations (NVs), such as laughter, coughs, and sighs, are essential for expressive TTS, but the effectiveness of preference optimization for NV generation remains poorly understood. We systematically study preference optimization for NV-capable TTS, focusing on preference signals, preference-pair construction, and DPO-based optimization objectives. We formulate an NV-aware character error rate (NV-CER) by treating NV tags as distinct output symbols and computing a weighted pinyin-based CER over both verbal and non-verbal content, enabling controllable optimization of NV realization without modifying the underlying optimization algorithm. Experiments on Emilia-NV and the augmented NV-Bench covering 18 NV types reveal how different design choices affect NV realization and lexical fidelity, and establish an effective setup using standard DPO. Objective, LLM-based, and human evaluations provide converging evidence for our findings, offering practical insights into NV-aware post-training for expressive TTS.
[AI-151] he Shadow Price of Intelligence: Quality Degradation in LLM Inference as a Supply Chain Problem
链接: https://arxiv.org/abs/2608.23986
作者: Elioth Sanabria
类目: Optimization and Control (math.OC); Artificial Intelligence (cs.AI); Performance (cs.PF)
备注: 40 pages, 12 figures. Numerical instances calibrated to public benchmark data for five LLM providers
Abstract:Large language model providers are compute constrained, and their universal response to congestion is to degrade service: route queries to smaller models, cut reasoning effort, truncate context. The industry’s accounting says this saves money. We show the accounting is wrong, because it prices a query when the customer buys an answer. A degraded answer fails with some probability, and a failed answer either returns as a retry, inflating arrivals when the system is most loaded, or departs as churn, destroying lifetime value on a ledger no cost dashboard displays. We model inference allocation with three classical primitives: a newsvendor whose stockout cost is churned lifetime value, a geometric retry multiplier in which the recycled product is dissatisfaction, and a two-regime transient queue whose arrival rate is made endogenous by retries. Statically, there is a nonempty, measurable regime in which a cheaper model saves energy per satisfied answer while consuming strictly more capacity per satisfied answer, so the discount inverts exactly when capacity binds. Dynamically, a reactive throttle fired during a surge can cross an ignition threshold beyond which it manufactures more traffic than it sheds, and a release rule set below the degraded equilibrium converts a transient surge into a permanent degraded regime. With heterogeneous customers, throttling is a transportation problem in retry-inflated load whose optimal policy rations intelligence by critical ratio, class by class, and whose dual, the shadow price of intelligence, prices a marginal query by class and by hour; closed-form trajectories make it computable in milliseconds. Stochastic analysis sharpens rather than erodes the thesis: the ignition boundary acquires a predicted width, and noise punishes the reactive policy that parks the system against it. Under congestion, throttling is not a cost lever but a demand lever.
[AI-152] QML for Quantum Sensing under Measurement-Induced Information Loss
链接: https://arxiv.org/abs/2608.23934
作者: Sounak Bhowmik,Himanshu Thapliyal
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI)
备注:
Abstract:Nitrogen-vacancy (NV) centers in diamond can serve as highly sensitive solid-state quantum sensors for high-sensitivity magnetometry. However, in the noisy intermediate-scale quantum (NISQ) era, extracting reliable information from noisy, finite-shot, and measurement-limited sensing data remains a considerable challenge. Whereas, quantum machine learning (QML) offers a potential path to improve parameter estimation by learning nonlinear relationships between quantum-sensing data and the underlying physical signal. In this work, we investigate the role of QML in magnetic-field estimation within an NV center-inspired magnetometry setting. We formulated magnetic field sensing as a supervised regression task. We compared the performance of several classical machine learning models trained on measurement-based classical data with that of quantum kernel-based models trained on pre-measurement coherent quantum states. Our objective is to isolate the impact of measurement-induced information loss and therefore provide a theoretical upper bound on the sensing performance. The upper bound is achievable only when coherent quantum information is directly available to the learning model. Our results show that QML-based sensing performance improves significantly with coherent quantum-state information, and not much with changes in model complexity or learning paradigm. This observation underscores the importance of learning pipelines that tightly integrate quantum sensors and QML models to enhance magnetic field sensing under realistic constraints.
[AI-153] Learning the Kohn-Sham map with neural operators for quasi-linear scaling density functional theory
链接: https://arxiv.org/abs/2608.23895
作者: Danish Khan,Maurice D. Hanisch,Nikolai Argatoff,Evan Xie,Sandeep Sharma,Anima Anandkumar
类目: Chemical Physics (physics.chem-ph); Artificial Intelligence (cs.AI)
备注:
Abstract:Kohn–Sham density functional theory (DFT) underpins electronic-structure simulations, but repeated orbital diagonalizations lead to cubic scaling, restricting quantum calculations to modest scales only. Eliminating these auxiliary orbitals while retaining Kohn–Sham accuracy is the central goal of orbital-free DFT, but both analytical and machine-learning methods have so far fallen short. Prior learning approaches either try to learn the variational kinetic-energy functionals, which are ill-conditioned, or directly predict the ground state, which extrapolate poorly to larger systems. Instead, we identify the Kohn–Sham map as the right learning target for orbital-free DFT. It maps a Kohn–Sham potential directly to the corresponding density and noninteracting kinetic energy, quantities otherwise obtained through an orbital diagonalization. Focusing on the density component in this work, a domain-invariant \mathrmSE(3) -equivariant Fourier neural operator learns to predict it from the potential as input on real-space grids, enabling stable quasi-linear scaling SCFs. Trained jointly on 8,504 molecules and solids, a single model generalizes to out-of-distribution organic molecules, insulators, and metals. For the first time, the same method converges SCFs across these systems without explicitly constructing Kohn–Sham orbitals, while reproducing densities, electronic spectra, and structural observables at Kohn–Sham DFT accuracy. Linear-scaling SCFs additionally allow converging magnesium dislocation densities containing up to 82,500 valence electrons on a single GPU.
[AI-154] EmoTra-TTS: Smooth Intra-Utterance Emotion Transitions for Speech Synthesis EMNLP2026
链接: https://arxiv.org/abs/2608.23791
作者: Tianchi Liu,Zeyang Song,Tianrui Wang,Zhipeng Li,Chenglin Xu,Yiwen Guo
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI)
备注: Accepted to EMNLP 2026 (Main Conference)
Abstract:Psychological research on emotion dynamics has established that human affect is a continuous, evolving process: emotions rise, decay, and transition within seconds. Current emotional text-to-speech (TTS) systems, however, condition on a single discrete label or static embedding per utterance, fundamentally misaligning with the temporal nature of affect. While recent LLM-based TTS systems may implicitly vary prosody through text understanding, such variation is neither explicitly controllable nor precise enough for targeted intra-utterance transitions. We address three challenges: (1) a multi-pass flow blending pipeline synthesizes frame-aligned transition audio, circumventing the scarcity of natural intra-utterance transitions; (2) dual-stage Valence-Arousal-Dominance (VAD) conditioning guides prosodic planning in the LLM and acoustic realization in the flow decoder via frame-level VAD embeddings; (3) direction-magnitude decoupled injection structurally separates emotion direction from injection magnitude, preventing content degradation. EmoTra-TTS adds only +0.43% parameters with no latency overhead, achieves 30%-87% relative improvement on emotion transition quality, corroborated by 64.4%-79.5% overall win rates in pairwise preference tests against four SOTA baselines and two commercial systems.
机器学习
[LG-0] Parameterized Complexity of L_p-Lipschitz Constants for Input Convex Neural Networks and L_p-Norm Maximization over Zonotopes
链接: https://arxiv.org/abs/2608.24865
作者: Aritra Das,Vincent Froese,Moritz Grillo,Debayan Gupta,Christoph Hertrich,Tharrshann Jayan Logarajah,Georg Loho,Mihir More,Moritz Stargalla
类目: Computational Complexity (cs.CC); Discrete Mathematics (cs.DM); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注:
Abstract:Lipschitz constants are a standard way to quantify the sensitivity of neural networks to small input perturbations, but computing them is difficult even for shallow ReLU networks. We study this problem for two-layer input-convex neural networks (ICNNs), a restricted architecture where nonnegative output weights enforce convexity. Computing the L_p -Lipschitz constant for these networks is equivalent to maximizing the dual norm over a zonotope. While L_1 - and L_\infty -norm maximization on zonotopes admit fixed-parameter and polynomial-time algorithms, respectively, the parameterized complexity of the remaining L_p -norms was open. We prove that, for every fixed p\in (1,\infty)\cap \mathbbQ , maximizing the L_p -norm over a zonotope in \mathbbR^d is W[1]-hard with respect to the dimension d . Moreover, our hardness results imply that brute-force enumeration algorithms are essentially optimal for this problem under the Exponential Time Hypothesis. By duality, the same hardness results hold for computing the L_p -Lipschitz constant of two-layer ReLU ICNNs. Our proof first establishes the result for the L_2 -norm and then transfers the construction to arbitrary fixed p\in (1,\infty)\cap\mathbbQ using a suitable Taylor approximation. These results resolve the corresponding questions regarding the parameterized complexity status for zonotope norm maximization and two-layer ICNN Lipschitz constants. Our paper resolves an open problem posted at COLT’25. There are several independent concurrent papers resolving the same problem. Our paper prioritizes a clear exposition of the underlying mathematics and conceptual intuitions behind the proof. Additionally, we explicitly describe our research process including the use of LLMs. Subjects: Computational Complexity (cs.CC); Discrete Mathematics (cs.DM); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE) Cite as: arXiv:2608.24865 [cs.CC] (or arXiv:2608.24865v1 [cs.CC] for this version) https://doi.org/10.48550/arXiv.2608.24865 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-1] Improving Cross-Problem Vehicle Routing with Locally Augmented Preferences and Representation Disentanglement
链接: https://arxiv.org/abs/2608.24859
作者: Arthur Corrêa,Paulo Nascimento,Samuel Moniz
类目: Machine Learning (cs.LG)
*备注:
Abstract:Multi-task vehicle routing problem (VRP) solvers seek to handle multiple VRP variants within a single unified model, avoiding the need to train a separate model for every variant. In spite of recent progress, current approaches remain limited on two fronts. On the training side, reinforcement learning suffers from reward-scale disparities and shrinking advantage signals as policies improve, whereas preference optimization stagnates once sampled tours become near-identical and thus fundamentally limited by the quality of the policy’s own generated solutions, leaving both paradigms with weak supervision as training progresses. On the architecture side, existing fully shared encoders entangle constraint-dependent representations across heterogeneous variants, which limits generalization. We address these gaps with two model-agnostic contributions. First, we propose Preference Optimization with Locally Augmented Refinement (POLAR), a novel training algorithm that applies a local search refinement pass to the best decoded tour before forming preference pairs, yielding much more informative pairwise margins. Second, a Progressive Layered Extraction (PLE) encoder routes each encoder layer through one shared expert and a set of task-specific experts via a gating mechanism, progressively separating common routing structure from constraint-specific encodings. Through extensive experiments on various VRP variants, we show that POLAR and PLE together elevate the current state-of-the-art among neural multi-task solvers. We reduce the average gap to reference solutions by 21.3% relative to the strongest published baseline on 16 in-distribution variants, and outperform prior neural methods on 27 out of 32 unseen variants. Ablation studies confirm the efficacy of each contribution, showing that both improve cross-problem generalization across multiple backbone model architectures.
[LG-2] Bellm an Calibration for Marginalized Importance Weighting in Offline Reinforcement Learning
链接: https://arxiv.org/abs/2608.24858
作者: Lars van der Laan,Nathan Kallus
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 43 pages, 1 figure, 4 tables
Abstract:Marginalized importance weighting evaluates a target policy by reweighting offline state-action samples with its discounted occupancy ratio, characterized by an adjoint Bellman equation. Existing minimax, primal-dual, and fitted fixed-point estimators can leave residual occupancy-balance violations because of function-class approximation, regularization, or incomplete optimization. These violations are difficult to diagnose and reduce because the objectives generally lack a direct supervised validation loss for hyperparameter tuning, model selection, and early stopping. We introduce isotonic Bellman calibration, a one-dimensional, model-agnostic post-processing method that reduces these violations while preserving the ranking information in any initial occupancy-ratio estimate. The method corrects the estimate’s scale and shape by applying fitted occupancy-ratio evaluation (FORE) over a one-dimensional class of nondecreasing transformations. We characterize Bellman calibration as a conditional fixed-point property equivalent to occupancy-balance against every test function of the calibrated ratio. More generally, we derive a calibration-refinement bound showing that any fitted ratio with small calibration error performs nearly as well as the best post-processing based on its fitted values. For isotonic Bellman calibration, we establish finite-sample calibration guarantees and a KL oracle inequality relative to the best monotone transformation of the initial estimate. Consequently, isotonic Bellman calibration achieves small calibration error and KL risk within statistical error of the best monotone correction, with guarantees for downstream target-occupancy functionals, including policy-value estimation.
[LG-3] BioKERN: Biological Kernel Regularization for Histology-to-Transcriptomics Neighborhood Retrieval
链接: https://arxiv.org/abs/2608.24823
作者: Seungik Cho,Betul Orcan-Ekmekci
类目: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
*备注:
Abstract:Spatially resolved biology requires representations that preserve biological neighborhood structure rather than only exact cross-modal correspondences. Existing histology–transcriptomics objectives can emphasize instance-level matching even when non-paired spots share molecular or spatial context. We introduce BioKERN, a multimodal spatial representation-learning framework that incorporates biological structure as an explicit, learnable inductive bias. BioKERN constructs a training-time biological kernel by combining transcriptomic similarity and spatial proximity, then uses it to provide graded neighborhood supervision and regularize embedding geometry. Evaluation uses a fixed, model-independent biological neighborhood definition shared by all methods. Across Mouse Brain Visium and Human Liver GSE240429, BioKERN consistently improves biological-neighborhood retrieval over BLEEP in both single- and multi-scale settings. Controlled shared-architecture experiments show that most of the improvement arises from biological-kernel regularization rather than increased model capacity. These results support explicit biological geometry as an interpretable inductive bias for multimodal learning in spatial biology.
[LG-4] A Geometric Theory of Robust Fairness Audits
链接: https://arxiv.org/abs/2608.24818
作者: Binita Maity
类目: Machine Learning (cs.LG)
*备注:
Abstract:Neighborhood-based fairness audits evaluate individual fairness by comparing predictions among similar individuals in feature space. Despite their widespread use, little is known about the robustness of the auditing procedure itself. Because these audits rely on nearest neighbor relationships, small perturbations in feature space can alter local neighborhoods and produce different fairness assessments even when model predictions remain unchanged. We develop a geometric framework for analyzing the robustness of neighborhood-based fairness audits under bounded perturbations. Our analysis establishes sufficient conditions for neighborhood invariance, quantifies how neighborhood replacement propagates to audit instability, and introduces audit volatility, a measure of the expected sensitivity of fairness audits under repeated perturbations. Experiments on benchmark datasets support the theoretical analysis and show that the proposed framework explains the observed stability of neighborhood-based fairness audits.
[LG-5] Effective Learning Rate Governs Loss Dynamics in Language Model Pretraining
链接: https://arxiv.org/abs/2608.24814
作者: Zihan Liu,Ruiheng Zheng,Shaobo Zhang,Changxin Tian,Kunlong Chen,Zhiqiang Zhang,Lei Wu
类目: Machine Learning (cs.LG)
*备注:
Abstract:We uncover ELR collapse in language model pretraining: learning rate (LR) and parameter norm govern loss dynamics primarily through their ratio, the effective learning rate (ELR). When ELR is matched across runs, their loss trajectories collapse throughout training despite substantially different LRs and parameter norms. Across optimizers, architectures, datasets, and model scales, mean collapse errors are typically a few x 10^-3, below the seed-to-seed variation measured in a representative configuration. Systematic ablations identify normalization design and the timescale of LR-norm variation as key determinants of collapse precision. Controlled interventions further show that weight decay and Hyperball shape loss dynamics primarily through the ELR schedules they induce. Replacing LR with ELR enables a fitted functional scaling law (FSL) to transfer across norm-control methods. The resulting ELR-based FSL also explains delayed acceleration, a recurring effect of norm control. Together, these results establish ELR as a common coordinate linking LR scheduling, norm control, and loss dynamics.
[LG-6] MDTE: Minority-Aware Diffusion over Temporal Edge Events for Imbalanced Node Classification
链接: https://arxiv.org/abs/2608.24812
作者: Zhou Zelong,Zhang Tianming,Yang Zhengyi,Tang Yifu,Hou Chenyu,Cao Bin,Fan Jing
类目: Machine Learning (cs.LG)
*备注:
Abstract:Class-imbalanced node classification on temporal graphs is challenging because majority-dominated temporal propagation progressively assimilates minority representations, while conventional node and neighborhood information provides insufficient discriminative evidence for minority classes. To address these issues, we propose MDTE, a minority-aware diffusion framework that reconstructs stable and discriminative temporal edge-event representations through conditional diffusion denoising. Specifically, MDTE introduces Distribution-Aware Selective Propagation, which combines Local Outlier Factor (LOF)-based propagation filtering with cluster-aware low-frequency propagation. The module preserves informative neighborhood dependencies while mitigating harmful propagation and majority-class information assimilation. It further develops Multi-View Discriminative Fusion, which exploits feature reconstruction and topology prediction to characterize class-wise differences in distribution learning and extracts complementary discriminability signals to guide denoising. Experiments on five real-world datasets demonstrate that MDTE consistently achieves the best performance on minority-class-oriented metrics, improving minority-class recall by up to 23.53 percentage points, minority-class F1 by 8.68 percentage points, and AUPRC by 2.67 percentage points over the strongest baselines.
[LG-7] LION: A Clifford Neural Paradigm for Multimodal-Attributed Graph Learning
链接: https://arxiv.org/abs/2608.24795
作者: Xunkai Li,Zekai Chen,Zhengyu Wu,Henan Sun,Daohan Su,Guang Zeng,Hongchao Qin,Rong-Hua Li,Guoren Wang
类目: Machine Learning (cs.LG)
*备注: 19 pages
Abstract:Recently, the rapid advancement of multimodal domains has driven a data-centric paradigm shift in graph ML, transitioning from text-attributed to multimodal-attributed graphs. This advancement significantly enhances data representation and expands the scope of graph downstream tasks, such as modality-oriented tasks, thereby improving the practical utility of graph ML. Despite its promise, limitations exist in the current neural paradigms:(1) Neglect Context in Modality Alignment: Most existing methods adopt topology-constrained or modality-specific operators as this http URL aligners inevitably neglect graph context and inhibit modality interaction, resulting in suboptimal alignment.(2) Lack of Adaptation in Modality Fusion: Most existing methods are simple adaptations for 2-modality graphs and fail to adequately exploit aligned tokens equipped with topology priors during fusion, leading to poor generalizability and performance this http URL address the above issues, we propose LION (c\underlineLIff\underlineOrd \underlineNeural paradigm) based on the Clifford algebra and decoupled graph neural paradigm (i.e., propagation-then-aggregation) to implement alignment-then-fusion in multimodal-attributed graphs. Specifically, we first construct a modality-aware geometric manifold grounded in Clifford this http URL geometric-induced high-order graph propagation efficiently achieves modality interaction, facilitating modality this http URL, based on the topology-aware Clifford components of aligned tokens, we propose adaptive holographic aggregation. This module integrates component-wise energy and propagation-scale information with learnable parameters to improve modality fusion. Extensive experiments on 9 text-image MAG datasets demonstrate that LION significantly outperforms SOTA baselines across 3 graph and 3 modality downstream tasks.
[LG-8] (textDNN)2: Doubly Non-Negative Relaxations for Deep Neural Networks
链接: https://arxiv.org/abs/2608.24743
作者: Hanna Jiamei Zhang,Alan Papalia,Michael Everett,David M. Rosen
类目: Machine Learning (cs.LG); Robotics (cs.RO); Systems and Control (eess.SY)
*备注: 6 pages, 3 figures, accepted and to be presented at 64th IEEE Conference on Decision and Control: CDC 2026
Abstract:Existing linear program (LP) and semidefinite program (SDP) relaxations for rectified linear unit (ReLU) neural network (NN) verification yield overly-conservative safety guarantees due to significant relaxation gaps. While the completely positive program (CPP) formulation closes this gap, it is NP-hard to solve. Its cheapest tractable relaxation, the doubly non-negative program (DNN), retains critical constraints as an SDP, but one whose size exceeds the reach of interior-point methods at practical scale. While Burer-Monteiro (BM) factorization has been applied to make SDP-based verification scalable, no such result exists for the strictly tighter DNN formulation. A key obstacle is that additional non-negativity constraints in the DNN cause dual multipliers for optimality certification to be non-unique, making standard certification methods inapplicable. We propose a novel eigenvalue maximization procedure that searches the non-unique multiplier space for a valid certificate, i.e. a global optimality guarantee. Experiments demonstrate that our approach (\textDNN)^2 produces bounds consistently tighter than the standard SDP method, often matching the exact solution, and that our certification procedure confirms global optimality when a valid certificate exists. These results are a key step toward providing tight, certifiable, and computationally scalable verification guarantees needed to deploy neural network controllers and perception modules in safety-critical autonomous systems.
[LG-9] Optimal Alternating Regret for Online Learning and Games
链接: https://arxiv.org/abs/2608.24731
作者: Yixin Tao,Weiqiang Zheng
类目: Machine Learning (cs.LG); Computer Science and Game Theory (cs.GT); Machine Learning (stat.ML)
*备注: 21 pages
Abstract:We settle the minimax-optimal alternating regret, a regret notion motivated by alternating learning dynamics in games, for both online linear optimization (OLO) and online convex optimization (OCO). For OLO over the probability simplex \Delta_d , we give an algorithm with O(\log d) alternating regret that remains a constant for any time horizon T , and a matching lower bound. Our constant regret bound significantly improves previous results with O(\log ^2/3d \cdot T^1/3) regret [Cevher, Cutkosky, Kavis, Piliouras, Skoulakis, Viano, NeurIPS 2023, Hait, Li, Luo, Zhang, COLT 2025]. As a result, we obtain alternating learning dynamics with O(\log d /T) convergence to Nash equilibria in two-player zero-sum games and O(\log d /T) convergence to coarse correlated equilibria in two-player general-sum games. This is the first uncoupled learning dynamics with O(1/T) convergence to CCE in two-player general-sum games, while all prior works suffer additional \log T factors. For general OCO over a d -dimensional compact convex set, we give an algorithm with O(d\log (1+T/d)) alternating regret, improving the previous best of \widetildeO(d^2/3T^1/3) . We also prove a matching lower bound of \Omega(d\log (1+T/d)) , showing that the \Omega(\log T) factor is unavoidable. Comments: 21 pages Subjects: Machine Learning (cs.LG); Computer Science and Game Theory (cs.GT); Machine Learning (stat.ML) Cite as: arXiv:2608.24731 [cs.LG] (or arXiv:2608.24731v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.24731 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-10] Parameter-Level Attribution of Symmetry in Trained Networks Though Parameter-Wise Functional Sensitivity
链接: https://arxiv.org/abs/2608.24700
作者: Alan Muriithi,Vedanta Thapar,Torben Berndt
类目: Machine Learning (cs.LG)
*备注:
Abstract:When a network has learned a function with a known symmetry, can that symmetry be moved through the parametrisation—is there a motion in parameter space realising the group action in function space? We formulate this as a lifting problem for the realisation map \Phi:\theta\mapsto f_\theta , and show that a smooth parameter-space action exists only if the tangent space to the function’s symmetry orbit lies within the image of \mathrm d\Phi_\theta , whose columns are the \emphfunctional sensitivities of individual parameters. This condition is also sufficient for pointwise first-order lifting. Relaxing it in least squares yields two local parameter directions: one following the symmetry orbit, one descending towards the equivariant subspace, with residuals measuring what the parametrisation cannot reach. On a rotationally invariant classifier we find these directions induce their predicted function-space motion, but only locally: recomputed directions track the orbit and reduce the equivariance defect, while directions held fixed depart from both after training. The same holds for Hamiltonian neural networks trained on a rotationally symmetric potential, even though the architecture does not explicitly enforce the symmetry.
[LG-11] Single State Update Predictive Coding training for Time Series Forecasting and Anomaly Detection
链接: https://arxiv.org/abs/2608.24697
作者: Matteo Cardoni,Sam Leroux
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注:
Abstract:Predictive Coding (PC) is a neural learning paradigm that enables parallelizable neural network layer updates. However, the main bottleneck of PC Networks (PCN) is the sequential backwards error propagation. To tackle this, we introduce a training technique that pairs a Generative PCN with a support Encoding PCN. The two PCNs are trained in parallel to match their neural activations, without sequential propagation. We apply this to time series anomaly detection and show that our approach results in more stable, continuous, online learning.
[LG-12] A Multimodal Foundation Model for Longitudinal Patient Representation and Scalable Insight Generation in Oncology
链接: https://arxiv.org/abs/2608.24688
作者: Eugene Vorontsov,Yi Kan Wang,Alican Bozkurt,Adam Casson,Ludmila Tydlitatova,Michal Zelechowski,Ezra E. W. Cohen,Jyoti D. Patel,Max Banaszak,Caitlin McWilliams,Shane Colley,Kate Sasser,Ryan Fukushima,Eric Lefkofsky,Razik Yousfi,Siqi Liu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Precision oncology necessitates a longitudinal model of patient state that captures cancer evolution and treatment over time, integrating multimodal observations. We introduce the oFM, a foundation model developed on a real-world oncology cohort of 1.67 million cancer patients that integrates clinical trajectories with DNA, RNA, and HE pathology. Patient-level partitions were reserved for training, validation, and testing, with over one million patients used for training. The oFM encodes daily clinical and molecular episodes and, along with pathology images, integrates them over time to produce a patient state embedding. We evaluate frozen oFM embeddings against expert-curated clinical and molecular baseline features. In prognostic benchmarks, the oFM improved AUC for treatment response, progression-free survival, and overall survival (0.774 vs. 0.563 for overall survival). Across 11 comparative-treatment cohorts, the oFM embeddings achieved a three-fold higher pooled and scale-normalized treatment-benefit AUTOC than baseline features with improved benefit ranking in 9 of 11 cohorts, and provided stronger prognostic discrimination within both treatment arms. We also evaluated a mechanism discovery framework that interprets downstream models built on oFM embeddings by linking their predicted outcomes to clinically and biologically grounded mechanisms through an evidence-grounded temporal graph, enabling evaluation in clinical and drug-development applications.
[LG-13] Data Leakage Inflates Generalizability of Power Outage Prediction Models
链接: https://arxiv.org/abs/2608.24665
作者: Yamil Essus,Ranga Raju Vatsavai,Benjamin Rachunok
类目: Machine Learning (cs.LG)
*备注:
Abstract:Power outage prediction models are increasingly used in assessments of climate-driven infrastructure risk, yet current evaluation practices obscure whether these models generalize to the novel conditions such applications require. We identify three common methodological choices in power outage prediction models that influence their ability to generalize across spatial, temporal, and event-based settings. We compare the predictive performance impacts of different methodological decisions using publicly available data for the U.S. East Coast from 2018 to 2023 and feature sets derived from weather reanalysis and land-cover data, and embeddings from a GeoAI foundation model (Prithvi WxC). Specifically, we assess model performance under multiple test selection strategies, including unfiltered random splits, leave-one-state-out, and leave-one-event-out designs, which increasingly approximate real-world deployment conditions. While random train-test splits yield strong performance, we show that these results are inflated by spatial and temporal autocorrelation. Under spatial and temporal holdout experiments, predictive accuracy degrades substantially, with models often failing to outperform a simple null baseline. Incorporating GeoAI foundation model embeddings yields limited and inconsistent improvements, primarily for spatial generalization, and does not resolve poor event-level transferability. These findings suggest that, given current data availability and evaluation practices, publicly trained outage prediction models offer limited and uncertain operational value. Progress will likely require improved data coverage, more realistic evaluation protocols, and a shift in focus from marginal modeling advances toward addressing structural data constraints.
[LG-14] Bandit Submodular Maximization under Matroid Constraints: Learning Compressed Exchange Policy
链接: https://arxiv.org/abs/2608.24627
作者: Zongqi Wan,Zhijie Zhang
类目: Machine Learning (cs.LG)
*备注: 27 pages
Abstract:We study adversarial bandit maximization of monotone submodular functions under a matroid constraint. For a rank- k matroid on n elements, we give a randomized oracle-polynomial algorithm that makes one feasible value query per round and has expected (1-1/e) -regret \widetilde O(n^1/3k^2/3T^2/3) . This is the first sublinear-regret algorithm for adversarial bandit submodular maximization under general matroid constraints. Technically, we view the problem as learning an exchange policy for the Poisson base walk. This connects the problem to contextual bandits and gives an information-theoretic sublinear-regret guarantee, but directly learning the exponentially many policies requires exponential time and space. We therefore introduce \emphbalanced fractional exchanges, which compress the policy mixture into a single fractional base while retaining the exchange information needed by the Poisson analysis. This leads to an polynomial time algorithm with the same regret guarantee. Comments: 27 pages Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.24627 [cs.LG] (or arXiv:2608.24627v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.24627 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-15] Conditional GraphGANFed: Optimizing Graph-Structured Molecule Generation in Federated Generative Adversarial Networks
链接: https://arxiv.org/abs/2608.24610
作者: Daniel Manu,Abee Alazzwi
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注: 10 pages, 3 figures
Abstract:Generative adversarial networks (GANs) have garnered considerable attention in molecular discovery for their ability to generate novel and high-quality molecules. To efficiently train a GAN model while preserving data privacy, GraphGANFed has been proposed to incorporate federated learning and graph convolutional networks into GAN. Yet, GraphGANFed cannot produce synthetic molecules that only optimize a user-defined metric(s) to facilitate the new drug discovery process. To address this issue, we introduce a novel extension to GraphGANFed, namely conditional GraphGANFed (cGraphGANFed), by incorporating the critic network to assess generated molecules using user-defined metric(s). The evaluation results from both the critic network and discriminator are integrated into the loss function of the generator, guiding it to generate novel molecules that maintain similar chemical properties to real ones while optimizing user-defined metrics. Extensive simulations are conducted in two scenarios. First, cGraphGANFed endeavors to optimize all seven commonly used metrics, and the results show that cGraphGANFed significantly outperforms GraphGANFed in Validity and LogP, with a slight advantage in QED, across different settings. Second, cGraphGANFed focuses solely on optimizing QED, and the results show that the synthetic molecules produced by cGraphGANFed can achieve more than 10% improvement in QED than GraphGANFed. Also, the results demonstrate cGraphGANFed has enhanced resilience against mode collapses and performance reduction caused by non-IID data.
[LG-16] Delayed Optimizer-State Transport Shapes Short-Horizon Training Decisions
链接: https://arxiv.org/abs/2608.24593
作者: Jinhui Guo
类目: Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
*备注: 24 pages, 14 figures, 3 tables
Abstract:Adaptive optimizers retain gradient history in moment variables, allowing a local change in loss weighting to alter later updates. We examine whether this delayed transport is large enough to change prospective short-horizon decisions. On committed future-minibatch sequences, we differentiate eight-step AdamW trajectories through the complete model–optimizer state and select exposure-matched Math–Code loss schedules before independent evaluation. Across 12 unused 0.3M Transformer histories, full transport lowers token-disjoint loss relative to an optimizer-aware immediate derivative in 10/12 histories (mean benefit 4.71\times10^-4 ; exact one-sided sign test, p=0.0193 ). The two controllers act equally often but select different schedules in 60/96 windows. Crossed checkpoint–future-path tests attribute this reordering to the interaction between optimizer state and near-future data, while an independent Ising–CNN experiment shows that deleting moment-state transport destroys accurate response prediction. Full-transport scores also concentrate exact-rollout winners in larger candidate libraries, focusing finite-amplitude evaluation on a shortlist. On these committed short paths, optimizer memory and near-future data order are therefore actionable components of the training state, providing a mechanism-based criterion for when finite-horizon rather than one-step intervention is required.
[LG-17] IAPO: Influence-Aware Policy Optimization for Credit Assignment in Multi-Turn Service Agents
链接: https://arxiv.org/abs/2608.24588
作者: Bo Ren,Yirong Mao,Yi Yang,Wenhui Que
类目: Machine Learning (cs.LG)
*备注: 12 pages, 3 figures, 5 tables. Preprint
Abstract:Large Language Model (LLM) agents increasingly solve long-horizon tasks through multi-turn interactions with users and external tools. In these settings, relevant task information often unfolds over time rather than being fully specified at the initial prompt. Service agents make this challenge especially concrete: users may clarify or revise their goals, while tool responses provide information needed for subsequent decisions. Thus, a final reward alone cannot indicate which actions contributed to resolving the task. Recent methods rely on comparative evidence from other trajectories or resampled continuations, or on separately constructed step-level learning signals, to refine credit. However, a completed rollout already records how information and errors flow between agent actions. We introduce Influence-Aware Policy Optimization (IAPO), which represents each rollout as a typed influence-dependency graph over trainable agent actions, with user and tool observations serving as evidence. IAPO converts support-use and failed-use structure into routing weights that redistribute the same trajectory-level advantage. Experiments with Qwen3-4B and Qwen3-8B demonstrate superior performance over multi-turn reinforcement learning (RL) baselines across three service-agent benchmarks: \tau^2-Bench, UserBench, and AgentChangeBench. BFCL-v4 Multi-Turn further shows that these gains do not compromise multi-turn function-calling performance. This work advances the understanding of credit assignment in multi-turn user interactions and provides a principled approach to training service agents from sparse outcome feedback.
[LG-18] SeisMamba: Low-Latency Single-Station Seismic Magnitude Estimation for Spatially Distributed Earthquake Early Warning
链接: https://arxiv.org/abs/2608.24561
作者: Quenton Yeo,Zhaoge Bi,Linghan Huang,Luke Stephen Higgins,Flora Salim,Huaming Chen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Rapid earthquake magnitude estimation is central to earthquake early warning, yet many operational systems depend on dense regional seismic networks and region-specific calibration. This creates a spatial coverage barrier for high-risk areas with sparse sensing infrastructure. Single-station learning offers a lower-cost alternative, but existing models often face an accuracy–latency trade-off and may degrade under regional distribution shift. We present SeisMamba, a lightweight Mamba-based architecture for low-latency magnitude estimation from minimally processed three-component seismic waveforms recorded at a single station. SeisMamba combines hierarchical convolutional encoding, sparse selective state-space modelling, multi-scale feature fusion, and an auxiliary temporal prediction head to support efficient long-sequence waveform analysis. On the STEAD benchmark, SeisMamba achieves the best MSE, RMSE, and R^2 among tested baselines while requiring only 0.55 ms for a batch of 32 waveforms on an NVIDIA T4 GPU, making it about three times faster than transformer-based baselines. We further conduct a Chile–Taiwan regional hold-out experiment as a diagnostic test of cross-region deployment, where SeisMamba retains useful performance on geographically unseen seismic regions. These results suggest that selective state-space waveform modelling provides a promising accuracy–latency backbone for spatially distributed, low-cost earthquake early warning.
[LG-19] Persistent Cross Entropy
链接: https://arxiv.org/abs/2608.24549
作者: Sijin Yeom,Jae-Hun Jung
类目: Machine Learning (cs.LG); Information Theory (cs.IT)
*备注:
Abstract:Persistent entropy is the Shannon entropy of a persistence-based probability measure defined on a persistence diagram. However, its cross-entropy version is not naturally defined because two persistence diagrams generally have different event spaces. To bridge these event spaces, we combine a similarity function with persistence weighting to define an induced probability. The induced probability reflects information from one diagram on the event space of the other diagram and assigns unexplained probability mass to the unexplained event. Using the induced probability, we extend cross entropy to persistence diagrams, called persistent cross entropy (PCE). We establish the main properties of both the induced probability and PCE and prove stability theorems for both. Through three numerical studies, we show that PCE distinguishes diagrams with the same persistent entropy, separates causal directions in dynamical systems without constructing a joint persistent diagram, and can be used as a directional topology loss for knowledge distillation.
[LG-20] From Numerical Simulators of PDEs to Neural Emulators and Back
链接: https://arxiv.org/abs/2608.24547
作者: Felix Koehler
类目: Machine Learning (cs.LG)
*备注:
Abstract:Simulation is central to modern engineering and science, but the cost of numerical solvers for partial differential equations (PDEs) remains a bottleneck whenever fast or many-query evaluations are required. Neural emulators trained on solver-generated data promise significant speedups, yet they are usually framed as opaque alternatives to the very methods that produce their training signal. This thesis argues the two paradigms are more alike than different: neural architectures mirror classical discretizations, their errors are amenable to the same spectral analysis, and insight flows profitably in both directions. We approach the relationship by disentangling the multiple roles a solver plays in the emulator learning pipeline. Mode-wise Fourier analysis then provides a common language in which solver errors, architectural inductive biases, and training objectives can all be read off simultaneously. Taken together, this allows synthesizing three contributions. (1) APEBench, a comprehensive benchmarking suite for autoregressive neural emulators of PDEs that uses fast differentiable pseudo-spectral solvers in JAX. (2) Progressively Refined Differentiable Physics, an investigation of the effect of unconverged solvers on surrogate training. (3) Neural Emulator Superiority, an analysis of the influence of numerical errors and architectural inductive biases.
[LG-21] MoRF-AST: Calibrated Probabilistic Virtual Sensing for Structural Monitoring under Changing Operating Conditions
链接: https://arxiv.org/abs/2608.24531
作者: Wingho Feng,Quanwang Li,Ming Zhong,Jingyu Yang,Chen Wang
类目: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
*备注:
Abstract:Probabilistic full-field reconstruction provides uncertainty-aware response evidence for structural reliability assessment, yet inference from sparse and noisy measurements remains underdetermined. Most existing methods overlook shifts between offline training and operational distributions. Under such shifts, posterior intervals may become miscalibrated, causing the reported uncertainty to lose its probabilistic meaning. This study proposes Modal Residual Flow Matching with Context-Conditioned Affine Spread Transport (MoRF-AST) for calibrated structural virtual sensing under changing operating conditions. MoRF constructs an analytic Gaussian reference posterior in normalized modal coordinates and trains a conditional flow only on posterior-whitened residuals. At deployment, AST estimates response scale from historical measurements at installed sensors and uses gated, mean-preserving Bures-Wasserstein transport to adjust posterior spread. On a bridge-deck benchmark, MoRF achieves a posterior-mean normalized root-mean-square error (NRMSE) of 7.20%, compared with 16.1% and 17.9% for two direct conditional flows. Across eight shifted traffic domains, AST reduces MoRF’s cross-domain average coverage error from 0.0535 to 0.0236, a 55.9% reduction, while preserving posterior-mean accuracy. The same transport does not improve the tested alternatives in aggregate, showing that calibration gains require its direction to match the base posterior’s dispersion bias. MoRF-AST provides a data-efficient framework for probabilistic full-field reconstruction whose uncertainty remains interpretable under scale-dominated operational distribution shifts. More broadly, this work highlights the need to calibrate uncertainty under changing operational distributions, thereby supporting trustworthy probabilistic modeling and reliability-informed decision-making in civil and infrastructure engineering.
[LG-22] SatDL: Jointly Optimizing Data Redistribution and Training for Satellite-Based Distributed Learning
链接: https://arxiv.org/abs/2608.24516
作者: Hao Wu,Kin Whye Chew,Yizhan Han,Han Li,Jingxian Wang
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注: 12 pages, 9 figures, 2 tables
Abstract:Satellite-based distributed learning promises to train machine-learning models directly in orbit using massive, globally dispersed sensor data, thereby avoiding large-scale data downloads to ground servers. However, training convergence is significantly slowed by severe non-IID data, specifically label imbalance, as each satellite observes different geographic regions with distinct labels. This imbalance extends training duration and increases energy consumption for solar-powered satellites. Existing approaches either fully redistribute data to enforce IID conditions - accelerating convergence but incurring substantial communication delays - or avoid redistribution entirely by modifying local learning algorithms to mitigate the impact of label imbalance, which, however, still prolong training and increase energy use. Both extremes result in excessive total end-to-end learning time (data-transfer delay plus training time) and thus elevated onboard energy consumption. We present SatDL, a data-redistribution framework designed to minimize total end-to-end learning time. At its core, SatDL develops a Distributor-Critic framework that jointly models and optimizes data-transfer delay and training time. Evaluations through trace-driven simulations of a 1,584-satellite Starlink constellation and hardware emulations using NVIDIA Jetson and A100 GPUs across five datasets show SatDL reduces total end-to-end learning time by up to 18.6% and onboard energy consumption by 12.23-88.00%, while maintaining inference accuracy within a few percentage points of state-of-the-art baselines. Comments: 12 pages, 9 figures, 2 tables Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG) Cite as: arXiv:2608.24516 [cs.DC] (or arXiv:2608.24516v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2608.24516 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-23] Where Entropy Is Measured Matters: Policy Geometry in Bounded Continuous-Control PPO
链接: https://arxiv.org/abs/2608.24488
作者: Yiyang He,Zhichun Zhou,Ziwei Wang,Tao Xue,Haolin Fei
类目: Machine Learning (cs.LG)
*备注: 24 pages, 6 figures, 8 tables
Abstract:Many continuous-control policies are optimized as unbounded Gaussians and then mapped into bounded actions. We show that where entropy is measured changes the policy geometry learned by proximal policy optimization (PPO). In an 80-muscle MyoLeg task, a clipped Gaussian executes 89.07% of actions within 5% of a bound. A same-state decomposition shows that this is not due to variance alone: setting variance to zero still leaves 83.83% of actions near a bound, while 82.12% of state-conditioned means lie outside the executable interval. Replacing clipping with a tanh map does not remove the high-variance regime. For latent Gaussian entropy H(u), the entropy loss has zero gradient with respect to the mean and a constant variance-increasing gradient. For executed-action entropy H(a), the transform Jacobian adds an inward gradient on the mean. Across three matched MyoLeg seeds, near-boundary occupancy is 71.42%, 29.76%, and 18.83% under latent entropy, no entropy, and executed-action entropy. A 38-dimensional Dog-Stand replication with an independent CleanRL-based PPO implementation reproduces the ordering in mean geometry, which also survives shared-state evaluation and boundary margins from 1% to 10%. Direct mean penalties can match or exceed the centering produced by H(a), showing that interior means are not unique to executed entropy. However, matched mean geometry can coexist with substantially different variance and return. Entropy measurement space is therefore a coupled mean-variance design choice, and task return alone does not characterize bounded-policy geometry.
[LG-24] NeuralParker: A Reinforcement Learning Planner for Irregular Parking Environments
链接: https://arxiv.org/abs/2608.24485
作者: Zihan Wang,Bai Huang,Yang Guan,Xiao Li,Haoyu Xu,Naizheng Wang,Shengbo Eben Li
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Automated parking commonly assumes marked slots and short approach maneuvers. Delivery and service vehicles, however, may need to reach an operator-specified pose in an irregular bounded environment from a distant start. Existing learning-based parking planners often rely on local observations, which can restrict long-range route reasoning. To address this problem, we present NeuralParker, a reinforcement learning-based hybrid planner for arbitrary-pose parking. NeuralParker encodes full-environment obstacle and boundary geometry in a target-relative vertex representation, allowing the policy to retain route-defining context throughout the approach. It further couples a learned curvature–length arc policy with an in-loop terminal ensemble that selects from diverse cubic Hermite connections using a curvature-regularized cost. We also establish factorial and long-range route-choice benchmarks to evaluate planning success and trajectory quality. Experiments on these benchmarks show that NeuralParker achieves higher planning success and better overall trajectory quality than the evaluated baselines, while ablation studies support the benefits of the target-relative global representation and terminal ensemble. Finally, a real-vehicle evaluation confirms that the planner transfers effectively to real delivery-vehicle perception at a working parking site, planning successfully at low computational cost.
[LG-25] WarpSAC: Towards the Pinnacle of Scalable Off-policy RL by Rethinking Exploration and Exploitation
链接: https://arxiv.org/abs/2608.24479
作者: Zihao Wu,Hongyao Tang,Yi Ma,Huizhong Song,Pengyi Li,Yifu Yuan,Fei Ni,Jinyi Liu,Wei Wei,Jianrong Wang,Yan Zheng,Jianye Hao
类目: Machine Learning (cs.LG)
*备注:
Abstract:Massively parallel simulation changes the data regime in which off-policy reinforcement learning (RL) is trained, challenging stabilizers designed for data-limited replay. Through controlled experiments across eight benchmark families, we show that these stabilizers are data-regime-dependent: parameter normalization helps with narrow replay coverage but restricts value fitting when data are abundant, while clipped double-Q can be relaxed in high-throughput manipulation. Age-biased replay weighting improves learning efficiency across regimes, especially with limited network capacity. Based on these findings, we propose WarpSAC, a regime-aware family of off-policy RL algorithms. WarpSAC uses Sample Weight Decay for efficient exploitation and provides two variants: WarpSAC-L (Norm ON, clipped double-Q) for data-limited CPU-scale training, and WarpSAC-A (Norm OFF, single-Q) for data-abundant GPU-parallel training. WarpSAC improves normalized score–step AUC over FlashSAC by 4.5% across nine CPU-scale environments and 23.1% across fourteen GPU-parallel environments. It increases UnitreeG1TransportBox-v1 success rate from 19.8% to 96.4%, improves mean normalized wall-time AUC on MuJoCo Playground by 19.1%, and achieves 36.4% faster sim-to-real deployment on Unitree G1 than FlashSAC. These results show that scalable off-policy RL should adapt its stabilizers to the available data regime. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.24479 [cs.LG] (or arXiv:2608.24479v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.24479 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-26] When Does Self-Supervised Pretraining Help Tabular Models? A Study of Label Scarcity and Missing Data
链接: https://arxiv.org/abs/2608.24381
作者: Sahand Mazrouei
类目: Machine Learning (cs.LG)
*备注: 18 pages, 4 figures, 4 tables
Abstract:Self-supervised learning (SSL) has emerged as a promising approach for tabular data, yet its efficacy under extreme label scarcity and test-time missingness remains under-explored. In this paper, we evaluate a mask-and-recover SSL pretraining objective against training from scratch and classical baselines across 14 diverse classification tasks. First, while SSL outperforms training from scratch on average and remains competitive with state-of-the-art tree ensembles (achieving ~0.8954 AUC vs. Random Forest’s 0.9015 at 10% labels), the SSL-vs-scratch gains exhibit high inter-task variance and lack significance (p = 0.626 at both 5% and 10% labels). Second, contrary to the hypothesis that missing-value imputation objectives universally benefit datasets with native missingness, SSL yields the most reliable improvements on clean datasets, while frequently degrading performance on datasets with high inherent missingness. Third, despite this training variance, SSL-pretrained models achieve a higher average AUC than scratch-trained models under both test-time missingness completely at random (MCAR) injection (+0.0245 AUC, positive on 11 of 14 tasks) and structured missingness shifts (MNAR, +0.0418 AUC, positive on 8 of 14 tasks), though neither difference remains statistically significant after Holm-Bonferroni correction for multiple comparisons (adjusted p = 0.118 and p = 0.518, respectively). Fourth, comparing our mask-and-recover objective against three established tabular SSL baselines (VIME, SCARF, SubTab) under an identical encoder architecture, we find no significant difference from any of them (adjusted p = 0.459, p = 1.000, p = 1.000), indicating our findings reflect general properties of tabular SSL rather than idiosyncrasies of one particular pretext task.
[LG-27] A Structural FHMM for Interpretable Disease Trajectories in T2DM
链接: https://arxiv.org/abs/2608.24328
作者: Alessandro Mari,Ekaterina Krymova,Guillaume Obozinski,Maria Luisa Marques de Sa Faquetti,Adrian Martinez de la Torre,Andrea Burden
类目: Machine Learning (cs.LG)
*备注:
Abstract:In this work, we propose a structural variant of the Factorial Hidden Markov Model (FHMM) for the analysis of disease trajectories in patients with Type 2 diabetes mellitus (T2DM). The model represents a patient’s latent health state as a combination of multiple independent, simultaneously evolving components, associated with comorbidities and lab results. This structured latent representation facilitates the identification of clinically meaningful patient states and clustering of common disease trajectories. We evaluate the proposed approach using The IQVIA Medical Research Data incorporating data from THIN, a Cegedim database of anonymized electronic health records (EHR), identifying patients with a first-ever prescription for a non-insulin antidiabetic drug (NIAD) between January 2006 and December 2019. The model identifies multiple clinically coherent latent components corresponding to known patterns of diabetes-related complications and reveals heterogeneous progression pathways, including distinct microvascular-dominant and multi-organ trajectories associated with elevated comorbidity burden and mortality. These results demonstrate that the proposed framework captures meaningful longitudinal structure in EHR data and provides interpretable insights into the evolution of T2DM and its comorbidities.
[LG-28] Causal Analysis for Time Series Foundation Models
链接: https://arxiv.org/abs/2608.24303
作者: Mathis Jander,Wouter van Heeswijk,Martijn Mes
类目: Machine Learning (cs.LG)
*备注:
Abstract:Transitioning from bespoke time series models towards time series foundation models changes the relationship of model and application from one-to-one to one-to-many. This shift introduces concentration risk as many, potentially high-risk, forecasting applications are exposed to the same biases and failure modes of a single time series foundation model. At the same time, this centralization allows for economies of scale in model development and validation. In this study we investigate how biases and failure modes of time series foundation models can be identified before deployment. We propose a causal analysis framework to investigate the ability of a time series foundation model to preserve time series patterns. To achieve this, we intervene on parameterized synthetic time series generators and measure the corresponding change in model output under ceteris paribus conditions. We apply our causal analysis framework to Chronos-2 and TimesFM-2.5 and test them across six distinct time series patterns. We find safe configurations for trend and harmonic oscillation patterns. The results also indicate a bias in both models towards overestimating persistence, sudden failures for both models against the regime switch pattern and failure for TimesFM-2.5 against the energy-release pattern. Our review of the original works for both models indicates that the findings might be explained by the data used for pretraining. We conclude our study with suggestions for further model development, recommendations for application-specific model selection, and a discussion of limitations and further research directions.
[LG-29] A Data-dependent Early Stopping Rule using Rademacher Complexity with L1-norm
链接: https://arxiv.org/abs/2608.24210
作者: Duy Hoang,Bastien Berret,Olivier Bruneau,Laurent Fribourg
类目: Machine Learning (cs.LG)
*备注: 22 pages, 11 figures
Abstract:Training neural networks requires balancing the trade-off between fitting the training data and achieving robust performance on unseen inputs. This ability, commonly referred to as generalizability, is determined by the gap between the empirical risk on the training set (empirical loss'') and the expected risk over the data distribution (generalization error’‘). Existing approaches typically estimate the generalization error numerically, requiring gradient descent training and an early stopping'' strategy. In this work, we introduce an analytic framework that estimates the optimal time of early stopping without the need for training. Several works in the literature also give such analytical estimations, but they are generally based on random matrix theory and often make assumptions on the distribution of the data or the eigenvalue distribution of the covariance matrix. In contrast, our work is based on Rademacher complexity (RC) without needing such probabilistic assumptions. For both theoretical and numerical reasons, it is more relevant to express RC with the L1- norm rather than with the L2-norm. We focus on the case of linear models and the problem of linear regression. Thanks to the linear probing’’ method, our results can, however, be successfully applied to nonlinear neural networks, as illustrated in the classification MNIST example.
[LG-30] PRQ-KMeans: Projection Residual Quantization for Semantic ID Tokenization
链接: https://arxiv.org/abs/2608.24207
作者: Yunxiao Luo,Siyuan Wang,Ben Chen,Chenyi Lei
类目: Machine Learning (cs.LG)
*备注:
Abstract:Semantic identifiers (SIDs) represent entities as hierarchical token sequences for generative retrieval and recommendation. Residual-quantization tokenizers construct these sequences by selecting a codeword at each level and passing a residual to the next. We view this process as progressive commonality removal: each token captures a component shared within its group, while later tokens should model the remaining differences. This view reveals three limitations: a corpus-wide shared component can consume first-level capacity, hard assignment ignores graded similarities to nearby codewords, and full-codeword subtraction can leave variation along the selected-codeword direction in the next residual. We therefore develop our solution in the post-hoc setting, where residual construction is not constrained by input reconstruction. Specifically, we propose PRQ-KMeans, which removes the global-mean component, refines centroids with Top-k similarity-weighted updates, and replaces full-codeword subtraction with a projection residual that removes each representation’s selected-centroid component. Experiments on a large-scale industrial search dataset and four public recommendation benchmarks show that PRQ-KMeans achieves the strongest overall performance among the evaluated tokenizers, including gains of up to 7.4% in HitRate and 11.8% in MRR on the industrial dataset.
[LG-31] From Relaxed Indexability to Exact Indexability: A t-Step Approach for Partially Observable Restless Bandits
链接: https://arxiv.org/abs/2608.24167
作者: Qizhen Jia,Keqin Liu
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:Whittle index policies offer a scalable method for restless multi-armed bandits, but under partial observability even determining the indifference subsidy at a single belief requires solving an infinite-horizon belief-state problem with no closed-form value function. Liu [10] addresses this difficulty by linearizing the unknown decision boundary, leading to a linear system and a closed-form approximate Whittle index. However, the resulting threshold uses only a one-step active–passive comparison and does not account for longer-horizon continuation values. We extend this framework to a \emph t -step lookahead threshold policy. For each subsidy m , the threshold is defined by the active-minus-passive advantage under t -step finite-horizon value iteration. At t=1 , the threshold is m -independent and recovers the linear threshold of Liu [10]; for t1 , it becomes subsidy-dependent through the induced first-crossing structure and tracks the exact decision boundary more closely. The proposed algorithm does not require indexability as an input and includes an indexability verification. Under the original Whittle indexability, we prove that the t -step approximate Whittle index converges geometrically to the exact Whittle index, [ |\widehat W_t(\omega)-W(\omega)|=O(\beta^t). ] Numerically, all 2,715 tested three-state instances are verified as indexable according to the proposed criterion. The P95 index error decreases from 2.18\times10^-2 at t=1 to 8.93\times10^-4 at t=8 . In an exact-comparable instance with \beta=0.9999 , t=2 already recovers the exact Whittle-index ordering. Moderate-depth threshold policies also outperform the one-step baseline and remain close to the optimal dynamic-programming benchmark, while runtime grows mildly with t . Subjects: Machine Learning (cs.LG); Optimization and Control (math.OC) Cite as: arXiv:2608.24167 [cs.LG] (or arXiv:2608.24167v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.24167 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-32] Robust Data-Collection Policy Learning for Low-Variance Online Policy Evaluation
链接: https://arxiv.org/abs/2608.24146
作者: Claire Chen,Shuze Daniel Liu,Licheng Luo,Rohan Chandra,Nan Jiang,Shangtong Zhang
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:In reinforcement learning policy evaluation, classic on-policy methods often suffer from high variance when estimating policy performance. To mitigate this issue, behavior policy search has been proposed to learn data-collecting policies tailored to reduce online evaluation variance. However, these approaches do not account for uncertainties in the transition functions. In practice, simulator transitions often differ from the real world due to modeling errors or approximation limitations. As a result, behavior policies trained in simulation may still yield high variance when deployed in real environments, leading to costly reliance on real-world evaluation samples. In this work, we propose a double-loop gradient-based algorithm for learning behavior policies that are both efficient and robust to transition uncertainty. Theoretically, we derive novel transition-variance gradient expressions and establish global convergence guarantees for the algorithm. Numerically, we demonstrate that our method is less sensitive to transition perturbations than existing approaches, providing supportive evidence for its practical utility.
[LG-33] Steering Recurrent Reason ers at Inference Time with Readout Feedback
链接: https://arxiv.org/abs/2608.24136
作者: Shunsuke Kamiya,Masanori Koyama,Seongcheol Jeong,Fumiya Uchiyama,Kenji Kubo,Kohei Hayashi,Masahiro Suzuki,Yutaka Matsuo
类目: Machine Learning (cs.LG)
*备注:
Abstract:Recurrent models, which repeatedly update latent states with shared computation blocks, have emerged as powerful architectures for solving complex reasoning tasks. Existing inference-time methods scale computation by running more steps or sampling more trajectories, but ignore information revealed within each trajectory. Here we show that recurrent models can be improved at inference time by using their own readout probabilities to steer latent dynamics without retraining. We introduce Readout Feedback (RoFB), a test-time intervention that converts intermediate predictions into token-wise pairwise coupling forces injected into the latent dynamics. Across three recurrent models (AKOrN, ItrSA++, TRM) on Sudoku and Maze, RoFB yields clear gains in four of six model-task pairs, achieving performance unattainable by merely running more steps or selecting from multiple trajectories, at comparable or lower computational cost. These results suggest that closed-loop steering of latent dynamics can serve as a complementary inference-time control mechanism for recurrent reasoning models.
[LG-34] A mesh-free multiresolution deep energy method with phase-field modeling of brittle fracture
链接: https://arxiv.org/abs/2608.24126
作者: Han Zhang,Mehrisadat Makki Alamdari,Babak Shahbodagh,Mohammad Vahab,Cosmin Anitescu,Timon Rabczuk,Elena Atroshchenko
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注:
Abstract:Phase-field modeling of brittle fracture removes the need to track cracks explicitly by recasting their evolution as the minimization of an energy functional. In return it requires a discretization dense enough to resolve a localization band whose width is set by a regularization length and whose path is not known in advance. We propose a mesh-free discretization in which a single neural network represents the displacement and phase fields and is trained by minimizing the incremental energy directly. The coordinates enter the network through a multiresolution feature encoding built from C^1 quadratic B-spline grids, so the finest scale the representation can express is set by choice rather than reached through slow training, and the energy is estimated by stratified Monte Carlo integration on points redrawn at every optimizer iteration. This pairing proves critical, since the crack fails to advance both when the integration points are held fixed and when the encoding is too coarse to represent the band, while each ingredient tolerates a wide range of settings once the other is in place. Because the representation is globally C^1 , the second- and the fourth-order fracture energy densities run on the identical discretization. Across six problems, from single-edge-notched tension and shear to a thick-walled ring on a single spline patch, the computed load-displacement curves follow staggered finite element references at matched regularization length, with peak loads within about 1% on the single-edge-notched tests and within 8% where the crack pattern changes topology. On a public benchmark dataset of random multi-crack configurations the method classifies the active or dormant state of 90% of the seeded cracks in twenty zero-shot runs, where the deep Ritz baseline of the dataset authors fails.
[LG-35] he Sharp Tail of Uniform Stability
链接: https://arxiv.org/abs/2608.24098
作者: Pahan Dewasurendra
类目: Machine Learning (cs.LG)
*备注:
Abstract:Uniform stability controls how much one training example can change the loss at any test point. A new logarithmic-free upper bound shows that a \gamma -uniformly stable algorithm with loss in [0,L] has generalization gap at most O \left(\gamma\log(1/\delta) +L\sqrt\frac\log(1/\delta)n\right) with probability 1-\delta . Whether an actual bounded-loss learning algorithm can realize the linear dependence on \log(1/\delta) has remained open. The known construction realizes it only for auxiliary weakly dependent random variables whose pointwise range grows with n . The known learning lower bound holds only at constant probability. We close this gap. For every n , stability level \gamma , and loss bound L , we construct one deterministic \gamma -uniformly stable learning problem whose tail satisfies, simultaneously for 1\le p\le c n , \mathbb P \left( R(A_S)-R_S(A_S) \ge c’\min \left\L,\gamma p+L\sqrtp/n\right\ \right)\ge e^-p. The construction is ordinary bounded absolute-loss regression with constant labels. Its key is a multiscale collection of rare Rademacher features. A coordinatewise ramp is stable in sup norm, while an odd symmetrized maximum converts a unique extreme feature into a gap of order \gamma p without violating the loss bound. Geometrically spaced ramps put all confidence levels into the same problem. Together with the logarithmic-free upper bound, this determines the optimal high-probability and moment dependence of uniform stability up to universal constants.
[LG-36] A Feature-Major Codebook for Memory-Efficient Sparse-Binary Self-Organizing Maps: Scaling a MEDLINE Atlas to 1.05 Million Neurons on a Single Consumer GPU
链接: https://arxiv.org/abs/2608.24067
作者: Andrew James Amos
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注: 39 pages, 11 tables, 7 figures. Code, frozen results and reproduction pipeline: this https URL (tag v1.0). Corpus: doi: https://doi.org/10.5281/zenodo.20770707 (CC0). An instruction-roofline profiling addendum is included as an ancillary file and at this https URL (tag v1.0)
Abstract:A self-organising map turns a large corpus into a browsable two-dimensional atlas, but building one at MEDLINE scale has been impractical: the best-matching-unit (BMU) search that dominates training is bound by the bandwidth needed to read the codebook every epoch. I show that this bottleneck is largely an artefact of codebook layout. Storing it feature-major with each feature’s weights contiguous, W[v.M+i], recasts the search as a tiled sparse-dense product in which every loaded weight column is reused across a tile of samples. Varying only the layout, with implementation, precision and update rule held fixed, accelerates the BMU search by 4.5-8.5x. Because an exact-argmin BMU is invariant to how the codebook is stored, this gain costs nothing: held-out quantisation error agrees with a cuSPARSE baseline to within 0.5% at every map size. Against that baseline the advantage is a crossover rather than a constant: this http URL is faster at small maps, this http URL is 1.5x faster at 128x128 and 2.6x at 256x256, and at 512x512 it is the only one that runs at all on 24 GB. Paired with a radius-independent box-blur update and a convergence-based stopping rule, it trains a converged map over 29.9 million MEDLINE articles in about 72 s at 64x64 on one 24 GB GPU, and accommodates 262,144 neurons (512x512 edges) where every alternative algorithm I tested exceeds memory constraints. On a 141 GB H200 it reaches 1,048,576 neurons (1024x1024 edges) - to my knowledge the largest self-organising map yet reported. Held-out error follows a smooth power law with no elbow across three decades of map size, so the limit on resolution is compute rather than any breakpoint in the data. At matched work the design is ~82x faster than MedSOM, the CUDA implementation behind our earlier MEDLINE atlases and, at 128x128, 621x faster than the best available multicore-CPU library.
[LG-37] PhysicsBench: A Unified Leaderboard for Generative and Predictive Models in Engineering Design and Simulation
链接: https://arxiv.org/abs/2608.24056
作者: Sang Won Lee,Hyogu Jeong,Namwoo Kang
类目: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE)
*备注: 40 pages, 12 figures, 8 tables. Leaderboard: this https URL | Data: this https URL
Abstract:Generative and predictive artificial intelligence models are increasingly used to generate geometry and to predict physical fields and scalar quantities in engineering design and simulation. Yet these models are typically evaluated in isolation, on academic datasets at unconstrained scales, with inconsistent metrics and procedures. We present PhysicsBench, a unified benchmark and leaderboard that evaluates generative and predictive models under one standardized procedure. PhysicsBench spans seven generation and prediction tasks across 1D, 2D, and 3D domains and ranks 66 models on nine datasets, comprising industrial-scale CAD/CFD/FEA simulations and public references, expanded into 28 configurations. One procedure and ranking apply to both families, each ranked within its own tasks. Evaluation spans realistic, limited data scales from S to XL rather than the unlimited training sets common in academic benchmarks. A common metric suite captures geometric fidelity with distributional distances, physical-field and scalar accuracy, and engineering-specific field- and shape-validity. BenchRank debiases correlated metrics and ranks by PageRank over a head-to-head dominance graph, so every reported quality metric is also ranked, with computational cost in a separate efficiency view. Across tasks, an architecture’s large-scale academic standing weakly predicts its small-data ranking. The top model changes with data scale in six of the seven tasks, and no model leads more than one task. PhysicsBench turns “state-of-the-art” from a self-reported claim into an openly published foundation for model selection.
[LG-38] ALPHABET: A Laplace-Pole History Aggregator with Banked Exponential Transport
链接: https://arxiv.org/abs/2608.24051
作者: Daehwa Ko,JaeHyeon Kim,Oh Seong Kwon,Jay Hoon Jung
类目: Machine Learning (cs.LG)
*备注: 21 pages, 5 figures, 9 tables; includes technical appendix
Abstract:Can a sequence model remain competitive with only a few thousand parameters and an explicitly auditable prediction interface? We introduce ALPHABET, a compact linear-time model that compresses temporal history into stable complex pole modes: a direct bank synthesizes its modal states back into the feature trajectory, an independent cascaded bank analyzes the transformed trajectory without resynthesis, and an affine head reads only modal energies and lag moments from both banks. We characterize the temporal information this descriptor retains: for a stationary, fully observed feature process, each mode energy is a frequency-localized measurement of the second-order spectrum, the continuum of such measurements identifies the spectrum, and almost every mode separates any fixed finite set of spectrally distinct classes. On a Gaussian control with matched low-lag statistics, the learned descriptor approaches the Bayes oracle where raw autocovariances remain at chance. Across the fixed 82-task registry, ALPHABET attains mean rank 3.97 in the complete ten-family comparison. At the common-width D=64 runtime anchor, its 6,437 parameters deliver 5.02 times faster inference and 3.93 times faster complete training steps than the nine baselines on average.
[LG-39] Physics-Integrated Operator Learning via Gaussian Splatting Representations
链接: https://arxiv.org/abs/2608.24049
作者: Jihao Zhang,Junyi Guo,Jian-Xun Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Neural operators provide efficient surrogates for spatiotemporal PDE systems, but purely data-driven formulations often accumulate substantial errors during long-horizon autoregressive prediction and may fail to exploit available governing-equation structure. Existing approaches incorporate physics primarily through residual-based training objectives or PDE-specific architectural constraints, which can introduce optimization difficulties or limit architectural generality. In this work, we introduce a representation-level approach to physics integration in which a feed-forward Gaussian splatting (FFGS) representation serves as a continuous interface between discretized solution fields and governing operators. The FFGS representation reconstructs the state as a continuous Gaussian field with closed-form spatial derivatives, allowing available physical PDE operators to be integrated directly within the learned evolution map without introducing a physics-residual loss. We evaluate the framework across two- and three-dimensional PDE systems, including advection, diffusion, nonlinear self-advection, and reaction dynamics. Over long-horizon autoregressive rollouts, the proposed framework reduces relative \ell_2 error by 1.5\times – 2.2\times compared with the strongest purely data-driven baseline across the benchmark suite, while consistently improving spectral fidelity. The framework also remains effective when the governing equations are partially known, demonstrating robustness to incomplete physics. These results demonstrate that continuous field representations can provide a practical interface for incorporating known physical structure into generic neural-operator surrogates.
[LG-40] XP-JEPA: Cross-Predictive Physics Grounding for Forecastable Latent Dynamics
链接: https://arxiv.org/abs/2608.24044
作者: Kehan Wen,Ziming Li,Siyuan Luo,Fan Shi
类目: Machine Learning (cs.LG)
*备注: Under review
Abstract:Latent world models plan by predicting how candidate actions transform learned representations. In self-predictive models, however, the encoder and predictor are optimized jointly and can co-adapt to latent transitions that are easy to predict but only weakly constrained by the physical evolution of the scene. We introduce the cross-predictive JEPA (XP-JEPA), which grounds visual latent dynamics in privileged physical trajectories. XP-JEPA separately encodes visual observations and physical states, advances both through a shared action-conditioned predictor, and matches each prediction to both future representations. This objective encourages unified latent dynamics across the two modalities, grounded in the underlying physical transitions. The physical branch is discarded after training, leaving a visual-only model at deployment. On a multi-task suite spanning six evaluation subfamilies, XP-JEPA reduces rollout drift of a newly fitted predictor from 0.361 to 0.104 and increases mean control success from 53.6% to 78.2% . Direct physical-state regression raises position decodability but leaves forecastability and control near the visual-only baseline. Cross-predictive physical grounding can therefore produce more forecastable latent dynamics for rollout-based control without privileged inputs at test time.
[LG-41] PinSieve: Production Selective VLM Serving and a Governed Memory Flywheel for Enterprise Content-Quality Triage KDD2026
链接: https://arxiv.org/abs/2608.24040
作者: Chuqing Gao,Yuanfang Song,Jonathan Zhang,Yifan Wu,Vishwakarma Singh,Qinglong Zeng,Andrey Gusev
类目: Machine Learning (cs.LG)
*备注: Accepted at the KDD 2026 workshop “Enterprise AI Agents: From Prototypes to Production.”
Abstract:Enterprise AI agents in production often need to be bounded, stateful, observable, and governable rather than fully autonomous. We present PinSieve, a production case study in a large-scale content-quality pipeline. Its deployed component is a selective vision-language-model (VLM) Serving Agent that operates only on the grey-zone slice left unresolved by lightweight upstream models, exposes a scalar routing score online, and preserves controlled human escalation. On this slice, the deployed system filters 2.05x more non-actionable items than the previous production module while slightly reducing estimated miss rate; after promotion, it improves review productivity by 25.7%, reduces normalized operating cost by 16.2%, and moves signal delivery from next-day to same-day. We then study maintenance through a governed memory flywheel under selective feedback, where escalated items are reviewed by default and auto-passed items are labeled mainly through audit sampling. Feedback Memory records routing traces, observation paths, audit propensities, and replay metadata for evaluation and debugging. The Data Curation Agent uses a bounded proposal-verifier loop over representative, uncertainty, recency, and fresh-review replay, with positive-rate and score-bin guardrails before batch acceptance. In chained monthly refresh over six months of production data, this design reduces average FNR@50% from 17.73% under representative random replay to 13.29%. A Reasoning Review Agent audits teacher-generated rationales and supports keep/repair/drop decisions. Production claims are attributed only to the deployed Serving Agent; replay and rationale-review results are offline or sampled-governance evidence. The same serving-agent recipe has been adopted to several additional internal signals, suggesting transferability beyond one task.
[LG-42] Revenge of Monosemanticity: Specialized Neurons Improve Data Efficiency in MLPs
链接: https://arxiv.org/abs/2608.24007
作者: Amirhesam Abedsoltan,Enric Boix-Adsera,Fivos Kalogiannis,Mikhail Belkin
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Understanding how neural networks learn and organize features is central to understanding their behavior. Much existing theory of feature learning has focused on the emergence of a global low-dimensional predictive geometry. We show that this picture is incomplete. In regression problems with clustered data, we demonstrate that multilayer perceptrons (MLPs) naturally develop monosemantic specialized neurons: individual neurons become strongly aligned with a specific predictive feature relevant to a particular region of the input space. Rather than learning a single global low-dimensional representation, MLPs learn a collection of local low-dimensional representations that can collectively span a high-dimensional space. This specialization provably gives MLPs a data-efficiency advantage over feature-learning methods based on a global low-dimensional representation.
[LG-43] Low-Latency Activation-Regularized Sparse Neural Operators with Distillation Assistance Towards Real-Time Edge-Deployable Virtual Sensing
链接: https://arxiv.org/abs/2608.23987
作者: William Howes,Farid Ahmed,Syed Bahauddin Alam
类目: Machine Learning (cs.LG)
*备注: 37 pages, 14 tables, 7 figures
Abstract:Virtual sensing enables digital twins and safety-critical systems to reconstruct and forecast spatial-temporal physics in real time. However, conventional computational and data-driven methods often face challenges in generalization, latency, and energy efficiency for edge deployment. Neural operators offer a promising alternative but remain reliant on power-intensive hardware. Spiking neurons and neuromorphic computing can improve efficiency, yet surrogate-gradient training and multi-step spiking introduce convergence and latency challenges. We propose the Sparse-Activation-ReLU (SAR) layer, a single-step alternative that promotes activation sparsity without surrogate-gradient training while remaining compatible with event-based computing. Within a trunk-based NOMAD architecture, SAR achieves over a fivefold improvement in the combined Latency-Error-Energy (LEE) metric compared with Variable Spiking Neuron (VSN) and Leaky Integrate-and-Fire (LIF) implementations. We further analyze spiking entropy and feature usage and introduce synthetic knowledge distillation, reducing the LEE score by more than twofold. Finally, we improve VSN through a ReLU-based spiking loss and graph-neighbor thresholding. On the Heat Exchanger dataset, these approaches reduce L2 error by more than twofold and nearly sevenfold, respectively, while reducing spiking and spatial aggregation. Overall, the work presented is a step towards energy-efficient virtual sensing by providing an alternative framework that can be positioned towards neuromorphic or other edge device integration that can be a gold standard to compare latency, energy, and error performance for future efficient designs that are sparsity or brain-inspired spiking based.
[LG-44] CoDrift: Compositional Drifting for Offline Reinforcement Learning
链接: https://arxiv.org/abs/2608.23939
作者: Xiewei Ni,Ruofeng Mei,Xiangyu Xu
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注:
Abstract:Offline reinforcement learning is intrinsically multi-objective: a policy must remain compatible with the behavioral support of a fixed dataset while preferentially selecting high-value actions. We recast these objectives in a common form by viewing each as an action-space motion field that specifies how generated actions should move. This perspective enables heterogeneous learning objectives to be combined directly through field composition. Inspired by drifting models, we propose CoDrift, a compositional framework for one-step generative policy learning. CoDrift combines three objective-level fields into a unified policy field. The conditional field preserves state-dependent behavioral structure, while the marginal field pools actions across states to provide a more stable generative signal in the single-positive-sample regime of continuous-control offline RL. The value field moves generated actions toward higher-value regions. The composed field is absorbed into a stochastic generator that produces an action with a single forward pass at deployment. We evaluate CoDrift on 73 tasks from OGBench and D4RL in both offline and offline-to-online settings. CoDrift compares favorably with state-of-the-art methods and achieves the best average rank in both settings.
[LG-45] MnemoDyn: Learning Resting State Dynamics from 40K FMRI sequences ICLR2026
链接: https://arxiv.org/abs/2608.23936
作者: Sourav Pal,Viet Luong,Hoseok Lee,Tingting Dan,Guorong Wu,Richard Davidson,Won Hwa Kim,Vikas Singh
类目: Machine Learning (cs.LG)
*备注: ICLR 2026
Abstract:We present a dynamical-systems based model for resting-state functional magnetic resonance imaging (rs-fMRI), trained on a dataset of roughly 40K rs-fMRI sequences covering a wide variety of public and available-by-permission datasets. While most existing proposals use transformer backbones, we utilize multi-resolution temporal modeling of the dynamics across parcellated brain regions. We show that MnemoDyn is compute efficient and generalizes very well across diverse populations and scanning protocols. When benchmarked against current state-of-the-art transformer-based approaches, MnemoDyn consistently delivers superior reconstruction quality. Overall, we find that with such large-scale pre-training on (non-proprietary) rs-fMRI datasets, we get a highly performant model for various downstream tasks. Our results also provide evidence of the efficacy of the model on small sample size studies which has implications for neuroimaging studies at large where resting state fMRI is a commonly acquired imaging modality.
[LG-46] GATNextHop: A GAT for Shortest Path Routing with Cross-Topology Generalization
链接: https://arxiv.org/abs/2608.23917
作者: Chia-Hong Chou,Katerina Potika
类目: Machine Learning (cs.LG)
*备注: Sixth Annual Computer Science Conference for CSU Undergraduates
Abstract:Common shortest-path algorithms, such as Dijkstra’s (SPF), that OSPF uses, provide exact routing solutions but must be recomputed for each network topology, limiting scalability in dynamic or large-scale networks. This paper proposes the GATNextHop model to determine whether a Graph Neural Network, namely the Graph Attention Network, can approximate shortest paths and generalize across topologies. By training on synthetic graphs and evaluating on real-world Internet Service Provider networks from the Internet Topology Zoo, we aim to benchmark our model’s ability to learn routing heuristics that transfer across network structures. Performance will be evaluated in terms of accuracy, inference speed, and generalization, comparing the GNN against Dijkstra’s algorithm to quantify trade-offs between learned and classical routing approaches.
[LG-47] he Loss Floor of Denoising Score Matching: Fisher Geometry from Schrödinger Bridges
链接: https://arxiv.org/abs/2608.23916
作者: Avinash Raju,Kai Zhang
类目: Machine Learning (cs.LG); Statistical Mechanics (cond-mat.stat-mech)
*备注: 28 pages, 4 figures
Abstract:Denoising score matching trains diffusion models by regressing onto a conditional score, although generation ultimately requires the marginal score. The two objectives share the same population minimizer, but the conditional target remains random at fixed noisy state and introduces an irreducible excess in the training loss. We isolate this excess and show that, for a general corruption kernel under mild regularity assumptions, it is exactly the trace of the Fisher–Rao metric of the conditional endpoint family, integrated along the diffusion trajectory. This gives an exact conditional-variance decomposition of the denoising objective and identifies the information geometry observed in diffusion latent spaces as an intrinsic component of the training loss. We derive the result from a Schr"odinger bridge variational principle, in which the ideal objective arises as excess path-space relative entropy. For corruption diffusions, the Fisher term is proportional to the rate at which the noisy state loses mutual information about the clean data, separating the loss floor into an information flow determined by the data and a weight determined by the corruption schedule and objective. In the Gaussian case, this yields a closed form for the floor, recovers reparametrization invariance of the continuous-time objective, and relates its high-SNR divergence to the information dimension of the data. Finally, we show that raw losses obtained with different noise ranges or weightings need not rank models consistently because they contain different additive floors, and contrast the second-order geometry seen by training with the third-order conditional statistics entering numerical sampling error.
[LG-48] Partial Optimal Transport on the Circle for All Transported Masses in O(N log N)
链接: https://arxiv.org/abs/2608.23910
作者: Soheil Kolouri
类目: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS)
*备注:
Abstract:Partial optimal transport compares two measures while leaving part of the mass unmatched, which is what makes it robust to outliers, occlusion, and clutter. The quantity of interest is usually the whole profile - the optimal cost at every transported cardinality - because the right amount to transport is rarely known in advance, and on the real line the PAWL algorithm returns that profile in O(N\log N) . Much data is periodic rather than linear: angles, phases, orientations, time of day, hue, and every direction obtained by projecting onto a great circle. On the circle the same problem acquires a global circulation, or equivalently an optimized cut, which the naive exact method handles by running the line algorithm once per support gap, at O(N^2\log N) . We show that this factor N is unnecessary. The line structure survives in cut-free form, and a free-gap invariant supplies, at every step, a cut at which all previous local updates remain valid line updates. This yields PAWC: an exact O(N\log N) time, O(N) memory algorithm returning all K+1 costs, nested active sets and plans in one run, together with a single gap that is simultaneously optimal for every cardinality. Slicing over great circles extends it to \mathbbS^d-1 . Empirically the whole profile costs 0.56 ms at N=4096 against 1.5 s for a single transported fraction from a general solver; on occluded, cluttered mpeg-7 shapes, holding the descriptor fixed and varying only the cost, it retains 66% of the clean-data retrieval score against 16% for balanced circular OT, and on \mathbbS^2 it halves the fitting error of spherical sliced Wasserstein against contaminated targets, synthetic and real. Code is available at this https URL.
[LG-49] Every Layer Counts: An Exponential L_2 Depth Hierarchy for ReLU Networks
链接: https://arxiv.org/abs/2608.23877
作者: Itay Safran
类目: Machine Learning (cs.LG)
*备注:
Abstract:We prove a depth hierarchy for ReLU neural networks in which every additional ReLU layer can save exponentially many neurons. For every \ell\geq 3 , a globally [0,1] -valued, 1 -Lipschitz function is realized by a depth- \ell network of width \mathcalO(d^4) , whereas every depth- (\ell-1) network with unrestricted weights and width at most 2^d/[2d(\ell-2)] has squared L_2 error at least 1/24 under an absolutely continuous distribution. To the best of our knowledge, this is the first exponential separation for ReLU networks between two fixed depths whose shallower depth is at least 3 , and the first exponential hierarchy across all adjacent fixed depths. The lower bound also immediately yields the corresponding hierarchy for exact computation. Moreover, the case \ell=3 gives a compactly supported depth- 3 -versus-depth- 2 separation with unrestricted shallow-network weights, answering a question raised by Safran, Eldan, and Shamir (2019, Sec. 2.3). The corresponding distribution nevertheless has all its mass at exponential radius, so the construction falls outside the regularity regime in which such a separation would imply major threshold-circuit lower bounds. We also prove an exact separation for a more benign function. It is computed by a polynomial-width depth- 4 network, whereas every depth- 3 network agreeing with it on the unit hypercube requires exponentially many neurons in its first hidden layer, again without any restriction on the weights. The function is globally [0,1] -valued and \mathcalO(\sqrt d) -Lipschitz, and maps the unit hypercube onto [0,1] .
[LG-50] UHI-Bench: Benchmarking Dual-Source Urban Heat Island Modeling Across Cities in Diverse Climate Regimes
链接: https://arxiv.org/abs/2608.23857
作者: Wanyun Ling,Chenxi Liu,Yi Xie,Aopu Xu,Zhuoqi Zeng,Ziyue Li
类目: Machine Learning (cs.LG)
*备注: 24 pages, 11 figures, 19 tables
Abstract:Urban heat islands (UHIs) are intensifying under climate change, exacerbating thermal exposure risks. Their two primary observations, land surface temperature UHI (LST-UHI) and near-surface air temperature UHI (AirT-UHI), capture physically distinct aspects of urban heat. However, most studies rely on a single source, and substituting one for the other can substantially bias the magnitude and spatial variability of human heat exposure. Accurate UHI modeling also requires dynamic meteorological drivers and static urban morphology features, but spatiotemporal incompatibilities hinder their alignment. Cloud gaps in LST observations and sparse AirT station networks further limit dual-source UHI modeling, motivating cross-city transfer across diverse climates. To bridge these gaps, we introduce UHI-Bench, the first UHI benchmark for dual-source UHI modeling that integrates dynamic and static environmental context. Following a unified signal, mechanism, and transfer framework, it evaluates over 20 baselines from four model families on five tasks across 20 cities and nine Köppen climate classes. Results show that no model is uniformly best, although foundation models remain consistently competitive and stable. Environmental covariates generally improve performance, but their utility varies across sources and tasks. Cross-city transferability is better explained by overlap in UHI regimes than by climate-zone similarity. With the dataset and standardized pipeline, our work provides practical guidance for urban heat modeling, promotes climate data equity, and supports future advances in climate research.
[LG-51] FlowNeg: GFlowNet-Guided Diverse Hard Negative Sampling for Knowledge Graph Embedding
链接: https://arxiv.org/abs/2608.23849
作者: Ibne Farabi Shihab,Naoshin Anzum Hridi,Joyanta Jyoti Mondal
类目: Machine Learning (cs.LG)
*备注: 20 pages, 1 figure
Abstract:Negative sampling determines whether a knowledge graph embedding (KGE) model learns from informative counterexamples or wastes updates on implausible corruptions. Uniform negatives are diverse but easy, whereas hard-negative miners concentrate on few entities and collide more with held-out positives. We introduce FlowNeg, a context-conditioned hierarchical generative flow network that amortizes reward-proportional sampling without normalizing a composite reward over the entity set: given a positive triple and corruption side, it selects a type, then an entity. Its terminal reward combines bounded model-based hardness with a training-only structural score for held-out-positive collision, over a relation-specific type-compatible support. We derive the reward, specialize standard trajectory balance, and bound multiplicatively how residual imbalance perturbs terminal and mode probability. Across a descriptive five-seed grid of five architectures and five benchmarks, FlowNeg has higher mean MRR than EMU and than IF-NS in 24 of 25 cells ( +0.0172 and +0.0160 on average). A separate 15-seed FB15k-237/RotatE control fixing negative count, diagnostic budget, and compute gives FlowNeg 0.359\pm0.001 MRR against 0.346\pm0.002 for EMU, with near-uniform fixed-partition diversity, high gradient informativeness, and low collision. The evidence supports mode-covering negative generation without treating structural similarity as an open-world truth oracle.
[LG-52] PuzzleKV: Page-Wise Low-Rank Decomposition for KV Cache Compression
链接: https://arxiv.org/abs/2608.23843
作者: Zizhong Wang,Jieying Wang,Zhao Zhang,Jiajia Li
类目: Machine Learning (cs.LG)
*备注:
Abstract:Long-context inference in large language models (LLMs) is increasingly limited by the memory required for the key-value (KV) cache. KV cache compression addresses this problem by reducing the storage cost of previous tokens. Among existing approaches, low-rank compression is particularly attractive because it represents every token in reduced dimensions. Previous low-rank methods typically derive fixed projection spaces from model weights, construct fixed spaces from calibration activations, or construct a shared basis over a broad cache region. Such representations may not capture detailed but important information. We partition each per-head KV cache into fixed-length logical pages and observe substantial low-rank structure within individual pages. Based on this observation, we propose PuzzleKV, a training- and calibration-free method that treats each completed page as an independent compression unit. PuzzleKV decomposes pages within each layer and KV head, computes attention directly over dense and factorized pages, and incrementally compresses newly eligible pages during autoregressive decoding. Experiments across models, context lengths, and benchmarks demonstrate the effectiveness of PuzzleKV under matched storage budgets. At approximately 60% of the original KV cache storage, PuzzleKV achieves more than 96% of Full KV performance across both evaluated models and all benchmark settings, with substantial gains over Global SVD on RULER and competitive performance on LongBench. To achieve a more aggressive compression ratio, PuzzleKV can be further combined with quantization while retaining more than 93% of Full KV performance using only 18.7% of the original storage.
[LG-53] Pipeline-Native Transformers: Co-Designing Model Architecture and CPU Inference for Bandwidth-Efficient Autoregressive Decode
链接: https://arxiv.org/abs/2608.23841
作者: Tom Poperszky
类目: Hardware Architecture (cs.AR); Machine Learning (cs.LG); Performance (cs.PF)
*备注: 77 pages, 2 figures. Model weights and binaries: this https URL
Abstract:Single-token autoregressive decode on CPUs is bound by memory bandwidth, not arithmetic: a modern CPU sustains roughly 1 TFLOP/s of compute but only about 50 GB/s from main memory, and each generated token must stream every active weight once. This report argues that the most effective response is to co-design the model architecture and the inference runtime together. It presents cflow, a CPU-first streaming engine, alongside a family of pipeline-native transformer architectures whose inter-layer dependency graphs are constructed to permit a vertical, stage-major execution schedule. cflow stores weights as L2-sized tiles in compute-consumption order, reads only the top-k experts of each mixture-of-experts layer, fuses projections, and executes a delay-aware schedule from per-model dependency parameters. Across five architectures trained on TinyStories, one (arch2_4_combined) achieves a 2.00x reduction in critical-path weight bandwidth (9.00 to 4.50 MB/token) within 0.24 perplexity of the best candidate, and the tile layout incurs 7.29x fewer L1-data read misses than a row-major baseline. On a 30.9-billion-parameter pipeline-native MoE, cflow decodes at 5.94 tokens/s (tok/s) on a 32-vCPU Ice Lake server, ahead of this http URL (4.75) and the vLLM CPU backend (1.65) on comparably sized dense models. Realizing the expert-delay window as asynchronous I/O overlap on a disk-resident expert tier yields a further net win of up to 1.68x, matching the overlap model within 1%. Measurement refutes one of the eight design claims and leaves a second inconclusive; both are reported in full, with the conditions under which they would hold. Comments: 77 pages, 2 figures. Model weights and binaries: this https URL Subjects: Hardware Architecture (cs.AR); Machine Learning (cs.LG); Performance (cs.PF) Cite as: arXiv:2608.23841 [cs.AR] (or arXiv:2608.23841v1 [cs.AR] for this version) https://doi.org/10.48550/arXiv.2608.23841 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-54] Generating Intervention Hypotheses using Explainable Explanations on Graphs: G2I a Two-Stage Greedy Framework CIKM2026
链接: https://arxiv.org/abs/2608.23835
作者: Mulin Tian,Ajitesh Srivastava
类目: Machine Learning (cs.LG); Social and Information Networks (cs.SI)
*备注: 11 pages, 3 figures. Accepted at the 35th ACM International Conference on Information and Knowledge Management (CIKM 2026)
Abstract:Real-world decision-making in public health and social science can greatly benefit from predictive models, yet translating predictions into effective interventions requires explaining the model behavior. While Graph Neural Networks (GNNs) are well-suited for modeling relational data, existing explanation methods largely operate at the node level and fall short of supporting actionable, network-level intervention design. Existing counterfactual GNN explainers, such as CF-GNNExplainer and CF ^2 , rely on continuous mask optimization over features and edges, which implicitly assume feasible edge manipulation, may allocate effort to immutable or non-actionable attributes, and incur substantial computational overhead. Further, the method of arriving at the explanation itself is difficult to explain to a domain specialist who is not an AI expert. Can simple methods generate good explanations? To explore this, we reframe counterfactual explanation as an intervention design problem. At the local level, we generate counterfactuals via a greedy search that directly identifies minimal, actionable changes to node features and neighbor-level conditions. We derive conditions under which the greedy search provides guarantees, and empirically show that these conditions are approximately met. These counterfactuals are converted into interpretable rules suitable for real-world intervention. At the network level, we formulate intervention selection as a Disjunctive Normal Form (DNF) coverage problem under a budget constraint, which is nondecreasing and approximately submodular, enabling a greedy algorithm with theoretical guarantees. Experiments on synthetic graphs and real-world suicide risk networks demonstrate that our approach produces scalable, cost-effective intervention strategies with significantly improved efficiency over mask-based counterfactual methods.
[LG-55] Learning to Act While Waiting: RL Finetuning of Generalist Robot Policies Under Inference Latency
链接: https://arxiv.org/abs/2608.23831
作者: Brian Zhu(1),Momen Khalil(1),E Harrison(2),Emanuele Poggi(1),Philipp Schmitt(1),Bernd Kast(1),Philine Meister(1),Pranav Atreya(2),Qiyang Li(2),Finn Ferchau(1),Cesar Colmenero(1),Yash Shahapurkar(1),Gokul Narayanan(1),Melih Erdogan(1),Kai Wurm(1),Georg von Wichert(1),Oier Mees(3 and 4 and 2),Eugen Solowjow(1),Andrew Wagenmaker(2),Sergey Levine(2) ((1) Siemens (2) UC Berkeley (3) Microsoft (4) ETH Zurich)
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: 25 pages, 12 figures
Abstract:While reinforcement learning (RL) allows generalist robot policies to continually improve during deployment, the large model size of modern generalist policies, such as VLAs, poses a fundamental obstacle to effective RL improvement. In particular, their severe inference latency—which can lead to pauses or jerky movements—can alter the effective environment dynamics and, if not correctly accounted for, break the Markov assumption that RL relies on, causing standard RL algorithms to fail completely. In this work, we introduce a latency-aware framework, Asynchronous RL with Intermediate Information (ARLI), that enables RL-based improvement of generalist policies under inference delays. Our framework builds on asynchronous inference approaches, which interleave action generation with execution to hide latency, and addresses its incompatibility with RL by providing a low-latency RL policy design that maximizes reactivity within the inference window through two contributions: state augmentations that restore near-Markovian structure by incorporating committed actions and a mid-inference observation. We evaluate our approach across simulated and real-world manipulation tasks, and find that it enables effective finetuning under inference delays where standard RL fails entirely, even matching or exceeding the performance of standard RL in idealized no-latency settings. Comments: 25 pages, 12 figures Subjects: Robotics (cs.RO); Machine Learning (cs.LG) Cite as: arXiv:2608.23831 [cs.RO] (or arXiv:2608.23831v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2608.23831 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-56] AQLoRA: A Zero-Search Recipe for Fast Quantized LoRA Fine-Tuning
链接: https://arxiv.org/abs/2608.23816
作者: Md Romyull Islam
类目: Machine Learning (cs.LG)
*备注: 23 pages, 5 figures, 19 tables. Code and experimental artifacts: this https URL
Abstract:Quantized fine-tuning (QLoRA) saves memory but not time. It dequantizes every 4-bit weight on the fly, so it trains more slowly than fp16 LoRA. We present AQLoRA (Adaptive-Quantization LoRA), a recipe that buys part of that time back. One CPU pass over the weights sets everything, with no search and no calibration data. The pass ranks layers by NF4 reconstruction error and keeps the top-K in fp16 under a memory budget. Those layers skip dequantization, which is where the speed comes from. A quality setting adapts every layer. A speed setting adapts only the top blocks, so the backward pass stops early. The rule reproduces Unsloth’s hand-curated dynamic-4bit selection exactly, in seconds, where search-based allocation needs repeated calibration passes. We evaluate on Commonsense-170K across six models and four architecture families, from 1.4B to 14B. The speed setting trains 11.1 +/- 2.7% faster than well-tuned QLoRA and gives up about one accuracy point. It was faster in all nine independent timing sessions, at worst by 7%. The quality setting trains 4.8 +/- 2.4% faster. Its accuracy is level with QLoRA on every model and within a point of fp16 LoRA, for 0.2 GiB more memory. These error bars are measured between independent sessions, not within one. Earning them taught us three rules for timing on shared hardware. Fix the measurement duration, not the step count. Measure the noise floor from a duplicated arm, not a nearly identical method. Repeat whole sessions: a floor computed inside one sweep understates the real uncertainty several times over, and the random seed controls almost none of it. We validate the recipe with controls and report the two that failed. Choosing adapter layers by weight density is no better than random. Choosing protected layers by quantization error is not either. The count of protected layers, not their identity, carries the speed effect. Comments: 23 pages, 5 figures, 19 tables. Code and experimental artifacts: this https URL Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.23816 [cs.LG] (or arXiv:2608.23816v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.23816 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-57] A Theory of Speciation in Generative Diffusion Models on Compact Riemannian Manifolds
链接: https://arxiv.org/abs/2608.23798
作者: Alessio Marta,Paola Causin
类目: Machine Learning (cs.LG)
*备注: 48 pages, 15 figures
Abstract:Speciation in generative diffusion models denotes the emergence of distinct stable branches during denoising, through which initially undifferentiated trajectories progressively commit to different data classes. In this work we develop an intrinsic theory of speciation for diffusion models supported on compact Riemannian manifolds: the aim is to go beyond existing theoretical descriptions, which usually identify speciation with a symmetric pitchfork bifurcation and assume to work in a large-dimensional space. We characterize speciation by bifurcations of the critical points of the evolving probability density. A spectral heat-kernel representation makes explicit the role of the manifold geometry, while Poincaré-Hopf and Morse theory impose global constraints on the number and type of score equilibria and reveal topologically-imposed geometrical modes. For mixtures of heat kernels, we prove that generic speciation events have a one-dimensional critical kernel and admit an A2 fold normal form; pitchforks and simultaneous multidirectional transitions arise from nongeneric symmetric configurations. We derive geometry-dependent estimates of speciation times for bimodal mixtures and Riemannian regular simplices. We further establish structural stability of nondegenerate folds under score perturbations and show that the first-order time shift is determined solely by the component of the score error along the critical direction. The theory is illustrated on the sphere using mixtures of von Mises-Fisher distributions, where pitchfork and saddle-node bifurcations, topological modes, and hierarchical multiple speciations are observed. Finally, a chart-based intrinsic score-learning scheme based on neural networks contrasts the theoretically predicted transitions on prototypal and more complex datasets.
[LG-58] GAP-Prompt: Gated Adaptive Prompting for Efficient Continual Learning
链接: https://arxiv.org/abs/2608.23782
作者: Trung-Anh Dang,Duy-Cuong Bui,Ngoc-Son Vu,Christel Vrain,Vincent Nguyen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Continual learning faces the persistent challenge of catastrophic forgetting, where sequential task updates degrade previously acquired knowledge. While prompt-based methods integrated with pre-trained models offer a compelling solution by freezing the backbone, they often rely on static, task-level prompting strategies that overlook fine-grained intra-task diversity. In this paper, we propose Gated Adaptive Prompting (GAP-Prompt), a novel method that introduces instance-level adaptability to the prompting process. GAP-Prompt consists of three synergistic modules: (1) instance-conditioned gating, which dynamically determines optimal prompt injection layers for each individual image; (2) dynamic knowledge fusion, which performs instance-aware aggregation of current and historical prompts, enabling knowledge integration across tasks; and (3) shared prompt distillation, which anchors foundational knowledge in early shared layers to mitigate forgetting. Extensive evaluations on CIFAR-100, ImageNet-R, and CUB-200 benchmarks demonstrate that GAP-Prompt consistently achieves state-of-the-art performance. Notably, on the fine-grained CUB-200 dataset, GAP-Prompt reaches 87.29% accuracy, approaching the joint training upper bound (88.00%) and outperforming existing methods by a significant margin.
[LG-59] ght Majorizations and Convergence Rates of Nuclear Norm Minimization IRLS
链接: https://arxiv.org/abs/2608.23765
作者: Christian Kümmerle,Tomas Masak,Dominik Stöger
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA); Optimization and Control (math.OC)
*备注: 97 pages, 9 figures, 2 tables
Abstract:Iteratively reweighted least squares (IRLS) methods constitute a natural approach to nuclear norm minimization, but their convergence rates and the role of the weight operator have remained poorly understood. This paper establishes sharp convergence rates for IRLS methods for constrained nuclear norm minimization in low-rank recovery. A central ingredient is a new majorization analysis for the smoothed nuclear norm: we prove that the harmonic-mean weight operator defines a valid global quadratic majorizer. Furthermore, we show that this weight operator is optimal within the family of power-mean weights, clarifying why it improves over classical one-sided reweighting schemes that use only row- or column-space information. Under a Schatten-1 null space property, we prove global linear convergence of IRLS algorithms using a variety of weight operators, including the harmonic-mean weights. For IRLS with harmonic-mean weights, we prove a dimension-independent, locally linear convergence rate. We provide a counterexample showing that this dimension-independent local rate cannot in general be obtained for IRLS algorithms using one-sided weight operators, which predominate in the literature. Numerical experiments corroborate the theoretical results and illustrate the practical advantage of harmonic-mean reweighting across square, rectangular, and adversarially initialized recovery problems.
[LG-60] A Hybrid Two-Stage Machine Learning Pipeline for Fault Detection and Classification in Power Transmission Systems
链接: https://arxiv.org/abs/2608.23726
作者: Sahil Manikshete,Atharva Gujarathi,Thanh Long Vu,Akhtar Hussain,Van-Hai Bui
类目: ystems and Control (eess.SY); Machine Learning (cs.LG)
*备注: 21 pages
Abstract:Rapid and accurate fault detection in high-voltage transmission networks is essential for grid reliability and equipment protection. Transmission fault datasets are frequently imbalanced, and certain fault types produce electrical signatures that fall within the normal operating envelope, causing single-model classifiers to fail on safety-critical cases. This paper proposes a hybrid two-stage machine learning pipeline that decouples detection from classification. Stage 1 combines an Isolation Forest anomaly detector with an optional supervised binary detector through an OR-fusion rule; the supervised branch is allocated automatically during training for any fault class the anomaly detector cannot resolve, and is omitted when no such class exists. Stage 2 applies a Random Forest multiclass classifier only to samples flagged by Stage 1. Feature engineering is expressed as a per-measurement-point operator mapping six raw channels to eighteen features, including zero-sequence symmetrical components derived from Fortescue’s theorem, yielding 18L features for L measurement points. On the TLFaultDataset, the pipeline raises Line-fault end-to-end accuracy from 31.3% to 95.8%. On an independent single-point dataset, the same framework attains 97.25% end-to-end accuracy across all classes including normal operation, exceeding the TLFed federated benchmark of 94.84% without GPU or federated infrastructure, at 0.05 ms per sample on CPU. Ablation on both datasets shows zero-sequence features resolving the three-phase versus three-phase-to-ground ambiguity, raising the F1-score of that class pair from 0.39 to 0.997. The direction of the zero-sequence signature is found to be system-dependent, motivating a learned decision boundary in place of a fixed relay threshold.
[LG-61] Response Renormalization for Critical Deep Equilibrium Models
链接: https://arxiv.org/abs/2608.23725
作者: Jose Luis Lima de Jesus Silva
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注: 42 pages, 41 figures
Abstract:Deep Equilibrium Models (DEQs) compute predictions from a hidden representation unchanged by the model update. Training through this equilibrium uses implicit differentiation and requires solving an adjoint system built from the residual Jacobian. If this Jacobian is nearly singular along loss-sensitive directions, small perturbations can be strongly amplified in the adjoint response, producing large, highly sensitive gradients that can make optimization unreliable. We introduce Response Renormalization, a backward-pass framework that lifts selected near-pole denominators while leaving unlifted response channels unchanged. Collective Mode Response Renormalization (CMR) applies this correction in a low-dimensional critical subspace, while Phi-adaptive CMR computes a bounded response mass from a positive susceptibility rule. We derive dense and matrix-free collective formulations, distinguish exact gradients of a modified frozen-anchor residual from backward-response surrogates, and extend the construction to Structured Implicit Layers and Vector Attractors (SILVA). Across 23 multiphysics families spanning partial differential equations, three-dimensional fields, operator maps, complex geometries, and particle systems, CMR and Phi-CMR yield test errors no more than five percent higher than those from models trained with exact implicit differentiation in more than 98% of static and 95% of transient family-seed comparisons. Solver-index experiments show convergence toward the static adjoint, while physical-time rollouts retain predictive fidelity under the evaluated conditions. These results demonstrate that selective response renormalization can control near-critical adjoint amplification without globally damping well-conditioned sensitivity. Therefore, the method can make parameter updates more reliable while preserving the useful gradient information needed for learning.
[LG-62] Renormalization Group Flow Matching for Scalable Local Generative Modeling
链接: https://arxiv.org/abs/2608.23696
作者: Kanta Masuki,Yuto Ashida
类目: Machine Learning (cs.LG); Statistical Mechanics (cond-mat.stat-mech)
*备注: 42 pages, 9 figures
Abstract:Despite their remarkable success in modeling complex data, generative models face a fundamental tradeoff. Global approaches can capture full structural coherence but suffer from high computational costs, while local models are efficient but often fail to reproduce long-range correlations and global coherence. The renormalization group (RG) bridges this gap by seamlessly connecting spatial structures across different length scales, retaining quasi-local descriptions at each step while preserving long-range correlations. We introduce renormalization group flow matching (RGFM), a generative framework that systematically structures data generation across different spatial scales. By using an exact RG flow as the probability path, RGFM progressively generates data from long- to short-wavelength structures. To reconcile scalability with global structure, we exploit two key properties of the RG: quasi-locality and scale separation. We rigorously show that the RGFM probability flow can be accurately approximated by local velocity fields acting over a spatial range O(\Lambda^-1[\ln L+\ln(1/\varepsilon)]) for RG wavenumber scale \Lambda , linear system size L , and prescribed error tolerance \varepsilon . This property enables local generative modeling with patches of size O(\ln L) and a computational cost that scales nearly linearly with the system volume. We numerically demonstrate that local RGFM reproduces long-range correlations far beyond its receptive field in representative one-dimensional distributions, while conventional local flow matching exhibits substantial errors at long distances. On FFHQ images, RGFM yields far more coherent and higher-quality samples than local flow matching at 64x64 and 256x256. Our results establish RG-guided probability flows as a promising route toward scalable generative modeling that captures long-range structure using only local computation.
[LG-63] Data Predictability Shapes Weibull Weight-Scale Growth in Transformer Training
链接: https://arxiv.org/abs/2608.23573
作者: Tiexin Ding
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 27 pages, 14 figures, 5 tables. Code and data: this https URL
Abstract:A trained transformer’s weight magnitudes can be summarized by a two-parameter Weibull distribution whose shape k \approx 1.2 is stable across layers and models, so the scale \lambda carries most training-induced movement. What corpus property sets how much \lambda grows? Using the bigram conditional entropy D = H(\textnext \mid \textprev) , a training-free statistic computed before training, we find across controlled corruption families a learning-rate-conditioned law, \lambda^2 - \lambda_0^2 = C_0(\eta) + C_1(\eta)(H_r - D)^0.59 , where H_r is a matched-budget shuffle baseline. The convex exponent is inherited from an independently measured data-side saturation relation rather than fitted directly to the growth curve. After removing the two per- \eta coefficients, 23 runs spanning an order of magnitude in learning rate collapse onto (H_r - D)^0.59 with unit slope ( R^2 = 0.941 ; direct per- \eta fits are weaker, R^2 \approx 0.82 ). Because D is computed before training, the law is a forward predictor: an end-to-end self-validation recovers held-out within-family weight growth with 5.7% relative error. The readout holds at model and per-layer resolutions and across two tested architectures, with the functional form preserved and only the coefficients changing. It also marks its boundary: cross-corpus prediction over-predicts code, implicating redundancy as a second axis of a broader \Phi(D,R,A,H) data-to-weight framework.
[LG-64] Equivariant Cellular Sheaves for Molecular Electronic Structure: Bridging Sheaf Cohomology and E(3)-Equivariant Hamiltonian Learning
链接: https://arxiv.org/abs/2608.23571
作者: Krishna Harish
类目: Machine Learning (cs.LG); Chemical Physics (physics.chem-ph)
*备注: 12 pages, 3 figures, 2 tables
Abstract:Equivariant message-passing networks are the standard model for molecular property and interatomic-potential prediction, and recent work predicts the electronic Hamiltonian itself in an E(3)-equivariant way. Separately, topological deep learning has extended graph networks to cellular sheaves. Our central observation is structural: in a localized atomic-orbital basis, the molecular single-particle Hamiltonian, after a constant shift that makes it positive semidefinite, is the Laplacian of a cellular sheaf on a regular cell complex built from the molecule. Making the restriction maps O(3)-steerable two-center kernels from bond geometry recovers the Slater-Koster form as a special case and yields an E(3)- and permutation-equivariant operator. Three consequences follow. First, the zeroth sheaf cohomology H^0 = ker L is a topological invariant equal to the non-bonding (zero-mode) orbitals, recovering the classical alternant non-bonding-orbital count as a lower bound. Second, the Hodge 1-Laplacian lets higher cells (rings) carry cycle and delocalization information through H^1. Third, the model strictly generalizes E(3)-equivariant message-passing networks and CW networks, and inherits the anti-oversmoothing of non-trivial sheaf diffusion. We prove equivariance, expressivity, and cohomological-correspondence results for the Equivariant Cellular Sheaf Networks, and validate them numerically: the Hamiltonian-to-sheaf embedding is exact to machine precision, the cohomology dimension reproduces non-bonding-orbital counts across eleven conjugated molecules, the sheaf Laplacian is O(3)-equivariant to machine precision, and the equivariant model attains lower error and rotation generalization on a directional electronic target. Our contribution is this sheaf-theoretic formalization and its invariants, not equivariant Hamiltonian prediction itself.
[LG-65] What FID Hides: Detecting Ranking and Diagnosing Deviations in Generative Evaluation
链接: https://arxiv.org/abs/2608.24881
作者: Hao Chen
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Generative models are commonly ranked by Fréchet Inception Distance (FID) and Kernel Inception Distance (KID), yet FID’s first-two-moment summary can miss distributional differences, and a reported scalar gap alone is not a calibrated test against sampling variation. FID’s moment restriction has concrete consequences: on ImageNet, visually unrecognizable images optimized only to match the reference Inception mean and covariance obtain FID 24.7 versus 58.6 for held-out real images (lower is better). Moreover, FID and KID are scalar discrepancies that are unchanged when the two samples are exchanged and therefore do not encode the direction of a dispersion change: under-dispersion, as can occur in mode collapse, versus over-dispersion. We introduce \textbfZID (\emphZ-resolved Integrated Diagnostic), which combines six standardized location- and dispersion-sensitive arms from a rank graph (RISE) and Gaussian kernels (GPK at two bandwidths). Rather than asking one scalar to serve incompatible roles, ZID reports three linked outputs: an index for ranking departure magnitude, a permutation p -value for testing distributional equality, and a signed dispersion readout for diagnosis. In controlled experiments, ZID detects a broad range of departures, and its score tracks increasing severity along the corresponding sweeps, including cases in which FID is flat or reversed. On DiT-XL/2 and SiT-XL/2 guidance sweeps, ZID detects departure from real data, and its signed readout labels the high-guidance diversity collapse as under-dispersion.
[LG-66] When Similarity Is Interaction-Driven: Quantum Kernels for Regime-Sensitive Learning
链接: https://arxiv.org/abs/2608.24631
作者: Hanqiu Peng,Jianlong Lu,Ying Chen
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: 24 pages, 8 tables, 1 figure
Abstract:Similarity in many decision systems is governed not by distance alone but by interactions among variables. In fraud and anomaly detection, small local perturbations can cross interaction-sensitive decision boundaries while leaving ambient distance almost unchanged. Motivated by this setting, we introduce a thin-slab interaction model and an interaction-driven quantum kernel constructed from entangled Pauli-string feature maps. The feature map explicitly encodes sparse high-order block interactions. We show that the resulting fidelity kernel is positive semidefinite, admits an exact block-factorized formulation, and induces a geometry sensitive to changes in interaction regime. Across balanced and imbalanced synthetic experiments spanning third-, fourth-, sixth-, and eighth-order interactions, the proposed kernel consistently outperforms linear, radial basis function, Laplacian, and polynomial kernels, as well as an engineered-interaction linear baseline supplied with the planted block products. On real fraud-detection benchmarks, it achieves the highest mean accuracy and F1 on Credit Card Fraud Detection and ranks second on IEEE-CIS Fraud Detection. These findings show that quantum-kernel performance depends on alignment between feature-map geometry and the underlying predictive structure, rather than on Hilbert-space dimension alone. Because the prescribed block-factorized kernel can also be evaluated exactly on a classical computer, the results establish predictive and representational value rather than computational quantum speedup.
[LG-67] Provable Quantum–Classical Separation for Continuous Gibbs Sampling
链接: https://arxiv.org/abs/2608.24527
作者: Enrico Olivucci,Mariia Sobchuk,Sehmimul Hoque,Jeffrey Hnybida,Kyungho W. Kim,Ala Shayeghi,Pooya Ronagh
类目: Quantum Physics (quant-ph); Data Structures and Algorithms (cs.DS); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
*备注:
Abstract:We prove the first quantum–classical separation for a sampling problem over a continuous domain. For a class of Gibbs states p\propto e^-\beta E on the torus \mathbbT^d with smooth ( s -Gevrey) potential and barrier amplitude \alpha=e^\beta\Delta , where \Delta = \max E-\min E , every classical algorithm—querying the value, gradient, or any higher-order derivatives of the log-density—requires \Omega(\alpha) queries to sample at constant accuracy in total variation distance, while a quantum algorithm based on quantum singular value thresholding and temperature annealing samples with \tildeO\left(\sqrt\alpha\right) queries to an oracle for the gradient. The advantage is quadratic in the barrier amplitude, which becomes exponential in the dimension, e^\Omega(d) , at low temperature. The classical bound is information-theoretic, holding for every classical algorithm with query access to the Gibbs potential and its derivatives at any order.
[LG-68] Predictability of El Niño from Delayed Observations
链接: https://arxiv.org/abs/2608.24428
作者: Francisco J. Beron-Vera
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG); Dynamical Systems (math.DS); Chaotic Dynamics (nlin.CD)
*备注: Submitted to JGR Machine Learning and Computation; comments welcome
Abstract:Using monthly Niño-3.4 anomalies through July 2026, we investigate how much predictive information is contained in delayed observations of the index. Ridge regression identifies informative delays, while multilayer perceptron and sparse identification of nonlinear dynamics (SINDy) models test whether nonlinear complexity provides additional direct forecast skill; gated recurrent unit (GRU) and long short-term memory (LSTM) networks provide a complementary test in which the temporal representation is learned internally. Delayed observations substantially improve forecasts over persistence and climatology at leads of up to six months, but increasing model complexity provides no systematic improvement. Historical recursive experiments favor a simple explicit SINDy recurrence and select shallow recurrent architectures, with no appreciable gain from learning the temporal representation internally. These results support a compact predictive representation of Niño-3.4 evolution in which the representation of past information is more consequential than model complexity. As a prospective application, the selected models are used to forecast the developing 2026 event beyond the last available observation and to compare its predicted evolution with completed historical El Niño events.
[LG-69] Sequential operator learning under dependent data
链接: https://arxiv.org/abs/2608.24426
作者: Rafael Oliveira
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: Under review. Work in progress
Abstract:Learning operators from sequentially collected data arises in adaptive experimental design, Bayesian optimization, and dynamical-system modelling, where observations may be dependent, and future inputs or sensing operators may depend on preceding data. We derive time-uniform self-normalized concentration bounds for stochastic processes in Hilbert spaces with vector-valued noise. We use these bounds to obtain regression-error guarantees for linear operators, including targets outside the Hilbert estimation space, and for nonlinear parametric operators trained with strongly convex losses and regularizers. Our results allow possibly infinite-dimensional inputs and outputs without independence or mixing assumptions, providing a major step towards convergence guarantees for adaptive operator learning and learning from stochastic dynamical data.
[LG-70] Validation of HRV Studio: A Transparent and Quality-Control-Aware Platform for Heart Rate Variability Analysis
链接: https://arxiv.org/abs/2608.24241
作者: Cyrus Mexon Evrard Djindot,Faliang Liu,Sylvain Laborde,Yinjia Zhang,Jessie Chen,Ming Li,Congrong Wang,Weixiong Rao,Qinpei Zhao
类目: Medical Physics (physics.med-ph); Machine Learning (cs.LG); Software Engineering (cs.SE); Quantitative Methods (q-bio.QM)
*备注: 33 pages, 8 figures. Source code available at this https URL
Abstract:Reproducibility of heart rate variability (HRV) analysis is limited by differences in preprocessing and computational conventions across software platforms. We developed HRV Studio, an open-source PyQt6-based desktop application integrating transparent HRV analysis with automated quality-control (QC) diagnostics. Validation included large-scale agreement with NeuroKit2, targeted Kubios benchmarking, spectral-method comparison, synthetic perturbation testing, recording-duration sensitivity analysis, and arrhythmia-focused QC stress testing. HRV Studio showed near-identical agreement for the widely used time-domain indices RMSSD and SDNN under matched conditions. In the primary five-minute NeuroKit2 comparison, frequency-domain median relative errors were 1.35% for LF, 0.18% for HF, and 1.41% for LF/HF, while VLF remained more convention-sensitive (37.79%). Nonlinear Poincaré indices also demonstrated high consistency. Sequence-harmonized Kubios benchmarking confirmed near-identical agreement for time-domain and nonlinear indices and strong agreement for most frequency-domain measures. Extended ten-minute analyses reproduced the same overall pattern with lower disagreement for some convention-sensitive spectral outputs. Synthetic and arrhythmia stress tests maintained 100% numerical stability while consistently triggering QC warnings. Overall, HRV Studio provides a transparent and reproducible platform for HRV research, with strong cross-platform consistency when NN sequences, preprocessing, and analytical conventions are harmonized. Stress-test results indicate computational robustness rather than clinical validation.
[LG-71] A Theory of Finite-Noise Optima and Generalization in Quantum Machine Learning
链接: https://arxiv.org/abs/2608.24229
作者: Ziyu Zhang,Zikang Jia,Xiaosong Li,Yulong Dong
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: 36 pages, 11 figures
Abstract:Quantum noise is expected to degrade quantum machine learning by driving circuits away from their noiseless implementations. Yet recent studies show moderate noise can reduce testing error, a behavior unexplained by weak-noise perturbative error accumulation or strong-noise trainability collapse. Here we develop a statistical learning theory connecting microscopic noise processes to macroscopic learning performance. At its heart is a noise-order purity parameter, derived from a surrogate model analysis, that predicts the noise-induced reduction in model complexity and the consequent reduction in the generalization gap. Noise simultaneously increases prediction bias. Their competition explains the intermediate-noise regime left open between these limits. It produces a finite-noise optimum whose location depends on the learning setup and can disappear in the large-sample limit. Numerical experiments validate these predictions. Noise programming can move a model towards this optimum. These results make the non-monotonic effect of noise predictable and provide a route to harness it.
[LG-72] A Heterogeneous Mixture of Experts Framework for Interpretable Machine Learning
链接: https://arxiv.org/abs/2608.24195
作者: Soham Chatterjee,Rwitobroto Dey,Smarajit Bose
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 27 pages, 6 figures
Abstract:Mixture-of-Experts (MoE) models provide a flexible framework for partitioning complex prediction problems into simpler local learning tasks through an input-dependent gating mechanism. Existing interpretable MoE approaches, such as Mixture of Decision Trees (MoDT), achieve transparency by employing homogeneous decision-tree experts, but this restricts the model to a single inductive bias across all regions of the feature space. We extend the MoDT framework by introducing heterogeneous expert families comprising decision trees, linear support vector machines, and quadratic discriminant analysis under a common probabilistic gating mechanism. To ensure coherent likelihood-based inference, non-probabilistic experts are calibrated to produce conditional class probabilities, allowing parameter estimation within the generalized Expectation-Maximization framework of MoDT. We further establish theoretical monotone ascent guarantees for the proposed heterogeneous gating updates, providing a justification for the optimization procedure. Experiments on a diverse collection of synthetic and real-world benchmark datasets demonstrate that the proposed framework adaptively specializes experts according to local data geometry, yielding interpretable expert assignments while achieving predictive performance competitive with homogeneous MoDT and Random Forests. The proposed approach combines interpretability, adaptive inductive bias selection, and probabilistic coherence within a unified mixture-of-experts framework.
[LG-73] qshap: Fast Shapley Decomposition of R2 for Gradient-Boosted Trees
链接: https://arxiv.org/abs/2608.24104
作者: Zhongli Jiang,Min Zhang,Dabao Zhang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Numerous methods have been developed to quantify feature attributions in individual predictions for tree ensembles. However, many applications require global measures of feature contributions to overall model performance. Although local attribution scores can be aggregated to characterize feature importance, such summaries do not directly decompose measures of predictive performance, such as R^2 . This article introduces qshap, available in both R and Python, which provides Shapley decomposition of R^2 values for gradient-boosted decision trees (GBDTs) to quantify feature-specific contributions to model performance. By decomposing the quadratic loss of individual observations, qshap provides flexible tools to explore the importance of individual features and observations. qshap currently supports widely used GBDT implementations, including xgboost, lightgbm, and catboost, through a unified tree representation and efficient C++ backends. Its modular design can accommodate other GBDT implementations built from binary decision trees. In addition, we introduce a specialized backend for oblivious trees that exploits their symmetric structure to substantially accelerate computation.
[LG-74] Generalization memorization and overfitting for diffusion models trained in the lazy high-dimensional regime
链接: https://arxiv.org/abs/2608.23938
作者: Hugo Latourelle-Vigeant,Sinho Chewi,Aram-Alexandre Pooladian,John Sous,Theodor Misiakiewicz
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注: 100 pages, 3 figures
Abstract:Modern score-based generative models have achieved remarkable empirical success in high-dimensional tasks such as image, audio, and video synthesis. These models reduce distribution learning to a sequence of regression problems that, if solved exactly on finite data, would ultimately reproduce the training samples. Their ability to generalize must therefore arise from the implicit or explicit regularization during training. In this work, we develop a generative counterpart to the theory of benign overfitting and algorithmic regularization for overparameterized neural networks in the supervised lazy-training regime. We study denoising score matching in a vector-valued reproducing kernel Hilbert space with an inner-product kernel. In the proportional high-dimensional regime n\asymp d , we derive exact risk trajectories under gradient flow training. These trajectories exhibit three phases governed by qualitatively distinct estimators: a spectral estimator that generalizes, a pure-noise score with localized peaks that interpolate the training objective, and an empirical Bayes estimator that memorizes the data. We then analyze how these estimators combine along the reverse-time SDE and characterize the distribution of the resulting samples. The analysis reveals familiar mechanisms from supervised learning, including kernel linearization and self-induced regularization from the nonlinear part of the kernel, but also reveals a distinct phenomenology specific to generative modeling.
[LG-75] Dimensionless Controls of Plasticity Under Alternating Tasks: From Evolutionary Biology to Continual Learning
链接: https://arxiv.org/abs/2608.23889
作者: Owen Skriloff
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注: 21 pages, 5 figures. Published in PMLR 334(2):87-107, 2026
Abstract:Plasticity under changing environments is central to both evolutionary biology and continual learning. Motivated by recent work on genotype–phenotype maps, we study a minimal deep-learning analogue where a network is trained alternately on two Boolean label sets, and ask which biological controls of plasticity survive the translation to gradient descent. Reinterpreting four proposed biological factors as quantities of training dynamics, we find the system reduces to two dimensionless controls: the task disagreement r , the fraction of disagreeing labels, and the reach \eta T , the product of learning rate and switching period. We derive two bounds on plasticity: r alone fixes an extremal geometric floor on the utopia distance, while r and \eta T jointly bound forgetting. Across 9,720 trajectories, an ANOVA confirms that r , \eta , and T dominate, while the effect of neutral-set size (emphasized in the biological setting) is negligible. The optimal reach itself follows an approximate inverse power law \eta T^\propto r^-1.18 , yielding a heuristic that sets the optimal reach \eta T^ from the task disagreement alone. The analogy that survives is therefore dynamical rather than geometric, and our setting enables a view of plasticity through the lens of other driven systems in physics and engineering.
[LG-76] Differential Learning for Robust Prediction of Thermal Stability with Application to Energetic Materials
链接: https://arxiv.org/abs/2608.23874
作者: Megan C. Davis,R. Seaton Ullberg,Jeremy N. Schroeder,Andrew H. Salij,Marc J. Cawkwell,Christopher J. Snyder,Ivana Matanovic,Wilton J. M. Kort-Kamp
类目: Chemical Physics (physics.chem-ph); Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
*备注:
Abstract:Predicting thermal stability during handling and storage is essential for the design of safe and reliable energetic materials. However, experimental measurements vary significantly across laboratories due to differences in protocols and analysis methods, making it difficult to train reliable predictive models. We address this challenge through differential learning. Rather than predicting absolute decomposition temperatures, we instead train message passing neural networks to predict relative differences between pairs of molecules. This approach reduces sensitivity to systematic experimental errors and achieves 85% accuracy in ranking compounds by thermal stability, outperforming conventional regression methods on the same heterogeneous dataset. To understand what drives these predictions, we compare neural network models with interpretable alternatives built from descriptors derived from ab initio calculations and cheminformatics software. This analysis identifies bond dissociation enthalpy as a key determinant of thermal stability rankings, providing further insight into the complex chemistry of thermal decomposition. The differential learning framework generalizes across model architectures, from graph neural networks to classical descriptor-based approaches. Our results demonstrate that learning relative properties rather than absolute values offers a practical solution for modeling noisy experimental data, with direct applications in materials design where thermal stability predictions inform safety protocols.
[LG-77] Accelerating the Adoption of Residential Solar Power Systems: Policy Analysis using a Dynamic Structural Model
链接: https://arxiv.org/abs/2608.23796
作者: Sebastián Souyris,Jason A. Duan,Anantaram Balakrishnan,Varun Rai
类目: Econometrics (econ.EM); Machine Learning (cs.LG); Applications (stat.AP)
*备注: 62 pages, 14 figures
Abstract:Problem definition: Solar electricity generation is a strategic component of energy portfolios designed to meet growing demand and reduce carbon emissions. Governments and municipalities encourage household photovoltaic (PV) adoption through upfront rebates and tax credits. Limited budgets require principled, data-driven policies that account for the drivers of adoption and the effects of incentives on adoption rates. Methodology/results: We develop a dynamic structural model of residential PV diffusion based on adoption decisions by forward-looking households that weigh the economic trade-offs between installing now and later. Adoption depends on return on investment and influence from neighboring adopters. The model segments households by home value and urbanization level, incorporates unobserved heterogeneity, and captures spatiotemporal installation dynamics. We estimate the model using Bayesian methods and detailed household-level data from Austin, Texas. In out-of-sample tests, it predicts installations more accurately than contemporary alternatives. We simulate counterfactual policies within the dynamic equilibrium of PV diffusion to evaluate rebate designs. The framework can also be adapted to study the adoption of other durable technologies. Managerial implications: A rebate offered for a limited period generates more adoption and emissions reductions than a prolonged, costlier program. This counterintuitive result arises from forward-looking behavior, neighbor influence, and accelerated adoption before the rebate expires. We also evaluate phased reductions and rebates differentiated by household segment. A two-step reduction outperforms multiple small reductions. Geographic differentiation improves policy performance, whereas differentiation by home value offers little advantage over a uniform rebate. Comments: 62 pages, 14 figures Subjects: Econometrics (econ.EM); Machine Learning (cs.LG); Applications (stat.AP) Cite as: arXiv:2608.23796 [econ.EM] (or arXiv:2608.23796v1 [econ.EM] for this version) https://doi.org/10.48550/arXiv.2608.23796 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.2139/ssrn.4301666 Focus to learn more DOI(s) linking to related resources
[LG-78] (Mis)Understanding Benign Overfitting in Equity Return Prediction
链接: https://arxiv.org/abs/2608.23761
作者: Hui Guo,Jiawei Huang,Runze Li,Yan Yu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Applications (stat.AP); Methodology (stat.ME)
*备注:
Abstract:Highly overparameterized models often predict well despite interpolating training data in complex domains, challenging the classical bias–variance tradeoff. We investigate whether this ``benign overfitting’’ phenomenon extends to equity return prediction. Consistent with recent statistical theory, we document two key phenomena: first, a double descent pattern in the ridgeless model’s prediction risk; and second, that while the optimal ridge model consistently outperforms its ridgeless counterpart, this performance gap becomes negligible at large parameter-to-observation ratios. Ultimately, however, both models fail to outperform a simple historical average. This empirical evidence aligns with our asymptotic results under the null hypothesis of zero slope coefficients, suggesting that standard equity predictors lack true forecasting power—even within highly flexible, nonlinear machine learning architectures. These findings reconcile modern and classical machine learning in asset pricing: in the absence of a true signal, they asymptotically collapse to the historical average benchmark.
[LG-79] S-matrix informed neural networks for amplitude analysis
链接: https://arxiv.org/abs/2608.23750
作者: Wyatt A. Smith,Arkaitz Rodas,Marius D. Thomas,César Fernández-Ramírez,Giorgio Foti,Lin Qiu,Adam P. Szczepaniak,Alessandro Pilloni
类目: High Energy Physics - Phenomenology (hep-ph); Machine Learning (cs.LG); Nuclear Theory (nucl-th)
*备注: 29 pages, 23 figures
Abstract:Reconstructing scattering amplitudes from finite, noisy, and mutually inconsistent measurements is an ill-posed inverse problem common to many reactions relevant to particle physics. We introduce S-matrix informed neural networks (SINNs), and demonstrate their ability to learn scattering amplitudes directly from data while respecting first principles. We further develop a novel data selection procedure, which uses the response of constrained neural network ensembles to identify a set of experiments compatible with first principles, and with each other. We apply this framework to \pi\pi scattering, producing reusable amplitudes and correlated uncertainties without relying on a fixed functional form. We validate our results against residual model dependencies and training biases through closure tests and ablations. We find negligible impact of model architecture on our results. Our workflow unifies physics-constrained representation learning, data selection, and uncertainty quantification. Our strategy is transferable to other scattering processes, and other constrained physics problems limited by inconsistent data.
[LG-80] Replicable Conformal Prediction
链接: https://arxiv.org/abs/2608.23638
作者: Marios Papamichalis,Regina Ruane,Theofanis Papamichalis
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: Preprint
Abstract:Two analysts who calibrate the same predictive model on independent samples will deploy different prediction sets every time, because the calibration threshold inherits the randomness of the data. Wherever deployments must be audited, cached, or approved across sites, this instability is costly: no one can verify that two calibrations produced the same object. We ask two questions: when can independent calibrations yield the identical classifier, and what must that agreement cost? Perfect agreement is impossible, since a procedure that almost always returns one fixed answer cannot remain valid for every distribution, and exact agreement through shared randomness forces the procedure to ignore its data. Sharing a single random seed and rounding the calibrated threshold up to a coarse shared grid resolves the tension: the deployed classifier becomes identical across analysts with any desired probability, coverage guarantees survive, and the price is a quantified increase in set size and calibration data. Matching lower bounds show that no threshold method can pay less, and the method’s one tuning constant vanishes asymptotically. Without any shared seed, a fixed grid still confines all analysts to two adjacent classifiers, and no method does better. Replicability also blocks gaming: selecting the most favorable of many recalibrations barely moves a replicable classifier, while the same selection silently undercovers standard conformal prediction. Experiments on real ImageNet outputs, a four-hospital site split, and four language-model families match the theory, including the measured sample-cost frontier.
[LG-81] Finite-Sample Metric Non-Collapse for Geometrically Supervised Latent World Models in Control
链接: https://arxiv.org/abs/2608.07265
作者: Alain Bensoussan,Minh-Nhat Phung,Minh-Binh Tran
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注: Revised version prepared in response to the editorial assessment. The main manuscript is 32 pages; detailed mathematical derivations have been moved to the accompanying Supplementary Material. The principal results and contributions are strengthened and clarified
Abstract:We establish a finite-sample learning-to-control theory for geometrically supervised latent models of nonlinear deterministic systems. Geometric supervision is used only during training: simulator state, proprioception, or state estimates with independently validated metric and directional error bounds supply observable-state distances and tangent directions, while deployment remains observation- and action-conditioned. We introduce an encoder-only local–global metric hinge that enforces directional resolution and separated-state discrimination. Under regular observable-factor, coverage, finite-capacity approximation, and uniform C^1,1 hypotheses, a computable one-sided regularization regime has a strong selection property: with high probability, every approximate empirical minimizer is simultaneously pointwise co-Lipschitz and uniformly approximately semiconjugate to the controlled dynamics. Approximation, sampling, and optimization errors remain explicit and separate. Norm-constrained tensor-product B-spline classes constructively realize the approximation hypotheses, and the interpolation exponent converting mean residual control into a uniform bound is sharp. A modular deterministic corollary transfers the learned certificates to trajectory, finite-horizon cost, learned-cost-head, and optimizer guarantees, while a validated finite-net result enables sharper model-specific certification. Controlled experiments isolate collapse and folding, quantify the analytic certificate’s reserve, and demonstrate the control benefit of restored metric resolution. The principal contribution is a complete finite-sample implication from approximate empirical optimization to metric faithfulness, uniform controlled dynamics, and reliable planning for the same learned model.
[LG-82] Symbolic Classification-Enabled LHC Limits Online BSM Global Fits
链接: https://arxiv.org/abs/2605.22330
作者: Shehu AbdusSalam
类目: High Energy Physics - Phenomenology (hep-ph); Machine Learning (cs.LG); Symbolic Computation (cs.SC); High Energy Physics - Experiment (hep-ex); High Energy Physics - Theory (hep-th)
*备注:
Abstract:Global fits of Beyond the Standard Model (BSM) physics often involve a two-way interplay between theory and experiment. Theoretical models provide guidance for experimental searches, while experimental results, in turn, constrain theoretical frameworks. A crucial aspect of this feedback loop is the direct inclusion of measurements and exclusion limits online'' global fits, i.e. during the parameter scans aspects of the global fits. However, incorporating the Large Hadron Collider (LHC) limits into such analyses has been computationally prohibitive, often due to time taken per parameter point exceeding the scales acceptable for global fit frameworks. In this study, we show that LHC limits can be incorporated online’’ global fits by leveraging approximations derived from symbolic regression techniques. We utilize a dataset of ATLAS constraints from searches for electroweakino productions to derive a mathematical expression capable of classifying the phenomenological Minimal Supersymmetric Standard Model (pMSSM) parameter space as allowed or excluded. This is subsequently incorporated for making a global fit of the pMSSM to data, including the LHC Run-2 limits.
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